CONSTRUCTING INTELLIGENT SYSTEM PROCESSING UNCERTAIN CAUSAL RELATIONSHIP TYPE INFORMATION

Abstract

Techniques for an intelligent system processing uncertain causal relationship type information are disclosed herein. The disclosed techniques comprise determining, by using a new logic gate and new action variables, an effect of evidences and a combination thereof on the probability of occurrence of a root cause event; determining, by using an universal logic gate, a logic relationship between a cause event and a combination of more than one result event, and performing reasoning; determining, by using a specific event, a corresponding relationship between the specific event and a root cause event, and directly determining a cause when the specific event is observed; determining, by using a degree of attention of a result event, a degree of decrease of a probability that a reasoning result is true due to that the result event cannot be explained by the reasoning result, and involving said degree of attention in probability calculation; and determining, by using a degree of risk of the root cause event, a degree of damage to a system caused by the root cause event, and involving said degree of risk in reasoning calculation.

Description

FIELD OF THE INVENTION

This invention involves the AI technology processing information, and is a further development of the technical schemes recorded in granted Chinese patent METHOD FOR CONSTRUCTING AN INTELLIGENT SYSTEM PROCESSING UNCERTAIN CAUSAL RELATIONSHIP (in Chinese, Patent Number: ZL 2006 8 0055266.X), granted US patent METHOD FOR CONSTRUCTING AN INLLIGENT SYSTEM PROCESSING UNCERTAIN CAUSAL RELATIONSHIP INFORMATION (Patent Number: U.S. Pat. No. 8,255,353 B2), and granted Chinese patent A HEURISTIC CHECK METHOD TO FIND CAUSES OF SYSTEM ABNORMALITY BASED ON DYNAMIC UNCERTAIN CAUSALITY GRAPH (in Chinese, Patent Number: ZL 2016 1 0282052.1). Based on the technical scheme proposed in this invention, through the computations of computer, the ability to represent and utilize causal knowledge of the so-called DUCG (Dynamic Uncertain Causality Graph) can be further improved, to make it more satisfied with the actual demands and to accurately diagnose the cause of abnormality of the object system more conveniently, so as to help people take effective measures to get the current system back to normal.

BACKGROUND OF THE INVENTION

As granted patents METHOD FOR CONSTRUCTING AN INTELLIGENT SYSTEM PROCESSING UNCERTAIN CAUSAL RELATIONSHIP, METHOD FOR CONSTRUCTING AN INLLIGENT SYSTEM PROCESSING UNCERTAIN CAUSAL RELATIONSHIP INFORMATION and A HEURISTIC CHECK METHOD TO FIND CAUSES OF SYSTEM ABNORMALITY BASED ON DYNAMIC UNCERTAIN CAUSALITY GRAPH recorded, there exist enormous cause events which may lead to the abnormality of systems in industrial systems, social systems and biological systems (abbreviated as object system in the rest of this invention), such as short circuit of coils, fail to stop of pumps, failure of components, malfunction of sub-systems, blocking of transduction pathways, the entry of non-self, the mutation, necrosis, pollution, infection, damage and natural failure of tissues or body. These cause events can be represented by event variable B_k or BX_k indexed by k, B_kj or BX_kj represents that B_k or BX_k is in state j. As FIG. 1 and FIG. 2 show, B_k, B_kj, BX_k, and BX_kj can be drawn as graphical symbols such as or , or , or , and or respectively. The difference between B_k and BX_k is that B_k represents root cause variable with no input while BX_k may have inputs and can be affected by other factors, that is to say, BX_k means the B_k affected by other factors. In general, j=0 means that B_k or BX_k is in the normal state, and j=1, 2, 3 . . . means that B_k or BX_k is in the abnormal state indexed by j.

If there is only one index in the graphical symbol, it indexes the variable and its state is unknown. For simplicity, the two indices kj can be separated by a comma as k,j (the same in what follows).

Most of the states of B_k and BX_k cannot be or are hard to be detected directly, thus the DUCG intelligent system is needed to reason whether B_k and BX_k are in any abnormal state.

Furthermore, there are a large number of variables that are causal to B_k or BX_k such as temperature, pressure, flow, velocity, frequency, switch state, various laboratory reports or physical test results, investigation reports, imaging examination results, feeling, symptom, sign, region, time, environment, season, religion, skin color, experience, sibship, hobby, personality, living condition, working condition and so on. These are called causal variable, represented by X_y, in which y=0, 1, 2 . . . while X_yg represents the state of X_y indexed by g. in general, g=0 means that X_y is in the normal state, and g≠0 means that X_y is in an abnormal state. X-type variable has at least one input (cause) variable and can have or have no output (consequence) variables. As FIG. 1 and FIG. 2 show, X_y and X_yg can be drawn as graphical symbols or , or respectively.

Based on the DUCG technical theme, people can get Evidence E by detecting the states of X-type variables, to diagnose corresponding B_k, or BX_kj (j≠0) that is the root cause of the system abnormality, so as to take effective measures to get the system back to normal. E is composed of at least one state-known X-type variable, e.g. E=X_1,2X_2,3X_3,1X_4,0X_5,0.

The DUCG intelligent reasoning is to calculate Pr{H_kj|E}=Pr{H_kjE}/Pr{E}, where H_kj is a hypothesis event, which is a state combination of the variables defined in DUCG, for example, H_1,2=B_1,2, H_2,1=BX_2,1, H_3,2=BX_3,2X_4,1, etc., and subscript k in H_kj indexes the variable combination, e.g. H₁=B₁, H₂=BX₂, H₃=BX₃X₄, etc., subscript j in H_kj indexes the state combination of the variables in H_k, as illustrated above. Denote the set of all hypothesis events H_kj conditioned on E as S_H, i.e. H_kj∈S_H.

The following variables are also defined in DUCG:

Logic gate variable, which has at least two input variables and one output variable, can be represented by G_i. G_ij is state j of G_i. G_i is used to represent the logic combinations of input variable states in concern, and these logic combinations are specified by logic gate specification LGS_i. For example, G₁ is specified by LGS_i: G_1,1=B_3,1X_1,1, G_1,2=B_3,1X_1,2, G_1,0=Remnant State that is defined as all other state combinations, etc. Assume G_ijG_ij=0 (null set, where j≠j′), that is to say, different states of G are mutually exclusive. Similarly, G_i and G_ij can be represented by graphical symbols or , or respectively.

Default cause variable of X can be represented by D_i. For example, D₄ is the default cause variable of X₄. It is assumed that Pr{D_i}=1. As FIG. 1 depicts, D_i can be represented as graphical symbol or .

DUCG is comprised of the above-mentioned variables and the certain/uncertain causal relationship between them, which is usually represented by graphical symbols. An example of DUCG is illustrated in FIG. 1, in which B-type variable or event is drawn as rectangle, and X-type variable or event is drawn as circle, and BX-type variable or event is drawn as double-circle, and G-type variable or event is drawn as gate with a directed arc such as to connect its input, and D-type variable or event is drawn as pentagon.

{B-, X-, BX-, D-, G-}-type variables/events are also called nodes. Their states can be defined according to the described object. All of {B-, X-, BX-, D-, G-}-type variables/events can be a direct cause variable/event called parent variables/events, and can be represented by V in general, i.e. V∈{B, X, BX, D, G}, with the same subscript. For example, V₂=X₂, V_3,2=B_3,2, etc. Consequence variable/event can only be {X-, BX-}-type variables/events. A state-known variable is an event, for example, X_yg, B_kj, BX_kj, G_ij, H_kj, and V_ij are all events.

A DUCG is comprised of the above-mentioned variables along with the certain/uncertain causal relationships among them. An example of DUCG is illustrated in FIG. 1, in which the directed arc is from cause to consequence, denoting the functional variable F_n;i representing the causal relationship between parent variable V_i and child variable X_n or BX_n. In F_n;i, which is an event matrix, F_nk;ij is a member representing the causal relationship between parent event V_ij and child event X_nk or BX_nk, F_nk;i represents the causal relationship between parent variable V_i and child event X_nk or BX_nk, F_n;ij, represents the causal relationship between parent event V_ij and child variable X_n or BX_n, and F_nk;i represents the causal relationship between parent event V_nk and child variable X_i or BX_i. In details, F_nk;ij(r_n;i/r_n)A_nk;ij, where r_n;i>0 quantifies the uncertain causal relationship intensity between parent variable V_i and child variable X_n or BX_n, r_n≡Σ_ir_n;i, A_nk;ij represents the virtual random causal event that V_ij may cause X_nk or BX_nk and the probability of A_nk;ij is defined as a_nk;ij≡Pr{A_nk;ij} satisfying Σ_k a_nk;ij≤1. Define f_nk;ij=Pr{F_nk;ij}(r_n;i/r_n)a_nk;ij, in which f_nk;ij means the probabilistic contributions from V_ij to X_nk, satisfying

$\mathrm{Pr}\left\{X_{\mathrm{nk}}\right\}\sum _{i,j}^{}f_{\mathrm{nk};\mathrm{ij}}\mathrm{Pr}\left\{V_{\mathrm{ij}}\right\}.$

In general, v_ij=Pr{V_ij}, in which v∈{b, x, bx, d, g}, and V_ij or v_ij is a member of event vector V_i or parameter vector v_i respectively. When cause variable is D_i, define F_nk;ij≡F_nk;iD, i.e. j=D. The other causal variables and relationships can be represented similarly.

