A method and system for managing work permits

Abstract

This invention relates to the field of safety management technology for special operations in hazardous chemical enterprises, and discloses a method and system for managing work permits. The method includes: constructing a hybrid storage architecture and establishing two-layer field constraints; planning capability unit boundaries, encapsulating capability units, and establishing a capability unit registry; performing process status awareness and data version control; adaptive distribution of capability unit results and semantic distribution of risk identification results; performing process rules and status control; designing standard API interfaces and establishing API call monitoring and exception handling mechanisms; designing multi-dimensional query interfaces and constructing a complete traceability chain for the work process; statistically implementing risk analysis; and obtaining a full-process traceability and intelligent analysis strategy. This invention balances compliance and flexibility, reduces redundant reporting, accurately shares results, facilitates efficient collaborative workflow, shortens integration cycles, and supports full-process traceability and intelligent risk analysis.

Description

Technical Field

[0001] This invention relates to the field of safety management and control technology for special operations in hazardous chemical enterprises, and more specifically, to a method and system for managing work permits. Background Technology

[0002] In the safety production management of hazardous chemical enterprises, the management of special operation permits is a core link in ensuring the safe and controlled operation of high-risk activities such as hot work, confined space work, and work at height. The traditional paper-based operation permit management method has inherent defects such as cumbersome approval process, lack of on-site supervision, and difficulty in data traceability, which makes it difficult to meet the requirements of modern safety management.

[0003] With the development of information technology, electronic work permit systems have gradually replaced paper work permits, realizing online management of work permit processes. However, existing electronic work permit systems still face many technical bottlenecks in practical applications: fixed template systems use hard-coded methods to implement business logic, which, while ensuring compliance, cannot adapt to the personalized management needs of enterprises. Any functional adjustment requires modification of code and database structure, resulting in a disconnect between the system and the actual business process; while visual configuration systems offer some flexibility, their use of semi-structured JSON format for data storage leads to poor query performance, difficult data analysis, complex system integration, and the potential for erroneous compliance fields. Deletion or tampering leads to an exponential increase in system maintenance costs as configuration complexity increases. In multi-ticket collaborative management, existing systems generally adopt a "ticket-centric" management model, requiring the issuance of each ticket individually, repeated filling of general information, and repeated risk assessments, resulting in extremely low invoicing efficiency. Regarding business logic encapsulation, complex business logic such as gas analysis and risk identification is either hard-coded, making it difficult to adjust, or configured visually, leading to fragmented logic that is difficult to maintain, lacking effective modular encapsulation and reuse mechanisms. In cross-system integration, the use of semi-structured data storage or fixed integration interfaces necessitates customized development for each specific scenario, resulting in long response cycles and high costs.

[0004] Therefore, a work permit management technology solution is needed that can both guarantee rigid compliance requirements and possess high flexibility and scalability to address the deep-seated technical deficiencies of existing technologies in terms of business flexibility, architectural robustness, and data linkage. Summary of the Invention

[0005] This invention provides a method and system for managing work tickets, which solves the technical problems of related technologies such as inability to adapt to the personalized management needs of enterprises, low query performance, and low invoicing efficiency.

[0006] This invention provides a method for managing work permits, comprising: Construct a hybrid storage architecture and establish two-layer field constraints to obtain a hybrid storage strategy; Based on a hybrid storage strategy, capability unit boundaries are planned, capability units are encapsulated, a capability unit registry is established, and a business capability unit library is obtained. Based on the business capability unit library, process status awareness and data version control are performed to obtain data management strategies. Based on the data management strategy, the system automatically maps and populates general business data, and adaptively distributes the processing results of the capability units according to their business attributes, thereby obtaining risk identification results and a distribution record set. Based on the risk identification results and distribution record set, process rules and status control are implemented to obtain a closed-loop management strategy for task tickets. Based on the task ticket closed-loop management strategy, a standard API interface was designed and an API call monitoring and exception handling mechanism was established to obtain an agile integration strategy. Based on the agile integration strategy, a multi-dimensional query interface was designed and a complete traceability chain for the operation process was constructed. Statistical risk analysis was implemented to obtain a full-process traceability and intelligent analysis strategy.

[0007] In a preferred embodiment, constructing the hybrid storage architecture and establishing two-tier field constraints includes: Based on the national standard compliant field set, a fixed physical table containing a job task table and a job ticket table is created. Non-null constraints and value range constraints are set at the database level, as well as non-deletable and non-editable identifiers at the system metadata level, to build a rigid storage layer. Reserve extended columns in each physical table, create a field configuration table to realize dynamic mapping from extended columns to business fields, and realize dynamic parsing and data access of extended fields through the configuration engine to build a flexible extension layer; Strong constraints are imposed on national standard compliant fields, while weak constraints are imposed on extended fields, constructing a hybrid storage architecture consisting of a rigid storage layer, a flexible extension layer, and a unified access layer.

[0008] In a preferred embodiment, the planning of capability unit boundaries, the encapsulation of capability units, and the establishment of a capability unit registry include: Based on the complexity and non-standardization of business processes, complex business logic that needs to be encapsulated using a hybrid storage architecture is identified. The plan includes gas analysis capability unit, risk identification capability unit, safety measure capability unit, JSA analysis capability unit, safety briefing capability unit, approval capability unit, and monitoring capability unit. The business logic of the capability unit is implemented using object-oriented programming methods, and a standard API interface including input parameters, output results and processing logic is defined. Establish a capability unit registry that includes capability unit identifiers, API interface paths, input parameter definitions, and output result definitions, and design capability unit reference components.

[0009] In a preferred embodiment, the process status awareness and data version control include: Establish a mapping table from task table fields to ticket table fields, and create a data change history table containing the changed fields, the version number before the change, the version number after the change, the value before the change, and the value after the change; Save common field values ​​to the job task table and automatically increment the data version number. Build a field value distribution plan based on the mapping relationship table. Perform data distribution operations according to the process status of the sub-ticket using automatic synchronization, prompt synchronization, or prohibit synchronization. When the ticketing process reaches the approval stage, a data consistency check is performed. The current values ​​of common fields in the task table and the ticketing table are compared field by field. Change records are queried from the data change history table, and the data state at the target version time is reconstructed.

[0010] In a preferred embodiment, the automatic mapping and filling of general business data, and the adaptive distribution of capability unit processing results based on the business attributes of the capability units, include: The factory area is discretized into a preset number of spatial grids using a grid partitioning method, and the work area is converted into a set of grid identifiers. Add the operation range grid set parameter to the input parameters of the capability unit, and add the result coverage grid set parameter and spatial location field to the output results to associate the detection data with the corresponding spatial grid. Perform automatic mapping and filling of general data from tasks to sub-tickets, calculate the intersection grid set of the gas detection result coverage area and the sub-ticket operation area, and filter out the detection data of the intersection grid to construct the exclusive detection dataset for the sub-tickets; Establish a risk distribution rule table that includes a risk type and an applicable set of job types. Calculate the intersection of the applicable set of job types and the job type set of the task. Distribute risk records to sub-tickets whose job types belong to the intersection.

[0011] In a preferred embodiment, the process rules and status control include: Based on the sequential relationships and branching conditions between nodes, a process state machine model is constructed, consisting of a set of states, a set of events, a state transition function, an initial state, and a set of final states. Create a task process instance and initialize the current state as the starting node. Determine the flow strategy based on the node's business identifier. If the identifier is to start a sub-ticket process, create an independent process instance for each sub-ticket, establish a parent-child relationship in the process association table, and update the current state of the task process instance to waiting for the sub-process. The process association table is queried periodically to identify task process instances that are in the waiting sub-process state. If the status flag of all sub-ticket processes is "completed", the task process continue execution event is triggered, the execution of the task process is resumed and the task process status flag is updated to "completed".

[0012] In a preferred embodiment, the design of the standard API interface and the establishment of API call monitoring and exception handling mechanisms include: Identify scenarios such as operator qualification verification, on-site monitoring, gas detection data acquisition, and on-site visualization; design a standard API interface that includes input parameters and output results; and encapsulate data interaction with external systems using the adapter pattern. The design supports selector components that support external data sources. The configuration panel provides options for data sources such as manual input, local data tables, and external API interfaces. It also configures the parameter sources required for API calls and generates the runtime logic of the selector component. Log recording and monitoring points are added before and after each call to record the call time, interface identifier, input parameters, call time and return status code, and a unified exception handling mechanism is used for retry and degradation processing.

[0013] In a preferred embodiment, the design of a multi-dimensional query interface and the construction of a complete traceability chain for the work process, along with the statistical implementation of risk analysis, include: Design a logging specification, register event listeners before and after each node execution to capture node events and insert log entries; The design supports a multi-dimensional query interface that allows filtering and statistics based on time range, job type, job unit, job location, and job level, and uses SQL aggregation queries to implement statistical functions. The system uses multi-table join query technology to query task information, sub-ticket process instance identifiers, process execution log records, and business component processing records to build a structured traceability report. The frequency statistics method is used to group and statistically analyze risk types according to specified dimensions, calculate the frequency of occurrence of risk types, identify high-frequency risk points with frequencies exceeding a preset threshold, and conduct risk co-occurrence analysis.

[0014] In a preferred embodiment, when the number of reserved extended columns is insufficient, a work ticket attribute table is created, which includes attribute identifier, ticket identifier, attribute name and attribute value. The newly added extended fields are stored in the attribute table format. When accessing data, the configuration engine automatically determines the storage location of the extended fields and returns the data after merging the two parts. Materialized view technology is used to optimize the query efficiency of extended fields. Materialized views are generated periodically based on the configuration information of the field configuration table, and the data of reserved extended columns and JSON extended columns are reorganized according to the business field names.

