An Intrinsic Safety Management Method Integrating Functional Safety and Information Security

Abstract

This invention provides an inherently safe management method that integrates functional safety and information security, relating to the field of intelligent industrial control technology. The method includes: determining multi-dimensional scenario data records and unique scenario coding identifiers; calculating risk indicators based on a preset functional safety and information security risk assessment model; and correcting and integrating these indicators according to dynamic parameters combined with personnel exposure levels, ignition probability, and accident risk impact coefficients to obtain a comprehensive dynamic risk level. For unacceptable risks, dynamic risk decision content is automatically generated, such as instrument-equipment failure decisions, maintenance decisions, personnel management decisions, and functional safety and information security protection measures decisions, achieving collaborative risk management throughout the entire lifecycle of industrial processes. This invention solves the problems of existing technologies in integrated dynamic risk analysis and decision-making for functional safety and information security, such as difficulty in unifying multi-source data, lack of modeling and scenario-based expression, and missing risk analysis algorithms.

Description

Technical Field

[0001] This invention relates to the field of intelligent industrial control technology, and in particular to an intrinsically safe management method that integrates functional safety and information security. Background Technology

[0002] Industrial control systems characterized by automation, intelligence, and wide-area interconnection face risks of wide-ranging and diverse attack patterns. Attacks penetrating from the information domain to the physical domain are gradually becoming one of the major sources of risk for safe production. Traditional production safety control strategies with functional safety as the core are facing severe challenges. With the continuous increase in the scale and complexity of industrial control systems, process control systems, online equipment monitoring systems, safety protection systems, personnel positioning and operation management systems, and information security monitoring systems are widely used in industrial enterprises. Existing technologies can already complete data acquisition and alarm management within different systems, but the collaborative utilization of multi-source data is still limited.

[0003] While existing technologies can aggregate multi-source data on a unified platform, significant shortcomings remain in achieving integrated dynamic risk decision-making for functional safety and information security. The lack of an integrated assessment mechanism for dynamic risk identification of multi-source data hinders real-time analysis. Furthermore, functional safety and information security risk assessments are often conducted independently, without unified consideration during the analysis, assessment, and decision-making stages. This results in risk assessments failing to accurately reflect the overall risk level in specific scenarios, and the dynamic risk decision-making instructions lack specificity, effectiveness, and comprehensiveness. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an inherently safe management method that integrates functional safety and information security. This invention solves the problem of the lack of methods in the integrated dynamic risk analysis and decision-making of functional safety and information security in existing technologies.

[0005] To achieve the above objectives, the present invention provides the following solution: An inherently safe management method that integrates functional safety and information security includes: Collect multi-source data from industrial systems; Establish a dynamic influencing factor identification model for integrated safety; The multi-source data is input into the integrated safety dynamic influencing factor identification model to obtain the corresponding device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields; The device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields are correlated in both time and space to obtain multi-dimensional scene data records; The multidimensional scene data records are encoded to obtain a unique scene code identifier; The unique scenario code identifier is analyzed using a preset functional safety risk assessment model and an information security risk assessment model to obtain functional safety risk indicators and information security risk indicators, respectively. Based on the device type, instrument and equipment operating status (fault / abnormality), current operating conditions, conveying medium, operating pressure, operating temperature, personnel job category, number of personnel, relative distance between personnel and hazard sources, environmental conditions, and real-time status of functional safety protection measures and information security protection measures, real-time functional safety risk indicators and real-time information security risk indicators are obtained; the real-time functional safety risk indicators and real-time information security risk indicators are integrated to obtain a comprehensive dynamic risk level. Based on the comprehensive dynamic risk level, corresponding dynamic risk decision instructions are generated, including: instrument-equipment failure decisions, maintenance and repair decisions, personnel management decisions, and functional safety and information security protection measures decisions.