F_nk;ij can also be a conditional functional event, which can be drawn as dashed directed arc. The conditional functional event is used to represent the conditional functional relationship between its cause event and its consequence event X_nk/BX_nk. The condition event Z_nk;ij encoded in determines whether F_nk;ij holds or not. Taken Z_nk;ij=X_1,2 as an example, when X_1,2 is observed as true, Z_nk;ij is met and F_nk;ij is held; when X_1,2 is observed as false, Z_nk;ij is not met and F_nk;ij is not held. Condition events Z_nk;ij can be a single event Z_n;i, e.g. Z_n;i,=X_1,2. When X_1,2 is observed as true, Z_n;i is met and F_n;i is held, causing to become ; when X_1,2 is observed as false, Z_n;i is not met and F_n;i is not held, causing to be eliminated.

For simplicity, the complete set is denoted as 1 and the null set is denoted as 0. Users can also choose other graphical symbols or signs to represent the aforementioned variables and their states.

With the received evidence E, the following rules can be used to simplify the DUCG:

Rule 1: If E shows that Z_nk;ij or Z_n;i is not met, F_nk;ij or F_n;i is eliminated from the DUCG. If E shows that Z_nk;ij or Z_n;i is met, the dashed F_nk;ij or F_n;i becomes the solid F_nk;ij or F_n;i.Rule 2: If E shows that V_ij (V∈{B, X}) is true while V_ij is not a parent event of X_n or BX_n, F_n;i is eliminated from the DUCG.Rule 3: If E shows that X_nk is true while X_nk cannot be caused by any state of V_i, V∈{B, X, BX, G, D}, F_n;i is eliminated from the DUCG, except that X_nk is a descendant of a variable whose state is to be determined and there is no state-known variable to block them.Rule 4: If E shows that state-unknown {B-, X-}-type node does not have any output directed arc, the node and all its input directed arcs are eliminated from the DUCG.Rule 5: If E shows X_n0 is true, and X_n0 has no causal connection with abnormal evidence E′, then X_n0 is eliminated from the DUCG, except that X_n0 is a descendant of a variable whose state is to be determined and there is no state-known variable to block them.Rule 6: If E shows that a group of state-known nodes have no causal connection with X_nk (k≠0), unless through X_n0, then this group of state-known nodes and their connected directed arcs and D-type nodes are eliminated from the DUCG.Rule 7: If G_i without any output is encountered for any reason, G_i and its input directed arcs are eliminated from the DUCG; If G_i without input is encountered, G_i and its output directed arcs are eliminated from the DUCG.Rule 8: If a directed arc has no parent nodes or no child nodes, then it is eliminated from the DUCG.Rule 9: If there is such a group of nodes and directed arcs that have no causal connection with those nodes included in E, this group of nodes and connected directed arcs can be eliminated from the DUCG.Rule 10: If E shows that abnormal state X_nk is true while X_nk does not have any input due to any reason, add a virtual parent event D_n to X_nk as its input, in the directed arc from D_n to X_nk, a_nk;nD=1 and a_nk;nD=0 (k≠k′) and r_n;D, can be any value. D_n can be drawn as or .Rule 11: If E shows that there exists a group of state normal X-type events X_n0∈S_I(0 indexes normal state), which are connected to only state-unknown variables but not the hypothesis event H_kj in concern, the state-known variables are blocked by X_n0∈S_I with the state-unknown variables, then this group of state-unknown variables and X_n0∈S_I are eliminated.Rule 12: The above rules can be applied in any order: separately, together or repeatedly.

By assuming that only one B-type variable exist while the others do not exist, the simplified DUCG graph can be divided as a group of sub-DUCGs with each containing only one B-type variable. The sub-DUCGs can be simplified according to the above simplification rules again. After these simplifications, the sub-DUCG whose B-type or BX-type variable has no descendent abnormal evidence is eliminated. The abnormal states of the B-type and BX-type variables in the remnant sub-DUCGs make up the possible hypothesis space S_H which may lead to system abnormality. S_H usually consists of B_kj or BX_kj (k≠0). For H_kj∈S_H, calculate the posterior probability of H_kj=B_kj or H_kj=BX_kj (k≠0): h_kj^s=ξ_kPr{H_kj|E, sub-DUCG_k}, in which

${\xi }_k={\zeta }_k/\sum _k^{}{\zeta }_k,$

and ζ_k=Pr{E|sub-DUCG_k}. According to h_kj^s, people can know the possible causes and their ranks of system abnormality, so as to take corrective measures to make the system back to normal as soon as possible.

In order to collect evidence more effectively, granted Chinese patent A HEURISTIC CHECK METHOD TO FIND CAUSES OF SYSTEM ABNORMALITY BASED ON DYNAMIC UNCERTAIN CAUSALITY GRAPH (in Chinese, Patent Number: ZL 2016 1 0282052.1) proposes a method to recommend detecting the states of state-unknown X-type variables, thus to make the above-mentioned evidence ampler and more effective, in order to diagnose and reason more accurately. In the Claim 5 of the above patent, when calculating the probability importance measurement ρ_i of state-to-test X_i, many calculation formulas are adopted, such as:

${\rho }_i\left(y\right)=\frac{1}{m_i\left(y\right)}\sum _{k\in S_{\mathrm{iK}}\left(y\right)}^{}{\omega }_k\sum _{j\in S_{\mathrm{kJ}}}^{}\sum _{g\in S_{\mathrm{iG}}\left(y\right)}^{}\mathrm{Pr}\left\{X_{\mathrm{ig}}|E\left(y\right)\right\}\mathrm{Pr}\left\{H_{\mathrm{kj}}|X_{\mathrm{ig}}E\left(y\right)\right\}-\mathrm{Pr}\left\{H_{\mathrm{kj}}|E\left(y\right)\right\}$

In which, y=0, 1, 2, . . . indexes the step of check, ω_k is irrelevant to subscript j, that means ω_k is irrelevant to the abnormal state of root cause variable. In reality, however, the degree of concern for different abnormal states of root cause variable may be different.

The above technical schemes have following restrictions: (1) G_ijG_ij′=0 (j≠j′), which is a strict requirement for representing logic combinations. When people only take into account the impact of the combination of different factors X_nk on the probabilistic distribution of each state of B_i, they want a more flexible combination, without or less restricted by the above assumption.

$\begin{array}{cc}\sum _ka_{\mathrm{nk};\mathrm{ij}}\le 1,& \left(2\right)\end{array}$

which indicates a_nk;ij≤1. However, sometimes the meaning of parameter a is the increasing or decreasing rate of the occurrence probability of an event, thus both a_nk;ij and

$\sum _ka_{\mathrm{nk};\mathrm{ij}}$

can be larger than 1. (3) Logic gate G only considers the state combinations of its input variables, while sometimes people need to represent the state combinations of the output variables of logic gate G. (4) There exists special kind of X-type variable in real applications, once its abnormal state is observed, its corresponding B-type or BX-type cause event can be determined, no complex probabilistic reasoning is needed. (5) The reasoning of DUCG is based on E, yet sometimes the abnormality of state of X-type variable is not caused by current B-type or BX-type variable but caused by other unknown cause. For different states of different X-type variables, the degree that people consider them are different. Thus the concern degree of X_nk (k≠0) and the corresponding calculation method need defining, so that when other conditions are the same, the more unexplained X-type evidence with an abnormal state, the less likely its corresponding B-type or BX-type variable will be the cause of the system abnormality.

This invention proposes an extended technical scheme to solve the aforementioned issues.

