Patent Application
20190155869
The subject matter herein generally relates to devices and methods for managing area risk.
A current risk evaluation method can detect multiple risk factors of a monitored area. But the current risk evaluation method cannot generate a processing sequence of the multiple risk factors to manage the monitored area in response to the monitored area having a risk warning.
Implementations of the present technology will now be described, by way of example only, with reference to the attached figures.
It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. Also, the description is not to be considered as limiting the scope of the embodiments described herein. The drawings are not necessarily to scale and the proportions of certain parts may be exaggerated to better illustrate details and features of the present disclosure. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean “at least one”.
Several definitions that apply throughout this disclosure will now be presented.
The term “comprising,” when utilized, means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in the so-described combination, group, series, and the like.
The risk managing device 100 monitors a plurality of areas and obtains risk information of the plurality of areas through accessing a network. The plurality of areas in this embodiment can comprise three areas 200a, 200b, and 200c. The risk information can be a fire risk, a dust risk, a hazardous material leak risk, and others. The risk managing device 100 can transmit the risk information of areas 200a, 200b, and 200c, to a monitoring client 300 to inform an administrator.
In one embodiment, the monitoring client 300 can be a mobile phone or a computer. The risk managing device 100 can be operated in a cloud server.
Referring to
Referring to
The obtaining module 10 is configured to obtain live inspection information of the plurality of safety devices, 1a to 1d, of the area 200a. Each of the plurality of safety devices, 1a to 1d, can comprise a plurality of failure factors.
In one embodiment, the live inspection information of the plurality of safety devices, 1a to 1d, of the area 200a can be stored in the storage 11 or the database 400. The obtaining module 10 can communicate with the database 400 to obtain the live inspection information of the plurality of safety devices, 1a to 1d, of the area 200a.
In one embodiment, the live inspection information can comprise periodic inspection recorded information, periodic maintenance recorded information, environmental pollution information, equipment damage information, oxidation damage information, audit system executing information, line aging information, line connection information, and switch state information.
The setting module 20 is configured to set an SOD score for each failure factor of each safety device, 1a to 1d. The SOD score represents a risk grade of a risk priority number (RPN). The SOD score comprises a severity (Sev) score, an occurrence (Occ) score, and a detection (Det) score.
In one embodiment, the setting module 20 can set the Sev score, the Occ score, and the Det score for each failure factor of each safety device, 1a to 1d, according to a failure mode and effect analysis (FMEA) scoring rule.
In one embodiment, a first table as below shows a scoring rule of the Sev score and the Det score:
A second table as below shows a scoring rule of the Occ score:
In one embodiment, the Sev score and the Det score of each failure factor of each safety device, 1a to 1d, can be scored according to the table 1. The Occ score of each failure factor of each safety device, 1a to 1d, is set as an initial score. For example, an initial probability of each failure factor of each safety device, 1a to 1d, is 1/200. According to the table 2, the initial score of the Occ score of each failure factor of each safety device, 1a to 1d, is 6.
A third table as below shows a first RPN sequence of the plurality of safety devices, 1a to 1d, of the area 200a. In the table 3, the Sev score and the Det score of each failure factor of each safety device, 1a to 1d, can be scored according to the table 1:
In the table 3, failure factors of the smoke sensor 1a comprise the periodic inspection recorded information, the periodic maintenance recorded information, the environmental pollution information, the equipment damage information, and the audit system executing information. Failure factors of the alarm button 1b comprise the periodic inspection recorded information, the periodic maintenance recorded information, the audit system executing information, the line aging information, and the equipment damage information. Failure factors of the spray valve 1c comprise the periodic inspection recorded information, the periodic maintenance recorded information, the audit system executing information, the line connection information, the oxidation damage information, and the line aging information. Failure factors of the spray pressure switch 1d comprise the periodic inspection recorded information, the periodic maintenance recorded information, the audit system executing information, and the switch state information.
The updating module 30 is configured to update the SOD score of each failure factor of each safety device, 1a to 1d, according to the live inspection information of each safety device, 1a to 1d.
In one embodiment, the updating module 30 updates the initial score of the Occ score of each failure factor of each safety device, 1a to 1d, according to the live inspection information of each safety device, 1a to 1d.
For example, the updating module 30 updates the initial score of the Occ score of the periodic inspection recorded information of the smoke sensor 1a. A fourth table as below shows a periodic inspection record of the smoke sensor 1a.