[0006] The present invention discloses the following technical effects: This invention provides an inherently safe management method that integrates functional safety and information security. It constructs a dynamic, integrated safety influencing factor analysis model encompassing device status, process status, personnel status, environmental status, and information security status. This model performs temporal and spatial dual-path correlation on multi-source data, including process parameters, equipment operating status, interlocking and protection actions, personnel identity and location information, network access logs, and security events. Using a unique scenario code identifier as a link, it drives both the functional safety risk assessment model and the information security risk assessment model. Furthermore, it incorporates factors such as device type, instrument and equipment operating status (fault / abnormality), current operating conditions, conveying medium, operating pressure, operating temperature, personnel job category, number of personnel, relative distance between personnel and hazards, and environmental factors. Based on factors such as environmental conditions, functional safety protection measures, and real-time status of information security protection measures, real-time risk indicators for functional safety and information security are obtained. These indicators are then fused across scenarios to obtain a comprehensive dynamic risk level that can directly trigger decisions regarding instrument-equipment failures, maintenance, personnel management, and functional and information security protection measures. This overcomes the problems of functional safety assessment and information security assessment being separate in the background technologies, risk identification relying on a single data source, and static lag in risk levels. It achieves unified modeling and coordinated assessment of industrial system operating conditions and network security posture, significantly improving the real-time nature and completeness of risk identification, as well as the relevance and executability of decision-making instructions. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A flowchart of an intrinsically safe management method that integrates functional safety and information security, provided as an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0011] like Figure 1 As shown, this invention provides an intrinsically safe management method that integrates functional safety and information security, comprising: Step 100: Collect multi-source data from the industrial system; Step 200: Establish a dynamic influencing factor identification model for integrated safety; Step 300: Input the multi-source data into the integrated safety dynamic influencing factor identification model to obtain the corresponding device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields; Step 400: Perform time and space dual-path association on the device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields to obtain multi-dimensional scene data records; Step 500: Encode the multidimensional scene data records to obtain a unique scene code identifier; Step 600: Analyze the unique scenario code identifier using the preset functional safety risk assessment model and information security risk assessment model respectively to obtain functional safety risk indicators and information security risk indicators; Step 700: Based on the device type, instrument and equipment operating status (fault / abnormality), current operating conditions, conveying medium, operating pressure, operating temperature, personnel job category, number of personnel, relative distance between personnel and hazard sources, environmental conditions, and real-time status of functional safety protection measures and information security protection measures, obtain the real-time functional safety risk index and the real-time information security risk index; integrate the real-time functional safety risk index and the real-time information security risk index to obtain the comprehensive dynamic risk level; Step 800: Generate corresponding dynamic risk decision instructions based on the comprehensive dynamic risk level, wherein the dynamic risk decision instructions include: instrument-equipment failure decision, maintenance and repair decision, personnel management decision, functional safety and information security protection measure decision, etc.

[0012] In this embodiment, step 100 acquires multi-source data from the industrial device during operation through various communication protocols, and records it on the timeline according to a preset sampling period or triggering conditions to ensure that the collected data reflects the dynamic changes in the actual operating conditions. Specifically, without changing the existing control and protection logic, this embodiment configures data access interfaces and acquisition tasks to uniformly incorporate available operation records from the process control layer, equipment layer, and safety protection layer into the multi-source data acquisition scope, providing complete basic data support for subsequent identification of dynamic influencing factors in integrated safety.

[0013] In this embodiment, when collecting process parameter data and equipment operating status data, priority is given to parameters that can characterize the safety margin of key process units and the health status of equipment. These include, but are not limited to, real-time values ​​and alarm statuses of process variables such as temperature, pressure, flow rate, and liquid level, as well as the start-up and shutdown operating status of key moving equipment such as pumps or compressors, and the operating status and self-diagnostic results of equipment such as valves and instruments. By synchronously collecting the above data, this embodiment can depict the correspondence between the degree of process deviation and the equipment operating status under the same reference at the same time, providing a direct data source for calculating indicators such as process deviation, equipment status difference, and failure frequency in subsequent functional safety risk assessments.

[0014] This embodiment, when collecting equipment data, operational data, information security data, environmental data, personnel data, emergency response data, and risk decision-making data, uniformly includes relevant records from industrial site process control systems, safety instrumented systems, alarm and event recording modules, personnel positioning systems, environmental monitoring systems, and industrial information security protection equipment within the collection scope. The focus is on acquiring the trigger conditions and interlocking actions of various interlocking and protection functions, recording the occurrence time, duration, and handling of faults and abnormal events, obtaining the identification and spatial location information of personnel entering or approaching hazardous areas, and extracting the access source, access target, access type, and identified security event characteristics during network access. Through the synchronous collection of multi-source safety-related data, this embodiment achieves unified data acquisition of device operating status, personnel positioning status, and information security status, laying a complete data foundation for subsequent time and space dual-path correlation, multi-dimensional scenario construction, and functional safety and information security risk assessment.