TECHNICAL REFERENCES FOR THIS INVENTION

• [1] Q. Zhang and Z. Zhang. Method for constructing an intelligent system processing uncertain causal relationship information, ZL 200680055266.X, 2010, (in Chinese).
• [2] Q. Zhang and Z. Zhang. Method for constructing an intelligent system processing uncertain causal relationship information, U.S. Pat. No. 8,255,353 B2, 2012.
• [3] Q. Zhang and C. Dong. Method for constructing cubic DUCG for dynamic fault diagnosis, CN 2013107185964, 2015, (in Chinese).
• [4] Q. Zhang. A heuristic check method to find causes of system abnormality based on Dynamic Uncertain Causality Graph, Z L 2016 1 0282052.1, 2016, (in Chinese).
• [5] Q. Zhang. “Dynamic uncertain causality graph for knowledge representation and reasoning: discrete DAG cases”, Journal of Computer Science and Technology, vol. 27, no. 1, pp. 1-23, 2012.
• [6] Q. Zhang, C. Dong, Y. Cui and Z. Yang. “Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: statistics base, matrix and fault diagnosis”, IEEE Trans. Neural Networks and Learning Systems, vol. 25, no. 4, pp. 645-663, 2014.
• [7] Q. Zhang. “Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution”, IEEE Trans. Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1503-1517, 2015.
• [8] Q. Zhang. “Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: continuous variable, uncertain evidence and failure forecast”, IEEE Trans. Systems, Man and Cybernetics, vol. 45, no. 7, pp. 990-1003, 2015.
• [9] Q. Zhang and S. Geng. “Dynamic uncertain causality graph applied to dynamic fault diagnosis of large and complex systems”, IEEE Trans. Reliability, vol. 64, no. 3, pp 910-927, 2015
• [10] Q. Zhang and Z. Zhang. “Dynamic uncertain causality graph applied to dynamic fault diagnoses and predictions with negative feedbacks”, IEEE Trans. Reliability, vol. 65, no. 2, pp 1030-1044, 2016.
• [11] Q. Zhang & Q. Yao. Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases. IEEE Trans. Neural Networks and Learning Systems, vol. 20, no. 5, pp 1637-1651, 2018.

DISCLOSURE OF THE INVENTION

This invention discloses a technical scheme, which further develops granted Chinese patents No. CN 200680055266.X and No. CN 2013107185964, and granted U.S. Pat. No. 8,255,353 B2 and the DUCG technical schemes disclosed in the above-mentioned literature.

Detailed Descriptions of the Technical Scheme of this Invention:

1. A method of construction and reasoning of an extended DUCG intelligent system for processing uncertain causal relationship information, by using a storage medium characterized in that: the storage medium stores computer programs, when the computer programs are executed by a computing device comprising at least one processor and at least one memory, they can execute the method that, based on previous DUCG technical schemes, adds new methods to represent and reason the cause B_k of object system abnormality, which include (1) Use a new type of logic gate SG_k and a new functional variable SA_k;k to represent the direct influences of evidence X_yg and its combinations on every state of B_k, B_k after the influences is denoted as BX_k, X_y and B_k are inputs of SG_k, and event matrix SA_k;k is the output of SG_k, the member event of SA_k;k is SA_kj;kn; (2) Use reversal logic gate RG_i to represent the logic relationship between every state of cause variable and the state combination of more than one consequence variable, and determine the state of the reversal logic gate based on the meaningful state combination evidence of consequence variables, then make the DUCG reasoning according to the determined state of the reversal logic gate; (3) Use SX_y variable to represent the special X-type variable that corresponds to an abnormal state of a certain B-type variable, characterized in that when SX_yg (g≠0) is observed, it can be concluded that the corresponding abnormal state of the B-type variable is true without reasoning or calculating about SX_yg; (4) Use concern degree ε_yg (g≠0) of X_yg or SX_yg to represent the degree of the decreased likelihood when X_yg or SX_yg cannot be explained by a reasoning result H_kj, and includes ε_yg in the calculation of the state probability of H_kj, so that the more ε_yg included in the calculation and the bigger the value of ε_yg, the smaller the possibility of H_kj is; (5) Use danger degree μ_kj of abnormal state B_kj of B_k to represent the degree of B_kj to damage the object system, so that the bigger the value of μ_kj, the larger the demand to detect the states of X-type variables helpful to determine the state of B_k is.

2. As the said 1(1), which also characterized in that: 1) When B_k=B_kj, then BX_k=BX_kj and vice versa; 2) Use a graphical symbol to represent SG_k, and a type of directed arc to represent the input relationship from B_k or X_y to SG_k; 3) Use another type of directed arc to represent SA_k;k from SG_k to BX_k; 4) sa_kj;kn≡Pr{SA_kj;kn} represents the zoom ratio to increase or decrease Pr{B_kj} as Pr{BX_kj}, sa_kj;kn is not restricted by Pr{SA_kj;kn}≤1; 5) SA_k;k can be a conditional event matrix, which is represented by a directed arc different from the directed arc in the said 3), pointing from SG_k to BX_k, the conditional event of SA_k;k is represented by Z_k;k, which is an observable event, when Z_k;k is not met, SA_k;k is eliminated, otherwise is kept as ordinary SA_k;k; 6) In the logic gate specification LGS_k of SG_k, use event combination expression indexed by n (n≠1) to represent the X-type input event combination of SG_kn; 7) When n=1, the input event combination of SG_k1 is the remnant state of other state combination of input variables, the remnant state can also be indexed by n≠1; 8) n is given to indicate the rank of priorities of expressions; 9) According to the X-type evidence collected on site, match the event combination expression according to the rank of n to determine SG_k=SG_kn, stop the match once an event combination expression indexed by n is matched; 10) When the event combination expression indexed by a special n such as n=0 is matched, B_k does not exists, and B_k, SG_k and its input/output directed arcs can be eliminated; 11) The directed arc pointing from the state-unknown or state-normal X-type variable not included in the matched event combination expression n to SG_kn can be eliminated; 12) When the matched n is not the special index mentioned above, replace Pr{B_kj|E} with Pr{BX_kj|E}, BX_kj=SA_kj;knB_kj, thus Pr {B_kj|E}=sa_kj;knb_kj, where E is the collected evidence.

3. As the said 1(2), which also characterized in that: 1) Use a graphical symbol to represent RG_i, with at least one input variable connected with an F-type directed arc pointing from the input variable to RG_i, and with at least two output variables connected with directed arcs pointing from RG_i to the output variables; 2) RG_in is the state of RG_i indexed by n, represents the output variable state combination indexed by n, and is denoted as event combination expression n; 3) In the process of reasoning, the DUCG logic expanding of RG_in is as an X-type variable; 4) When n is a special index such as 0, which means no meaningful state combination of output variables, then RG_i0 and its input/output directed arcs are eliminated; 5) n is given to indicate the rank of the priorities of the output variable state combinations, when evidence E is received, match the state combination expression of RG_in according to the rank of n till matched to determine RG_k=RG_kn; 6) The a parameters encoded in the output F-type directed arc of RG_i can be generated automatically according to the LGS_i of RG_i. The rule of generation is: Check if there exists X_yg in the event combination expression of RG_i, if yes then a_yg;in=1 that is A_yg;in=1, otherwise a_yg;in=0 or “-” which means A_yg;in=0.

4. As the said 1(3), which also characterized in that: use 1≥θ_yg>0 to denote how much confidence of SX_yg to determine that an abnormal state of B_kj, j≠0 (indicate abnormal state), is true directly. θ_yg is used as h_kj^s to join the rank of possible hypotheses.

5. As the said 1(4), which also characterized in that: 1) ε_yg is included in the calculation of the state probability h_kj^s of H_kj, only when H_kj cannot be the cause explaining X_yg or SX_yg in sub-DUCG_k. 2) The way to include ϵ_yg in the calculation is: in the calculation of the weighting coefficient

${\xi }_k={\zeta }_k/\sum _k^{}{\zeta }_k$

in the sub-DUCG_k containing H_kj, when calculating ζ_k,

${\zeta }_k=\mathrm{Pr}\left\{\prod _{y^{\prime }\in S_1}^{}E_{y^{\prime }}|\mathrm{sub}\text{-}{\mathrm{DUCG}}_k\right\}\prod _{y\in S_2}^{}$

(expression that the bigger ε_yg, the smaller the value is), where S₁ represents the set of index of evidence that is explained by H_kj in the sub-DUCG_k, and S₂ represents the set of index of X_yg-type or SX_yg-type evidence that is not explained by H_kj in the sub-DUCG_k.