In the table 4, the “null” represents detection time not yet determinable. The updating module 30 can calculate a failure rate Ø1 of the periodic inspection recorded information Post probability density function of the smoke sensor 1a through a Bayesian analytic algorithm. For example, a prior probability density function of the failure rate Ø1 is a gamma distribution, a prior expected value al is 5*10−3 time/day, and a coefficient of variation is 200%. A gamma distribution probability density function is f(Ø) ∝ Øk-1exp(Ø/θ), where k represents a shape parameter and θ represents a scale parameter. A post probability density function of the failure rate Ø1 is f(Ø|T1, T2, T3, T4) ∝ f(T1, T2, T3, T4|Ø)*f(Ø). The updating module 30 can calculate k=3.25, θ=0.002824 time/day, and a probability density expected value a2=k*θ=3.25*0.002824 time/day=1/109 time/day. According to the table 2, the probability density expected value a2 is between a first probability of occurrence ( 1/200) and a second probability of occurrence ( 1/100). The updating module 30 updates from 6 to 7 the initial score of the Occ score of the periodic inspection recorded information of the smoke sensor 1a. The updating module 30 updates the first RPN sequence of the table 3 to obtain an updated first RPN sequence of the plurality of safety devices, 1a to 1d, of the area 200a.
A fifth table as below shows the updated first RPN sequence of the plurality of safety devices, 1a to 1d, of the area 200a:
The calculating module 40 is configured to calculate a grey correlation degree of each safety device, 1a to 1d, according to updated SOD score.
In one embodiment, the calculating module 40 calculates a difference value between the updated SOD score and an SOD score of a reference sequence and calculate a grey relational coefficient of each failure factor of each safety device, 1a to 1d, according to the difference value. The calculating module 40 further calculates the grey correlation degree of each safety device, 1a to 1d, according to the grey relational coefficient of each failure factor of each safety device, 1a to 1d.
In one embodiment, the calculating module 40 can calculate a grey correlation degree of each failure factor of each safety device, 1a to 1d, according to the grey relational coefficient of each failure factor of each safety device, 1a to 1d. The calculating module 40d calculates a mean value of the plurality of failure factors of each safety device, 1a to 1d, to obtain the grey correlation degree of each safety device, 1a to 1d.
In one embodiment, an RPN value of the reference sequence is 1. A sixth table as below shows the reference sequence:
The calculating module 40d calculates a difference value between the SOD score of the table 5 and the SOD score of the table 6 to obtain a difference sequence, and a seventh table as below shows the difference sequence:
The calculating module 40d can calculate the grey relational coefficient of each failure factor of each safety device, 1a to 1d, through the table 7 and a grey relational coefficient formula. The grey relational coefficient formula is shown below:
An eighth table as below shows calculated grey relational coefficient of each failure factor of each safety device, 1a to 1d:
The calculating module 40 can calculate the grey correlation degree of each failure factor of each safety device, 1a to 1d, according to the table 8. The grey correlation degree of each failure factor of each safety device, 1a to 1d, is equal to a mean value of SOD grey relational coefficients.
For example, the grey correlation degree of a first failure factor (the periodic inspection recorded information) of the smoke sensor 1a is 0.8667 (i.e., 1+0.6+1)/3=0.8667). The grey correlation degree of a second failure factor (the periodic maintenance recorded information) of the smoke sensor 1a is 0.8889 (i.e., (1+0.6667+1)/3=0.8889). The grey correlation degree of a third failure factor (the environmental pollution information) of the smoke sensor 1a is 0.6667 (i.e., (0.6667+0.6667+0.6667)/3=0.6667). The grey correlation degree of a fourth failure factor (the equipment damage information) of the smoke sensor 1a is 0.5556 (i.e., (0.5+0.6667+0.5)/3=0.5556). The grey correlation degree of a fifth failure factor (the audit system executing information) of the smoke sensor 1a is 0.8889 (i.e., (1+0.6667+1)/3=0.8889). The grey correlation degree of the safety device 1a is 0.7773 (i.e., (0.8667+0.8889+0.6667+0.5556+0.8889)/5=0.7733).
The sorting module 50 is configured to output a risk sorting list of the plurality of safety devices, 1a to 1d, of the area 200a according to the grey correlation degree of each safety device, 1a to 1d.