[0015] Furthermore, in this embodiment, step 200 involves sorting out and analyzing the functional safety and information security requirements of the industrial system. Firstly, from the perspectives of process safety, equipment operation, personnel work, and information security protection, key business elements that can characterize the operating status of the device, process status, personnel status, environmental status, and information security status are selected. These elements are then abstracted into device-related fields, process-related fields, personnel-related fields, environmental-related fields, and information security-related fields. Specifically, this embodiment uses the ability to reflect changes in the state of hazards, the degree of personnel exposure, network attack paths, and the effectiveness of security protection as screening criteria. The names, data types, value ranges, update frequencies, and their correspondence with actual physical or logical quantities of the fields are standardized, forming a set of field definitions for device-related fields, process-related fields, personnel-related fields, environmental-related fields, and information security-related fields. This provides a clear semantic foundation for subsequent unified modeling.

[0016] After obtaining the field definition set, this embodiment, combined with the multi-source data sources and data formats collected in step 100, establishes parsing and mapping rules between multi-source data and device-related fields, personnel-related fields, and information security-related fields. On the one hand, based on the storage structure and encoding method of various data sources, this embodiment clarifies the differences in timestamps, tag naming, units, and precision of data output by different devices and systems. By formulating parsing rules, the key content in the original data stream is parsed into identifiable intermediate variables. On the other hand, based on the field meanings and value requirements given in the field definition set, this embodiment constructs a mapping relationship from the parsed intermediate variables to the target fields, clarifying the value source, calculation method, and necessary data cleaning or transformation logic of each field, thereby forming a multi-source data parsing and field mapping rule set, ensuring that the subsequent transformation from the original data to the unified fields is deterministic and repeatable.

[0017] After obtaining the set of rules for multi-source data parsing and field mapping, this embodiment further combines the process operation information, equipment information, hazardous area classification information, personnel activity information, and information security protection equipment information of the industrial site to formulate time association rules and spatial association rules for multi-source data. Based on this, the generation rules for unique scene coding identifiers are determined, and finally, a safety integrated dynamic influencing factor identification model is generated. Specifically, this embodiment uses time association rules to align device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields generated under different systems and sampling periods to a unified time reference point. It uses spatial association rules to establish correspondences between process information, equipment information, hazardous areas, personnel location information, and the network segments where information security nodes are located. Then, based on these time and spatial association results, it designs the field composition, field order, and encoding method for unique scene code identifiers, ensuring that each group of time-synchronized and spatially associated device, personnel, and information security states can correspond to a unique scene identifier. Furthermore, this embodiment combines "multi-source data parsing and field mapping rule sets, time and spatial association rules, and unique scene code identifier generation rules" into a unified processing flow. This results in inputs of multi-source raw data and outputs device-related fields, personnel-related fields, and information security-related fields with unique scene codes, thus forming a safety-integrated dynamic influencing factor identification model.

[0018] Furthermore, in this embodiment, the integrated safety dynamic influencing factor identification model processes raw data related to the device, process, personnel, environment, and information security. It summarizes the risk points involved in the device using process-related and information security-related data. Based on the device-related raw data, it uses mathematical induction to determine the probability of occurrence and the severity of the consequences of each risk point, multiplying the two to obtain an initial risk level. Then, it combines process-related and environmental-related data to determine functional safety risk parameters such as protective measures, common-cause failures, and instrument-equipment operating status, or information security risk reduction factors such as redundant configuration and effectiveness of information security protection, obtaining static risk influencing factor identification results. Real-time data is introduced into the static analysis results. In this embodiment, at each time index, the collected raw data related to the device, process, personnel, environment, and information security are mapped to the static risk analysis.

[0019] Specifically, in this embodiment, the source and value of the above parameters can be explained in conjunction with a specific scenario. Assume that the time index is the sampling sequence number corresponding to 10:00 AM for a certain oil and gas storage and transportation device. At this time, the device-related raw data contains 3 components, namely: the current liquid level of a certain storage tank is 10m (1), the liquid level limit is set to 12m (2), and the current process flow is in the oil recovery condition (3). The process-related raw data includes 2 components, namely: the valve status controlling the oil recovery process is normal (1), and the liquid level alarm and interlock instrument readings are normal and there is no fault alarm. The personnel-related raw data includes 3 components, namely: the on-duty status flag of the on-duty operator is 1, indicating that the operator is on duty; the job category code is 2, for example, 2 represents the on-site operation position; the distance between the operator and the nearest hazard source of the reactor is 10 meters. The environmental-related raw data includes 2 components, namely: the ambient temperature is 25℃, and the ambient pressure is 101Kpa. The raw data related to information security includes three components: the number of abnormal accesses detected in the last 10 minutes is 1, the number of session connections currently connected to the control network is 100, and the flag indicating whether an information security event has occurred in the last 10 minutes is 0, indicating that no security alarm has been detected. This results in a unified dynamic influencing factor identification result that includes device data, process data, personnel data, environmental data, and information security data.