6. As the said 1(5), which also characterized in that: 1) When calculating the probability importance measurement ρ_i of the X_i variable to be detected, replace ω_k with ω_kj. 2) When calculating the probability importance measurement ρ_i, put ω_kj into the inner layer of subscript j in the formulas to calculate ρ_i, which includes but are not limited to:

${\rho }_i\left(y\right)=\frac{1}{m_i\left(y\right)}\sum _{k\in S_{\mathrm{iK}}\left(y\right)}^{}{\omega }_k\sum _{j\in S_{\mathrm{kJ}}}^{}\sum _{g\in S_{\mathrm{iG}}\left(y\right)}^{}\mathrm{Pr}\left\{X_{\mathrm{ig}}|E\left(y\right)\right\}\mathrm{Pr}\left\{H_{\mathrm{kj}}|X_{\mathrm{ig}}E\left(y\right)\right\}-\mathrm{Pr}\left\{H_{\mathrm{kj}}|E\left(y\right)\right\}$

is replaced with

${\rho }_i\left(y\right)=\frac{1}{m_i\left(y\right)}\sum _{k\in S_{\mathrm{iK}}\left(y\right)}^{}\sum _{j\in S_{\mathrm{kJ}}}^{}{\omega }_{\mathrm{kj}}\sum _{g\in S_{\mathrm{iG}}\left(y\right)}^{}\mathrm{Pr}\left\{X_{\mathrm{ig}}|E\left(y\right)\right\}\mathrm{Pr}\left\{X_{\mathrm{ig}}E\left(y\right)\right\}-\mathrm{Pr}\left\{E\left(y\right)\right\}$ $\mathrm{or}$ ${\rho }_i\left(y\right)=\frac{1}{m_i\left(y\right)}\sum _{k\in S_{\mathrm{iK}}\left(y\right)}^{}\frac{1}{J_k}\sum _{j\in S_{\mathrm{kJ}}}^{}{\omega }_{\mathrm{kj}}\sum _{g\in S_{\mathrm{iG}}\left(y\right)}^{}\mathrm{Pr}\left\{X_{\mathrm{ig}}|E\left(y\right)\right\}\mathrm{Pr}\left\{X_{\mathrm{ig}}E\left(y\right)\right\}-\mathrm{Pr}\left\{E\left(y\right)\right\}$

where J_k denotes the number of abnormal states of B_k.

BRIEF DESCRIPTIONS OF FIGURES

FIG. 1: An example of DUCG.

FIG. 2: Simplified FIG. 1 based on received evidence E=X_6,2X_7,1X_14,1.

FIG. 3: New type of DUCG in Example 3.

FIG. 4: The case of FIG. 3 conditional on E=X_1,1X_2,1.

FIG. 5: The case of Example 2 after receiving evidence.

FIG. 6: Simplified FIG. 5 according to Claim 2-9).

FIG. 7: FIG. 3 after receiving evidence E=1.

FIG. 8: The further simplification result of FIG. 7.

FIG. 9: The further simplification result of FIG. 8.

FIG. 10: The further simplification result of FIG. 8.

FIG. 11: The case of SA_k;k as a conditional functional variable.

FIG. 12: The case of SA_k;k being eliminated.

FIG. 13: An example of the reversal logic gate.

FIG. 14: FIG. 13 after receiving evidence E=X_1,1X_2,1X_4,1X_5,1.

FIG. 15: Another example of the reversal logic gate.

FIG. 16: FIG. 15 after receiving evidence E=X_1,1X_2,1X_4,1X_5,1.

FIG. 17: sub-DUCG after receiving evidence E=X_2,1X_4,1X_5,1SX_8,1.

FIG. 18: The simplified sub-DUCG conditioned on E=X_1,1X_2,1X_4,1X_5,1X_6,2X_7,1.

FIG. 19: The subgraph of nasal septum deviation in the DUCG of nasal obstruction.

FIG. 20: The subgraph of encephalomeningocele in the DUCG of nasal obstruction.

FIG. 21: The DUCG of nasal obstruction.

FIG. 22: The graphical explanation to the diagnostic result of case 1 in example 9.

FIG. 23: A realistic example system that may be used in some embodiments.

EXAMPLES OF IMPLEMENTING THIS INVENTION

Example 1

As is shown in FIG. 3, SG_k is represented by graphical symbol , SG_kj is represented by graphical symbol , and its input directed arc is represented by , SA_k;k is represented by directed arc . In B_kj and BX_kj, it is assumed that j∈{0, 1, 2}. As claimed in Claim 1(2) and Claim 2, X₁, X₂, X₃, and B_k comprise the input variables of SG_k, BX_k is the output variable of SG_k connected by SA_k;k. Encoded in FIG. 3, the logic expressions LGS_k (in TABLE 1), the parameter of SA_k;k, and the parameters of B_k are:

TABLE 1
LGSk in FIG. 3
n SGkn
0 X1, 0∪X2, 0X3, 0
1 Remnant state
2 X1, 1X2, 1
3 X1, 1X3, 1
4 X1, 1X2, 1X3, 1

where n=0 is the special index;In FIG. 3, the parameters of B_k is: b_k=(−0.01 0.002)^T, and the parameters of SA_k;k are Sa_k;k:

${\mathrm{sa}}_{k;k}=\left(\begin{array}{ccccc}-& -& -& -& -\\ -& 1& 10& 15& 0.5\\ -& 1& 2& 2.5& 15\end{array}\right),$

where “-” represents not in concern (equivalent to 0), the meaning of sa_kj;kn is: In the case that the state combination of input X-type variables of double-line logic gate SG_k is determined as the event combination expression n of LGS_k based on evidence E, the value Pr{B_kj}=b_kj is decreased/increased to Pr{BX_kj}, in other words, for each E, only one column parameters in sa_k;k is included in calculations, e.g. when n=2 is determined based on E, only sa_k;k2 (−10 2)^T is included in calculations.

Assume H_kj=B_kj, E=X_1,1X_2,1, and the rank of n is 0, 3, 2, and 1. According to Claim 2-9), when E is received, match event combination expression according to the rank of n, till SG_k2=X_1,1X_2,1 is matched, thus SG_k2 is true. Since X₃ is not included in E, According to Claim 2-11), input and output of X₃ are eliminated. In the end, FIG. 3 is simplified as FIG. 4 (wherein the colors of states can be self-defined).

Based on FIG. 4, according to Claims 1(1) and 2-12), we have

$\begin{array}{c}\mathrm{Pr}\left\{H_{\mathrm{kj}}|E\right\}=\mathrm{Pr}\left\{B_{\mathrm{kj}}|E\right\}\\ =\mathrm{Pr}\left\{{\mathrm{BX}}_{\mathrm{kj}}|E\right\}\\ =\mathrm{Pr}\left\{{\mathrm{SA}}_{\mathrm{kj};k2}B_{\mathrm{kj}}|X_{1,1}X_{2,1}\right\}\\ =\frac{\mathrm{Pr}\left\{{\mathrm{SA}}_{\mathrm{kj};k2}B_{\mathrm{kj}}X_{1,1}X_{2,1}\right\}}{\mathrm{Pr}\left\{X_{1,1}X_{2,1}\right\}}\\ =\frac{\mathrm{Pr}\left\{{\mathrm{SA}}_{\mathrm{kj};k2}B_{\mathrm{kj}}\right\}\mathrm{Pr}\left\{X_{1,1}X_{2,1}\right\}}{\mathrm{Pr}\left\{X_{1,1}X_{2,1}\right\}}\\ =\mathrm{Pr}\left\{{\mathrm{SA}}_{\mathrm{kj};k2}B_{\mathrm{kj}}\right\}\\ ={\mathrm{sa}}_{\mathrm{kj};k2}b_{\mathrm{kj}}\end{array}$

when j=0, Pr{H_k0|E}=sa_k0;k2b_k0=“-”ד-”=“-”,when j=1, Pr{H_k1|E}=sa_k1;k2b_k1=10×0.01=0.1,when j=2, Pr{H_k2|E}=sa_k2;k2b_k2=2×0.002=0.004.

