Multi-security collaborative protection architecture implementation method and device

Abstract

The embodiment of the invention provides a multi-security collaborative protection architecture implementation method and device, and the method comprises the steps: distributing each monitoring function of production operation into a plurality of relatively independent monitoring micro-services for modeling; the plurality of monitoring micro-services subscribe related data through a data platform, realize data-driven analysis logic based on the related data, and realize dynamic risk intelligent management and control of the process industry; and establishing a risk collaborative micro-service, and carrying out information intercommunication, cross validation and collaborative management and control with the plurality of monitoring micro-services through the risk collaborative micro-service.

Description

Technical Field

[0001] This document relates to the field of computer technology, and in particular to a method and device for implementing a multi-security collaborative protection architecture based on a distributed microservice system. Background Art

[0002] Among the existing technologies, the research on dynamic risk intelligent control technology and the establishment of tool sets for process industries are in their infancy. my country's process industry is at a critical stage of intelligent upgrading, facing challenges such as improving the scientific nature of production and operation management, enhancing the accuracy of risk analysis to cope with complex process mechanisms, strong variable coupling and process dynamics. The existing risk control systems at home and abroad often show problems such as weak stability of risk control systems and low levels of collaborative innovation in dealing with the complexity and dynamics of the process industry. In particular, there are obvious deficiencies in multi-security collaborative protection that integrates functional safety, information security and physical security, real-time risk monitoring and early warning, and intelligent decision support. Therefore, it is urgent to establish a multi-security collaborative protection technology architecture to support the collaborative control of dynamic risks in the process industry. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for implementing a multi-security collaborative protection architecture based on a distributed microservice system, aiming to solve the above-mentioned problems in the prior art.

[0004] The present invention provides a method for implementing a multi-security collaborative protection architecture based on a distributed microservice system, comprising:

[0005] Distribute the various monitoring functions of production operations into multiple relatively independent monitoring microservices for modeling;

[0006] The multiple monitoring microservices subscribe to relevant data through the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry;

[0007] A risk collaboration microservice is established, through which information is exchanged, cross-validated, and collaboratively managed with the multiple monitoring microservices.

[0008] The present invention provides a device for implementing a multi-security collaborative protection architecture based on a distributed microservice system, comprising:

[0009] Multiple monitoring microservices are connected to the risk collaboration microservices to model the various monitoring functions of production operations, subscribe to relevant data through the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry;

[0010] The risk collaboration microservice is connected to the multiple monitoring microservices and is used for information exchange, cross-verification and collaborative management and control with the multiple monitoring microservices.

[0011] The data middle platform is connected to the multiple monitoring microservices and is used to push relevant data to the multiple monitoring microservices.

[0012] An embodiment of the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned method for implementing a multi-security collaborative protection architecture based on a distributed microservice system are implemented.

[0013] An embodiment of the present invention also provides a computer-readable storage medium, on which an implementation program for information transmission is stored. When the program is executed by a processor, the steps of the above-mentioned method for implementing a multi-security collaborative protection architecture based on a distributed microservice system are implemented.

[0014] By adopting the embodiment of the present invention, a multi-security collaborative protection architecture technology for information exchange, cross-validation and collaborative management between microservices is proposed to implement data-driven analysis logic, and interact with verification data sets, security rule sets and algorithm tool sets to jointly realize dynamic risk intelligent management and control in the process industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 It is a flowchart of a method for implementing a multi-security collaborative protection architecture based on a distributed microservice system according to an embodiment of the present invention;

[0017] Figure 2 Schematic diagram of the system architecture of a method for implementing a multi-security collaborative protection architecture based on a distributed microservice system according to an embodiment of the present invention;

[0018] Figure 3 is a schematic diagram of a verification data set according to an embodiment of the present invention;

[0019] Figure 4 is a schematic diagram of a security rule set according to an embodiment of the present invention;

[0020] Figure 5 is a schematic diagram of an algorithm tool set according to an embodiment of the present invention;

[0021] Figure 6 Schematic diagram of a multi-security collaborative protection architecture based on a distributed microservice system according to an embodiment of the present invention;

[0022] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0024] Method Embodiment

[0025] According to an embodiment of the present invention, a method for implementing a multi-security collaborative protection architecture based on a distributed microservice system is provided. Figure 1 : is a flowchart of a method for implementing a multi-security collaborative protection architecture based on a distributed microservice system according to an embodiment of the present invention. Figure 1 As shown, the method for implementing a multi-security collaborative protection architecture based on a distributed microservice system according to an embodiment of the present invention specifically includes:

[0026] Step S101, distribute various monitoring functions of production operation into multiple relatively independent monitoring microservices for modeling; specifically includes:

[0027] Personnel safety monitoring, process monitoring, equipment monitoring, environment monitoring and network monitoring are distributed into multiple relatively independent monitoring microservices for modeling.

