Risk chain-based special operation accident case data management method

Abstract

The invention provides a special operation accident case data management method based on a risk chain, and the method comprises the steps: 1, constructing a digital special operation safety risk chain through an accident case library of a corresponding operation safety coverage range, and carrying out the safety risk assessment of all links and all element indexes of safety risk prevention, a chain structure of danger sources, risks, safety measures, hidden dangers and accidents is formed; the method comprises the steps of 1, constructing a digital special operation safety risk chain, 2, implementing an accident case library data treatment method based on the constructed digital special operation safety risk chain, and 3, generating safety early warning and prevention suggestions for different special operation scenes based on a data treatment result, and optimizing safety measures of high-risk operation. The method has the following beneficial effects that the standardized and systematized treatment of the special operation accident case library can be realized, and more direct and accurate support is provided for the special operation risk analysis of the high-risk industry.

Description

Technical Field

[0001] The present invention belongs to the field of safe production, and in particular relates to a special operation accident case data management method based on a risk chain. Background Art

[0002] In chemical production and other high-risk industries, special operation safety management is an important part of ensuring production safety. However, existing accident case analysis methods usually have problems such as data dispersion, insufficient governance, and low accuracy of risk analysis, making it difficult to fully support the safety assessment and early warning of special operation tasks. GB30871-2022 "Safety Specifications for Special Operations in Chemical Production Units" proposes strict safety specifications, but lacks tool support based on systematic data analysis. There are many articles on various methods for accident case safety analysis, but after searching, it was found that they are mainly data analysis methods and fault tree methods from the macro-level of "man-machine-material-method-environment". No public special operation accident case data governance methods based on risk chains similar to the present invention have been retrieved. Summary of the invention

[0003] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a special operation accident case data management method based on risk chain to solve the problems raised in the above background technology.

[0004] The present invention is implemented through the following technical solutions: A special operation accident case data management method based on risk chain, comprising:

[0005] A special operation accident case data governance method based on risk chain, including:

[0006] Step 1: construct a digital special operation safety risk chain through the accident case library corresponding to the operation safety coverage. The construction of the digital special operation safety risk chain is based on the input special operation task book and safety specifications. By conducting safety risk assessment on each link and each element indicator of safety risk prevention, a chain structure of danger source, risk, safety measures, hidden danger and accident is formed;

[0007] Step 2: Based on the constructed digital special operation safety risk chain, implement the accident case library data governance method to form data governance results. The data governance method includes:

[0008] a. Metadata management;

[0009] b. Resource directory management;

[0010] c. Data desensitization and control;

[0011] d. Data specification definition and modeling;

[0012] e. Determination of data blood relationship;

[0013] f. Data quality assessment;

[0014] Step 3: Based on the data governance results, generate safety warnings and prevention suggestions for different special operation scenarios, and optimize safety measures for high-risk operations. The specific operation steps of Step 3 are: Combined with the analysis results of the accident case library and risk chain, through the information after data governance, generate effective hidden danger prevention and control suggestions for specific operation scenarios. The suggestions include hidden danger checklists and optimization plans to help managers identify potential dangers, evaluate existing safety measures, and propose optimization measures.

[0015] As a preferred embodiment, the specific steps of step 1 are:

[0016] Step s1, data input

[0017] Standard specification input: Based on the specific requirements for special operations in GB30871-2022 "Safety Specifications for Special Operations in Chemical Production Units", including operation approval, risk assessment, implementation of safety measures, etc.

[0018] Job assignment input: By parsing the job assignment, key information such as job type, work environment, work tools, personnel information and time schedule can be extracted.

[0019] Accident case input: Extract the data of special operation accidents that have occurred from the historical accident case library as the initial basis for building the risk chain.

[0020] Step s2: Risk chain analysis

[0021] Hazard source identification: Classify and record the hazards that may cause accidents (such as flammable and explosive gases, corrosive substances, high-altitude working environment, etc.) based on the working environment and task book; use the risk matrix method or HAZOP analysis method to evaluate the possibility and severity of the hazards in combination with accident case data;

[0022] Risk assessment: Analyze possible safety risks (such as fire, explosion, poisoning, etc.) based on the hazard sources and operating conditions; use FTA (fault tree analysis) and FMEA (failure mode and effects analysis) techniques to quantitatively assess risks and obtain risk priorities;

[0023] Safety measures matching: Based on the risk assessment results, extract corresponding safety precautions from standard specifications (such as wearing protective equipment, setting up isolation zones, real-time monitoring, etc.); verify the effectiveness of the selected safety measures through historical case data and optimize the plan;

[0024] Hazard investigation combines the implementation of safety measures to analyze possible remaining safety hazards (such as equipment aging, personnel negligence, etc.), classify the hazards, and propose targeted rectification measures;

[0025] Accident modeling: Integrate hazards, risks, safety measures and hidden dangers to build a logical model of accident occurrence; use event tree analysis (ETA) technology to simulate the evolution path of the accident and determine the final result.