Adopt the operator “*” defined in DUCG, the above calculations can be abbreviated as follows:

$\begin{array}{c}\mathrm{Pr}\left\{H_k|E\right\}=\mathrm{Pr}\left\{B_k|E\right\}\\ =\mathrm{Pr}\left\{{\mathrm{BX}}_{\mathrm{kj}}|E\right\}\\ =\mathrm{Pr}\left\{{\mathrm{SA}}_{k;k2}*B_k|X_{1,1}X_{2,1}\right\}\\ =\frac{\mathrm{Pr}\left\{\left({\mathrm{SA}}_{k;k2}*B_k\right)X_{1,1}X_{2,1}\right\}}{\mathrm{Pr}\left\{X_{1,1}X_{2,1}\right\}}\\ =\frac{\mathrm{Pr}\left\{{\mathrm{SA}}_{k;k2}*B_k\right\}\mathrm{Pr}\left\{X_{1,1}X_{2,1}\right\}}{\mathrm{Pr}\left\{X_{1,1}X_{2,1}\right\}}\\ =\mathrm{Pr}\left\{{\mathrm{SA}}_{k;k2}*B_{\mathrm{kj}}\right\}\\ ={\mathrm{sa}}_{k;k2}*b_k\\ =\left(\begin{array}{c}-\\ 10\\ 2\end{array}\right)*\left(\begin{array}{c}-\\ 0.01\\ 0.002\end{array}\right)\\ =\left(\begin{array}{c}-\times -\\ 10\times 0.01\\ 2\times 0.002\end{array}\right)\\ =\left(\begin{array}{c}-\\ 0.1\\ 0.004\end{array}\right)\end{array}$

where the definition of operator “*” in DUCG is to conduct logic AND or to multiply the event/data in the same row of two matrices with a same number of rows (see Ref [6] for more details).

Example 2

Except E=X_1,0, other conditions are the same with Example 1.

According to the rank of n and TABLE 1, SG_k0=X_0,0∪X_2,0X_3,0 is matched. Thus FIG. 3 becomes FIG. 5.

As is claimed in Claim 2-10), B_k, SG_k0 and its input/output directed arcs are eliminated, resulting in FIG. 6. Assume X_1,0 is the normal state of X₁, according to the simplification rules in DUCG, all variables in FIG. 6 are eliminated, i.e. FIG. 6 is eliminated, resulting in the elimination of B_k, i.e. the abnormal state of B_k does not exist, thus no further calculation is needed.

Example 3

Except for that E=1, other conditions are the same with Example 1. E=1 means all the states of X₁, X₂, and X₃ are unknown so that only the remnant state in TABLE 1 is matched. Thus SG_k1 is matched, and FIG. 3 becomes FIG. 7. As is claimed in Claim 2-11), FIG. 7 is simplified as FIG. 8. Based on the simplification rules in DUCG, FIG. 8 is further simplified as FIG. 9.

Since SG_k=SG_k1, we can see that from TABLE 1, sa_k0;k1=“-” and a_k1;k1=Sa_k2;k1=1. As is claimed in Claim 2-12), similar to the calculations in Example 1, we have

Pr{H_k0|E}=sa_k0;k2b_k0=“-”ד-”=“-”;Pr{H_k1|E}=sa_k1;k2b_k1=1×0.01=0.01;Pr{H_k2|E}=sa_k2;k2b_k2=1×0.002=0.002.In other words, the probability distribution of the state of BX_k is exactly the same with that of B_k. In this case, B_k is exactly equal to BX_k, thus we can substitute B_k for BX_k. That means FIG. 9 can be further simplified as FIG. 10.

Example 4

Change FIG. 3 as FIG. 11 with Z_k;k=X_1,0∪X_2,0X_3,0 and E=X_1,0, while other conditions remain unchanged, in which the said directed arc in Claim 2-5) is represented by →.

According to Claim 2-5), Z_k;k is met conditioned on E, and SA_k;k is eliminated, FIG. 11 is changed as FIG. 12. Then based on the simplification rules in DUCG, the entire FIG. 12 (including B_k) is eliminated, and no further calculation is needed. This example is equivalent to Example 2 although with different representation modes, thus they have the same result.

Example 5

The reverse logic gate stated in Claims 1 and 3 is illustrated in FIG. 13, reversal logic gate RG_i is represented by graphical symbol , RG_in is represented by graphical symbol , BX_k is the input of reversal logic gate RG_i, X₄ and X₅ are the outputs of RG_i=(RG_i0 RG_i1 RG_i2)^T, the LGS_i is shown in TABLE 2.

TABLE 2
LGSi in FIG. 13
n RGin
0 Remnant state
1 X4, 1
2 X5, 1
3 X4, 1X5, 1

Additionally, we have

$a_{i;k}=\left(\begin{array}{ccc}-& -& -\\ -& 0.4& 0.1\\ -& 0.5& 0.1\\ -& 0.1& 0.8\end{array}\right),$

the rank of n is 3, 2, 1 and 0, others are the same with Example 1.

According to Claim 3-6) and TABLE 2, the generated parameters a are

$a_{4;i}=\left(\begin{array}{cccc}-& -& -& -\\ -& 1& 0& 1\end{array}\right)\mathrm{and}a_{5;i}=\left(\begin{array}{cccc}-& -& -& -\\ -& 0& 1& 1\end{array}\right).$

Assume E=X_1,1X_2,1X_4,1X_5,1, according to LGS_i, we have RG_i=RG_i3, then FIG. 13 becomes FIG. 14. Based on FIG. 14, according to the DUCG expanding algorithm, we expand step by step from downstream to upstream:

$X_{4,1}=F_{4,1;i3}RG_{i3}=A_{4,1;i3}RG_{i3}\mathrm{with}\mathrm{single}\mathrm{input},r\mathrm{does}\mathrm{not}\mathrm{work}.X_{5,1}=F_{5,1;i3}RG_{i3}=A_{5,1;i3}RG_{i3}$ $X_{4,1}X_{5,1}=A_{4,1;i3}RG_{i3}A_{5,1;i3}RG_{i3}=\left(A_{4,1;i3}*A_{5,1;i3}\right)RG_{i3}$

Since a_4,1;i3=a_5,1;i3=1, i.e. A_4,1;i3=A_5,1;i3=1, the above equation becomes:

$\begin{array}{c}{X}_{4,1}X_{5,1}=\left(A_{4,1;i3}*A_{5,1;i3}\right)RG_{i3}\\ =RG_{i3}\\ =A_{i3;k}BX_k\\ =A_{i3;k}\left(SA_{k;k2}*B_k\right)\mathrm{based}\mathrm{on}\mathrm{Claim}2\text{-}12)\end{array}$

For simplicity, operator “*” in DUCG is used, its definition is explained in Example 1.

Then,

$X_{1,1}=F_{1,1;1D}D_1=A_{1,1;1D}D_1$ $X_{2,1}=F_{2,1;2D}D_2=A_{2,1;2D}D_2$ $\begin{array}{c}E=X_{1,1}X_{2,1}X_{4,1}X_{5,1}\\ =A_{1,1;1D}D_1A_{2,1;2D}D_2A_{i3;k}\left(SA_{k;k2}*B_k\right)\end{array}$

Assume H_kj=B_kj, then we have

$\begin{array}{c}\mathrm{Pr}\left\{H_{\mathrm{kj}}|E\right\}=Pr\left\{B_k|E\right\}\\ =\mathrm{Pr}\left\{BX_k|E\right\}\mathrm{based}\mathrm{on}\mathrm{Claim}2\text{-}12)\\ =\mathrm{Pr}\left\{SA_{\mathrm{kj};k2}B_{\mathrm{kj}}|E\right\}\mathrm{based}\mathrm{on}\mathrm{Claim}2\text{-}12)\\ =\frac{Pr\left\{SA_{\mathrm{kj};k2}B_{\mathrm{kj}}E\right\}}{Pr\left\{E\right\}}\\ =\frac{Pr\left\{SA_{\mathrm{kj};k2}B_{\mathrm{kj}}A_{1,1;1D}D_1A_{2,1;2D}D_2A_{i3;k}\left(SA_{k;k2}*B_k\right)\right\}}{Pr\left\{A_{1,1;1D}D_1A_{2,1;2D}D_2A_{i3;k}\left(SA_{k;k2}*B_k\right)\right\}}\\ =\frac{Pr\left\{A_{1,1;1D}D_1A_{2,1;2D}D_2A_{i3;k2}\left(SA_{\mathrm{kj};k2}B_{\mathrm{kj}}\right)\right\}}{Pr\left\{A_{1,1;1D}D_1A_{2,1;2D}D_2A_{i3;k}\left(SA_{\left(k;k2\right)}*B_k\right)\right\}}\\ =\frac{a_{1,1;1D}a_{2,1;2D}a_{i3;\mathrm{kj}}\left(sa_{\mathrm{kj};k2}b_{\mathrm{kj}}\right)}{a_{1,1;1D}a_{2,1;2D}a_{i3;k}\left(sa_{k;k2}*b_k\right)}\\ =\frac{a_{i3,\mathrm{kj}}\left(sa_{\mathrm{kj};k2}b_{\mathrm{kj}}\right)}{a_{i3;k}\left(sa_{k;k2}*b_k\right)}\end{array}$