[0028] Step S102, the multiple monitoring microservices subscribe to relevant data through the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry; specifically including:

[0029] The multiple monitoring microservices subscribe to relevant data based on the data subscription and push functions provided by the distributed message queue of the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry by interacting with the verification data set, safety rule set and algorithm tool set. The verification data set includes a mixture of dynamic and static data under multiple working conditions, multi-rate sampling under multi-source and multi-type data conditions, and multi-source heterogeneous data, which are used to verify the model. The safety rule set includes safety rule information under scenario characteristics constructed based on the analysis of detailed historical data and accident cases and the use of corpus data mining and analysis technology; the algorithm tool set includes: online SIL verification algorithm, SIS autonomous anomaly detection algorithm based on dynamic interpretable graph neural network and adaptive threshold, fault diagnosis algorithm based on deep neural network, fault tracing algorithm based on Bayesian network reasoning, self-learning safety early warning model construction algorithm and multi-safety collaborative dynamic adaptation algorithm.

[0030] The data middle platform specifically includes: cleaning, correcting, aligning and integrating the dynamic risk data of the process industry to form a standardized database after these pre-processing. The standardized database specifically includes: enterprise security rule library, risk matrix and grade library, personnel management database, environmental monitoring database, information security database, physical security database, and other multi-source time series production databases.

[0031] Step S103: establish a risk collaboration microservice, and perform information exchange, cross-verification, and collaborative management and control with the multiple monitoring microservices through the risk collaboration microservice.

[0032] Typical process industry production process risk management involves multi-level risk identification and management, including functional safety, information security, and physical safety. Figure 2As shown in the figure, the dynamic risk data in the process industry often has very complex structures and situations such as multi-source, heterogeneous, heterogeneous, multi-modal, nonlinear, and non-stationary. The full-factor data perception module built through the platform can perform pre-processing such as cleaning, correction, alignment, and fusion on the collected complex data to form standardized data that is easy to manage and use later, and then store it in the data middle platform database of the platform; the data middle platform relies on efficient distributed message queues to provide convenient data subscription and push functions; with the data middle platform as the support, this project designs a multi-security collaborative protection architecture based on a distributed microservice system, which distributes personnel safety, process monitoring, equipment monitoring, environmental monitoring, and network monitoring to relatively independent microservices for subsequent modeling and analysis, and at the same time establishes a risk collaborative microservice to exchange information, cross-validate, and coordinate management with the above five microservices; each microservice subscribes to relevant data from the data middle platform according to its own needs, implements data-driven analysis logic, and interacts with the verification data set, security rule set, and algorithm tool set to jointly realize dynamic risk intelligent management and control in the process industry.

[0033] like Figure 3 As shown in the figure, when constructing a verification data set for the dynamic risk analysis industry, it is necessary to integrate multi-rate sampling and multi-source heterogeneous data. Research on data preprocessing suitable for mixed dynamic and static data in multiple working conditions and multi-source and multi-type data conditions is a reliable guarantee for data quality. On this basis, the quality assessment and verification methods of the data set are studied, including effective verification in terms of robustness, completeness and representativeness.

[0034] like Figure 4 As shown in the figure, the construction of safety rule sets for oil and gas, chemical and steel production is based on detailed historical data and accident case analysis, using corpus data mining and analysis technology to construct scenario features - safety rule information, build safety rule sets, and verify the effectiveness and integrity of rule sets under different working conditions based on advanced professional process simulation technology. The rule set needs to go through multiple verification links such as integrity verification, expert review and actual data comparison to fully cover risk points and optimize rule content.

