Patent Grant
11082433
The presented invention generally relates to the field of factory planning and management systems. More specifically, it relates to the authentication of production machines and machine sub-units configuration and operation, in respect to scenarios and actions that are executed in the plant.
Known in art processes of managing in plants and factories world-wide, enable gathering of information from the factories machine and sensor to monitoring.
The current state of the art does not relate to platforms that would accommodate a comprehensive, configurable process of automatic authentication of the actual raw data received from the said origins. Raw data input streams are thus susceptible to drifts in accuracy, failures in authenticity, and may be maliciously compromised by perpetrators from within and outside the plant.
The present invention discloses a method for analyzing actions that are taking place in a plant or a factory, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the steps of:
According to one embodiment of the said method, at least one said correlated data stream originates from tapping onto machines or machine sub-units within the plant, and another said correlated data stream originates from sensors or indicators located within the plant's production systems or environment;
According to one embodiment of the said method, at least one said correlated data stream originates from tapping onto communication lines within the plant.
According to one embodiment of the said method, at least one said correlated data stream originates from IT information, communicated over the plant's network.
According to one embodiment, the said method further comprises at least one of the following steps:
According to one embodiment, the said method further comprises at least one of the following steps:
According to one embodiment, the said method further comprises the step of applying machine learning algorithms to the said extracted characteristics and parameters of said correlations, thus iteratively refining the analysis of actions and scenarios taking place within the plant.
According to one embodiment, the said method further comprises the step of obtaining expected characteristics and parameters of monitored data streams by:
According to one embodiment, the said method further comprises the steps of:
According to one embodiment, the said method further comprises the steps of:
According to one embodiment, the said method further comprises the steps of:
According to one embodiment, the said method further comprises the steps of:
According to one embodiment of the said method, the communication between the sources of input data streams and the system which analyzes the said streams is unidirectional, thus disabling the configuration of data sources from within the said system 10, and providing security against cyber attacks targeting the said input data sources.
According to one embodiment of the said method, at least part of the said sources of input data streams are communicatively connected to the said analysis system through secured P2P connections, and are thus controllable by dedicated components of the analysis system.
According to one embodiment the said method further comprises the step of maintaining a knowledgebase withholding at least part of the following information:
According to one embodiment of the said method, the said front-end subsystem is the only human interface to the entire system, said front-end subsystem is set as an encrypted, separated environment, and is connected to the rest of the system via unidirectional communication, thus facilitating at least one of the following measures of precaution for maintaining cyber security:
The present invention further discloses a system for analyzing actions that are taking place in a plant or a factory, comprising one or more non-transitory computer readable storage devices and one or more processors operatively coupled to the storage device(s) on which are stored modules of instruction code executable by the one or more processors, said system comprising at least part of:
According to one embodiment of the said system, at least one of said data stream originates from tapping onto machines or machine sub-units within the plant (101), and at least one other said data stream originates from sensors and indicators located within the plant's production systems or environment (103);
According to one embodiment of the said system, at least one said data stream originates from tapping onto communication lines within the plant.
According to one embodiment of the said system, at least one said data stream originates from IT information, communicated over the plant's network.
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to perform at least one of:
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to perform at least one of the following:
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to apply machine learning algorithms to the said extracted characteristics and parameters of said correlations, thus iteratively refining the analysis of actions and scenarios taking place within the plant.
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to obtain expected characteristics and parameters of monitored data streams by:
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to:
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to:
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to:
According to one embodiment of the said system, the scenario analysis module (3300) is further configured to:
According to one embodiment of the said system, the communication between the sources of input data streams (100) and the scenario analysis module (3300) is unidirectional, thus the configuration of data sources (100) is disabled from within the said scenario analysis module (3300), providing security against cyber attacks targeting the said input data sources.
According to one embodiment of the said system, at least part of the said sources of input data streams 100 are communicatively connected to dedicated collectors (1101) via secured P2P connections, and are thus controllable by said dedicated collectors (1101).
According to one embodiment, the said system further comprises a knowledgebase (3500) withholding at least part of the following information:
According to one embodiment of the said system, said front-end subsystem (2000) is an encrypted environment, communicatively connected to said collectors (1101) knowledgebase (3500) and scenario analysis module (3300) via unidirectional communication, thus providing at least one of the following measures of precaution for maintaining cyber security:
The present invention will be more readily understood from the detailed description of embodiments thereof made in conjunction with the accompanying drawings of which:
Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is applicable to other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting.