[0026] Step s3, digital implementation

[0027] Through databases and graphical tools, each link in the risk chain and their relationships are digitized to form a chain structure of hazards, risks, safety measures, hidden dangers, and accidents, providing a basis for subsequent risk assessment and early warning.

[0028] As a preferred implementation, in step 2, the accident case database data management method includes:

[0029] Metadata management: Unified management of the description information and structured information of case library data to achieve data standardization;

[0030] Resource directory management: Establish a directory system for accident case data to facilitate quick query and retrieval;

[0031] Data desensitization and control: protect sensitive data in accident cases and ensure data is used in compliance with regulations;

[0032] Data specification definition and modeling: formulate classification standards for case data and establish data models based on the risk chain;

[0033] Data lineage determination: trace the source and change process of accident case data to ensure data traceability and integrity;

[0034] Data quality assessment: Assess the integrity, consistency, and accuracy of accident case data to improve the credibility of the data.

[0035] The metadata management includes:

[0036] Metadata definition;

[0037] Metadata storage and maintenance;

[0038] Metadata application.

[0039] As a preferred implementation, the metadata definition includes:

[0040] Case ID: unique identifier of each accident case;

[0041] Type of operation: the type of special operation involved;

[0042] Hazard source description: the main hazard source that may cause the accident;

[0043] Safety measures: the main precautions taken;

[0044] Hidden danger description: potential problems that existed before the accident but were not dealt with;

[0045] Accident outcome: the immediate consequences of the accident;

[0046] Data source: how the data is collected.

[0047] The metadata storage and maintenance includes: recording and classifying the above fields, and dynamically updating them.

[0048] The metadata application includes: providing a field query function to support users to quickly understand the data structure and field meaning.

[0049] As a preferred implementation, the specific operations of metadata management are: clarifying data fields and attributes, including operation type, hazard source, hidden danger, accident result, etc., to form a metadata dictionary; dynamically updating metadata to support field expansion (such as adding a new "ambient temperature" field);

[0050] The specific operations of resource directory management are: establishing a multi-level directory structure according to the operation type and risk level, supporting classified storage and fast retrieval;

[0051] The specific operations of data desensitization and management are: desensitize sensitive information (such as company name, job manager) to ensure data security; assign permissions to restrict low-level users from viewing sensitive data.

[0052] The specific operations of data specification definition and modeling are: setting data standards and modeling rules to ensure data consistency and standardization; building a risk chain model to display the causes and impacts of accidents through correlation analysis.

[0053] The specific operations of data lineage determination are: tracking the entire process of data from collection to analysis to ensure data traceability and credibility;

[0054] The specific operations of data quality assessment are: evaluating data integrity, consistency, and accuracy to ensure that the data meets risk analysis requirements.

[0055] After adopting the above technical solution, the beneficial effect of the present invention is that it can realize the standardized and systematic management of the special operation accident case library, and provide more direct and accurate support for the special operation risk analysis of high-risk industries. At the same time, this method can improve the efficiency of accident case analysis and enhance the enterprise's safety production risk prevention and control capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention 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 of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 The present invention is a flowchart of the steps of a special operation accident case data management method based on a risk chain. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 The present invention provides a technical solution: a special operation accident case data management method based on risk chain, comprising:

[0060] A special operation accident case data governance method based on risk chain, including:

[0061] Step 1: construct a digital special operation safety risk chain through the accident case library corresponding to the operation safety coverage. The construction of the digital special operation safety risk chain is based on the input special operation task book and safety specifications. By conducting safety risk assessment on each link and each element indicator of safety risk prevention, a chain structure of danger source, risk, safety measures, hidden danger and accident is formed;

[0062] Step 2: Based on the constructed digital special operation safety risk chain, implement the accident case library data governance method to form data governance results. The data governance method includes:

[0063] a. Metadata management;

[0064] b. Resource directory management;

[0065] c. Data desensitization and control;

[0066] d. Data specification definition and modeling;

[0067] e. Determination of data blood relationship;

[0068] f. Data quality assessment;

[0069] Step 3: Based on the data governance results, generate safety warnings and prevention suggestions for different special operation scenarios, and optimize safety measures for high-risk operations.

[0070] Embodiment 1, as an embodiment of the present invention, the specific steps of step 1 are:

[0071] Step s1, data input

[0072] Standard specification input: Based on the specific requirements for special operations in GB30871-2022 "Safety Specifications for Special Operations in Chemical Production Units", including operation approval, risk assessment, implementation of safety measures, etc.