[0020] Furthermore, in this embodiment, step 300 uses the aforementioned integrated safety dynamic influencing factor identification model to extract scenario-based features from the multi-source data collected in step 100 and mapped in step 200. Specifically, under each time index, the device data, process data, personnel data, environmental data, and information security data obtained through multi-source data parsing and field mapping rule set processing are sequentially input into the integrated safety dynamic influencing factor identification model according to a unified feature space dimension and field order. Through data processing operations within the model, the corresponding device-related fields, process-related fields, personnel-related fields, environmental-related fields, and information security-related fields are output.

[0021] In step 400 of this embodiment, the device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields are first aligned according to preset time association rules, and an initial scene record is generated. The time association rules consist of three parts: scene time granularity, maximum allowable time deviation, and alignment strategy. The scene time granularity is used to determine the time resolution of the scene record, such as using 1 second or 5 seconds as a scene unit; the maximum allowable time deviation is used to specify whether the timestamps from different data sources can still be considered the same scene when there is a slight offset; the alignment strategy is used to clarify the selection and interpolation method when there are multiple records or missing records within a certain time granularity. After the above processing, this embodiment obtains a time alignment result containing time identifiers and preliminary combination relationships of the five types of fields, and on this basis, an initial scene record with a time index but without embedded spatial information is formed.

[0022] After obtaining the initial scene record, this embodiment further establishes spatial correspondences between device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields in the initial scene record according to preset spatial association rules, based on equipment layout information, hazardous area classification information, personnel location information, environmental information, and information security node topology information. Specifically, this embodiment maps each device-related field to a specific equipment unit, pipeline segment, or process area number through equipment layout information; binds the aforementioned equipment units to different levels of hazardous areas through hazardous area classification information; associates each personnel-related field record with the nearest equipment unit or hazardous area through personnel location information, and calculates or marks the distance relationship and regional affiliation between personnel and key hazard sources; determines the current environmental temperature and pressure through environmental information to calculate the impact range of hazardous events; and associates information security-related fields with corresponding network nodes, control units, or regional network segments through information security node topology information. Spatial association rules clarify the matching methods between different device nodes, personnel locations, and information security nodes within the same time window. For example, using the device number as the primary key, the personnel field and device field of the same device or the same hazardous area are bound together, and the information security field corresponding to the network node controlling that device or area is also associated. Finally, this embodiment integrates the time alignment results with the above spatial correspondence to obtain a multi-dimensional scene data record that can fully reflect the physical operation status, process operation status, personnel exposure status, environmental status, and information security status within a specific spatial range at a specific time, as well as their interrelationships.

[0023] Furthermore, in this embodiment, step 500 generates a unique scenario code identifier that can be retrieved and used for decision-making by performing rule-based encoding on the aforementioned multi-dimensional scenario data records. Specifically, this embodiment first analyzes the scenario feature elements that must be clearly distinguished in the process of risk identification, risk warning, and post-event tracing, based on the business requirements of functional safety and information security risk decision-making, and selects a set of key fields for encoding from the multi-dimensional scenario data records. This set of key fields includes at least the number of process flow divisions of the device, the number of risk analysis keywords, and the number of scenarios, and the order of each key field in the encoding is uniformly specified through scenario encoding generation rules. For example, taking a high-risk point of liquid level in a storage tank of an oil and gas storage and transportation device as an example, assuming that the oil and gas storage and transportation device can be divided into 3 parts according to the process flow, with the storage tank located in the first part of the process flow; the risk analysis keywords include multiple categories such as temperature, pressure, flow rate, and liquid level, with liquid level being the fourth risk analysis keyword; the number of scenarios involved under the liquid level keyword is 5, so the number of scenarios is 5, and the scenario number is determined as (1, 4, 5).

[0024] Furthermore, in this embodiment, step 600 evaluates the scenario identified by the aforementioned unique scenario code by invoking a preset functional safety risk assessment model and an information security risk assessment model to generate a quantitative risk indicator. Specifically, this embodiment first performs structural parsing on the unique scenario code, based on the time identifier, device area identifier, hazard level, personnel status information, and information security node status contained in the unique scenario code, restoring it to a set of scenario input parameters that can be directly used by the assessment model. For the functional safety risk assessment part, this embodiment focuses on inputting the parsed scenario parameters into the preset functional safety risk assessment model from dimensions such as device status, hazard level, degree of deviation from operating conditions, effectiveness of protection and interlocking configuration, and personnel exposure. Through the pre-set weight coefficients, risk matrix, logical rules, and data-driven assessment structure within the model, a functional safety risk indicator is output to characterize the functional safety risk level of the current scenario.