The invention described henceforth relates to a system and a method designed to produce a knowledgebase, withholding the relations and effect of different units in a plant on one another, and on independent sensors and indicators located throughout the plant.
The said Knowledgebase relates to all hierarchical levels in the plant; from a machine sub-unit level, via a production machines' level to the level of an entire plant or factory. The knowledgebase is assembled through a continuous process of correlation between independent input data streams 100, and extraction of correlation parameters to understand the conditions and events that are taking place in the plant.
The said Knowledgebase serves as a reference for authenticating the correctness of production machines' operation and configuration, and can be queried for further analysis according to timing, actions and events, and in regards to specific production machines and sub-units in the plant.
Following is a table of definitions of the terms used throughout this application.
Input data streams 100 flow into the system from a plurality of sources. This data and is acquired by:
The different data sources are categorized according to the following types:
The said information is aggregated and arranged by dedicated hardware and software systems (not shown in
The processed information obtained in the Scenario analysis module 3300 is stored in a database referred to as the knowledgebase 3300f. The knowledgebase includes (but is not limited to) the following information:
The information obtained in the scenario analysis module 3300 is further processed to obtain specific indications that are propagated to the Front-end administrative sub-system. These indications include for example:
Input data streams 100 flow into the system from a plurality of sources. These sources are categorized according to the following types:
The said input data is propagated to the Data collection sub unit 1000, where it is collected by the Collector Cluster 1100. Collectors 1101 are computers which reside on a customers' operation plant. They produce raw event data which is the basis for analysis in the system.
According to one embodiment, the communication between the sources of input data streams and collectors 1101 within the collector cluster 1100 is unidirectional, i.e. data is flowing from the sensors to the collectors. For example, a CCTV camera may be transferring video streams to a collector 1101 in a unidirectional manner. This constellation in this embodiment detaches the configuration of input data sources 100 from any machine within the plant, and provides security against cyber attacks targeting the said sources.
According to another embodiment, at least part of the said sources of input data streams are controllable sources 105. These sources are communicatively connected to specific controllers 1101 through secured P2P connections, and are configured to be controllable by dedicated collectors 1101. For example, the CCTV camera mentioned above may be connected via USB communication to a specific controller, and prompted to operate via that connection in response to a command from a collector 1101 (e.g. following detection of movement).
According to one embodiment, the said sources of input data streams forward raw data (e.g.: video data streams) to the collector cluster 1100. According to another embodiment, at least some of the said sources of input data streams perform basic analysis of acquired data, and forward the results of this analysis to the collector cluster 1100. In relation to the CCTV example above, the camera may be configured to recognize the presence of a person in the room, and propagate that information to the collector cluster 1100.
According to one embodiment, collectors receive control and feedback messages from the Front-end 2000 subsystem to change their status of operation.
Data collected by the collector 1101 members of the collector cluster 1100 is propagated to the “Event Aggregator” module 1300.
According to one embodiment, collectors 1101 may be configured to perform low-level analysis of the data they acquired, and forward the analyzed data to the event aggregator 1300. For example, a collector 1101 may be configured to combine information from two audio sensors, in order to cancel environmental noise on the acquired audio.
The event aggregator 1300 module aggregates and buffers raw data originating from the Collectors 1101, and relays it onto the Cluster's File system (e.g. HDFS) 1320. The functionality of this module is further elaborated below, in relation to
Aggregated data is propagated to the data analysis sub unit 3000. This unit is comprised of:
The results of the Data analysis subsystem are propagated to the Front-end subsystem. The Front-end subsystem is an encrypted environment, accessible to authorized personnel's use only. It is separated from the data collection 1000 and Data analysis 3000 subsystems. This separation is optionally implemented, according to one embodiment of the said invention, by means of unidirectional optical gateways.
The front-end 2000 subsystem serves to:
The Front end subsystem is comprised of the following components:
The Front end subsystem 2000 is the only human interface to the entire system. It affects the data flow in one direction only; from the collector nodes onwards, denying unauthorized access to the actual producers of data. This is a measure of precaution for maintaining cyber security.