A ninth table as below shows calculated grey correlation degree of each failure factor and each safety device, 1a to 1d:
When a grey correlation degree of a safety device is closer to 1, a failure risk of the safety device is lower. According to the table 9, the safety device 1d has the lowest failure risk among the safety devices, 1a to 1d, and the safety device 1c has the highest failure risk among the safety devices, 1a to 1d. When the area 200a has a risk warning, an administrator can preferentially check the safety device 1c.
In one embodiment, when the area 200a has the risk warning, the administrator can check the safety devices of the area 200a according to the risk sorting of the table 9.
Referring to
For example, the display module 60 displays the safety device 1c in red, the safety device 1b in pink, and the safety device 1a in orange.
In step 600, the obtaining module 10 obtains live inspection information of the plurality of safety devices, 1a to 1d, of the area 200a.
In step 602, the setting module 20 sets an SOD score for each failure factor of each safety device, 1a to 1d.
In step 604, the updating module 30 updates the SOD score of each failure factor of each safety device, 1a to 1d, according to the live inspection information of each safety device, 1a to 1d.
In step 606, the calculating module 40 calculates a grey correlation degree of each safety device, 1a to 1d, according to updated SOD score.
In step 608, the sorting module 50 outputs a risk sorting list of the plurality of safety devices, 1a to 1d, of the area 200a according to the grey correlation degree of each safety device, 1a to 1d.
In step 610, the display module 60 generates a risk guide map according to the risk sorting list of the safety devices, 1a to 1d, of the area 200a and displays the safety devices that have higher priorities through different colors.
In one embodiment, the live inspection information can comprise periodic inspection recorded information, periodic maintenance recorded information, environmental pollution information, equipment damage information, oxidation damage information, audit system executing information, line aging information, line connection information, and switch state information.
In one embodiment, the SOD score represents a risk grade of a risk priority number (RPN). The SOD score comprises a severity (Sev) score, an occurrence (Occ) score, and a detection (Det) score.
In one embodiment, the setting module 20 can set the Sev score, the Occ score, and the Det score for each failure factor of each safety device, 1a to 1d, according to a failure mode and effect analysis (FMEA) scoring rule.
In one embodiment, the Sev score and the Det score of each failure factor of each safety device, la to 1d, can be scored according to the table 1. The Occ score of each failure factor of each safety device, 1a to 1d, is set as an initial score.
In one embodiment, the updating module 30 updates the initial score of the Occ score of each failure factor of each safety device, 1a to 1d, according to the live inspection information of each safety device, 1a to 1d.
In one embodiment, the calculating module 40 calculates a difference value between the updated SOD score and an SOD score of a reference sequence and calculate a grey relational coefficient of each failure factor of each safety device, 1a to 1d, according to the difference value. The calculating module 40 further calculates the grey correlation degree of each safety device, 1a to 1d, according to the grey relational coefficient of each failure factor of each safety device , 1a to 1d.
In one embodiment, the calculating module 40 can calculate a grey correlation degree of each failure factor of each safety device, 1a to 1d, according to the grey relational coefficient of each failure factor of each safety device, 1a to 1d. The calculating module 40d calculates a mean value of the plurality of failure factors of each safety device, 1a to 1d, to obtain the grey correlation degree of each safety device, 1a to 1d.
In one embodiment, the calculating module 40d can calculate the grey relational coefficient of each failure factor of each safety device, 1a to 1d, through the table 7 and a grey relational coefficient formula. The grey relational coefficient formula is shown below:
In one embodiment, when a grey correlation degree of a safety device is closer to 1, a failure risk of the safety device is lower. According to the table 9, the safety device 1d has the lowest failure risk among the safety devices, 1a to 1d, and the safety device 1c has the highest failure risk among the safety devices, 1a to 1d. When the area 200a has a risk warning, an administrator can preferentially check the safety device 1c.
In one embodiment, when the area 200a has the risk warning, the administrator can check the safety devices of the area 200a according to the risk sorting of the table 9.
The embodiments shown and described above are only examples. Many such details are neither shown nor described. Even though numerous characteristics and advantages of the present technology have been set forth in the foregoing description, together with details of the structure and function of the present disclosure, the disclosure is illustrative only, and changes may be made in the detail, including in matters of shape, size, and arrangement of the parts within the principles of the present disclosure, up to and including the full extent established by the broad general meaning of the terms used in the claims. It will therefore be appreciated that the one embodiments described above may be modified within the scope of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 201711160799.0 | Nov 2017 | CN | national |