[0035] like Figure 5As shown in the figure, the algorithm tool set includes six types of algorithm tool sets, including online SIL verification algorithm, SIS autonomous anomaly detection algorithm based on dynamic interpretable graph neural network and adaptive threshold, fault diagnosis algorithm based on deep neural network, fault tracing algorithm based on Bayesian network reasoning, self-learning safety early warning model construction algorithm and multi-safety collaborative dynamic adaptation algorithm. Based on the dynamic risk intelligent management and control system, through big data modeling analysis and early warning calculation, in-depth research and judgment of production line risks are achieved, providing the platform with rich safety risk situation analysis and early warning capabilities. As a key component of the comprehensive analysis and early warning platform, the early warning algorithm set aims to deeply mine data value through advanced analysis technology and algorithm models, identify and evaluate potential risks in the production process in advance, and effectively prevent the occurrence of safety accidents. Optimize the early warning model algorithm, adjust and optimize the model algorithm according to the feedback of the early warning event, and improve the accuracy of the model algorithm. At the same time, ensure the flexibility and scalability of the algorithm to adapt to the continuous changes and technological progress of the future production environment.

[0036] Device Example 1

[0037] According to an embodiment of the present invention, a multi-security collaborative protection architecture based on a distributed microservice system is provided. Figure 6 Schematic diagram of a multi-security collaborative protection architecture based on a distributed microservice system according to an embodiment of the present invention. Figure 6 As shown, the multi-security collaborative protection architecture based on the distributed microservice system according to an embodiment of the present invention specifically includes:

[0038] Multiple monitoring microservices 60 are connected to the risk collaboration microservice to model each monitoring function of production operation, subscribe to relevant data through the data middle platform, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry;

[0039] The multiple monitoring microservices 60 specifically include:

[0040] Personnel safety microservices are used to model personnel safety monitoring functions, subscribe to personnel safety data through the data center, implement data-driven analysis logic based on the personnel safety data, and realize dynamic risk intelligent management and control of personnel safety;

[0041] Process monitoring microservices are used to model personnel safety monitoring functions, subscribe to personnel safety data through the data center, implement data-driven analysis logic based on the personnel safety data, and realize dynamic risk intelligent management and control of personnel safety;

[0042] Equipment monitoring microservices are used to model equipment monitoring functions, subscribe to equipment monitoring data through the data center, implement data-driven analysis logic based on the equipment monitoring data, and realize dynamic risk intelligent management and control of equipment monitoring;

[0043] Environmental monitoring microservices are used to model environmental monitoring functions, subscribe to environmental monitoring data through the data center, implement data-driven analysis logic based on the environmental monitoring data, and realize dynamic risk intelligent management and control of environmental monitoring;

[0044] The network monitoring microservice is used to model the network monitoring function, subscribe to the network monitoring data through the data middle platform, implement data-driven analysis logic based on the network monitoring data, and realize dynamic risk intelligent management and control of network monitoring.

[0045] The multiple monitoring microservices 60 are specifically used for:

[0046] Subscription to relevant data is carried out based on the data middle platform, and data-driven analysis logic is implemented based on the relevant data. Dynamic risk intelligent management and control of the process industry is realized by interacting with the verification data set, safety rule set and algorithm tool set. The verification data set includes a mixture of dynamic and static data under multiple working conditions, multi-rate sampling under multi-source and multi-type data conditions, and multi-source heterogeneous data, which are used to verify the model. The safety rule set includes safety rule information under scenario characteristics constructed based on the analysis of detailed historical data and accident cases and the use of corpus data mining and analysis technology; the algorithm tool set includes: online SIL verification algorithm, SIS autonomous anomaly detection algorithm based on dynamic interpretable graph neural network and adaptive threshold, fault diagnosis algorithm based on deep neural network, fault tracing algorithm based on Bayesian network reasoning, self-learning safety early warning model construction algorithm and multi-safety collaborative dynamic adaptation algorithm.

[0047] The risk collaboration microservice 62 is connected to the multiple monitoring microservices and is used for information exchange, cross-verification and collaborative management and control with the multiple monitoring microservices.

[0048] The data center 64 is connected to the multiple monitoring microservices and is used to push relevant data to the multiple monitoring microservices. The data center 64 is specifically used to:

[0049] The standardized database formed after cleaning, correction, alignment and fusion of dynamic risk data of process industries is stored, wherein the standardized database specifically includes: enterprise security rule library, risk matrix and grade library, personnel management database, environmental monitoring database, information security database, physical security database, and other multi-source time series production databases;

[0050] Data subscription and push functions provided based on distributed message queues.