The Front-end subsystem presents feedback and alert indications to authorized personnel in regards to:
The Event Aggregator module 1300 provides a solution to the following restrictions and limitations:
Inspector hosts 3110 are computers which serve as building blocks of the Inspector Cluster 3100. The Inspectors partake in cluster computing; analyzing scenarios according to incoming data, storing required information, and producing alerts information en-route the Front-end subsystem 2000. The data flow comprising the Scenario analysis and Alerts generation logic is described in detail further below.
The computational activity of Inspectors is divided into basic logic units referred to as “Inspector logic units” 3130. The operation of these Inspector logic units 3130 is managed by central modules referred to as ‘Engines’ 3201. The data flow comprising the Inspector logic units is described in detail further below.
Each Engine is responsible for the allocation of system resources (e.g. hosting computer 3110, dedicated memory and number of CPUs), ordering, and prioritizing of the activity of Inspector logic units 3130.
The block diagram in
The data input to each Inspector logic unit family 3120 is directly received from the Data collection subsystem 1000. It is received by input-level Inspector logic units 3130. The data output of each Inspector logic unit family 3120 is propagated to the Scenario analysis module 3300 by an output-level Inspector logic unit 3130.
According to some embodiments of the presented invention, the computational complexity of the Inspector cluster 3100 may be further enhanced, by implementing multiple Inspector logic unit families 3120, managed by a multiple Engines 3201, as depicted on
3131: Each Inspector logic unit module 3130 receives its input as a raw data input stream from a specific collector 1101, or processed data from a preceding Inspector logic unit 3130, in an Inspector logic unit family 3120.
3132: The Inspector logic unit module is assigned a task by a Cluster Engine 3201. This task will apply basic analysis to the received data. This task may optionally be part of the analysis of a greater scenario, assigned to an Inspector logic unit Family 3120.
3133: The Inspector logic unit is allocated system resources by a Cluster Engine 3201, to accommodate the execution of the assigned task. Examples for such resources are:
3134: The Inspector logic unit 3130 is assigned a priority and a dependency list by a Cluster Engine 3201. These will define the order and timing of an Inspector logic unit's task execution, and its dependency on the execution of other Inspector logic units' assigned tasks within an Inspector logic unit family 3120.
3135: The Inspector logic unit 3130 executes the assigned task according to the parameters conveyed by the Cluster Engine 3201, as described above (i.e. Resources, Priority and Dependency).
3136: The Inspector logic unit 3130 emits the following output to the respective modules:
3305: The Scenario analysis module 3300 receives time-stamped data input streams from different Inspector logic unit Families. Each data input stream is related to a specific real-world scenario or action, and to specific data Collectors. The said data may be originated from:
3310: The Scenario analysis module 3300 identifies different streams of data, emitted by different inspector logic unit families, that relate to the same action or scenario, based on metadata (e.g. data source and timing) of each data stream. It then extracts parameters of correlation between the said streams of data.
3315: The Scenario analysis module 3300 analyzes data inputs to identify correlations to real-world specific actions and scenarios. It extracts the parameters and characteristics of correlation between specific actions and scenarios that occur within the plant and different streams of data.
The scenario analysis module 3300 receives as an input captured video streams originating from various sources such as (but not limited to):
The said identified human actions are correlated with processes and conditions that take place in the plant, such as: machine status, production flow state, process status, stages within the process, configuration changes, user intervention, user identification, and misuse of equipment.
The scenario analysis module 3300 stores the parameters of the said correlations in the knowledgebase. According to one embodiment, The scenario analysis module 3300 stores selected video input streams according to predefined storage conditions and parameters for further reference and analysis; e.g. for improving human operators' reaction to predefined conditions.
3320: The scenario analysis module 3300 analyzes combinations of identified actions executed by machine sub-units, to indicate specific actions and scenarios that are being executed on a production-machine level. For example, the scenario analysis module 3300 may combine readings from actuator encoders, cameras and power meters to identify the occurrence of a specific action, such as a movement of a robotic arm on an assembly line. It may further combine several such identified actions to identify the occurrence of specific stages in a more comprehensive scenario, such as the process of painting a car by the said robotic arm. The scenario analysis module 3300 stores the indicated actions and scenarios alongside their respective, identified parameters and characteristics in the knowledgebase.