[0025] In terms of information security risk assessment, this embodiment extracts information security scenario parameters based on the unique scenario coding identifier, which is related to network structure, control system nodes, access behavior patterns, and security alarm levels. These parameters include key information security node identifiers, current abnormal access intensity, triggered security event levels, involved control unit categories, and mapping relationships with physical devices. These parameters are then input into a preset information security risk assessment model. This model can employ rule-based attack path assessment methods, anomaly scoring methods based on statistical and behavioral analysis, or risk prediction structures trained on historical attack samples to comprehensively assess potential information security threats in the current scenario, such as network intrusion, malicious operations, data tampering, or service interruptions. This embodiment normalizes or standardizes the assessment results into information security risk indicators, enabling horizontal comparisons across different times, device regions, and system nodes to reflect the relative level of information security risk in the current scenario.

[0026] Furthermore, in this embodiment, step 700 first calculates the accident risk impact coefficient, which characterizes the degree of impact of the accident risk, based on the device type, the instrument and equipment operating status (fault / abnormality), the current operating condition, and the functional safety protection measures. This embodiment maps the device type, the instrument and equipment operating status (fault / abnormality), the current operating condition, and the original probability of the risk to obtain the correction coefficient of the original probability of the risk in different scenarios. The functional safety protection measures are combined with the original probability of the risk to obtain the coefficient of the probability of the risk. The above coefficients are multiplied to obtain the accident risk impact coefficient, which characterizes the degree of impact of the accident risk.

[0027] Based on personnel job categories, number of personnel, and distance between personnel and hazard sources, a personnel exposure parameter is constructed to characterize the degree of personnel exposure. This embodiment maps job categories to the nature of contact with hazard sources and the importance of responsibilities, pre-setting job exposure weight values ​​for different job categories. For example, positions that operate on-site for extended periods and frequently enter hazardous areas are assigned higher weights; positions that primarily monitor from the control room and only enter the site occasionally are assigned medium weights; and positions that primarily provide management and technical support and rarely enter hazardous areas are assigned lower weights. Subsequently, this embodiment obtains the number of personnel actually involved in the current work situation under each multi-dimensional scenario data record, and calculates a distance correction factor or area correction factor based on the real-time distance between each person and the corresponding hazard source or the hazard zone level to which they belong, so that personnel closer to the hazard source or in a higher hazard level area contribute more. By combining job exposure weights, the number of personnel in the scenario, and the distance correction factor, this embodiment obtains a personnel exposure parameter that monotonically increases with the number of people in high-exposure positions, the total number of people, and the decrease in the average distance to the hazard source, used to quantify the overall level of personnel exposure in the current scenario.

[0028] Based on the physical properties of the medium, operating pressure, operating temperature, and environmental conditions, ignition probability parameters characterizing the probability of an ignition event occurring are calculated. In this implementation example, these parameters are input into a preset ignition probability calculation model. The direct ignition probability is determined by the transport medium and operating pressure, and the indirect ignition probability is calculated by the operating temperature and environmental conditions. Finally, the total ignition probability is calculated using the following formula. P 点火概率 =Probability of direct ignition + (1 - probability of direct ignition) Indirect ignition probability; Furthermore, this embodiment combines the accident risk impact coefficient, personnel exposure level parameter, and ignition probability to form a scenario risk correction coefficient. This scenario risk correction coefficient is then applied to functional safety risk indicators and information security risk indicators, enabling dynamic calculation based on risk scenario information to obtain dynamic functional safety risk values ​​and dynamic information security risk indicators. Taking ignition probability as an example, the direct ignition probability is positively correlated with on-site pressure. Therefore, an increase in on-site operating pressure will lead to an increase in the direct ignition probability, thereby affecting P. 点火概率 This causes it to increase in size. And P 点火概率 An increase in the risk level will increase the functional safety risk index and the information security risk index, causing a change in the total risk value of the scenario. Similarly, the dynamic risk of the device is calculated based on the real-time changes in the accident risk impact coefficient.