[0051] Typical process industry production process risk management involves multi-level risk identification and management, including functional safety, information security, and physical safety. Figure 2 As shown in the figure, the dynamic risk data in the process industry often has very complex structures and situations such as multi-source, heterogeneous, heterogeneous, multi-modal, nonlinear, and non-stationary. The full-factor data perception module built through the platform can perform pre-processing such as cleaning, correction, alignment, and fusion on the collected complex data to form standardized data that is easy to manage and use later, and then store it in the data middle platform database of the platform; the data middle platform relies on efficient distributed message queues to provide convenient data subscription and push functions; with the data middle platform as the support, this project designs a multi-security collaborative protection architecture based on a distributed microservice system, which distributes personnel safety, process monitoring, equipment monitoring, environmental monitoring, and network monitoring to relatively independent microservices for subsequent modeling and analysis, and at the same time establishes a risk collaborative microservice to exchange information, cross-validate, and coordinate management with the above five microservices; each microservice subscribes to relevant data from the data middle platform according to its own needs, implements data-driven analysis logic, and interacts with the verification data set, security rule set, and algorithm tool set to jointly realize dynamic risk intelligent management and control in the process industry.

[0052] like Figure 3 As shown in the figure, when constructing a verification data set for the dynamic risk analysis industry, it is necessary to integrate multi-rate sampling and multi-source heterogeneous data. Research on data preprocessing suitable for mixed dynamic and static data in multiple working conditions and multi-source and multi-type data conditions is a reliable guarantee for data quality. On this basis, the quality assessment and verification methods of the data set are studied, including effective verification in terms of robustness, completeness and representativeness.

[0053] like Figure 4 As shown in the figure, the construction of safety rule sets for oil and gas, chemical and steel production is based on detailed historical data and accident case analysis, using corpus data mining and analysis technology to construct scenario features - safety rule information, build safety rule sets, and verify the effectiveness and integrity of rule sets under different working conditions based on advanced professional process simulation technology. The rule set needs to go through multiple verification links such as integrity verification, expert review and actual data comparison to fully cover risk points and optimize rule content.

[0054] like Figure 5As shown in the figure, the algorithm tool set includes six types of algorithm tool sets, including online SIL verification algorithm, SIS autonomous anomaly detection algorithm based on dynamic interpretable graph neural network and adaptive threshold, fault diagnosis algorithm based on deep neural network, fault tracing algorithm based on Bayesian network reasoning, self-learning safety early warning model construction algorithm and multi-safety collaborative dynamic adaptation algorithm. Based on the dynamic risk intelligent management and control system, through big data modeling analysis and early warning calculation, in-depth research and judgment of production line risks are achieved, providing the platform with rich safety risk situation analysis and early warning capabilities. As a key component of the comprehensive analysis and early warning platform, the early warning algorithm set aims to deeply mine data value through advanced analysis technology and algorithm models, identify and evaluate potential risks in the production process in advance, and effectively prevent the occurrence of safety accidents. Optimize the early warning model algorithm, adjust and optimize the model algorithm according to the feedback of the early warning event, and improve the accuracy of the model algorithm. At the same time, ensure the flexibility and scalability of the algorithm to adapt to the continuous changes and technological progress of the future production environment.

[0055] Device Example 2

[0056] An embodiment of the present invention provides an electronic device, such as Figure 7 As shown, it includes: a memory 70, a processor 72, and a computer program stored in the memory 70 and executable on the processor 72. When the computer program is executed by the processor 72, the steps described in the method embodiment are implemented.

[0057] Device Example 3

[0058] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 72, the steps described in the method embodiment are implemented.

[0059] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for implementing a multi-security collaborative protection architecture based on a distributed microservice system, characterized in that: include: Distribute the various monitoring functions of production operations into multiple relatively independent monitoring microservices for modeling; The multiple monitoring microservices subscribe to relevant data through the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry; A risk collaboration microservice is established, through which information is exchanged, cross-validated, and collaboratively managed with the multiple monitoring microservices.

2. The method according to claim 1, characterized in that Distribute the various monitoring functions of production operations into multiple relatively independent monitoring microservices for modeling, including: Personnel safety monitoring, process monitoring, equipment monitoring, environment monitoring and network monitoring are distributed into multiple relatively independent monitoring microservices for modeling.