3325: The scenario analysis module 3300 analyzes combinations of identified actions and scenarios that are executed by production-machines, to indicate specific scenarios that are being executed on the plant level. It Stores the indicated actions and scenarios alongside their respective, identified parameters and characteristics in the knowledgebase.
3330: The scenario analysis module 3300 creates a log of identified actions and scenarios, and their respective parameters and characteristics, which reflect real world actions and scenarios performed in the plant.
3335: The scenario analysis module 3300 authenticates the correctness of production machines' operation during the execution of the said actions or scenarios, by correlating between data streams of monitored machines or machine sub-units and data streams of independent indicators and sensors (e.g. correlating the data of a robotic arm's motor decoders with the robot's position as indicated by a camera). The results of this authentication are propagated to the Alerts generation module.
3340: According to one embodiment, the scenario analysis module 3300 obtains expected input data streams 100 per each production machine's configuration (e.g.: the rotation speed of a motor in RPM, during a specific action). The said expected input data streams may be acquired for example by:
3345: The scenario analysis module 3300 identifies irregularities in action or scenario parameters and characteristics based on the said authentication steps, and comparison of emitted data streams 100 with expected input streams. The said irregularities are analyzed to identify any of the following conditions:
Suspected System Malfunction
Suspected Erroneous System Configuration
Suspected Breach of Cyber Security
The scenario analysis module 3300 propagates irregularity notifications to the alerts generation module. The Scenario analysis module 3300 stores suspected irregularity events in the knowledgebase.
3350: The scenario analysis module 3300 creates a log of the said identified suspected irregularities in machine configuration or operation.
3355: The scenario analysis module 3300 extracts parameters and characteristics of correlation between data streams emitted by different monitored machines and machine sub-units, and applies machine learning algorithms to enhance the knowledgebase, and to identify the mutual effect of the said monitored units on one-another, following the execution of scenarios and actions.
3360: The scenario analysis module 3300 extracts parameters and characteristics of correlation between data streams emitted by monitored machines and machine sub-units (which are respective to actions and scenarios performed by the said machines and machine sub-units) and independent indicators and sensors within the plant (which represent the conditions in the plant).
The Scenario analysis module 3300 applies machine learning algorithms to identify the effect of the operation of the said monitored machines and machine sub-units on plant conditions (e.g. Physical, Chemical, Electrical conditions). It stores this information within the knowledgebase.
According to one embodiment, the Scenario analysis module 3300 applies machine learning algorithms to the data accumulated in the knowledgebase (e.g. parameters and characteristics of specific correlations between different data streams 100) to iteratively refine the analysis of actions and scenarios that occur within the plant, or set-up new analyses for that purpose.
For example: Assume a high level of correlation is detected between a microphone-recorded sound pattern and the power consumption of an electric engine.
Administrators will be informed of this detected information via the front end sub-system. They may be presented the option to add the said correlation as an additional indication for the engine's correct operation, i.e. lack thereof will be considered indicative of a malfunction.
According to one embodiment, the Scenario analysis module 3300 may be configured to perform said iterative refinement of the analysis automatically, i.e. without involving the user.
The knowledgebase also contains patterns of input data streams that reflect correct functionality on a plant level. Pertaining to the previous example, the recorded audio data pattern may indicate correct functionality of an entire assembly line, and not just a specific machine sub-unit. Attribution of correct functionality to a specific pattern may be obtained through algorithms of machine-learning or through input by an administrator in the Front-end subsystem 2000. Deviation of the input data pattern from the expected “correct functionality” pattern may produce an alert by the Alerts generation module.
The knowledgebase also contains patterns of correlation between data streams representing the actions of human operators (e.g. communication of all sorts, computer monitor screen capture, audio recording, video recording, etc.), and independent data streams which relate to scenarios and actions that take place in the plant are also maintained in the knowledgebase. This information serves to study and promote the efficiency and security of human operators' actions in response to specific scenarios.
The information accumulated in the Knowledgebase also serves to enhance the efficiency and security of production machines throughout the plant, improve current processes and scenarios, and design new processes in accordance with the assembled information.