[0073] Job assignment input: By parsing the job assignment, key information such as job type, work environment, work tools, personnel information and time schedule can be extracted.

[0074] Accident case input: Extract the data of special operation accidents that have occurred from the historical accident case library as the initial basis for building the risk chain.

[0075] Step s2: Risk chain analysis

[0076] Hazard source identification: Classify and record the hazards that may cause accidents (such as flammable and explosive gases, corrosive substances, high-altitude working environment, etc.) based on the working environment and task book; use the risk matrix method or HAZOP analysis method to evaluate the possibility and severity of the hazards in combination with accident case data;

[0077] Risk assessment: Analyze possible safety risks (such as fire, explosion, poisoning, etc.) based on the hazard sources and operating conditions; use FTA (fault tree analysis) and FMEA (failure mode and effects analysis) techniques to quantitatively assess risks and obtain risk priorities;

[0078] Safety measures matching: Based on the risk assessment results, extract corresponding safety precautions from standard specifications (such as wearing protective equipment, setting up isolation zones, real-time monitoring, etc.); verify the effectiveness of the selected safety measures through historical case data and optimize the plan;

[0079] Hazard investigation combines the implementation of safety measures to analyze possible remaining safety hazards (such as equipment aging, personnel negligence, etc.), classify the hazards, and propose targeted rectification measures;

[0080] Accident modeling: Integrate hazards, risks, safety measures and hidden dangers to build a logical model of accident occurrence; use event tree analysis (ETA) technology to simulate the evolution path of the accident and determine the final result.

[0081] Step s3, digital implementation

[0082] Through databases and graphical tools, each link in the risk chain and their relationships are digitized to form a chain structure of hazards, risks, safety measures, hidden dangers, and accidents, providing a basis for subsequent risk assessment and early warning.

[0083] Embodiment 2, as an embodiment of the present invention, in step 2, the accident case database data management method includes:

[0084] Metadata management: Unified management of the description information and structured information of case library data to achieve data standardization;

[0085] Resource directory management: Establish a directory system for accident case data to facilitate quick query and retrieval;

[0086] Data desensitization and control: protect sensitive data in accident cases and ensure data is used in compliance with regulations;

[0087] Data specification definition and modeling: formulate classification standards for case data and establish data models based on the risk chain;

[0088] Data lineage determination: trace the source and change process of accident case data to ensure data traceability and integrity;

[0089] Data quality assessment: Assess the integrity, consistency, and accuracy of accident case data to improve the credibility of the data.

[0090] The specific operations of metadata management are: clarifying data fields and attributes, including operation types, hazard sources, hidden dangers, accident results, etc., to form a metadata dictionary; dynamically updating metadata and supporting field expansion (such as adding a new "ambient temperature" field);

[0091] The specific operations of resource directory management are: establishing a multi-level directory structure according to the operation type and risk level, supporting classified storage and fast retrieval;

[0092] The specific operations of data desensitization and management are: desensitize sensitive information (such as company name, job manager) to ensure data security; assign permissions to restrict low-level users from viewing sensitive data.

[0093] The specific operations of data specification definition and modeling are: setting data standards and modeling rules to ensure data consistency and standardization; building a risk chain model to display the causes and impacts of accidents through correlation analysis.

[0094] The specific operations of data lineage determination are: tracking the entire process of data from collection to analysis to ensure data traceability and credibility;

[0095] The specific operations of data quality assessment are: evaluating data integrity, consistency, and accuracy to ensure that the data meets risk analysis requirements.

[0096] The specific steps for the above operation are:

[0097] 1. Metadata Management

[0098] Metadata Definition

[0099] Define the attributes of the data in the accident case database (such as data source, collection time, format requirements).

[0100] Tag field level information such as job type, incident type, impact area, etc.

[0101] Metadata storage

[0102] Establish a metadata dictionary to uniformly manage the definitions, meanings, and relationships of each data field.

[0103] Ensure the consistency and updatability of metadata and data in the incident case database.

[0104] Metadata Update and Maintenance

[0105] Configure an automatic update mechanism to dynamically adjust metadata as case data changes.

[0106] Regularly review metadata integrity to ensure that key descriptive information is not lost.

[0107] 2. Resource Directory Management

[0108] Catalog system design

[0109] Establish a multi-level directory structure according to operation type, accident type, risk level, etc. to ensure clear logic.

[0110] Set unified naming rules and classification codes.

[0111] Data Archiving

[0112] Archive and store accident case data according to the directory system and establish a retrieval index.

[0113] Historical data is managed in a hierarchical manner, distinguishing between active data and archived data.