3. The method according to claim 1, characterized in that The multiple monitoring microservices subscribe to relevant data through the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry, specifically including: The multiple monitoring microservices subscribe to relevant data based on the data subscription and push functions provided by the distributed message queue of the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry by interacting with the verification data set, safety rule set and algorithm tool set. The verification data set includes a mixture of dynamic and static data under multiple working conditions, multi-rate sampling under multi-source and multi-type data conditions, and multi-source heterogeneous data, which are used to verify the model. The safety rule set includes safety rule information under scenario characteristics constructed based on the analysis of detailed historical data and accident cases and the use of corpus data mining and analysis technology; the algorithm tool set includes: online SIL verification algorithm, SIS autonomous anomaly detection algorithm based on dynamic interpretable graph neural network and adaptive threshold, fault diagnosis algorithm based on deep neural network, fault tracing algorithm based on Bayesian network reasoning, self-learning safety early warning model construction algorithm and multi-safety collaborative dynamic adaptation algorithm.

4. The method according to claim 1, characterized in that: The data middle platform specifically includes: cleaning, correcting, aligning and integrating the dynamic risk data of the process industry to form a standardized database after these pre-processing. The standardized database specifically includes: enterprise security rule library, risk matrix and grade library, personnel management database, environmental monitoring database, information security database, physical security database, and other multi-source time series production databases.

5. A multi-security collaborative protection architecture based on a distributed microservice system, characterized in that: Specifically include: Multiple monitoring microservices are connected to the risk collaboration microservices to model the various monitoring functions of production operations, subscribe to relevant data through the data center, implement data-driven analysis logic based on the relevant data, and realize dynamic risk intelligent management and control of the process industry; The risk collaboration microservice is connected to the multiple monitoring microservices and is used for information exchange, cross-verification and collaborative management and control with the multiple monitoring microservices. The data middle platform is connected to the multiple monitoring microservices and is used to push relevant data to the multiple monitoring microservices.

6. The device according to claim 5, characterized in that The multiple monitoring microservices specifically include: Personnel safety microservices are used to model personnel safety monitoring functions, subscribe to personnel safety data through the data center, implement data-driven analysis logic based on the personnel safety data, and realize dynamic risk intelligent management and control of personnel safety; Process monitoring microservices are used to model personnel safety monitoring functions, subscribe to personnel safety data through the data center, implement data-driven analysis logic based on the personnel safety data, and realize dynamic risk intelligent management and control of personnel safety; Equipment monitoring microservices are used to model equipment monitoring functions, subscribe to equipment monitoring data through the data center, implement data-driven analysis logic based on the equipment monitoring data, and realize dynamic risk intelligent management and control of equipment monitoring; Environmental monitoring microservices are used to model environmental monitoring functions, subscribe to environmental monitoring data through the data center, implement data-driven analysis logic based on the environmental monitoring data, and realize dynamic risk intelligent management and control of environmental monitoring; The network monitoring microservice is used to model the network monitoring function, subscribe to the network monitoring data through the data middle platform, implement data-driven analysis logic based on the network monitoring data, and realize dynamic risk intelligent management and control of network monitoring.

7. The device according to claim 5, characterized in that The multiple monitoring microservices are specifically used for: Subscription to relevant data is carried out based on the data middle platform, and data-driven analysis logic is implemented based on the relevant data. Dynamic risk intelligent management and control of the process industry is realized by interacting with the verification data set, safety rule set and algorithm tool set. The verification data set includes a mixture of dynamic and static data under multiple working conditions, multi-rate sampling under multi-source and multi-type data conditions, and multi-source heterogeneous data, which are used to verify the model. The safety rule set includes safety rule information under scenario characteristics constructed based on the analysis of detailed historical data and accident cases and the use of corpus data mining and analysis technology; the algorithm tool set includes: online SIL verification algorithm, SIS autonomous anomaly detection algorithm based on dynamic interpretable graph neural network and adaptive threshold, fault diagnosis algorithm based on deep neural network, fault tracing algorithm based on Bayesian network reasoning, self-learning safety early warning model construction algorithm and multi-safety collaborative dynamic adaptation algorithm.

8. The device according to claim 5, characterized in that The data center is specifically used for: The standardized database formed after cleaning, correction, alignment and fusion of dynamic risk data of process industries is stored, wherein the standardized database specifically includes: enterprise security rule library, risk matrix and grade library, personnel management database, environmental monitoring database, information security database, physical security database, and other multi-source time series production databases; Data subscription and push functions provided based on distributed message queues.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for implementing a multi-security collaborative protection architecture based on a distributed microservice system as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by the processor, the steps of the method for implementing a multi-security collaborative protection architecture based on a distributed microservice system as described in any one of claims 1 to 4 are implemented.

Images