[0015] This invention provides a work ticket management system for executing the aforementioned work ticket management method, comprising: The hybrid storage module is used to build a hybrid storage architecture and establish two-layer field constraints to obtain a hybrid storage strategy; The business capability unit library module, based on a hybrid storage strategy, plans the boundaries of capability units, encapsulates the capability units, establishes a capability unit registry, and obtains the business capability unit library. The data management module, based on the business capability unit library, performs process status awareness and data version control to obtain data management strategies; The distribution management module, based on the data management strategy, performs automatic mapping and filling of general business data, and performs adaptive distribution of capability unit processing results according to the business attributes of capability units, to obtain risk identification results and distribution record sets; The process control module, based on the risk identification results and distribution record set, performs process rules and status control to obtain a closed-loop management strategy for task tickets. The integrated management module, based on the task ticket closed-loop management strategy, designs standard API interfaces and establishes API call monitoring and exception handling mechanisms to obtain an agile integration strategy; The traceability analysis module, based on an agile integration strategy, designs a multi-dimensional query interface and constructs a complete traceability chain for the operation process, statistically implements risk analysis, and obtains a full-process traceability and intelligent analysis strategy.

[0016] The beneficial effects of this invention are as follows: By constructing a hybrid storage architecture with a rigid storage layer and a flexible extension layer, strong constraints are set on national standard compliant fields to ensure they cannot be deleted or tampered with, while flexible configuration capabilities are provided for extended fields, solving the dilemma of traditional systems being "either rigid or out of control." An innovative two-level management model of task-ticket is proposed, enabling one-time entry of general information and automatic distribution to all related sub-tickets, and merging of general assessments and sharing of results among sub-tickets, reducing repetitive work. Complex business logic such as gas analysis, risk identification, safety measures, and JSA analysis is encapsulated into highly cohesive, loosely coupled, reusable capability units. Accurate distribution of processing results is achieved through plant spatial grid partitioning and adaptive distribution algorithms, avoiding data mismatch. A structured relational database is used to store business data, supporting standard SQL queries and index optimization, with query performance superior to semi-structured storage methods. An intelligent synchronization mechanism with data version number and process status awareness ensures timely data updates while preventing tampering of approved data.

[0017] By employing node business identifiers and process state machine technology, collaborative management of task processes and ticketing processes is achieved, ensuring the correctness and integrity of process flow. Integration with external systems is achieved through standard API interfaces and configurable calls, allowing business personnel to complete integration through visual configuration, transforming customized development into agile configuration and shortening the integration cycle. Full-process traceability of the work process is achieved through multi-dimensional queries of process execution logs and structured data. Frequency statistics and risk co-occurrence analysis identify high-frequency risk points and risk correlation patterns, providing scientific decision-making support for management. A comprehensive monitoring system is established through API call logs, exception handling mechanisms, and performance monitoring to ensure system stability and availability. This invention constructs a work ticketing management system that meets rigid compliance requirements while possessing high flexibility and scalability, effectively addressing the technical deficiencies of existing technologies in terms of business flexibility, architectural robustness, and data linkage. Attached Figure Description

[0018] Figure 1 This is a flowchart of a work ticket management method according to the present invention; Figure 2 This is a module diagram of a work ticket management system according to the present invention. Detailed Implementation

[0019] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0020] At least one embodiment of the present invention discloses a work ticket management method, such as Figure 1 As shown, it includes: Step 1: Construct a hybrid storage architecture and establish two-layer field constraints to obtain a hybrid storage strategy; Specifically, the following steps are included: Step 1.1: Analyze the national standard requirements and construct a rigid storage layer; Based on the national standard document GB30871-2022, the definitions of eight special operation types are extracted, including hot work, confined space work, blind flange installation / removal work, work at height, hoisting work, temporary electrical work, earthwork, and circuit breaking work. For each operation type, the required fields specified in the standard document are parsed to construct a set of national standard compliant fields for that operation type. This set includes, but is not limited to, fields such as work unit, work supervisor, work content, work location, work time, work level, approver, and safety measures.

[0021] Based on the national standard compliant field set, nine fixed physical tables are created in the relational database, including one work task table and eight work ticket tables. The work ticket tables include hot work ticket tables, confined space work ticket tables, blind flange removal and installation work ticket tables, high-altitude work ticket tables, hoisting work ticket tables, temporary power supply work ticket tables, earthwork work ticket tables, and circuit breaker work ticket tables. These nine physical tables constitute the rigid storage layer of the system, and their core characteristics are fixed table structure, clearly defined fields, and standardized data types.

[0022] Based on the design principle of a rigid storage layer, a strong constraint mechanism is set for national standard compliant fields. The constraint rules include: adding a "NOT NULL" constraint at the database level to ensure that compliant fields are mandatory; adding a "CHECK" constraint to ensure that field values ​​conform to the range specified by national standards. For example, the value range for the hot work rating field is limited to "Special Grade," "Grade 1," and "Grade 2," and the value range for the oxygen concentration field is limited to values ​​from 0 to 100. At the system metadata level, the field attribute "is_deletable=false" indicates that the field cannot be deleted through the configuration interface, and the field attribute "is_editable=false" indicates that the field definition cannot be modified. The field definition includes: field name, data type, and constraint rules. The field attribute "is_required=true" indicates that the field is a required field.

[0023] Based on the data integration requirements of the rigid storage layer, a standardized data interface is designed for each physical table. This interface adopts a unified data format and field naming convention, supporting automatic integration with the data platform of the safety supervision department. The interface design includes: defining a standard data export format, organizing data using XML or JSON, with field names consistent with national standards; defining standard data validation rules to ensure the exported data meets the integrity and accuracy requirements of national standards; and defining a standard data transmission protocol, supporting multiple transmission methods such as HTTP and FTP. This results in a rigid storage layer with national standard compliance and data integration capabilities.

[0024] Step 1.2: Design a flexible extension layer and establish a dynamic mapping mechanism; Based on the personalized management needs of enterprises, a flexible extension layer is designed on the basis of the rigid storage layer. This extension layer adopts a hybrid mechanism of "physical reservation + logical mapping" to support the dynamic expansion of fields while maintaining the stability of the physical table structure.

[0025] Based on the physical reservation mechanism, extended columns are reserved in each physical table. There are two reservation methods: 1. Reserve a fixed number of general extended columns, with 20 extended columns reserved in the job task table and each job ticket table. The extended columns are named "ext_field_01" to "ext_field_20" and the data type is uniformly set to VARCHAR (500) to support the storage of multiple data types. 2. Reserve JSON type extended columns, by adding a JSON type field "ext_json_fields" to each physical table to store structured extended data.

[0026] Based on a logical mapping mechanism, a field configuration table and a field metadata table are established to achieve dynamic mapping from extended columns to business fields. The structure of the field configuration table includes: configuration identifier, table name, physical column name, business field name, field display name, field data type, field description, whether it is required, value range, display order, enabled status, creation time, creator, modification time, and modifier.

[0027] Based on the configuration engine, dynamic parsing and data access of extended fields are implemented. The configuration engine works as follows: When the system loads a work ticket form, the configuration engine queries the field configuration table for all extended field configurations corresponding to the form, and dynamically generates form controls, including text boxes, drop-down selection boxes, and date pickers, based on the configuration information. When the user fills out the form and submits it, the configuration engine maps the business field names to physical column names and writes the user-input values ​​into the corresponding reserved extended columns or JSON extended columns. When the system queries work ticket data, the configuration engine reads the values ​​of the reserved extended columns and JSON extended columns from the physical table, converts the physical column names to business field names according to the mapping relationship in the field configuration table, and returns the data to the caller in the form of business fields.

[0028] Based on the configuration capabilities of the flexible extension layer, enterprise administrators can perform add, delete, and modify operations on fields through the system configuration interface. The operation process is as follows: When adding a field, the administrator enters information such as the business field name, field type, and field description in the configuration interface. The system automatically allocates an unused reserved extension column and inserts a new configuration record into the field configuration table. When modifying a field, the administrator can change the field's display name, field description, whether it is required, and value range, and the system updates the corresponding record in the field configuration table. When deleting a field, the administrator can set the field's enabled status to disabled. The system will no longer display the field in the form, but the field configuration record and historical data will be retained to support data traceability. This results in a flexible extension layer with flexible configuration capabilities.

[0029] Step 1.3: Establish a two-layer field constraint mechanism and construct a hybrid storage architecture; Based on the design of a rigid storage layer and a flexible extension layer, a two-layer field constraint mechanism is established. This mechanism achieves the goal of "being able to connect with the data of the Safety Supervision Bureau (structured) and meet the individual needs of enterprises (flexibility)" through differentiated constraint strategies.

[0030] Based on rigid layer constraint rules, strong constraints are implemented on national standard compliant fields. These constraints include: field structure constraints: the physical column name, data type, and field length of national standard fields are locked at the database level, and no configuration operation can modify these attributes, ensuring data structure stability and standardization. Field deletion constraints: the "is_deletable=false" attribute is set in the field metadata, and the system configuration interface does not provide an entry point for deleting national standard fields; even direct database operations will be blocked by the system verification mechanism. Field modification constraints: the "is_editable=false" attribute is set in the field metadata, and the definition attributes of national standard fields (field name, data type, constraint rules) cannot be modified, but the display name and field description can be modified to suit the enterprise's expression habits. Data integrity constraints: through the database's "NOT NULL" constraint and application-layer mandatory field validation, it is ensured that national standard fields must be filled in; through the "CHECK" constraint and application-layer value range validation, it is ensured that field values ​​comply with national standard regulations.