[0114] Catalog search optimization

[0115] Configure the full-text search function to support quick search by keyword, classification code or field.

[0116] Set permission control to ensure that users can only access directories within the authorized scope.

[0117] 3. Data desensitization and control

[0118] Desensitization rule configuration

[0119] Configure desensitization rules (such as data masking and pseudo-anonymization) for sensitive data in accident cases (such as personnel information and company names).

[0120] Use the hash encryption algorithm to irreversibly encrypt key fields.

[0121] Desensitization application

[0122] When data is exported, shared, or analyzed, desensitization operations are performed in real time to ensure privacy protection.

[0123] Permission allocation and control

[0124] Set data access permissions based on user roles to limit direct access to non-masked data.

[0125] 4. Data specification definition and modeling

[0126] Defining data standards

[0127] Determine the standardized format of the fields in the accident case library (such as the date format is unified as YYYY-MM-DD).

[0128] Establish a valid range or value selection rule for field values ​​(such as limiting the risk level to 1-5).

[0129] Building a data model

[0130] Based on the structure of the risk chain, an accident case data model is established to define entities and their relationships (such as the one-to-many relationship between hazards and risks).

[0131] Use ER diagram tools to implement graphical design of models.

[0132] 5. Data lineage determination

[0133] Data tracking rules

[0134] Define data sources, flows, and change record fields to ensure that each piece of data is traceable.

[0135] Establish a data processing log to record the data cleaning, integration and modification process.

[0136] Visualization of blood relationship

[0137] Use graph database technology (such as Neo4j) to build a data lineage map to display the upstream and downstream relationships of the data.

[0138] 6. Data Quality Assessment

[0139] Evaluation indicator design

[0140] Evaluation indicators such as design completeness, consistency, and accuracy.

[0141] Set an evaluation threshold, such as the data missing rate must not exceed 5%.

[0142] Quality Inspection Process

[0143] Scan case data regularly to detect missing, duplicate, or outlier values.

[0144] Mark erroneous data and generate a quality assessment report.

[0145] Quality Improvement Measures

[0146] Design automated repair processes (such as filling missing values) for problems found during the assessment.

[0147] Continuously optimize the data collection process and reduce low-quality data input.

[0148] Example 2.1: For example, a chemical plant did not fully detect the concentration of combustible gas in the working area during the hot work, resulting in an explosion accident, causing minor injuries to three people. Based on this case, data is constructed and managed to support risk analysis and early warning work for similar special operation safety.

[0149] 1. Metadata Management

[0150] Metadata is information about data, and its management is the foundation of data governance.

[0151] Metadata Definition

[0152] Case ID: The unique identifier of each accident case.

[0153] Type of operation: special types of operations involved, such as hot work, confined space operations, etc.

[0154] Hazard source description: The main hazard source that may cause an accident, such as residual flammable gas.

[0155] Safety measures: Major precautions taken, such as monitoring gas concentrations or increasing ventilation.

[0156] Hazard description: Potential problems that existed before the accident but were not addressed, such as inadequate ventilation in the work area.

[0157] Accident outcomes: direct consequences of the accident, such as explosion, casualties or economic losses.

[0158] Data source: the method of collecting data, such as self-reporting by enterprises or third-party audits.

[0159] Metadata storage and maintenance

[0160] The above fields are recorded and classified, and updated dynamically. For example, after adding the field "economic loss due to accident", the data structure of the existing cases needs to be updated synchronously.

[0161] Metadata Application

[0162] Provides field query function to support users to quickly understand data structure and field meaning.

[0163] 2. Resource Directory Management

[0164] In order to facilitate management and query, a scientific catalog system is established:

[0165] Directory structure design

[0166] Classified by operation type, such as "hot work" and "confined space work".

[0167] This is further broken down into risk level categories, such as “high risk” and “medium risk”.

[0168] Case data archive

[0169] This case is stored in the "Hot Work / High Risk" category for quick location and retrieval.

[0170] Catalog search optimization

[0171] Provides keyword search function, for example, input "hot work + high risk" to return all related cases.

[0172] 3. Data desensitization and control

[0173] Data desensitization is to protect sensitive information and ensure data security.

[0174] Desensitization rule configuration

[0175] The specific content of the data source field (such as company name) is replaced by “****”.

[0176] The name of the person in charge of the task is partially anonymized, for example, "Zhang San" is displayed as "Z**".

[0177] Application after desensitization

[0178] When sharing cases externally, sensitive information is blocked, but the core data information can still support risk analysis.

[0179] Permission Assignment

[0180] Administrators can access non-masked data, while ordinary users can only view the masked version.

[0181] 4. Data specification definition and modeling

[0182] Data Standardization

[0183] Set the field value range. For example, the operation type must be selected from predefined options such as "Hot Work" and "Confined Space Work".