when j=0,

$Pr\left\{B_{k0}|E\right\}=\frac{a_{i3;k0}\left(sa_{k0;k2}b_{k0}\right)}{a_{i3,k}\left(sa_{k;k2^{*}}b_k\right)}=\frac{-\times \left(10\times -\right)}{\left(-00.8\right)\left(\left(\begin{array}{c}-\\ 10\\ 2\end{array}\right)*\left(\begin{array}{c}-\\ 0.01\\ 0.002\end{array}\right)\right)}=0$

when j=1,

$\mathrm{Pr}\left\{B_{k1}|E\right\}=\frac{a_{i3;.k1}\left(sa_{k1;k2}b_{k1}\right)}{a_{i3;k}\left(sa_{k;k2}*b_k\right)}=\frac{0.1\times \left(10\times 0.01\right)}{\left(-0.10.8\right)\left(\left(\begin{array}{c}-\\ 10\\ 2\end{array}\right)\left(\begin{array}{c}-\\ 0.01\\ 0.002\end{array}\right)\right)}=0.7576$

when j=2,

$\mathrm{Pr}\left\{B_{k2}|E\right\}=\frac{a_{i3;k1}\left(sa_{k2;k2}b_{k2}\right)}{a_{i3;k}\left(sa_{k;k2}*b_k\right)}=\frac{0.8\times \left(2\times 0.01\right)}{\left(-0.10.8\right)\left(\begin{array}{c}-\\ 10\\ 2\end{array}\right)*\left(\begin{array}{c}-\\ 0.01\\ 0.002\end{array}\right)}=0.2424$

Example 6

As shown in FIG. 15, compared to FIG. 13, the output directed arc of RG_i is changed to be represented by , a_4;i and a_5;i are eliminated, other conditions are the same with Example 5. Assume E=X_1,1X_2,1X_4,1X_5,1, according to LGS_i, we have RG_i=RG_i3, then FIG. 15 becomes FIG. 16.

As is claimed in Claim 3-3), RG_i3 is included into E as evidence to be expanded, in other words, E=X_1,1X_2,1X_4,1X_5,1RG_i3. Since there is no F-type directed arc in the upstream of X_4,1 and X_5,1, their expanding ends, the expanding of E=X_1,1X_2,1X_4,1X_5,1RG_i3 is equivalent to that of E=X_1,1X_2,1RG_i3. We also have X_4,1X_5,1=RG_i3 in Example 5, thus the calculation results are exactly the same with Example 5.

Example 7

As shown in FIG. 17, we have E=X_1,1X_2,1X_3,1X_4,1X_5,1SX_8,1. Compared to FIG. 13, evidence X_1,1 is deleted and specific evidence SX_8,1 is added. Assume the state of B_k corresponding to SX_8,1 is B_k2 and θ_8,1=1. According to Claims 1 and 4, we get Pr{B_k2|E}=1.

Example 8

Example 8 is illustrated in FIG. 18.

The only difference between FIG. 18 and FIG. 13 is that FIG. 18 has two pieces of evidence X_6,2 and X_7,1 which cannot be explained by a B-type or BX-type variable. Assume a_1,1;1,D=0.4, a_2,1;2D=0.5 and score concern degree E according to centesimal grade, then the concern degree of X_6,2 is ε_6,2=20, the concern degree of X_7,1 is ε_7,1=10, take “equation that the bigger ε_yg, the smaller the value is”=1/ε_yg. According to Claim 5, we have S₁={X_1,1, X_2,1, X_4,1, X_5,1} and S₂={X_6,2, X_7,1}. Then,

$\begin{array}{c}{\zeta }_k=\mathrm{Pr}\left\{\prod _{\gamma \text{'}\in S_1}E_{y^{\prime }}|\mathrm{sub}\mathrm{graph}k\right\}\prod _{y\in S_2}\left(\mathrm{expression}\mathrm{that}\mathrm{the}\mathrm{bigger}{\varepsilon }_{\mathrm{yg}},\mathrm{the}\mathrm{smaller}\mathrm{the}\mathrm{value}\mathrm{is}\right)\\ =\mathrm{Pr}\left\{X_{1,1}X_{2,1}X_{3,1}X_{4,1}\right\}\frac{1}{{\varepsilon }_{6,2}}\frac{1}{{\varepsilon }_{7,1}}\\ =\mathrm{Pr}\left\{A_{1,1;1D}D_1A_{2,1;2D}D_2A_{i3;k}\left(SA_{k;k2}*B_k\right)\right\}\frac{1}{{\varepsilon }_{6,2}}\frac{1}{{\varepsilon }_{7,1}}\mathrm{see}\mathrm{Example}5\mathrm{for}\mathrm{details}\\ =a_{1,1;1D}a_{2,1;2D}a_{i3;k}\left(sa_{k;k2}*b_k\right)\frac{1}{{\varepsilon }_{6,2}}\frac{1}{{\varepsilon }_{7,1}}\\ =0.4\times 0.5\left(-0.10.8\right)\left(\left(\begin{array}{c}-\\ 10\\ 2\end{array}\right)*\left(\begin{array}{c}-\\ 0.01\\ 0.002\end{array}\right)\right)\frac{1}{20}\times \frac{1}{10}\\ =0.000014\end{array}$

Example 9

Consider 25 diseases that may cause the chief complaint nasal obstruction. They belong to five categories as shown in TABLE 3.

TABLE 3
25 DISEASES INCLUDED IN THE DUCG OF NASAL OBSTRUCTION
Category	Variable	Disease description
Tumor lesion	B2	Nasal sinus malignancy (not including Ethmoid
		sinus carcinoma and Carcinoma of maxillary sinus)
	B7	Nasopharyngeal angiofibroma
	B8	Inverted papilloma of the nose and sinuses
	B15	Carcinoma of nasopharyngeal
	B21	Carcinoma of ethmoid sinus
	B22	Carcinoma of maxillary sinus
	B235	Nasal hemangioma
Physical	B13	Fracture of nasal bone
injury	B14	Fracture of ethmoidal sin
	B16	Fracture of frontal sinus
Congenital	B3	Nasal septum deviation
aplasia	B4	Encephalomeningocele
	B242	Congenital atresia of the posterior nares
Inflammation	B1	Atrophic rhinitis
and Infection	B10	Mycotic maxillary sinusitis
	B12	Bleeding polyp
	B198	Acute sinusitis
	B203	Acute rhinitis
	B208	Allergic rhinitis
	B217	Chronic nasosinusitis
	B237	Chronic hypertrophic rhinitis
	B239	Adenoid hypertrophy
	B238	Chronic rhinosinusitis with nasal polyps
	B240	Chronic simple rhinitis
Foreign body	B6	Nasal foreign body

For each disease, a corresponding subgraph is constructed, where a subgraph is a part of the DUCG of nasal obstruction. As examples, two subgraphs of the 25 subgraphs are as shown in FIGS. 19 and 20 respectively, in which the methods included in Claims 1-5 are used. The other 23 subgraphs are similar and ignored for simplicity. Combine all the 25 subgraphs by fusing the same variables in different subgraphs, the final DUCG is constructed as shown in FIG. 21, which is the DUCG used in the disease diagnoses of nasal obstruction. FIG. 21 includes 25 B-type variables/diseases and the corresponding 25 BX-type variables, 25 SG-type variables, 3 RG-type variables, 200 X-type variables, 17 SX-type variables, 17 D-type variables, 25 SA-type variables/matrices (double line directed arcs) and 531 F-type variables/matrices (single line directed arcs).

In FIG. 19, B₃ denotes nasal septum deviation, X₆₄ is a risk factor of B₃. BX₃ and SG₃ represent the influence of risk factor X₆₄ to B₃. In LGS₃, SG_3,1X_64,0, SG_3,2=X_64,1 and SG_3,0=“Remnant”. Other variables down-stream of BX₃ represent all consequences/effects possibly caused by BX₃. In particular, SX₂₈₈ (Sinus CT shows nasal septum bending) is a disease-specific manifestation variable. When SX_288,1 is observed, nasal septum deviation B_3,1 must be true, i.e. S_288,1=1. The other encoded parameters are as follows.