The Knowledgebase also maintains a database of historic events of real world scenarios and actions, and their respective data stream parameters and characteristics. This information can be queried by the Front-end administrator for further analysis according to timing, actions and events, and in regards to specific machines, units and modules in the plant.
3405: The Alerts generation module 3400 receives data from the scenario analysis module 3300, portraying the characteristics and parameters of a specific scenario or action performed in the real world.
3410: The Alerts generation module 3400 compares the scenario analysis module's 3300 information with expected predefined parameters, held in the knowledgebase, and may generate alert indications in case of a mismatch between the incoming data and expected results (e.g.: a machine has failed to perform a certain action as many times as it was supposed to)
3415, 3420, 3425: The Alerts generation module 3400 receives data from the scenario analysis module 3300 indicating:
3430: The Alerts generation module 3400 produces an alert indication of a suspected cyber security event, based on the authentication and irregularity indications emitted by the Scenario analysis module 3300.
3435: The Alerts generation module 3400 produces an alert indication of a suspected machine or machine sub-unit malfunction event, or of an erroneous configuration, based on the authentication and irregularity indications emitted by the scenario analysis module 3300.
The system of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein. Alternatively or in addition, the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may wherever suitable operate on signals representative of physical objects or substances.
Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions, utilizing terms such as, “processing”, “computing”, “estimating”, “selecting”, “ranking”, “grading”, “calculating”, “determining”, “generating”, “reassessing”, “classifying”, “generating”, “producing”, “stereo-matching”, “registering”, “detecting”, “associating”, “superimposing”, “obtaining” or the like, refer to the action and/or processes of a computer or computing system, or processor or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories, into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. The term “computer” should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, computing system, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.
The present invention may be described, merely for clarity, in terms of terminology specific to particular programming languages, operating systems, browsers, system versions, individual products, and the like. It will be appreciated that this terminology is intended to convey general principles of operation clearly and briefly, by way of example, and is not intended to limit the scope of the invention to any particular programming language, operating system, browser, system version, or individual product.
It is appreciated that software components of the present invention including programs and data may, if desired, be implemented in ROM (read only memory) form including CD-ROMs, EPROMs and EEPROMs, or may be stored in any other suitable typically non-transitory computer-readable medium such as but not limited to disks of various kinds, cards of various kinds and RAMs. Components described herein as software may, alternatively, be implemented wholly or partly in hardware, if desired, using conventional techniques. Conversely, components described herein as hardware may, alternatively, be implemented wholly or partly in software, if desired, using conventional techniques.
Included in the scope of the present invention, inter alia, are electromagnetic signals carrying computer-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; machine-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the steps of any of the methods shown and described herein, in any suitable order; a computer program product comprising a computer usable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the steps of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the steps of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programmed to perform, alone or in combination, any or all of the steps of any of the methods shown and described herein, in any suitable order; electronic devices each including a processor and a cooperating input device and/or output device and operative to perform in software any steps shown and described herein; information storage devices or physical records, such as disks or hard drives, causing a computer or other device to be configured so as to carry out any or all of the steps of any of the methods shown and described herein, in any suitable order; a program pre-stored e.g. in memory or on an information network such as the Internet, before or after being downloaded, which embodies any or all of the steps of any of the methods shown and described herein, in any suitable order, and the method of uploading or downloading such, and a system including server/s and/or client/s for using such; and hardware which performs any or all of the steps of any of the methods shown and described herein, in any suitable order, either alone or in conjunction with software. Any computer-readable or machine-readable media described herein is intended to include non-transitory computer- or machine-readable media.
Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any step described herein may be computer-implemented. The invention shown and described herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally include at least one of a decision, an action, a product, a service or any other information described herein that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.
The scope of the present invention is not limited to structures and functions specifically described herein and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein, such that even though users of the device may not use the capacity, they are, if they so desire, able to modify the device to obtain the structure or function.
Features of the present invention which are described in the context of separate embodiments may also be provided in combination in a single embodiment.
For example, a system embodiment is intended to include a corresponding process embodiment. Also, each system embodiment is intended to include a server-centered “view” or client centered “view”, or “view” from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities performed at that server or client or node.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IL2017/050635 | 6/6/2017 | WO | 00 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2017/212486 | 12/14/2017 | WO | A |
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| 20190268356 A1 | Aug 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62346681 | Jun 2016 | US |