[0029] Subsequently, this embodiment fuses the modified functional safety risk value and the modified information security risk value according to preset criteria. For example, functional safety risk values ​​more closely related to direct process failure and personal injury can be assigned higher consequences or frequencies of occurrence, while information security risk values ​​related to system intrusion, data tampering, and remote control risks can be assigned lower consequences or frequencies of occurrence. A single comprehensive risk quantification result is obtained through summation or other monotonic fusion functions. Finally, this embodiment maps the comprehensive risk quantification result into several discrete comprehensive dynamic risk levels according to pre-established risk classification criteria. For example, it divides the risk into four levels according to numerical ranges or quantiles: low risk, acceptable risk, warning risk, and high risk. The corresponding comprehensive dynamic risk level is bound to a unique scenario code identifier to drive subsequent alarm linkage, emergency response, and risk optimization decisions.

[0030] Furthermore, in this embodiment, step 800 automatically generates dynamic risk decision instructions matching the current scenario based on the aforementioned comprehensive dynamic risk level. It intelligently proposes several categories of decisions, including maintenance and repair decisions, personnel management decisions, and functional safety and information security protection measures, based on the risk level and scenarios such as instrument and equipment failures and process anomalies. This embodiment first configures a set of standardized handling templates for each risk level according to the correspondence between the comprehensive dynamic risk level and preset risk response rules. These templates include detailed handling options for different device areas, different operating conditions, and different information security node types.

[0031] Once the overall dynamic risk level is determined, this embodiment selects a set of basic measures from the corresponding level's handling template library. Then, combining this with contextual information such as device status, process status, personnel status, environmental status, and information security status carried by the unique scenario code identifier, the set of basic measures is adjusted according to the scenario to generate specific, executable dynamic risk decision-making instructions. In this way, this embodiment enables the generation of targeted handling plans for the same risk level under different operating conditions and spatial locations, rather than being limited to general, principle-based recommendations.

[0032] Regarding risk mitigation measures, this embodiment automatically plans a combination of device control actions, from mild to severe, based on the comprehensive dynamic risk level and the key equipment, hazardous process parameters, and protection configurations involved in the scenario. When the comprehensive dynamic risk level is low, the device mitigation measures can be limited to mild interventions such as adjusting process setpoints, optimizing interlock setpoints, and increasing the frequency of online monitoring. When the comprehensive dynamic risk level is medium, this embodiment can generate measures including limiting load operation, switching to safe operating conditions, activating redundant protection circuits, and implementing remote interlock control for some high-risk units. When the comprehensive dynamic risk level reaches a high or highest level, this embodiment prioritizes generating emergency response instructions, such as issuing emergency shutdown instructions for specific device units, closing critical valves, cutting off some material or energy supplies, and initiating intrinsically safe measures such as inerting or depressurization. The above-mentioned device mitigation measures are output in the form of structured instructions, including the target equipment or device unit identifier, specific control action type, action priority, and whether manual confirmation is required, thereby ensuring that the mitigation actions can be directly invoked in the control system or implemented after manual review.

[0033] Regarding personnel control measures and information security protection measures, this embodiment generates decision-making instructions based on personnel safety exposure and network security protection needs, respectively. For personnel control measures, this embodiment automatically generates instructions based on comprehensive dynamic risk levels and personnel exposure parameters, such as restricting unnecessary personnel from entering specific hazardous areas, organizing the orderly evacuation of on-site workers to safe assembly points, strengthening on-duty personnel or requiring two-person operations for key personnel, and suspending or postponing non-emergency maintenance and construction activities related to the current hazardous area. It also generates mandatory control requirements when necessary, such as immediately clearing a high-risk area, conducting roll call confirmations and safety briefings for specific groups of personnel. For information security protection measures, this embodiment combines the correction results of real-time information security risk indicators and the currently involved key information security nodes to generate protective action instructions, including increasing access authentication strength, temporarily tightening remote access policies, limiting or blocking abnormal traffic, isolating specific nodes from unnecessary external networks, upgrading security monitoring and log auditing levels, and isolating and investigating suspected controlled hosts.

[0034] More specifically, based on the generation of dynamic risk decision-making instructions, this embodiment further establishes a detailed handling suggestion library that matches the comprehensive dynamic risk level for different categories of influencing factors, such as instrument valves, process anomalies, equipment failures, personnel impacts, other categories, and information security. This enables automatic mapping from risk assessment results to specific operational instructions. Specifically, this embodiment first marks the category of influencing factors in the multi-dimensional scenario data records and unique scenario codes. When an event is identified as an instrument valve, process anomaly, equipment failure, personnel impact, other categories such as lightning strikes or combustible gas detector alarms, or information security-related events, the corresponding decision-making submodule is entered. For scenarios with a general risk level and where spare parts are verified to exist through equipment ledgers and spare parts management information, this embodiment automatically generates instructions such as "View corresponding maintenance operation card" and "Confirm completion of processing" to guide on-site or maintenance personnel to implement standardized handling according to the standard operation card. For scenarios where there are no spare parts and the overall dynamic risk level reaches a significant or major level, this embodiment adds instructions such as "Initiate procurement application" and "Simultaneously activate temporary control measures" to the device handling measures to prompt management personnel to replenish spare parts in a timely manner and temporarily reduce risks by reducing load, limiting operating conditions, or strengthening monitoring while spare parts are not available.