[0031] Based on flexible layer constraint rules, weak constraints are applied to extended fields. These constraints include: field configuration constraints, where the "is_configurable=true" attribute is set in the field metadata, allowing modification of attributes such as the extended field's business field name, field type, field description, required field status, and value range through the configuration interface; field optional constraints, where the "is_optional=true" attribute is set in the field metadata, making extended fields optional by default, but allowing enterprises to set certain extended fields as required according to their management requirements; and field change tracking, establishing a field configuration history table to record each field configuration change. The history table includes fields such as change time, changer, change type (add, modify, delete), configuration before change, and configuration after change, supporting auditing and rollback of configuration changes.

[0032] Based on a two-layer constraint mechanism, a complete hybrid storage architecture is constructed, consisting of three core components: The first part is a rigid storage layer, containing nine fixed physical tables and strong constraint rules for national standard compliant fields, ensuring system compliance and data integration capabilities. The second part is a flexible expansion layer, including reserved expansion columns, JSON expansion columns, field configuration tables, and a configuration engine, supporting flexible configuration of customized fields for enterprises. The third part is a unified access layer, providing a unified data access interface, shielding the storage differences between the rigid and flexible layers, and providing transparent data access services to upper-layer business logic.

[0033] Based on the hybrid storage architecture, the data access process works as follows: When business logic needs to read job ticket data, the unified access layer receives the query request, reads the data from the rigid storage layer (standard fields), and reads the data from the flexible extension layer (extended fields). The configuration engine maps physical column names to business field names, and merges the two parts of data into a complete business data object, which is then returned to the caller. When business logic needs to write job ticket data, the unified access layer receives the write request, determines whether each field belongs to the rigid or flexible layer based on the field metadata, writes the data from the standard fields to fixed physical columns, and writes the data from the extended fields to reserved extended columns or JSON extended columns. Database transactions are executed to ensure the atomicity and consistency of the data write.

[0034] Step 1 outputs a hybrid storage strategy, including: a rigid storage layer, a flexible expansion layer, a two-layer field constraint, and a unified access layer.

[0035] In some embodiments, due to the limited number of reserved extended columns, when an enterprise's extended field requirements exceed the reserved number, a relational attribute table mechanism can be used for expansion. The aim is to support unlimited expansion without modifying the main table structure. Specifically, a work ticket attribute table is created, containing fields such as attribute identifier, ticket identifier, attribute name, attribute value, and attribute type. When the reserved extended columns are exhausted, newly added extended fields are stored in an attribute table, with each field value corresponding to a record in the attribute table. The configuration engine automatically determines the storage location of the extended fields during data access. If stored in a reserved extended column, it reads from the main table; if stored in an attribute table, it reads through a relational query, merges the two sets of data, and returns the result. This relational attribute table mechanism supports unlimited field expansion for enterprises. However, it should be noted that the query performance of the attribute table is slightly lower than that of the reserved extended columns. Therefore, reserved extended columns are used first, and the attribute table is only used after the extended columns are exhausted.

[0036] In some embodiments, to further improve the query performance of the flexible extension layer, materialized view technology can be used to optimize the query efficiency of extended fields, aiming to reduce the dynamic parsing overhead of the configuration engine. Specifically, based on the configuration information in the field configuration table, the system periodically generates materialized views. These views reorganize the data of reserved extended columns and JSON extended columns according to the business field names, forming a data structure consistent with the business perspective. When business logic queries job ticket data, it prioritizes reading data from the materialized view, avoiding real-time parsing of configuration and mapping field names. When field configurations change, the system automatically refreshes the materialized view to ensure that the view data remains synchronized with the configuration. Using this materialized view technology can improve the query performance of extended fields to a level comparable to fixed fields, but it increases storage space overhead and view maintenance costs. It is suitable for scenarios with frequent queries but infrequent configuration changes.

[0037] Step 2: Based on the hybrid storage strategy, plan the capability unit boundaries, encapsulate the capability units, establish a capability unit registry, and obtain the business capability unit library; Specifically, the following steps are included: Step 2.1: Determine the boundaries of capability units based on business process planning; Based on the national standard GB30871-2022 and the enterprise's special operation management process, the business links of the entire life cycle of the work permit are systematically sorted out. The business links include core links such as work application, gas detection, risk identification, safety measure formulation, JSA analysis, safety briefing, approval and permitting, work monitoring, and completion acceptance.

[0038] Based on the complexity and non-standardization of business processes, identify complex business logic that requires encapsulation using a hybrid storage architecture. The identification principle is as follows: if the business logic involves complex calculation rules, multi-source data fusion, real-time data processing, external system interaction, or other non-standardized scenarios, then plan it as an independent capability unit. If the business logic only involves simple data entry, data display, process flow, or other standardized scenarios, then implement it through visual configuration and do not need to be encapsulated as a capability unit.

[0039] Based on the identification results of complex business logic, seven core capability units are planned, with the following criteria: Gas Analysis Capability Unit, responsible for handling complex logic such as automatic acquisition of gas detection data, concentration analysis, and qualification determination. This logic involves non-standardized processing such as real-time data interaction with the equipment management system, multi-detection point data fusion, and national standard threshold determination; Risk Identification Capability Unit, responsible for handling complex logic such as intelligent identification of operational risks, risk level assessment, and risk list generation. This logic involves non-standardized processing such as historical accident data analysis, hazard source knowledge base matching, and risk matrix calculation; Safety Measures Capability Unit, responsible for handling complex logic such as intelligent matching of safety measures, verification of measure effectiveness, and confirmation of measure implementation. This logic involves non-standardized processing such as measure library query, on-site photo recognition, and measure integrity verification; JSA Analysis Capability Unit, responsible for handling work step decomposition. The system comprises seven functional units: a hazard identification unit, a hazard control measure formulation unit, and a monitoring unit. The hazard identification unit handles complex logic such as hazard identification and control measure formulation, involving the correlation and intelligent recommendation of the work process knowledge base, hazard factor database, and control measure database. The safety briefing unit handles complex logic such as multi-party briefing records, briefing content generation, and briefing signature confirmation, involving non-standardized processing such as risk information aggregation, briefing template rendering, and electronic signature verification. The approval unit handles complex logic such as approval authority verification, approval condition determination, and approval workflow control, involving non-standardized processing such as multi-level permission systems, dynamic approval conditions, and parallel approval aggregation. The monitoring unit handles complex logic such as real-time monitoring of the work site, anomaly warnings, and emergency response, involving the real-time fusion and intelligent analysis of personnel location data, video surveillance data, and gas detection data. These functional units define the boundaries and responsibilities of their respective business units.

[0040] Step 2.2: Encapsulate capability units using a hybrid storage architecture and define standard API interfaces; Based on the boundary definition of capability units, a hybrid storage architecture is adopted to implement the business logic of capability units. The core goal of hybrid storage architecture development is to encapsulate complex and non-standardized business rules into highly cohesive and loosely coupled independent modules, and provide services to the outside world through standard API interfaces.

[0041] Based on the business logic of the gas analysis capability unit, an object-oriented programming approach is used to implement a hybrid storage architecture. The implementation steps include: defining a gas analysis capability unit class "GasAnalysisCapability", which encapsulates all business logic related to gas analysis; defining a standard API interface "analyzeGas", whose input parameters include the operating range, detection time, and detection mode, and whose output includes a list of detection data, pass / fail judgment results, and explanations of reasons for failure; implementing data acquisition logic, which, when the detection mode is automatic acquisition, queries the list of gas detectors associated with the grid based on the operating range grid set, calls the equipment management system API to obtain real-time detection data from each detector, and associates the detection data with the corresponding spatial grid; when the detection mode is manual input, it receives user-input detection data and performs format validation; and implementing pass / fail judgment logic, which performs pass / fail judgment on each grid... The detection data of the grid is judged according to the safety thresholds specified in the national standard GB30871-2022. The judgment conditions are that the oxygen concentration is within the range of 19.5% to 23.5%, the combustible gas concentration is less than 10% of the lower explosive limit, and the toxic gas concentration is less than the occupational exposure limit. If all detection points in the grid meet the conditions, the grid is judged to be qualified; otherwise, it is judged to be unqualified and the reason for the unqualification is recorded. The overall judgment logic is implemented: if all grids in the working area are qualified, the whole is judged to be qualified; otherwise, the whole is judged to be unqualified. The output result data structure is constructed, and the detection data organized by grid, the qualification judgment result, and the reason for the unqualification are encapsulated in a standard JSON format and returned. Unit test cases are written to cover automatic acquisition mode, manual input mode, all qualified scenarios, some unqualified scenarios, and all unqualified scenarios, etc., to verify the correctness of the capability unit and obtain the encapsulated gas analysis capability unit.

[0042] Based on the business logic of the risk identification capability unit, the same hybrid storage architecture is adopted. A risk identification capability unit class "RiskIdentificationCapability" is defined, along with a standard API interface "identifyRisk". Input parameters include job type, job location, job content, and job method. The output is a risk list. The risk identification logic is implemented by matching keywords from the job content hazard knowledge base to identify hazards involved in the job, querying historical accident databases based on the job location to identify location-related risks, and analyzing hazardous aspects of the job process based on the job method. The risk assessment logic is implemented by using a risk matrix method to determine the risk level based on the probability of occurrence and the severity of consequences. The risk list is constructed and output, resulting in the encapsulated risk identification capability unit.