[0184] The risk level is set on a numerical scale of 1 to 5, with 1 representing low risk and 5 representing high risk.

[0185] Data Modeling

[0186] Constructing the chain relationship of "hazard source-risk-safety measures-hidden danger-accident":

[0187] Hazard source: “Residual flammable gas”.

[0188] Risk: "May cause explosion".

[0189] Safety measures: "Check gas concentration" and "Ensure adequate ventilation".

[0190] Hazard: "The working area is not adequately ventilated."

[0191] Accident: "Explosion, 3 people slightly injured".

[0192] Model Application

[0193] Based on the data model, a risk chain diagram is generated to intuitively display the causes and impacts of accidents.

[0194] 5. Data lineage determination

[0195] Data lineage is the tracking of the flow and evolution of data throughout the governance process.

[0196] Tracking data sources

[0197] The data was originally self-reported by the companies and uploaded on November 20, 2024.

[0198] After desensitizing, modeling, archiving and other processing steps, the final usable data is formed.

[0199] Visualization of blood relationship

[0200] The entire process from data collection to use, including uploading, desensitizing, archiving, etc., each step can be traced.

[0201] 6. Data Quality Assessment

[0202] The completeness and accuracy of the data are assessed through a number of indicators:

[0203] Evaluation indicator design

[0204] Completeness: The missing rate should be less than 5%.

[0205] Consistency: The percentage of erroneous values ​​should be less than 2%.

[0206] Quality test results

[0207] During the test, it was found that the field "economic losses from accidents" was missing and the data needed to be further improved.

[0208] No duplication or errors were found in other fields such as case ID, meeting the quality requirements.

[0209] Improvement measures

[0210] For missing data, the average value of similar cases can be used to fill in the gaps, such as “economic losses from accidents” can be filled in with 500,000 yuan.

[0211] Example 3, the specific operating steps of step 3 are: combined with the analysis results of the accident case library and the risk chain, through the information after data governance, generate effective hidden danger prevention and control suggestions for specific operation scenarios. The suggestions include hidden danger checklists and optimization plans to help managers identify potential dangers, evaluate existing safety measures, and propose optimization measures.

[0212] Specific:

[0213] 1. Risk chain data after data governance

[0214] The risk chain data after data governance contains safety information in multiple dimensions, such as hazard sources, risk assessments, safety measures, hidden dangers, accidents, etc. The characteristics of the data after governance are structured, standardized, desensitized and highly reliable. Through data governance, it can be ensured that this information can be efficiently used for subsequent safety analysis.

[0215] Data governance includes:

[0216] Metadata management: Ensure that each type of data is standardized, named in a standardized manner, and has a clear classification.

[0217] Resource directory management: Manage the equipment, environment, operating procedures and other resources at the work site to ensure that all data is associated with the resource library.

[0218] Data lineage tracing: By tracing the data sources in the accident case, the associations and changes behind each piece of data can be determined.

[0219] Data quality assessment: Conduct a comprehensive assessment of the data to ensure its accuracy, completeness, and timeliness.

[0220] 2. Process for generating risk prevention and control recommendations for specific work scenarios

[0221] 2.1 Identify key risk factors in the operation scenario

[0222] Based on the governed data, we first need to identify the key risk factors of a specific operation scenario. We can identify them by the following steps:

[0223] Based on historical data and risk chain: By analyzing the accident case database, identify risk sources, hidden dangers and types of accidents that have occurred in similar operating scenarios.

[0224] Real-time monitoring data: Combine the real-time data on site (such as temperature, pressure, humidity, etc.) and compare it with historical data to determine whether there are potential risk factors.

[0225] Analysis of job assignment and working environment: Identify possible sources of danger based on the job content and working environment described in detail in the job assignment.

[0226] 2.2 Hidden danger investigation

[0227] The hidden danger checklist is the basis for generating hidden danger prevention and control recommendations. The design and generation of the hidden danger checklist is based on the specific risk chain information obtained after data governance.

[0228] Risk chain analysis: Through each link in the risk chain (hazard sources, risks, safety measures, hidden dangers, accidents), possible hidden dangers are listed according to different operating scenarios.

[0229] Hazard classification: Hazards are classified according to their nature (such as mechanical failure, electrical fire, chemical leakage, etc.) and risk level, and corresponding hazard inspection contents are proposed.

[0230] Contents of hidden danger checklist:

[0231] Hidden danger type: List all possible hidden danger types (such as equipment failure, operating error, etc.).

[0232] Hidden danger location: Mark the area or equipment where the hidden danger may occur.

[0233] Causes of hidden dangers: Analyze the possible causes of hidden dangers (such as aging equipment, poor management, etc.).