[0035] In terms of refining decision-making output, this embodiment configures a multi-level instruction structure combining automatic prompts and on-site handling for categories such as instrument valves, process anomalies, equipment failures, and personnel impacts. When instrument valves or equipment failures are detected and the overall dynamic risk level is at a moderate or significant level, this embodiment automatically generates a "handle by relevant professionals" instruction in the device handling measures, pushing the alarm information to the corresponding responsible persons such as instrumentation professionals, equipment professionals, or electrical professionals; at the same time, an "on-site handling" prompt is generated in the personnel control measures, requiring on-site operators to perform inspection, reset, or switching operations according to the work card.

[0036] Regarding information security and subsequent closed-loop management, this embodiment combines technical and management instructions in the "Information Security Protection Measures" for information security scenarios determined to be of medium to high risk after comprehensive dynamic risk level correction. On the one hand, this embodiment generates technical protection measures such as restricting access, isolating suspected nodes, and upgrading audit levels based on information security risk characteristics. On the other hand, from a management perspective, this embodiment adds an item to the decision-making instructions: "Report control measures at the next day's production morning meeting." This requires production management personnel to report and review the information security incident and the control measures taken at the subsequent production morning meeting, thereby strengthening security awareness and experience feedback among all employees.

[0037] Through the above methods, this embodiment ensures that decision-making recommendations under various influencing factors such as instrument valves, process anomalies, equipment failures, personnel impacts, other factors, and information security not only cover different risk levels such as general, major, and significant, but also provide clear textual handling suggestions and optional manual supplementation space in both scenarios with and without spare parts, and in both routine and unconventional scenarios, thereby achieving a closed-loop transformation of risk assessment results into specific management and technical actions.

[0038] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0039] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for inherently secure management based on the integration of functional safety and information security, characterized in that, include: Collect multi-source data from industrial systems; Establish a dynamic influencing factor identification model for integrated safety; The multi-source data is input into the integrated safety dynamic influencing factor identification model to obtain the corresponding device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields; The device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields are correlated in both time and space to obtain multi-dimensional scene data records; The multidimensional scene data records are encoded to obtain a unique scene code identifier; The unique scenario code identifier is analyzed using a preset functional safety risk assessment model and an information security risk assessment model to obtain functional safety risk indicators and information security risk indicators, respectively. Based on the device type, instrument and equipment operating status, current working conditions, media properties, operating pressure, operating temperature, personnel job category, number of personnel, relative distance between personnel and hazard sources, environmental conditions, and real-time status of functional safety protection measures and information security protection measures, real-time functional safety risk indicators and real-time information security risk indicators are obtained. By integrating real-time risk indicators for functional safety and real-time risk indicators for information security, a comprehensive dynamic risk level is obtained. Based on the comprehensive dynamic risk level, corresponding dynamic risk decision instructions are generated, wherein the dynamic risk decision instructions include: instrument-equipment failure decision, maintenance and repair decision, personnel management decision, functional safety protection measure decision, and information security protection measure decision; The expression for the functional safety risk assessment model is: ； in, For functional safety risk indicators; where L is the original probability of a risk event occurring, C p The severity of consequences related to personal safety, C f For the severity of property-related consequences, C E P represents the severity of environmental consequences, N represents the effectiveness of protective measures, β represents the code number of the instrument-equipment operating status, β represents the common cause failure score, and F represents the mapping matrix of multi-source functional safety related parameters. The expression for the information security risk assessment model is: ； in, For information security risk indicators; N att The code number for the type of information security attack the device is subjected to, F att The frequency of information security attacks suffered by the device The attacked device's network segment code number; DFE (Device Validation Equipment) indicates the effectiveness of the information protection device. The risk reduction factor of redundant configuration in information security The impact of information security attacks is represented by F, which is a mapping matrix of multi-source information security-related parameters.

2. The intrinsically safe management method integrating functional safety and information security according to claim 1, characterized in that, The multi-source data includes: Equipment data, operational data, information security data, environmental data, personnel data, emergency response data, and risk decision-making data.