[0043] Based on the same hybrid storage architecture encapsulation method, security measures capability unit, JSA analysis capability unit, security disclosure capability unit, approval capability unit, and monitoring capability unit are implemented in sequence. Each capability unit contains independent class definition, standard API interface, business logic implementation, and unit test cases, resulting in a set of 7 business capability units encapsulated by the hybrid storage architecture.

[0044] Step 2.3: Establish a capability unit registry and implement a referencing mechanism for the visual configuration layer; Based on the management requirements of capability units, a capability unit registry is established. This registry is stored in a database table structure, and the table structure includes: capability unit identifier, capability unit name, capability unit description, API interface name, API interface path, input parameter definition, output result definition, calling method, version number, creation time, creator, modification time, and modifier.

[0045] Based on the capability unit registry, the metadata information of all capability units is loaded when the system starts up, and a capability unit service directory is built. This service directory provides a list of available capability units and interface definitions for the visual configuration layer.

[0046] Based on the referencing requirements of the visual configuration layer, a capability unit referencing component was designed. The goal of this component is to allow business users to referencing capability units visually within the form designer or process designer, without writing any business logic code. The component's working mechanism is as follows: In the form designer, business users can drag and drop the "Capability Unit Call" component to the form design area. Clicking the component opens the configuration panel, which displays a directory of capability unit services. Business users select the capability unit to be called from the directory, and the system automatically loads the API interface definition for that capability unit, displaying the input parameter configuration area. Business users configure the source of input parameters, choosing to obtain them from form fields, process variables, or set them to fixed values. Business users configure the processing method for output results, choosing to write the output results to form fields, store them in process variables, or trigger subsequent process nodes. When business users click the "Save Configuration" button, the system generates the runtime logic for the capability unit call. This logic executes automatically when the form is submitted or the process flows, reading the input parameter values ​​according to the configured parameter source, calling the capability unit's API interface, receiving the output results, and performing subsequent processing according to the configured processing method.

[0047] Based on capability unit reference components, the front-end visual configuration layer does not contain any complex business logic code. All complex logic is encapsulated in the back-end capability units. The front-end only calls capability units by referencing components, achieving complete decoupling between the business logic layer and the presentation layer. The core advantage of this decoupling model is that: when business rules change, only the code of the back-end capability units needs to be modified, and the front-end configuration does not need to be changed; when new business capabilities need to be added, only the new capability unit needs to be developed and registered in the service directory, and the front-end can call it by referencing components; when performance needs to be optimized, capability units can be optimized independently (such as adding caching or asynchronous processing) without affecting the front-end configuration.

[0048] This step outputs a business capability unit library, containing seven highly cohesive and loosely coupled business capability units. Each capability unit includes standardized interface definitions, a capability unit registry, and a business logic module implemented using a hybrid storage architecture. This capability unit library provides reusable business logic modules for subsequent visual configuration and process orchestration, achieving modular encapsulation and standardized management of complex business logic.

[0049] Step 3: Based on the business capability unit library, perform process status awareness and data version control to obtain data management strategies; This step aims to solve the efficiency problem of repetitive filling of general information in the collaborative issuance of multiple invoices. It improves invoicing efficiency by automatically distributing information after a single entry, avoids data synchronization conflicts through process status awareness, and supports data traceability through version control.

[0050] Step 3.1: Establish task-ticket data mapping relationship and version control mechanism; Based on the user's operation of creating a job task in the system interface, a new record is inserted into the job task table, a unique task identifier is assigned, and the data version number field of the task record is initialized to 1.

[0051] Based on the user's selection of the required work permit type on the task interface, which includes confined space work permits, hot work permits, work at height permits, and temporary electrical work permits, a binding relationship is established between tasks and permits in the system. This binding relationship records the correspondence between task identifiers and all associated permit identifiers.

[0052] Based on the ticket type and task identifier, insert new records into the corresponding work ticket tables. Insertion operations include: inserting records and assigning ticket identifiers in the confined space work ticket table, hot work ticket table, work at height ticket table, and temporary power supply work ticket table. Initialize the data version number field to 1 and the process status field to "draft" for each ticket record.

[0053] Based on a set of common fields, a mapping table is established to represent the fields in the task table and the fields in the ticket table. This mapping table contains four fields: source table name, source field name, target table name, and target field name. For each field in the set of common fields, a record is inserted into the mapping table, indicating the corresponding field in each ticket table that maps from the task table.

[0054] Based on the need for data traceability, a data change history table is created. This history table includes fields such as change identifier, table name, record identifier, changed fields, version number before change, version number after change, value before change, value after change, change time, and change operator. It is used to record all change history of task data and ticket data, thus achieving a data management mechanism with version control capabilities.

[0055] Step 3.2: Perform automatic distribution and synchronous update of general data; Based on the user's input of common fields in the task form interface, including work unit, work supervisor, work content, work location, work time, and work scope, the user-entered field values ​​are saved to the corresponding record in the task table. The save operation automatically increments the data version number of the task record by 1, recording the set of fields involved in this change.

[0056] Based on the binding relationship between tasks and tickets, query all sub-ticket identifiers and corresponding ticket types associated with the task to obtain a list of sub-ticket information.

[0057] Based on the field mapping relationship table and the set of changed fields, for each changed field, query the target field name of the field in each ticket table and construct a field value distribution plan.

[0058] Based on the sub-ticket information list and field value distribution plan, for each sub-ticket, the following data distribution operations are performed: Query the current process status of the sub-ticket in the corresponding ticket table. If the process status is draft, automatic synchronization is used, directly copying the values ​​of common fields in the task table to the corresponding fields in the ticket table, executing an SQL update statement, automatically incrementing the data version number of the ticket record by 1, and inserting a change record in the data change history table. If the process status is under approval, a prompt synchronization method is used. The system generates a data change notification message containing the task identifier, ticket identifier, changed field, previous value, and new value, and sends the notification message to the processing personnel at the current approval node, waiting for confirmation from the processing personnel whether to accept the synchronization. If the process status is in operation or completed, synchronization is prohibited, the ticket table data is not updated, but an inconsistency warning record is inserted in the data consistency record table, recording the differences between the task data and the ticket data.

[0059] Based on the handler's response to the data change notification, if the handler clicks the "Accept Synchronization" button, the ticket table fields are updated, the ticket's workflow status is rolled back to the previous approval node, requiring re-approval, the ticket record's data version number is incremented by 1, and a change record is inserted into the data change history table. If the handler clicks the "Reject Synchronization" button, the ticket table data remains unchanged, and a rejection record is inserted into the data consistency record table. This results in a sub-ticket dataset that completes the general data distribution and a data consistency guarantee mechanism.

[0060] Step 3.3: Perform data consistency verification and version rollback; Based on the event triggering when the ticket process progresses to the permit approval node, the system automatically performs data consistency verification. The verification logic is as follows: query the task identifier associated with the sub-ticket, read the current value of the common field from the task table, read the current value of the corresponding field from the ticket table, and compare whether the two are consistent field by field.

[0061] Based on the consistency comparison results, if the values ​​of all common fields are consistent, the validation passes and the approval process can continue. If inconsistent fields are found, a consistency validation failure message is generated, which includes the name of the inconsistent field, its value in the task table, and its value in the ticket table. The approver is asked to confirm whether to force the synchronization of task data to the ticket table, or to continue the approval process with the ticket data unchanged.

[0062] To address the need for administrators to trace historical data, the system provides a data version rollback function. This function includes: a version number selector on the data query interface; administrators input a task identifier or ticket identifier and the target version number; the system retrieves all change records from the initial version to the target version from the data change history table; it applies the values ​​before the changes in reverse order to reconstruct the data state at the target version; and the reconstructed historical data is displayed to administrators in read-only mode. The system also supports the export and archiving of historical data, resulting in a data management strategy with data traceability and auditing capabilities.

[0063] Step 4: Based on the data management strategy, perform automatic mapping and filling of general business data, and according to the business attributes of the capability unit, perform adaptive distribution of the capability unit processing results to obtain risk identification results and distribution record set; Specifically, the following steps are included: Step 4.1: Construct a spatial grid model of the plant area and a representation of the work area; Based on the factory's floor plan and three-dimensional spatial coordinate system, the factory space is discretized into several spatial grids using a grid division method. The grid division adopts a uniform grid method, with the grid side length set at 10 meters. The number of grids is calculated based on the east-west and north-south spans of the factory area, and a unique identifier is assigned to each grid using a two-dimensional coordinate encoding method.

[0064] Based on the user's selection of the work area in the task form, the work area selection supports three methods: selecting an area by clicking on the factory's electronic map, associating an area by equipment / facility name, and manually entering the spatial coordinate range. The user-selected work area is converted into a set of grid identifiers. The conversion logic uses a point-within-a-polygon determination algorithm to calculate all grids covered by the polygon, representing the task's work area as a set of grid identifiers.

[0065] Based on the sub-ticket's work scope selection, users can choose the specific work scope involved in the ticket in the sub-ticket form. This scope can be a subset of the task scope or an intersection with the task scope. The same transformation logic is used to represent the sub-ticket's work scope as a set of grid identifiers. A work scope field is added to the ticket table to store the set of grid identifiers, resulting in task data and ticket data with spatial location information.