[0234] Hazards of hidden dangers: Assess the possible consequences when hidden dangers occur (such as casualties, property losses, etc.).

[0235] Inspection items and frequency: List the inspection contents and frequency for each hidden danger.

[0236] 2.3 Optimization plan for hidden danger prevention and control measures

[0237] The design of the optimization plan is based on the results of the hidden danger investigation, combined with the existing safety measures and the risk chain data after governance, to propose more effective prevention and control measures. The optimization plan generally includes the following aspects:

[0238] Technical measures: Reduce or eliminate hidden dangers by introducing new technologies and equipment or optimizing existing equipment. For example, install new safety monitoring systems, improve operating procedures, and update aging equipment.

[0239] If the hidden danger is caused by aging equipment, the optimization solution may be to update the equipment or install a redundant protection system.

[0240] Management measures: Strengthen operator training, regular inspections, work process optimization, etc. to improve operational safety.

[0241] If the hidden danger is caused by operator negligence, the optimization solution may be to strengthen safety training and drills and strictly implement safety regulations.

[0242] Environmental measures: Improve the working environment and enhance ventilation, lighting and safety facilities in the workplace.

[0243] If the hazard is related to the working environment, the optimization solution may be to improve the ventilation system or strengthen the monitoring of exhaust emissions.

[0244] 2.4 Safety early warning mechanism

[0245] Based on the governed risk chain data, a dynamic security early warning mechanism is established to enable timely response and handling when potential risks occur.

[0246] Real-time monitoring and alarm system: Based on on-site data collection, alarm thresholds are set. Once the monitoring data exceeds the preset threshold (such as excessive temperature or excessive pressure), an alarm is immediately issued.

[0247] Data-driven decision support: Provides decision support to management based on historical data and real-time data analysis to help them make timely adjustments to safety measures.

[0248] 2.5 Regular evaluation and improvement

[0249] Recommendations for hazard prevention and control are not static. As the working environment, equipment and technology change, prevention and control plans need to be constantly adjusted and optimized.

[0250] Regularly evaluate hidden danger checklists and prevention and control measures: Based on the newly collected accident case data, evaluate and revise hidden danger checklists and prevention and control measures.

[0251] Feedback mechanism: Based on the implementation status and accident occurrence, feedback on the effectiveness of the optimization plan is provided and the prevention and control strategy is adjusted.

[0252] Example 3.1, Suggestions for prevention and control of hidden dangers in chemical production plants

[0253] Assume that in the reaction tower operation scenario of a chemical production plant, the risk chain data after governance is as follows:

[0254] Hazard source: high-pressure reaction gas in the reaction tower.

[0255] Risk: Gas leakage or uncontrolled pressure in the reaction tower may cause explosion.

[0256] Safety measures: Install a high-pressure alarm system, set up a gas leak detector, and regularly check the sealing of equipment.

[0257] Hidden dangers: aging equipment and insensitive leakage alarm system.

[0258] Accident: Aging equipment caused leakage in the reaction tower, which was not discovered in time and eventually caused an explosion.

[0259] Based on this data, the generated risk prevention and control suggestions may include:

[0260] Hidden danger investigation table: lists the aging conditions of the reaction tower equipment, sealing inspection items, sensitivity detection of the alarm system, etc.

[0261] Optimization plan: Update the reaction tower valves and pipelines, install a redundant alarm system; increase the frequency of equipment inspections, and increase on-site inspections and real-time monitoring.

[0262] Early warning measures: Combined with real-time data monitoring, establish an automatic alarm system for abnormal pressure and temperature.

[0263] Improvement measures: Regularly replace and upgrade the reaction tower equipment to ensure that safety measures keep pace with technological developments.

[0264] Example 4: This example describes in detail the working principle of the special operation accident case data governance method based on the risk chain.

[0265] background

[0266] A chemical production plant conducts cleaning and maintenance operations on high-pressure reaction towers, which involves entering high-temperature and high-pressure environments, and there are high-risk operation scenarios such as gas leakage and explosion. Based on GB30871-2022 "Safety Specifications for Special Operations in Chemical Production Units" and special operation task books, it is necessary to build a digital safety risk chain of "hazard source-risk-safety measures-hidden danger-accident", conduct data governance, and ultimately generate safety warnings and prevention recommendations.

[0267] 1. Build a digital special operation safety risk chain

[0268] Input Data

[0269] Accident Case Library: Contains historical accident cases that occurred in similar operating scenarios (such as explosions caused by gas leaks).

[0270] Special operation task book: operation content, equipment environment, personnel arrangement, operating procedures, expected risks, etc.

[0271] Safety regulations: Relevant requirements for special operations in chemical production units in GB30871-2022.