3. The intrinsically safe management method integrating functional safety and information security according to claim 1, characterized in that, The integrated safety dynamic influencing factor identification model includes: Based on functional safety requirements and information security requirements, we determine the device-related fields used to characterize the device status, the process-related fields used to characterize the process status, the personnel-related fields used to characterize the personnel status, the environment-related fields used to characterize the environment status, and the information security-related fields used to characterize the information security status, thus obtaining a set of field definitions for device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields. Based on the set of field definitions, we establish parsing and mapping rules between multi-source data and equipment-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields, thus obtaining a set of multi-source data parsing and field mapping rules. Based on equipment layout information, hazardous area classification information, process equipment information, personnel activity range, environmental information and information security node topology in the industrial site, time association rules and spatial association rules for multi-source data are formulated. Based on the multi-source data parsing and field mapping rule set and the time association rules and spatial association rules of the multi-source data, the generation rules for the unique scene code identifier are determined; Based on the generation rules of the unique scene code identifier, a dynamic influencing factor identification model for security integration is generated.

4. The intrinsically safe management method integrating functional safety and information security according to claim 3, characterized in that, The expression for the integrated safety dynamic influencing factor identification model is: ； Where t is the time index of the multi-source data; This is the original data vector related to the device acquired at time t; This is the vector of raw process-related data collected at time t; This is the vector of raw personnel-related data collected at time t; This is the original vector of environment-related data collected at time t; This is the original data vector related to information security collected at time t; These are the transformation matrices that map the raw data related to the equipment, process, personnel, environment, and information security to a unified feature space, respectively; F is the output transformation matrix that maps the fused features to a joint representation of the equipment-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields.

5. The intrinsically safe management method integrating functional safety and information security according to claim 1, characterized in that, By performing temporal and spatial dual-path correlation on the aforementioned device-related fields, process-related fields, personnel-related fields, environment-related fields, and information security-related fields, multi-dimensional scene data records are obtained, including: Based on the timestamps carried by each field and the preset time association rules, the time-related fields of equipment, process, personnel, environment and information security are time-aligned to obtain the time alignment results and the initial scene record; Based on equipment layout information, hazardous area division information, personnel location information, and information security node topology information, according to preset spatial association rules, spatial correspondences are established between device-related fields, personnel-related fields, and information security-related fields in the initial scene record. By integrating the time alignment results and spatial correspondence, a multi-dimensional scene data record is formed, which includes time markers, device status, personnel status, information security status, and their spatial relationships.

6. The intrinsically safe management method integrating functional safety and information security according to claim 1, characterized in that, The multidimensional scene data records are encoded to obtain a unique scene code identifier, including: Based on the security control requirements of functional safety and information security, a set of key fields for coding is determined, and the field order, encoding format and length requirements are specified for each key field to form a scenario coding generation rule. According to the scene encoding generation rules, the corresponding key field values ​​are extracted from each multidimensional scene data record, and the key field values ​​are formatted to obtain the field sequence used for encoding. A unique scene code identifier is generated based on the field sequence, and a duplicate check is performed during the generation process. When a code conflict is detected, the code parameters are adjusted according to a preset conflict resolution strategy to ensure that different multidimensional scene data records correspond to different unique scene code identifiers.

7. The intrinsically safe management method integrating functional safety and information security according to claim 1, characterized in that, Based on the device type, instrument and equipment operating status, current working conditions, media properties, operating pressure, operating temperature, personnel job category, number of personnel, relative distance between personnel and hazard sources, environmental conditions, and real-time status of functional safety protection measures and information security protection measures, real-time functional safety risk indicators and real-time information security risk indicators are obtained. By integrating real-time risk indicators for functional safety and real-time risk indicators for information security, a comprehensive dynamic risk level is obtained, including: The accident risk impact coefficient, which characterizes the degree of accident risk impact, is calculated based on the device type, instrument and equipment operating status, current operating conditions, and functional safety protection measures. Based on the personnel job category, number of personnel, and relative distance between personnel and hazard sources, calculate personnel exposure level parameters that characterize the degree of personnel exposure; Based on the physical properties of the medium, operating pressure, operating temperature, and environmental conditions, calculate the ignition probability parameters that characterize the probability of an ignition event occurring. The scenario risk correction coefficient is obtained by combining the personnel exposure level parameter, the ignition probability parameter and the accident risk impact coefficient. The functional safety risk index and information security risk index are corrected using the scenario risk correction coefficient to obtain the dynamic risk value of functional safety and the dynamic risk value of information security. The functional safety dynamic risk value and the information security dynamic risk value are fused together to obtain the corresponding comprehensive dynamic risk level.

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