[0066] Step 4.2: Add a spatial dimension to the capability unit and generate a processing result with location identifiers; Based on the set of business capability units output in step 2, identify the capability units that need to support fine-grained spatial distribution. These mainly include gas analysis capability units, personnel positioning and monitoring units, video monitoring units, and other capability units related to the spatial location of the work site. The interfaces of these capability units are extended, including: adding a work range grid set parameter to the input parameter set to receive the work space range specified by the caller; adding a result coverage grid set parameter to the output result set to identify the actual spatial range processed by the capability unit; and adding a spatial location field to the data structure of the output results to identify the grid location corresponding to each result data point.

[0067] Based on the operational characteristics of the gas analysis capability unit, this unit needs to acquire gas detection data within its operating range and determine its compliance. When using the automatic data acquisition mode, the unit's processing logic is as follows: It receives the operating range grid set from the input parameters, iterates through each grid in the set, and queries the list of gas detectors associated with each grid. This association is pre-maintained in the equipment management system, recording the installation coordinates of each detector and its associated grid identifier. It then calls the equipment management system API to obtain real-time detection data from each detector, including fields such as oxygen concentration, combustible gas concentration, toxic gas concentration, and detection time.

[0068] Based on the location coordinates of the detectors and the detection data, the detection data is associated with the corresponding spatial grid, constructing a grid-detection data mapping table. For each grid, the detection data is judged as qualified. The qualification judgment rule is as follows: extract the oxygen concentration, combustible gas concentration, and toxic gas concentration data of all detectors within the grid, and judge according to the safety thresholds specified in the national standard GB30871-2022. If all detection points within the grid meet the above conditions, the grid is judged as qualified, and the qualified status of the grid is marked as "qualified" in the grid-detection data mapping table. If any detection point within the grid does not meet the conditions, the grid is judged as unqualified, the qualified status is marked as "unqualified," and the specific reason for the unqualification is recorded.

[0069] Based on the pass / fail results of all grids, an overall pass / fail judgment is performed. The judgment logic is as follows: if all grids within the working area are pass / fail, the overall judgment is pass / fail; if any grid is fail / fail, the overall judgment is fail / fail. The output results of the gas analysis capability unit are constructed. This output result contains three core parts: a list of detection data organized by grid, with each record including grid identifier, detector identifier, detection data, and pass / fail status; the set of grids that actually performed gas detection, used for subsequent spatial matching; and the overall pass / fail judgment result and explanation of the reasons for failure. Gas analysis results with spatial location identifiers are obtained.

[0070] Step 4.3: Adaptive distribution of the results of the execution capability unit based on the spatial matching algorithm, and automatic mapping and filling of general data; Before spatially matching and distributing the results of the capability units, it is necessary to ensure that each sub-ticket has complete basic data, including key information such as the scope of work and the type of work. This information is a necessary input parameter for the spatial matching algorithm. Therefore, this step first processes the automatic mapping and filling of general data from tasks to sub-tickets, laying the data foundation for the subsequent distribution of capability unit results.

[0071] Based on the data linkage mechanism between tasks and sub-tickets, after a user completes the general information entry at the task level, the system automatically performs a mapping and filling operation for the general data. The processing logic of this operation is as follows: First, identify the set of general fields in the task table. These general fields include data shared across multiple tickets, such as the work unit, work supervisor, work content, work location, work time, work scope grid set, list of participants, and list of supervisors. Second, query the set of sub-tickets associated with the task to obtain the identifier, work type, and corresponding ticket table name for each sub-ticket. Third, based on the field mapping relationship table, which predefines the correspondence between task table fields and fields in each ticket table, query the target field name in each ticket table for each general field. Fourth, for each sub-ticket, perform a batch data filling operation, copying the values ​​of the general fields in the task table to the corresponding fields in the sub-ticket table. This filling operation uses a database transaction mechanism to ensure atomicity; if the data filling of any sub-ticket fails, the filling operation of all sub-tickets is rolled back. Finally, insert a distribution record into the data distribution record table, recording information such as the task identifier, sub-ticket identifier, distribution field set, distribution time, and distribution status, for tracing the data distribution process. The above-mentioned automatic mapping and filling mechanism enables one-time entry and multiple reuse of general data, avoiding the need to repeatedly fill in the same information in each sub-ticket and improving invoicing efficiency.

[0072] Based on the user's operation of calling the gas analysis capability unit at the task level, the call takes the task's operating range grid set as input parameters, executes the gas analysis processing logic to obtain the output results, and extracts the grid coverage range and the detection data organized by grid from the output results.

[0073] Based on the set of task-related sub-tickets, the operation type and scope of each sub-ticket are queried to identify the sub-tickets that need to receive gas analysis results. The identification logic is as follows: A mapping table between operation types and gas detection requirements is established. This mapping table, defined according to the national standard GB30871-2022, records the operation types that require gas detection, including confined space operations, hot work, etc. The sub-ticket set is traversed, and the operation type of each sub-ticket is queried. If the operation type exists in the mapping table, the sub-ticket is added to the target distribution list.

[0074] Based on the spatial matching algorithm, for each sub-ticket in the target distribution list, the following matching and distribution operations are performed: Calculate the intersection grid set between the gas detection result coverage area and the sub-ticket's operating area. The intersection calculation method is to compare the grid set where gas detection was actually performed in the gas analysis capability unit output results with the sub-ticket's operating area grid set, find the grid identifiers that exist in both sets simultaneously, and form the intersection grid set from these grid identifiers; determine if the intersection is empty. If the intersection is empty, it means that no gas detection was performed within the sub-ticket's operating area, and the sub-ticket is marked as needing supplementary gas detection, a supplementary detection task is generated, and the grid set requiring supplementary detection is recorded as the sub-ticket's operating area grid set; if the intersection is not empty, then from the gas analysis capability unit output results, the sub-ticket's operating area grid set is determined. The detection data of the intersection grids are selected from the detection data list of the analysis results to construct a dedicated detection dataset for the sub-ticket. It is then determined whether the working area of ​​the sub-ticket is completely covered by the detection. The method is to calculate the difference between the sub-ticket's working area grid set and the intersection grid set. If the difference is empty, it indicates complete coverage; if the difference is not empty, it indicates partial coverage. The sub-ticket is then marked as needing supplementary detection, and the set of grids requiring supplementary detection is recorded as the difference grid set. For sub-tickets with complete coverage, a pass / fail judgment is made based on the dedicated detection dataset. If the pass / fail status of all intersection grids is "pass," the gas detection of the sub-ticket is deemed passable. If any grid is unqualified, the gas detection of the sub-ticket is deemed unqualified, and the reason for the unqualification is recorded.

[0075] Step 4.4: Perform semantic distribution of the risk identification results; Based on the business characteristics of the risk identification capability unit, the output of this capability unit is a risk list. Each risk record includes information such as risk type, risk description, risk level, and associated work type. Risk types include, but are not limited to, fire and explosion risks, poisoning and asphyxiation risks, fall from height risks, electric shock risks, and mechanical injury risks.

[0076] Based on the correlation between job type and risk type, a risk distribution rule table is established, defining the job types applicable to each risk type. The rule table is constructed as follows: based on the national standard GB30871-2022 and enterprise safety management experience, the potential risk types for each job type are analyzed. For example, hot work is associated with fire and explosion risks, and poisoning and asphyxiation risks; confined space work is associated with poisoning and asphyxiation risks, and oxygen deficiency and asphyxiation risks; work at heights is associated with fall from height risks and falling object risks; temporary electrical work is associated with electric shock risks, etc. These correlations are recorded in the risk distribution rule table, with each rule containing a risk type and a set of applicable job types.

[0077] Based on the user's operation of invoking the risk identification capability unit at the task level, this invocation takes information such as the task's content, location, and method as input parameters. The risk identification processing logic then generates a list of potential risks. The risk identification processing logic includes: identifying hazards involved in the task based on keyword matching of the task content (e.g., "welding" is associated with fire and explosion risks, and "entering a tank" is associated with poisoning and suffocation risks); identifying location-related risks by querying historical accident records and risk assessment reports for the work location; and identifying process risks by analyzing hazardous elements in the work process based on the method of operation. Each risk record undergoes a risk level assessment using a risk matrix method. The risk level is determined based on the probability of occurrence and the severity of consequences, and is divided into three levels: high risk, medium risk, and low risk.

[0078] Based on the risk list and risk distribution rule table, semantic distribution operations are performed: The set of sub-tickets associated with the task is queried, and the job type of each sub-ticket is extracted; each risk record in the risk list is traversed, and the set of applicable job types corresponding to that risk type in the risk distribution rule table is queried; the intersection of the set of applicable job types and the set of job types of the task is calculated, and this intersection indicates which job types of sub-tickets the risk record should be distributed to; the sub-ticket information list is traversed, and if the job types of the sub-tickets belong to the intersection, the risk record is distributed to that sub-ticket, and the risk record is added to the risk list field of the sub-ticket; for each sub-ticket, all received risk records are summarized, sorted according to risk level, and a complete risk list for that sub-ticket is generated, resulting in the risk identification result of semantic distribution and the risk list of each sub-ticket.

[0079] Based on the semantic distribution results, a related record is inserted into the capability unit result association table to record the calling information and distribution results of the risk identification capability unit for subsequent tracing and auditing.

[0080] Step 5: Based on the risk identification results and distribution record set, process rules and status control are implemented to obtain a closed-loop management strategy for task tickets. This step aims to address the complexity of collaborative management of tasks and ticketing processes. It clarifies node responsibilities through node business identifiers, achieves refined control through multi-level permission configuration, and ensures the correctness of process flow through state machine technology.