[0272] Build Process

[0273] 1.1 Hazard identification:

[0274] Hazard sources: flammable and explosive gases in the high-pressure reaction tower, aging equipment, and operational errors.

[0275] 1.2 Risk Analysis:

[0276] Risks: Fire or explosion caused by gas leakage; splash and personal injury caused by rupture of high-pressure containers.

[0277] 1.3 Safety measures determination:

[0278] Existing measures: regular inspection of equipment, installation of leak alarms, gas detection systems, and wearing of protective equipment.

[0279] 1.4 Hidden danger identification:

[0280] Potential hidden dangers: aging of alarm system, decreased sealing of equipment, and untimely calibration of testing instruments.

[0281] 1.5 Accident simulation:

[0282] Possible accidents: Failure of equipment sealing leads to gas leakage, which is not detected and handled in time, eventually causing an explosion.

[0283] 2. Data governance process

[0284] Data Source

[0285] Accident Case Library: 50 historical accident data involving detailed descriptions of chemical reaction tower operations.

[0286] Job assignment book: the working environment, equipment conditions, operating procedures, etc. of the current task.

[0287] Safety regulations: Clauses in GB30871-2022 regarding working environment, personnel operation and equipment requirements.

[0288] Governance

[0289] 2.1 Metadata Management:

[0290] Definition: Establish data fields, such as "hazard source", "hazard type", "accident type", etc.

[0291] The fields are uniformly named as follows: “Alarm failure rate” and “Leak detection delay”.

[0292] 2.2 Resource Directory Management:

[0293] Define the equipment list: high-pressure reaction tower number, alarm system model, personnel information, etc.

[0294] For: "Reaction tower number A-1001, commissioning date: 2018" is marked in the resource catalog.

[0295] 2.3 Data desensitization and control:

[0296] Desensitization: Hide the name of the person responsible for the accident and other sensitive information.

[0297] The person responsible for the accident is replaced by "Zhang San" by "Person Responsible 1".

[0298] 2.4 Data specification definition and modeling:

[0299] Definition specification: The operation types are divided into "cleaning and maintenance", "disassembly operation", etc., and the risk level is divided into 1-5 levels.

[0300] The current task is "cleaning and maintenance" and the risk level is 4 (high risk).

[0301] 2.5 Data bloodline determination:

[0302] Trace the source of data: clarify the complete path of data from the accident case library, equipment inspection records to the work task book.

[0303] The current hidden danger data comes from "Case number 20231015, inspection record number 20231212".

[0304] 2.6 Data Quality Assessment:

[0305] Evaluation method: Check data completeness, accuracy and consistency.

[0306] Among the 50 accident cases, 45 were complete and valid, and 5 were marked as invalid due to incomplete data.

[0307] 3. Generate security warnings and prevention suggestions

[0308] Based on the analysis of governance results:

[0309] 3.1. Discover hidden dangers:

[0310] The alarm system is aging, with a failure rate of 5%;

[0311] The seal of the reaction tower has been in operation for 5 years and has not reached the expected replacement standard;

[0312] The current gas detection instrument calibration records are 3 months out of date.

[0313] 3.2. Generate a hidden danger checklist

[0314] As shown in Table 1

[0315]

[0316] 3.3. Optimization plan:

[0317] Technical measures:

[0318] Replace the seals of the reaction tower and upgrade the alarm system;

[0319] Add automatic calibration function to the detector.

[0320] Management measures:

[0321] Increase the frequency of on-site inspections and appoint safety specialists to supervise the investigation of hidden dangers;

[0322] Strengthen the training of personnel on operating procedures to ensure timely detection of abnormalities.

[0323] Environmental measures:

[0324] Improve ventilation conditions in the working area to reduce the risk of accumulation of flammable gases.

[0325] 3.4. Safety early warning mechanism:

[0326] Monitor the pressure and temperature of the reaction tower in real time, and set alarm thresholds (such as pressure ≥10MPa triggers an alarm); enable abnormal analysis during operation, issue early warnings when abnormalities are found, and automatically interlock and shut down.

[0327] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A special operation accident case data management method based on risk chain, characterized in that: include: Step 1: construct a digital special operation safety risk chain through the accident case library corresponding to the operation safety coverage. The construction of the digital special operation safety risk chain is based on the input special operation task book and safety specifications. By conducting safety risk assessment on each link and each element indicator of safety risk prevention, a chain structure of danger source, risk, safety measures, hidden danger and accident is formed; Step 2: Based on the constructed digital special operation safety risk chain, implement the accident case library data governance method to form data governance results. The data governance method includes: a. Metadata management; b. Resource directory management; c. Data desensitization and control; d. Data specification definition and modeling; e. Determination of data blood relationship; f. Data quality assessment; Step 3: Based on the data governance results, generate safety warnings and prevention suggestions for different special operation scenarios, and optimize safety measures for high-risk operations.

2. A method for managing special operation accident case data based on risk chain as claimed in claim 1, characterized in that: The specific steps of step 1 are: Step s1, data input Standard specification input: based on the specific requirements of special operations, including operation approval, risk assessment, and implementation of safety measures; Job task book input: By parsing the job task book, the job type, work environment, work tools, personnel information and time arrangement information are extracted; Accident case input: Extract the data of special operation accidents that have occurred from the historical accident case library as the initial basis for building the risk chain; Step s2: Risk chain analysis Hazard source identification: Classify and record the hazards that may cause accidents based on the operating environment and task book; use the risk matrix method or HAZOP analysis method to assess the possibility and severity of hazards based on accident case data; Risk assessment: Analyze the possible safety risks based on the hazard sources and operating conditions; use FTA and FMEA techniques to quantitatively assess the risks and obtain risk priorities; Safety measures matching: extract corresponding safety precautions from standard specifications based on risk assessment results; Verify the effectiveness of selected safety measures through historical case data and optimize the solutions; Hazard inspection combines the implementation of safety measures, analyzes possible remaining safety hazards, classifies them, and proposes targeted rectification measures; Accident modeling: Integrate hazards, risks, safety measures and hidden dangers to build a logical model of accident occurrence; use event tree analysis technology to simulate the evolution path of the accident and determine the final result; Step s3, digital implementation Through databases and graphical tools, each link in the risk chain and their relationships are digitized to form a chain structure of hazards, risks, safety measures, hidden dangers, and accidents, providing a basis for subsequent risk assessment and early warning.

3. A method for managing special operation accident case data based on risk chain as claimed in claim 1, characterized in that: In step 2, the accident case database data governance method includes: Metadata management: Unified management of the description information and structured information of case library data to achieve data standardization; Resource directory management: Establish a directory system for accident case data to facilitate quick query and retrieval; Data desensitization and control: protect sensitive data in accident cases and ensure data is used in compliance with regulations; Data specification definition and modeling: formulate classification standards for case data and establish data models based on the risk chain; Data lineage determination: trace the source and change process of accident case data to ensure data traceability and integrity; Data quality assessment: Assess the integrity, consistency, and accuracy of accident case data to improve the credibility of the data.

4. A method for managing special operation accident case data based on risk chain as claimed in claim 3, characterized in that: The specific operations of metadata management are: clarifying data fields and attributes, including operation types, hazard sources, hidden dangers, accident results, etc., to form a metadata dictionary; dynamically updating metadata and supporting field expansion; The specific operations of resource directory management are: establishing a multi-level directory structure according to the operation type and risk level, supporting classified storage and fast retrieval; The specific operations of data desensitization and control are: desensitize sensitive information to ensure data security; assign permissions to restrict low-level users from viewing sensitive data. The specific operations of data specification definition and modeling are: setting data standards and modeling rules to ensure data consistency and standardization; building a risk chain model to display the causes and impacts of accidents through correlation analysis; The specific operations of data lineage determination are: tracking the entire process of data from collection to analysis to ensure data traceability and credibility; The specific operations of data quality assessment are: evaluating data integrity, consistency, and accuracy to ensure that the data meets risk analysis requirements.

5. A method for managing special operation accident case data based on risk chain as claimed in claim 3, characterized in that: The metadata management includes: Metadata definition; Metadata storage and maintenance; Metadata application.

6. A method for managing special operation accident case data based on risk chain as claimed in claim 5, characterized in that: The metadata definition includes: Case ID: unique identifier of each accident case; Type of operation: the type of special operation involved; Hazard source description: the main hazard source that may cause the accident; Safety measures: the main precautions taken; Hidden danger description: potential problems that existed before the accident but were not dealt with; Accident outcome: the immediate consequences of the accident; Data source: how the data is collected.

7. A method for managing special operation accident case data based on risk chain as claimed in claim 5, characterized in that: The metadata storage and maintenance includes: recording and classifying the above fields, and dynamically updating them.

8. A method for managing special operation accident case data based on risk chain as claimed in claim 5, characterized in that: The metadata application includes: providing a field query function to support users to quickly understand the data structure and field meaning.

9. A method for managing special operation accident case data based on risk chain as claimed in claim 1, characterized in that: The specific operation steps of step 3 are: combined with the analysis results of the accident case library and risk chain, through the information after data governance, generate effective hidden danger prevention and control suggestions for specific operation scenarios. The suggestions include hidden danger checklists and optimization plans to help managers identify potential dangers, evaluate existing safety measures, and propose optimization measures.

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