$r_{n;i}=1,b_3={\left(-0.006\right)}^T,a_{7;3}=\left(\begin{array}{cc}-& -\\ -& 0.7\\ -& 0.29\end{array}\right),a_{9;65}=\left(\begin{array}{cc}-& -\\ -& 0.8\\ -& 0.15\end{array}\right),a_{12;66}=\left(\begin{array}{cc}-& -\\ -& 0.9\end{array}\right),a_{55;3}=\left(\begin{array}{cc}-& -\\ -& 1\end{array}\right),a_{64D}=\left(-1\right),a_{65;3}=\left(\begin{array}{cc}-& -\\ -& 0.6\end{array}\right),a_{66;3}\left(\begin{array}{cc}-& -\\ -& 0.6\end{array}\right),a_{99;7}=\left(\begin{array}{cc}-& -\\ -& 0.9\\ -& 0.01\end{array}\right),a_{101;3}=\left(\begin{array}{cc}-& -\\ -& 0.2\\ -& 0.1\end{array}\right),a_{288;3}=\left(\begin{array}{cc}-& -\\ -& 1\end{array}\right),{\mathrm{sa}}_{3;3}=\left(\begin{array}{ccc}-& -& -\\ 1& 1& 1.5\end{array}\right),{\mathrm{LGS}}_3=\left(\begin{array}{cc}0& \mathrm{Remnant}\\ 1& X_{64,0}\\ 2& X_{64,1}\end{array}\right),{\varepsilon }_{7,1}=85,{\varepsilon }_{9,1}={\varepsilon }_{9,2}=50,{\varepsilon }_{12,1}=30,{\varepsilon }_{55,1}=20,{\varepsilon }_{64,1}=1,{\varepsilon }_{65,1}=70,{\varepsilon }_{66,1}=70,{\varepsilon }_{99,1}={\varepsilon }_{99,2}=35,{\varepsilon }_{101,1}={\varepsilon }_{102,2}=50,{\varepsilon }_{228,1}=95,{\theta }_{288,1}=1.$

TABLE 4 is the descriptions of {X-, SX-}-type variables in FIG. 19.

TABLE 4
{X-, SX-}-type variables in FIG. 19
Variable Variable description
X7       Nasal obstruction
X9       Hemorrhinia
X12      Headache
X55      Nasal septum bending (physical examination)
X64      History of external head injury
X65      Nasal mucosal erosion
X66      Partial compression of ipsilateral turbinate
X99      Progressive nasal congestion
X101     Volume of nasal bleeding
SX228    Nasal septum bending (sinuses CT)

In FIG. 20, B₄ denotes encephalomeningocele, X₂ and X₄ are two risk factors of B₄. BX₄ and SG₄ represent the influence of risk factors X₂ and X₄ to B₄. TABLE 5 is the descriptions of X-type variables in FIG. 20.

TABLE 5
X-type variables in FIG. 20
Variable Variable description
X2       Sex
X4       Age
X7       Nasal obstruction
X42      Hyposmia
X99      Progressive nasal congestion
X108     Eyeball displacement
X144     Angulus oculi medialis spacing increase
X235     Nasopharynx visible tumor
X236     Nasopharynx visible tumor (Nasal endoscopy)
X237     Visible tumor in nasal cavity
X238     Visible tumor in nasal cavity (Nasal endoscopy)
X244     Rhinolalia clausa
X262     Buccal respiration
X268     Infant lactation difficulty
X269     Mental decline
X270     The root of the tumor is located at the top of the nasal or nasopharynx
X271     Cystic masses can be seen at the root of the nose
X272     Tumor transmittance experiment
X273     The Tumors increases with crying
X274     X-ray suggests skull base bone defect
X275     CT suggests skull base bone defect
X276     Herniated meninges and brain tissue
X277     MIR suggests meningeal brain swelling

The parameters related to the method presented in this invention are as follows, while the others are similar to those given for FIG. 19 and are ignored for simplicity.

$a_{2;4}=\left(\begin{array}{cc}-& -\\ 0& 1\end{array}\right)\mathrm{for}F_{2;4}\mathrm{from}{\mathrm{BX}}_4\mathrm{to}{\mathrm{RG}}_2,a_{275;2}=\left(\begin{array}{cc}0& 0\\ 0& 1\end{array}\right)a_{276;2}=\left(\begin{array}{cc}0& 0\\ 0& 1\end{array}\right),\mathrm{LG}S_2=\left(\begin{array}{cc}0& \mathrm{Remnant}\\ 1& X_{275,1}X_{276,1}\end{array}\right),{\varepsilon }_{2,1}=99,{\varepsilon }_{275,1}=85,{\varepsilon }_{276,1}=95.$

Case 1:

A middle-aged male patient with no history of trauma, unilateral nasal congestion, persistent nasal obstruction, nasal itching, unilateral epistaxis, volume of nasal bleeding is less, deviation of nasal septum found by physical examination, other symptoms and physical signs are normal, no laboratory examination and imaging examination results provided. In other words, the abnormal evidence E′ of this patient is:

E′=X_7,1X_9,1X_55,1X_101,1X_221,1X_286,1

Some symptoms and physical signs are observed as in normal states included in E″; the other {X-, SX-}-type variables are state-unknown. Based on the above evidence, the possible diseases are computed and ranked as shown in TABLE 6, in which nasal septal deviation is correctly diagnosed by using the method presented in this invention and the existing methods given in the earlier published DUCG patents and papers listed in this invention.

TABLE 6
The Diagnostic Results of the Nasal Septum Deviation Case
Disease Hkj = Bkj                Ranked hskj
Nasal septal deviation           46.266%
Inverted papilloma of the nose and sinuses <0.01%
Hemorrhagic nasal polyps         <0.01%
Nasal hemangioma                 <0.01%
Mycotic maxillary sinusitis      <0.01%
Allergic rhinitis                <0.01%
Nasopharyngeal angiofibroma      <0.01%
Nasal sinus malignancy           <0.01%
Carcinoma of maxillary sinus     <0.01%
Carcinoma of ethmoid sinus       <0.01%
Atrophic rhinitis                <0.01%
Chronic nasosinusitis            <0.01%
Encephalomeningocele             <0.01%
Carcinoma of nasopharyngeal      <0.01%
Chronic hypertrophic rhinitis    <0.01%
Chronic simple rhinitis          <0.01%

FIG. 22 explains this diagnostic result, in which color nodes indicate meaningful evidence including all E′ and X_12,0 in E″ playing as the negative evidence.

Case 2:

A one-year-old girl patient has the following symptoms: unilateral nasal congestion, snoring, nasal cavity mass found by physical examination, and nasal endoscopy. Imaging examination: CT suggests skull base bone defect, herniated meninges and brain tissue. In other words, the abnormal evidence E′ of this patient is:

E′=X_2,1X_4,1X_7,1X_237,1X_238,1X_254,1X_275,1X_276,1

The other symptoms and physical signs are state-normal. No laboratory test or imaging examination is made (state-unknown). The diseases are diagnosed and ranked as shown in TABLE 7, in which encephalomeningocele is correctly diagnosed.

TABLE 7
The Diagnostic Results of the Encephalomeningocele Case
Disease Hkj = Bkj                Ranked hskj
Encephalomeningocele             50.658%
Nasal septum deviation           4.509%
Chronic rhinosinusitis with nasal polyps 3.653%
Nasal hemangioma                 2.11%
Inverted papilloma of the nose and sinuses <0.01%
Hemorrhagic nasal polyp          <0.01%
Nasal sinus malignancy           <0.01%
Mycotic maxillary proinflammatory <0.01%
Carcinoma of maxillary sinus     <0.01%
Carcinoma of ethmoid sinus       <0.01%
Chronic simple rhinitis          <0.01%
Atrophic rhinitis                <0.01%
Allergic rhinitis                <0.01%
Nasopharyngeal angiofibroma      <0.01%
Nasopharyngeal carcinoma         <0.01%
Acute sinusitis                  <0.01%
Chronic nasosinusitis            <0.01%
Chronic hypertrophic rhinitis    <0.01%

In the above calculations to get the diagnostic results, the methods included in Claims 1-5 and the methods in the patents and papers listed in this invention are used. Considering that examples 1-8 explain all the details of how to use these methods, the details of applying these methods in example 9 are ignored for simplicity.

The above described aspects of the disclosure have been described with regard to certain examples and embodiments, which are intended to illustrate but not to limit the disclosure. It should be appreciated that the subject matter presented herein may be implemented as a computer process, a computer-controlled apparatus or a computing system or an article of manufacture, such as a computer-readable storage medium.

Those skilled in the art will also appreciate that the subject matter described herein may be practiced on or in conjunction with other computer system configurations beyond those described herein, including multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, handheld computers, special-purposed hardware devices, network appliances, and the like. The embodiments described herein may also be practiced in distributed computing environments, where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

In at least some embodiments, a server or computing device that implements a portion or all of one or more of the technologies described herein may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 23 illustrates such a general-purpose computing device 200. In the illustrated embodiment, computing device 200 includes one or more processors 210 (which may be referred herein singularly as “a processor 210” or in the plural as “the processors 210”) are coupled through a bus 220 to a system memory 230. Computing device 200 further includes a permanent storage 240, an input/output (I/O) interface 250, and a network interface 260.