[0081] Step 5.1: Parse the process configuration and build the process state machine model; Based on the system configuration interface, the administrator can pre-compile the task flow configuration and the ticket flow configuration for each job type. The flow configuration includes node definition, node order, branch conditions, handler configuration, node business identifier, form permission configuration, and verification rule configuration.

[0082] Based on the task flow configuration, information of all nodes in the flow is extracted to build a node information table. The node information includes node identifier, node name, node type, node business identifier, handler configuration, form permission configuration, and validation rule configuration.

[0083] Based on the sequential relationship and branching conditions between nodes, a state machine model of the task flow is constructed. This state machine model consists of five elements: a set of states corresponding to a set of process nodes, a set of events including operations such as submission, approval, rejection, and rollback, a state transition function defining the flow rules between nodes, an initial state corresponding to the process start node, and a set of terminating states corresponding to the process end node.

[0084] Based on the state transition function, node flow rules are defined. This state transition function describes the logic of process state changes under different event triggers: for the submission event, if the current state is the starting node, it will move to the next approval node; for the approval passed event, if the current node is configured with branch conditions, it will evaluate the branch conditions based on the form data and move to the target node that meets the conditions; for the approval rejected event, it will move to the specified rollback node or return to the starting node according to the rejection policy configuration.

[0085] Using the same method, for each job type, the ticket process configuration is parsed to construct the ticket process state machine model, a node information table is established, and a set of task process state machines and ticket process state machines is obtained.

[0086] Step 5.2: Create a process instance and execute collaborative flow control; Based on the user's submitted job task, the system creates a task process instance. The creation operation includes inserting a new record into the process instance table, assigning a process instance identifier, associating a task identifier, setting the process type to task process, initializing the current state to the starting node, and initializing the process status identifier to in progress.

[0087] Based on the task flow state machine, the state transition begins from the starting node. The business identifier of the starting node is queried. If the identifier indicates that task information is to be filled in, the process is advanced to that node. The task applicant is determined as the handler based on the handler configuration of the node. The visibility, editability, and required fields in the task form are determined based on the form permission configuration.

[0088] The submission event is triggered when the task applicant completes the form and clicks the submit button. The system checks whether all required fields have been filled in according to the required fields in the form permission configuration. If all required fields have been filled in, the system calculates the next state according to the state transition function and queries the business identifier of the next node.

[0089] The flow strategy is determined based on the business identifier of the next node. If the identifier is to start a sub-ticket process, the sub-ticket process start logic is executed: query the set of sub-tickets associated with the task, query the corresponding ticket process configuration and state machine model for each sub-ticket according to its job type, insert a new record in the process instance table, assign a process instance identifier, associate the ticket identifier, set the process type to ticket process, and initialize the current state to the starting node.

[0090] Based on the parent-child process relationship, a related record is inserted into the process relationship table to establish the parent-child relationship between the task process instance and the sub-ticket process instance. The current status of the task process instance is updated to waiting for the sub-process, the progress of the task process is paused, and all sub-ticket processes are waited for to complete.

[0091] Based on the independent execution of sub-ticket processes, for each sub-ticket process instance, the process is independently advanced according to its state machine model. The business identifier of the current node is queried. If the identifier is a permission approval node, the approver is determined according to the processor configuration. The range of fields visible and editable by the approver is determined according to the form permission configuration. Face recognition and location verification are performed according to the verification rules.

[0092] Based on the approver's approval operation, if the approval is successful, the next state is calculated according to the state transition function. If the business identifier of the next node is a node in operation, the process status identifier of the ticket is updated to in operation, allowing operators to perform operations such as start operation, pause operation, and complete operation.

[0093] Based on the operator's action of completing the task and clicking the "Complete Task" button, a "Complete Task" event is triggered. The system then advances the process to the next node according to the state transition function, queries the node's business identifier, and if the identifier is a completion and acceptance node, determines the acceptance person based on the handler configuration, executes the acceptance process according to the form permission configuration and validation rules, and updates the process status identifier to "Completed" if the acceptance is successful. This yields a sub-ticket process instance that has completed its flow.

[0094] Step 5.3: Perform collaborative monitoring of the execution process and close the task flow loop; Based on the system's process monitoring mechanism, the process association table is queried periodically to identify task process instances that are in the waiting sub-process state. For each task process instance, all associated sub-ticket process instances are queried, and the process status identifier of each sub-process is read from the process instance table.

[0095] Based on the summary analysis of sub-process statuses, if the status flag of all sub-ticket processes is "completed," then all sub-processes are considered complete, triggering the task process to continue execution. If any sub-ticket process has a "rejected" status flag, then the task is considered to have an exception, generating an exception handling prompt and pushing it to the task manager.

[0096] Based on the trigger event of the completion of all sub-processes, the system resumes the execution of the task process, updates the current state of the task process instance from waiting for sub-processes to waiting for the next state of the node, calculates the next node according to the state transition function, queries the business identifier of the next node, and if the identifier is task completion confirmation, determines the task person in charge as the handler according to the handler configuration, and displays the summary information of the task and the completion status of all sub-tickets.

[0097] Based on the task leader's confirmation of task completion, the task process status is updated to "completed", the task status field in the task table is updated to "completed", the task completion time is recorded, a data archiving event is triggered, and the task process instance and task ticket closed-loop management strategy that complete the collaborative flow are obtained.

[0098] Step 6: Based on the task ticket closed-loop management strategy, design standard API interfaces and establish API call monitoring and exception handling mechanisms to build an agile integration strategy; Specifically, the following steps are included: Step 6.1: Design standard API interfaces and encapsulate external system integration logic; Based on the integration requirements analysis of work permit management and external business systems, key integration scenarios were identified, including: the worker qualification verification scenario needs to be integrated with the contractor management system to obtain information on workers who meet the qualification requirements; the work site monitoring scenario needs to be integrated with the personnel positioning system to obtain the real-time location information of workers and supervisors; the gas detection data acquisition scenario needs to be integrated with the equipment management system to automatically obtain the real-time detection data of gas detectors; and the work site visualization scenario needs to be integrated with the video surveillance system to obtain video surveillance footage of the work site.

[0099] Based on the scenario of verifying the qualifications of operational personnel, a standard API interface is designed. The goal of this interface is to provide a unified calling method for the visual configuration layer, shielding it from the differences of external systems. The interface definition includes: the interface name is "queryQualifiedPersonnel", the interface description is "querying operational personnel who meet the qualification requirements", the input parameters include job type, job date, qualification level requirements, and the output result is a list of qualified personnel. Each personnel record includes information such as personnel ID, personnel name, affiliated unit, certificate type, certificate number, and certificate validity period.

[0100] Based on the interface implementation logic, the adapter pattern is used to encapsulate data interaction with the contractor management system. The adapter's role is to convert the interface protocol and data format of the external system into the standard format of this system. The adapter processing steps include: querying the locally maintained mapping table of job types and qualification requirements based on the job type in the input parameters to obtain the set of certificate types required for that job type, such as hot work requiring a hot work permit and confined space work requiring a confined space work permit; calling the personnel query interface provided by the contractor management system, passing in the certificate type filtering conditions, and obtaining a list of candidates holding relevant certificates. Since different contractor management systems may provide different interface protocols, the adapter needs to select the appropriate calling method according to the configuration; for each person in the candidate list, extracting their certificate validity information, determining whether the job date in the input parameters is within the certificate validity period, and filtering out personnel with valid certificates; querying the local personnel training record table to verify whether the filtered personnel have completed the onboard safety training and that the training is within the validity period, and further filtering out qualified personnel; sorting the qualified personnel list according to certificate level and work experience, with the sorting rule being: personnel with higher certificate levels take priority, and among personnel of the same level, those with longer work experience take priority; converting the sorted qualified personnel list into a standard output format as the interface return result. Obtain the fully encapsulated qualification verification API interface.

[0101] Based on the same design methodology, standard API interfaces for other integration scenarios are designed and encapsulated, including: a personnel location query interface "queryPersonnelLocation", which takes personnel identifier and time range as input parameters and outputs personnel location trajectory data; a gas detection data interface "queryGasDetectionData", which takes detector identifier and time range as input parameters and outputs historical gas detection data; and a video surveillance interface "queryVideoStream", which takes camera identifier and time point as input parameters and outputs video stream address. This results in a set of standard API interfaces, which provides a unified external system integration capability for the visualization configuration layer.

[0102] Step 6.2: Implement configurable API calls in the visual configuration layer; Based on a component library with a visual configuration interface, a selector component supporting external data sources is designed. The goal of this selector component is to allow business users to access data from external systems without writing code. The selector component includes a configuration panel that provides data source options for three types: manual input, local data tables, and external API interfaces.

[0103] Based on the scenario of user-configured work ticket forms, when a user needs to add a worker field, the following configuration operations are performed: Drag and drop the worker selector component from the component library to the form design area. This operation uses a visual drag-and-drop method and requires no coding; Click the component to open the configuration panel, select "External API Interface" in the data source option, and the system displays a list of available API interfaces, which includes all the standard API interfaces encapsulated in step 6.1; Select the "queryQualifiedPersonnel" qualification verification interface from the API interface list, and the system automatically loads the parameter definition of this interface and displays the input parameter configuration area.

[0104] Based on the input parameter configuration area, users configure the parameter sources required for API calls. The configuration method is as follows: For the "Job Type" parameter, the configuration panel provides two value modes: fixed value mode, where users directly input a fixed job type value, such as "Hot Work"; and form field mode, where users select the job type field from the current form's field list as the parameter source, and the system automatically reads the value of that field from the form as the API parameter at runtime. The "Job Date" parameter uses the same configuration method, allowing users to choose either a fixed value or a form field. For the "Qualification Level Requirement" parameter, since this is optional, users can choose not to configure it, and the system will not pass this parameter when calling the API.

[0105] After the parameters are configured, the user clicks the "Save Configuration" button. The system then generates the runtime logic for the selector component, which includes the following steps: When the user clicks the personnel selector on the form entry page, the system triggers the selector's data loading event. The system reads the current values ​​of each parameter from the form according to the configured parameter source. The system verifies the completeness of the parameter values; if any required parameter has an empty value, the user is prompted to fill in the relevant field first. If the parameter values ​​are complete, the system calls the qualification verification interface, passing in the parameter values ​​to perform a qualification verification query.

[0106] Based on the list of qualified personnel returned by the API interface, the system performs the following rendering and interactive operations: The personnel list is rendered as candidate items in a selector, with each candidate displaying information such as the personnel's name, affiliated unit, and certificate type. Users can select one or multiple personnel from the list, with the selection mode determined by the component configuration. After the user completes their selection and clicks the "OK" button, the system stores the identifiers of the selected personnel in a form field; if multiple selections are used, they are stored as an array of personnel identifiers. The system displays the names of the selected personnel on the form interface for the user to confirm the selection results. This results in a fully configured selector component, which enables no-code calls to external system APIs.

[0107] With the above-mentioned configuration-based invocation mechanism, business personnel do not need to understand the technical details of the API interface or write interface call code. They can achieve data integration with external systems simply through visual configuration, which reduces the technical threshold and development cost of system integration.

[0108] Step 6.3: Establish an API call monitoring and exception handling mechanism; Based on the API interface call process, log recording and monitoring points are added before and after each call. The recorded content includes call time, interface identifier, input parameters, call time, return status code, return data size, exception information, etc. The log data is stored in the API call log table.

[0109] Based on the return results of API calls, a unified exception handling mechanism is adopted. If the return status code is success and the return data format is correct, the return data is processed normally. If the return status code is timeout or network error, a retry is performed. The maximum number of retries is set to 3, and the retry interval adopts an exponential backoff strategy. If the retry still fails, a user-friendly error message is displayed and a manual degradation solution is provided.

[0110] Based on API call log data, performance monitoring and statistical analysis functions are established. Scheduled tasks are used to collect metrics such as the number of calls, average time, success rate, and failure rate of each API interface every hour. When the failure rate of an API interface exceeds the first threshold, an alarm notification is triggered. When the average time of an API interface exceeds the second threshold, a performance warning is triggered, resulting in an agile integration strategy with monitoring and fault tolerance capabilities.

[0111] Step 7: Based on the agile integration strategy, design a multi-dimensional query interface and build a complete traceability chain for the operation process, statistically implement risk analysis, and obtain a full-process traceability and intelligent analysis strategy. Specifically, the following steps are included: Step 7.1: Design the process execution log recording specifications and collect log data in real time; Based on the node transition events during the execution of process instances, a unified log recording standard is designed. The log records include log identifier, process instance identifier, node identifier, node name, node business identifier, operation type, operator identifier, operator name, operation time, operation location coordinates, face recognition result, form changed fields, value before change, value after change, operation time, and operation device information.

[0112] Based on the event listening mechanism of the process engine, an event listener is registered before and after the execution of each node. The listener captures node start events, node completion events, node rejection events, and node rollback events. When an event is triggered, the listener automatically extracts the context information related to the event, constructs log entries according to the log recording specifications, and inserts the log entries into the process execution log table.

[0113] Based on form data change monitoring, the system captures change events when the values ​​of form fields change, extracts the changed field name, the value before the change, and the value after the change, and records the change information in the form change field of the log entry, supporting subsequent data traceability and audit analysis.

[0114] Based on the results of face recognition and location verification, the verification results and related information are recorded in the log when these verification operations are performed. The information includes the similarity score of face recognition, the judgment result of recognition pass or fail, the actual location coordinates of location verification, the offset distance from the required location, and the judgment result of verification pass or fail, resulting in a complete log dataset that records the execution process.

[0115] Step 7.2: Design a multidimensional query interface based on a structured data model; The business data model built in step 1 is stored in a structured relational database, which naturally supports standard SQL queries and index optimization.

[0116] Based on the need for statistical analysis of operations across the entire plant, a multi-dimensional query interface was designed. This interface supports filtering and statistics based on multiple dimensions, such as time range, operation type, operation unit, operation location, and operation level.

[0117] Based on the implementation logic of the query interface, SQL aggregation queries are used to implement statistical functions. Query condition clauses are constructed according to the time range and filtering conditions in the input parameters. For each job type, a statistical query is executed on the corresponding job ticket table to summarize the statistical results of all job types and obtain the quantity distribution of each job type. Grouping statistics by job level are then performed on each job ticket table to obtain the quantity distribution of each job level. A join query is performed between the job ticket table and the risk identification result table to statistically analyze the distribution of each risk level. The statistical results are organized into a structured JSON format and returned as the interface result, resulting in a multi-dimensional statistical query interface.

[0118] Step 7.3: Construct a complete traceability chain for the job process based on the association query; Based on the need for job process traceability, a traceability query interface is designed. This interface receives the task identifier as an input parameter and returns the complete job process record of the task.

[0119] Based on the implementation logic of traceability query, a multi-table join query technique is adopted. The basic information of the task is retrieved from the task table according to the task identifier. The task process instance identifier and all sub-ticket process instance identifiers associated with the task are retrieved from the process association table. All log records of the task process instance are retrieved from the process execution log table and sorted in ascending order of operation time to obtain the timeline record of the task process. All log records of each sub-ticket process are retrieved from the process execution log table according to the sub-ticket process instance identifier to obtain the process timeline of each sub-ticket. A list of sub-ticket identifiers is obtained from the task-ticket binding relationship. For each sub-ticket identifier, the corresponding task ticket table is retrieved based on its job type to extract the detailed data of the ticket. Finally, the processing records of the business components involved in the task are retrieved from the capability unit result association table.

[0120] Based on the data integration and formatting of the query results, a structured traceability report is constructed. This report includes a basic task information section that displays the general field data of the task, a process timeline section that displays the execution records of all nodes of the task process and sub-ticket process in chronological order, a ticket details section that displays the complete data and processing results of each sub-ticket, and a business component record section that displays the processing process and results of components such as gas analysis, risk identification, and JSA analysis. The traceability report is returned in JSON format to support the front end for visualization, thus obtaining a complete work process traceability interface.

[0121] Step 7.4: Perform risk analysis based on historical data statistics; Based on the accumulation of historical operation data, a risk hotspot analysis interface was designed. This interface accepts the time window and analysis dimension as input parameters and returns high-frequency risk points and risk distribution.

[0122] Based on the implementation logic of risk hotspot analysis, a frequency statistics method is adopted. According to the time window parameter, all work records within the time window are queried from each work ticket table, and fields such as work location, work unit, work type, and risk identification result are extracted for each record. The aggregation granularity of the analysis is determined according to the analysis dimension parameters, including statistics by work location, by work unit, and by work type. Grouping statistics are performed according to the specified dimensions, and for each group, the number of work items and the frequency of occurrence of each risk type are counted.

[0123] Based on the statistical results, the frequency of occurrence of risk types is calculated by dividing the number of times a specific risk type occurs in a group by the total number of tasks in that group. This frequency reflects the prevalence of a specific risk in that group. High-frequency risk points exceeding a preset threshold (e.g., 30%) are identified by sorting them from highest to lowest frequency. These high-frequency risk points are the risk areas that enterprises need to focus on and control.

[0124] Based on risk co-occurrence analysis, frequently occurring risk combinations are identified. The analysis method involves statistically analyzing the co-occurrence of different risk types in historical work records and calculating the frequency of simultaneous occurrences of two risk types. For each pair of risk types, the co-occurrence frequency is calculated by dividing the number of jobs where both risks occur simultaneously by the number of jobs containing either risk. If the co-occurrence frequency exceeds a preset threshold (e.g., 50%), the two risks are considered correlated and likely to occur simultaneously in actual operations. Identifying risk correlations helps companies achieve a more comprehensive risk identification process and avoid overlooking related risks.

[0125] The analysis results are compiled into an analysis report, which includes the following: a high-frequency risk ranking, displaying the top 10 most frequent risk types and their frequencies to help enterprises identify major risks; a risk hotspot map, showing the risk distribution at various work locations and identifying high-risk areas; a risk correlation table, showing frequently occurring risk combinations to alert enterprises to related risks during risk control; and a risk trend curve, showing the changing trends of risk types over different time periods to help enterprises assess the effectiveness of risk management measures. The analysis report is then returned to the user to support management in making safety decisions and continuous improvement, providing access to risk analysis interfaces and end-to-end traceability and intelligent analysis strategies.

[0126] A work ticket management system is used to execute the aforementioned work ticket management method, such as... Figure 2 As shown, it includes: The hybrid storage module is used to build a hybrid storage architecture and establish two-layer field constraints to obtain a hybrid storage strategy; The business capability unit library module, based on a hybrid storage strategy, plans the boundaries of capability units, encapsulates the capability units, establishes a capability unit registry, and obtains the business capability unit library. The data management module, based on the business capability unit library, performs process status awareness and data version control to obtain data management strategies; The distribution management module, based on the data management strategy, performs automatic mapping and filling of general business data, and performs adaptive distribution of capability unit processing results according to the business attributes of capability units, to obtain risk identification results and distribution record sets; The process control module, based on the risk identification results and distribution record set, performs process rules and status control to obtain a closed-loop management strategy for task tickets. The integrated management module, based on the task ticket closed-loop management strategy, designs standard API interfaces and establishes API call monitoring and exception handling mechanisms to obtain an agile integration strategy; The traceability analysis module, based on an agile integration strategy, designs a multi-dimensional query interface and constructs a complete traceability chain for the operation process, statistically implements risk analysis, and obtains a full-process traceability and intelligent analysis strategy.

[0127] In one embodiment of the present invention, a specific example is provided: A 30-day field test was conducted in the catalytic cracking unit area of ​​a large petrochemical enterprise. During the test, the work permit management system of this invention was deployed, covering core production areas such as the catalytic cracking unit, atmospheric and vacuum distillation unit, and hydrocracking unit, with a test area of ​​approximately 150,000 square meters. It involved eight special work types, processing a total of 126 work tasks and issuing 452 work permits. Multi-source field data and business logic rule data were collected during the test, verifying the effectiveness of the various technical solutions of this invention.

[0128] Examples of field classification rules are shown in Table 1: Table 1: Examples of Field Classification Rules; Table 2 shows an example of how business logic dependency graphs are constructed: Table 2: Example of data construction for business logic dependency graph; As can be seen from the above application effects and data examples, the job ticket management method based on the hybrid storage architecture and hybrid decoupling mode proposed in this invention has achieved good technical results in terms of business flexibility, ticketing efficiency, system performance, integration capability and security control. It effectively solves the core problems of existing technologies and has important practical value and broad application prospects.

[0129] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for managing work permits, characterized in that, Includes the following steps: Construct a hybrid storage architecture and establish two-layer field constraints to obtain a hybrid storage strategy; Based on a hybrid storage strategy, capability unit boundaries are planned, capability units are encapsulated, a capability unit registry is established, and a business capability unit library is obtained. Based on the business capability unit library, process status awareness and data version control are performed to obtain data management strategies. Based on the data management strategy, the system automatically maps and populates general business data, and adaptively distributes the processing results of the capability units according to their business attributes, thereby obtaining risk identification results and a distribution record set. Based on the risk identification results and distribution record set, process rules and status control are implemented to obtain a closed-loop management strategy for task tickets. Based on the task ticket closed-loop management strategy, a standard API interface was designed and an API call monitoring and exception handling mechanism was established to obtain an agile integration strategy. Based on the agile integration strategy, a multi-dimensional query interface was designed and a complete traceability chain for the operation process was constructed. Statistical risk analysis was implemented to obtain a full-process traceability and intelligent analysis strategy.

2. The work permit management method according to claim 1, characterized in that, The construction of the hybrid storage architecture and the establishment of two-layer field constraints include: Based on the national standard compliant field set, a fixed physical table containing a job task table and a job ticket table is created. Non-null constraints and value range constraints are set at the database level, as well as non-deletable and non-editable identifiers at the system metadata level, to build a rigid storage layer. Reserve extended columns in each physical table, create a field configuration table to realize dynamic mapping from extended columns to business fields, and realize dynamic parsing and data access of extended fields through the configuration engine to build a flexible extension layer; Strong constraints are imposed on national standard compliant fields, while weak constraints are imposed on extended fields, constructing a hybrid storage architecture consisting of a rigid storage layer, a flexible extension layer, and a unified access layer.

3. The work permit management method according to claim 1, characterized in that, The planning of capability unit boundaries, the encapsulation of capability units, and the establishment of a capability unit registry include: Based on the complexity and non-standardization of business processes, identify complex business logic that requires encapsulation using a hybrid storage architecture, and plan at least one business capability unit. The business capability unit is selected from one or more of the following: gas analysis capability unit, risk identification capability unit, safety measure capability unit, JSA analysis capability unit, safety briefing capability unit, approval capability unit, and monitoring capability unit. The business logic of the capability unit is implemented using object-oriented programming methods, and a standard API interface including input parameters, output results and processing logic is defined. Establish a capability unit registry that includes capability unit identifiers, API interface paths, input parameter definitions, and output result definitions, and design capability unit reference components.

4. The work permit management method according to claim 1, characterized in that, The process status awareness and data version control include: Establish a mapping table from task table fields to ticket table fields, and create a data change history table containing the changed fields, the version number before the change, the version number after the change, the value before the change, and the value after the change; Save common field values ​​to the job task table and automatically increment the data version number. Build a field value distribution plan based on the mapping relationship table. Perform data distribution operations according to the process status of the sub-ticket using automatic synchronization, prompt synchronization, or prohibit synchronization. When the ticketing process reaches the approval stage, a data consistency check is performed. The current values ​​of common fields in the task table and the ticketing table are compared field by field. Change records are queried from the data change history table, and the data state at the target version time is reconstructed.

5. The work permit management method according to claim 1, characterized in that, The automatic mapping and filling of general business data, and the adaptive distribution of capability unit processing results based on the business attributes of the capability units, include: The factory area is discretized into a preset number of spatial grids using a grid partitioning method, and the work area is converted into a set of grid identifiers. Add the operation range grid set parameter to the input parameters of the capability unit, and add the result coverage grid set parameter and spatial location field to the output results to associate the detection data with the corresponding spatial grid. Perform automatic mapping and filling of general data from tasks to sub-tickets, calculate the intersection grid set of the gas detection result coverage area and the sub-ticket operation area, and filter out the detection data of the intersection grid to construct the exclusive detection dataset for the sub-tickets; Establish a risk distribution rule table that includes a risk type and an applicable set of job types. Calculate the intersection of the applicable set of job types and the job type set of the task. Distribute risk records to sub-tickets whose job types belong to the intersection.

6. The work permit management method according to claim 1, characterized in that, The process rules and status control include: Based on the sequential relationships and branching conditions between nodes, a process state machine model is constructed, consisting of a set of states, a set of events, a state transition function, an initial state, and a set of final states. Create a task process instance and initialize the current state as the starting node. Determine the flow strategy based on the node's business identifier. If the identifier is to start a sub-ticket process, create an independent process instance for each sub-ticket, establish a parent-child relationship in the process association table, and update the current state of the task process instance to waiting for the sub-process. The process association table is queried periodically to identify task process instances that are in the waiting sub-process state. If the status flag of all sub-ticket processes is "completed", the task process continue execution event is triggered, the execution of the task process is resumed and the task process status flag is updated to "completed".

7. The work permit management method according to claim 1, characterized in that, The design of the standard API interface and the establishment of API call monitoring and exception handling mechanisms include: Identify scenarios such as operator qualification verification, on-site monitoring, gas detection data acquisition, and on-site visualization; design a standard API interface that includes input parameters and output results; and encapsulate data interaction with external systems using the adapter pattern. The design supports selector components that support external data sources. The configuration panel provides options for data sources such as manual input, local data tables, and external API interfaces. It also configures the parameter sources required for API calls and generates the runtime logic of the selector component. Log recording and monitoring points are added before and after each call to record the call time, interface identifier, input parameters, call time and return status code, and a unified exception handling mechanism is used for retry and degradation processing.

8. The method for managing work permits according to claim 1, characterized in that, The design of the multi-dimensional query interface and the construction of a complete traceability chain for the work process, along with the statistical implementation of risk analysis, include: Design a logging specification, register event listeners before and after each node execution to capture node events and insert log entries; The design supports a multi-dimensional query interface that allows filtering and statistics based on time range, job type, job unit, job location, and job level, and uses SQL aggregation queries to implement statistical functions. The system uses multi-table join query technology to query task information, sub-ticket process instance identifiers, process execution log records, and business component processing records to build a structured traceability report. The frequency statistics method is used to group and statistically analyze risk types according to specified dimensions, calculate the frequency of occurrence of risk types, identify high-frequency risk points with frequencies exceeding a preset threshold, and conduct risk co-occurrence analysis.

9. The method for managing work permits according to claim 2, characterized in that, When the number of reserved extended columns is insufficient, a job ticket attribute table containing attribute identifier, ticket identifier, attribute name and attribute value is created. The newly added extended fields are stored in the attribute table format. When accessing data, the configuration engine automatically determines the storage location of the extended fields and returns the data after merging the two parts. Materialized view technology is used to optimize the query efficiency of extended fields. Materialized views are generated periodically based on the configuration information of the field configuration table, and the data of reserved extended columns and JSON extended columns are reorganized according to the business field names.

10. A work permit management system, characterized in that, A method for implementing a work ticket management system according to any one of claims 1-9 includes: The hybrid storage module is used to build a hybrid storage architecture and establish two-layer field constraints to obtain a hybrid storage strategy; The business capability unit library module, based on a hybrid storage strategy, plans the boundaries of capability units, encapsulates the capability units, establishes a capability unit registry, and obtains the business capability unit library. The data management module, based on the business capability unit library, performs process status awareness and data version control to obtain data management strategies; The distribution management module, based on the data management strategy, performs automatic mapping and filling of general business data, and performs adaptive distribution of capability unit processing results according to the business attributes of capability units, to obtain risk identification results and distribution record sets; The process control module, based on the risk identification results and distribution record set, performs process rules and status control to obtain a closed-loop management strategy for task tickets. The integrated management module, based on the task ticket closed-loop management strategy, designs standard API interfaces and establishes API call monitoring and exception handling mechanisms to obtain an agile integration strategy; The traceability analysis module, based on an agile integration strategy, designs a multi-dimensional query interface and constructs a complete traceability chain for the operation process, statistically implements risk analysis, and obtains a full-process traceability and intelligent analysis strategy.

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