In various embodiments, the computing device 200 may be a uniprocessor system including one processor 210 or a multiprocessor system including several processors 210 (e.g., two, four, eight, or another suitable number). Processors 210 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 210 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 210 may commonly, but not necessarily, implement the same ISA.

System memory 230 may be configured to store instructions and data accessible by processor(s) 210. In various embodiments, system memory 230 may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory.

In one embodiment, I/O interface 250 may be configured to coordinate I/O traffic between processor 210, system memory 230, and any peripheral devices in the device, including network interface 260 or other peripheral interfaces. In some embodiments, I/O interface 250 may perform any necessary protocol, timing, or other data transformations to convert data signals from one component (e.g., system memory 230) into a format suitable for use by another component (e.g., processor 210). In some embodiments, I/O interface 250 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface 250 may be split into two or more separate components, such as a north bridge and a south bridge, for example. Also, in some embodiments some or all of the functionality of I/O interface 250, such as an interface to system memory 230, may be incorporated directly into processor 210.

Network interface 260 may be configured to allow data to be exchanged between computing device 200 and other device or devices attached to a network or network(s). In various embodiments, network interface 260 may support communication via any suitable wired or wireless general data networks, such as types of Ethernet networks, for example. Additionally, network interface 260 may support communication via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks, via storage area networks such as Fibre Channel SANs or via any other suitable type of network and/or protocol.

In some embodiments, system memory 230 may be one embodiment of a computer-accessible medium configured to store program instructions and data as described above for implementing embodiments of the corresponding methods and apparatus. However, in other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media. Generally speaking, a computer-accessible medium may include non-transitory storage media or memory media, such as magnetic or optical media, e.g., disk or DVD/CD coupled to computing device 200 via I/O interface 250. A non-transitory computer-accessible storage medium may also include any volatile or non-volatile media, such as RAM (e.g. SDRAM, DDR SDRAM, RDRAM, SRAM, etc.), ROM, etc., that may be included in some embodiments of computing device 200 as system memory 230 or another type of memory.

Further, a computer-accessible medium may include transmission media or signals such as electrical, electromagnetic or digital signals, conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 260. Portions or all of multiple computing devices may be used to implement the described functionality in various embodiments; for example, software components running on a variety of different devices and servers may collaborate to provide the functionality. In some embodiments, portions of the described functionality may be implemented using storage devices, network devices, or special-purpose computer systems, in addition to or instead of being implemented using general-purpose computer systems. The term “computing device,” as used herein, refers to at least all these types of devices and is not limited to these types of devices.

Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computers or computer processors. The code modules may be stored on any type of non-transitory computer-readable medium or computer storage device, such as hard drives, solid state memory, optical disc, and/or the like. The processes and algorithms may be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, e.g., volatile or non-volatile storage.

While certain example embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions disclosed herein. Thus, nothing in the foregoing description is intended to imply that any particular feature, characteristic, step, module, or block is necessary or indispensable. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the methods and systems described herein may be made without departing from the spirit of the inventions disclosed herein. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of certain of the inventions disclosed herein.

Claims

1. A method of construction and reasoning of an extended DUCG intelligent system for processing uncertain causal relationship information, by using a storage medium characterized in that: the storage medium stores computer programs, when the computer programs are executed, they can execute the method that, based on previous DUCG technical schemes, adds new methods to represent and reason the cause Bk of object system abnormality, which include (1) Use a new type of logic gate SGk and a new functional variable SAk;k to represent the direct influences of evidence Xyg and its combinations on every state of Bk, Bk after the influences is denoted as BXk, Xy and Bk are inputs of SGk, and event matrix SAk;k is the output of SGk, the member event of SAk;k is SAkj;kn; (2) Use reversal logic gate RGi to represent the logic relationship between every state of cause variable and the state combination of more than one consequence variable, and determine the state of the reversal logic gate based on the meaningful state combination evidence of consequence variables, then make the DUCG reasoning according to the determined state of the reversal logic gate; (3) Use SXy variable to represent the special X-type variable that corresponds to an abnormal state of a certain B-type variable, characterized in that when SXyg (g≠0) is observed, it can be concluded that the corresponding abnormal state of the B-type variable is true without reasoning or calculating about SXyg; (4) Use concern degree εyg (g≠0) of Xyg or SXyg to represent the degree of the decreased likelihood when Xyg or SXyg cannot be explained by a reasoning result Hkj, and includes εyg in the calculation of the state probability of Hkj, so that the more εyg included in the calculation and the bigger the value of εyg, the smaller the possibility of Hkj is; (5) Use danger degree μkj of abnormal state Bkj of Bk to represent the degree of Bkj to damage the object system, so that the bigger the value of μkj, the larger the demand to detect the states of X-type variables helpful to determine the state of Bk is.

2. As the said claim 1(1), which also characterized in that: 1) When Bk=Bkj, then BXk=BXkj and vice versa; 2) Use a graphical symbol to represent SGk, and a type of directed arc to represent the input relationship from Bk or Xy to SGk; 3) Use another type of directed arc to represent SAk;k from SGk to BXk; 4) sakj;kn≡Pr{SAkj;kn} represents the zoom ratio to increase or decrease Pr{Bkj} as Pr{BXkj}, sakj;kn is not restricted by Pr{SAkj;kn}≤1; 5) SAk;k can be a conditional event matrix, which is represented by a directed arc different from the directed arc in the said 3), pointing from SGk to BXk, the conditional event of SAk;k is represented by Zk;k, which is an observable event, when Zk;k is not met, SAk;k is eliminated, otherwise is kept as ordinary SAk;k, 6) In the logic gate specification LGSk of SGk, use event combination expression indexed by n (n≠1) to represent the X-type input event combination of SGkn; 7) When n=1, the input event combination of SGk1 is the remnant state of other state combination of input variables, the remnant state can also be indexed by n≠1; 8) n is given to indicate the rank of priorities of expressions; 9) According to the X-type evidence collected on site, match the event combination expression according to the rank of n to determine SGk=SGkn, stop the match once an event combination expression indexed by n is matched; 10) When the event combination expression indexed by a special n such as n=0 is matched, Bk does not exists, and Bk, SGk and its input/output directed arcs can be eliminated; 11) The directed arc pointing from the state-unknown or state-normal X-type variable not included in the matched event combination expression n to SGkn, can be eliminated; 12) When the matched n is not the special index mentioned above, replace Pr{Bkj|E} with Pr{BXkj|E}, BXkj=SAkj;knBkj, thus Pr {Bkj|E}=sakj;knbkj, where E is the collected evidence.

3. As the said claim 1(2), which also characterized in that: 1) Use a graphical symbol to represent RGi, with at least one input variable connected with an F-type directed arc pointing from the input variable to RGi, and with at least two output variables connected with directed arcs pointing from RGi to the output variables; 2) RGin is the state of RGi indexed by n, represents the output variable state combination indexed by n, and is denoted as event combination expression n; 3) In the process of reasoning, the DUCG logic expanding of RGin is as an X-type variable; 4) When n is a special index such as 0, which means no meaningful state combination of output variables, then RGi0 and its input/output directed arcs are eliminated; 5) n is given to indicate the rank of the priorities of the output variable state combinations, when evidence E is received, match the state combination expression of RGin according to the rank of n till matched to determine RGk=RGkn; 6) The a parameters encoded in the output F-type directed arc of RGi can be generated automatically according to the LGSi of RGi. The rule of generation is: Check if there exists Xyg in the event combination expression of RGi, if yes then ayg;in=1 that is Ayg;in=1, otherwise ayg;in=0 or “-” which means Ayg;in=0.

4. As the said claim 1(3), which also characterized in that: use 1≥θyg>0 to denote how much confidence of SXyg to determine that an abnormal state of Bkj, j≠0 (indicate abnormal state), is true directly. θyg is used as hkjs to join the rank of possible hypotheses.

5. As the said claim 1(4), which also characterized in that: 1) εyg is included in the calculation of the state probability hkjs of Hkj, only when Hkj cannot be the cause explaining Xyg or SXyg in sub-DUCGk. 2) The way to include εyg in the calculation is: in the calculation of the weighting coefficient

6. As the said claim 1(5), which also characterized in that: 1) When calculating the probability importance measurement ρi of the Xi variable to be detected, replace ωk with ωkj. 2) When calculating the probability importance measurement ρi, put ωkj into the inner layer of subscript j in the formulas to calculate ρi, which includes but are not limited to: