Government affair task dynamic distribution and multi-level auditing method and system

Abstract

The invention discloses a government affair task dynamic distribution and multi-level auditing method and system, belongs to the technical field of government affair systems, and aims to solve the technical problem of how to realize a government affair task management method of task dynamic disassembly, hierarchical intelligent auditing and multi-source data deep fusion. Comprising the following steps: disassembling a government affair task by adopting a heterogeneous task tree generation method to form multiple levels of subtasks; the subtasks are distributed to an execution end based on the optimal distribution path; after receiving the subtasks, the execution end executes data acquisition and data submission operations; performing multi-level auditing on the data passing the basic logic verification according to a preset multi-level auditing rule, and executing rejection correction if any link in the multi-level auditing process is rejected; the data are stored in a global database, and data tables in the global database are associated through unique identification fields; and associating fields existing in the global library through an intelligent filling engine, and matching filling contents according to an acquisition scene to realize data updating and multiplexing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of government affairs system, in particular to a government task dynamic assignment and multi-level auditing method and system. BACKGROUND

[0002] With the deepening of digital transformation of government affairs, grassroots governance faces the challenge of low efficiency of task assignment and auditing process. Traditional government affairs system relies on manual task allocation, often with disassembly errors and cross-level communication delays; the auditing process adopts a "one-size-fits-all" mode, and various data types (such as logical errors, business conflicts, and sensitive abnormalities) need to be approved by fixed levels one by one, resulting in long auditing period and difficulty in responsibility tracing. At the same time, multi-source government data is stored in scattered storage, lacking unified identification association and dynamic update mechanism, making it difficult to support cross-department collaboration and historical data reuse. Although existing technologies have introduced government-related tools, they still perform poorly in task intelligent disassembly, hierarchical auditing, and data deep fusion, restricting the improvement of grassroots governance efficiency.

[0003] How to realize the government task management method of task dynamic disassembly, hierarchical intelligent auditing, and multi-source data deep fusion is a technical problem to be solved. SUMMARY

[0004] The technical task of the present application is to solve the technical problem of how to realize the government task management method of task dynamic disassembly, hierarchical intelligent auditing, and multi-source data deep fusion by providing a government task dynamic assignment and multi-level auditing method and system.

[0005] In a first aspect, the present application provides a government task dynamic assignment and multi-level auditing method, comprising the following steps:

[0006] Government task management: creating a government task, defining the attributes of the government task, and using a heterogeneous task tree generation method to disassemble the government task to form multi-level sub-tasks;

[0007] Task assignment optimization: calculating the optimal assignment path based on the improved ant colony algorithm, and assigning the sub-tasks to the execution end based on the optimal assignment path;

[0008] Task execution: after the execution end receives the sub-tasks, the historical data is associated through the intelligent filling engine, and the data collection and data submission operations are performed;

[0009] Task auditing: performing basic logic verification on the submitted data, and performing multi-level auditing on the data that passes the basic logic verification according to the predetermined multi-level auditing rules, and performing rejection correction if any link in the multi-level auditing process is rejected;

[0010] Data storage: for the data passed through the audit, store the data to the global database, the data tables in the global database are associated through the unique identification field, the global database supports dynamic updating and intelligent filling of data, and the data related operation person, timestamp and modification trace are written into the block chain;

[0011] Dynamic updating and reuse: when the execution end executes data collection again, the intelligent filling engine is triggered based on the identifier of the data, the existing fields in the global library are associated through the intelligent filling engine, the filling content is matched according to the collection scene, and data updating and reuse are realized.

[0012] As preferred, when defining the attributes of the government task, the structured task description language JSON is used to define the attributes of the government task, and the attributes of the government task include task type, jurisdiction area, associated data source and priority.

[0013] As preferred, the improved ant colony algorithm parameters include pheromone evaporation coefficient, heuristic factor and expected factor, and the dispatch path is dynamically adjusted based on the ant colony algorithm parameters to balance the load.

[0014] As preferred, when the existing fields in the global library are associated through the intelligent filling engine and the filling content is matched according to the collection scene, for sensitive fields, authorization is required before display.

[0015] As preferred, the submitted data is subjected to basic logical verification, and the data is subjected to identity card number bit, date format, numerical range and mandatory field verification, and abnormal data is intercepted;

[0016] When the data passing through the basic logical verification is subjected to multi-level audit according to the predetermined multi-level audit rule, a double-check mechanism is adopted for each level of audit, the audit operation is recorded through the block chain storage technology, the block header includes task ID, timestamp and auditor digital signature.

[0017] In the second aspect, the present application is a kind of government task dynamic dispatch and multi-level audit system, including government task management module, task dispatch optimization module, task execution module, task audit module, data storage module and dynamic updating and reuse module;

[0018] The government task management module is used to execute as follows: creating a government task, defining the attributes of the government task, and using a heterogeneous task tree generation method to disassemble the government task to form multiple sub-tasks;

[0019] The task dispatch optimization module is used to execute as follows: calculating the optimal dispatch path based on the improved ant colony algorithm, and dispatching the sub-tasks to the execution end based on the optimal dispatch path;

[0020] The task execution module is configured to perform the following: after the subtask is received by the execution end, the historical data is associated by the intelligent filling engine, and the data collection and data submission operations are performed;

[0021] The task audit module is configured to perform the following: the submitted data is subjected to basic logical verification, and the data passing the basic logical verification is subjected to multi-level audit according to predetermined multi-level audit rules, and if any link in the multi-level audit process is rejected, the rejection correction is performed.

[0022] The data storage module is configured to perform the following: for the data passing the audit, the data is stored in a global database, the data tables in the global database are associated through unique identification fields, the global database supports dynamic updating and intelligent filling of the data, and the operation person, timestamp and modification trace related to the data are written into a blockchain.

[0023] The dynamic updating and reuse module is configured to perform the following: when the execution end performs data collection again, the intelligent filling engine is triggered based on the identifier of the data, the existing fields in the global library are associated through the intelligent filling engine, the filling content is matched according to the collection scene, and data updating and reuse are realized.

[0024] As a preferred, when defining the attributes of the government affair task, the government affair task management is configured to define the attributes of the government affair task by using a structured task description language JSON, and the attributes of the government affair task include a task type, a jurisdiction area, an associated data source and a priority.

[0025] As a preferred, the improved ant colony algorithm parameters include a pheromone evaporation coefficient, a heuristic factor and an expectation factor, and the dispatching path is dynamically adjusted based on the ant colony algorithm parameters to balance the load.

[0026] As a preferred, when associating the existing fields in the global library through the intelligent filling engine and matching the filling content according to the collection scene, for sensitive fields, the display needs to be authorized.

[0027] As a preferred, the task audit module is configured to perform the following: for the submitted data, identity card number bit, date format, numerical range and mandatory field verification are performed on the data, and abnormal data is intercepted.

[0028] When the data passing the basic logical verification is subjected to multi-level audit according to predetermined multi-level audit rules, the task audit module is configured to perform the following: a double-check mechanism is adopted for each level of audit, the audit operation is recorded by using a blockchain storage technology, the block header includes a task ID, a timestamp and an auditor digital signature.

[0029] The government affair task dynamic dispatching and multi-level audit method and system have the following advantages:

[0030] 1. Accurate and efficient task dispatching: based on heterogeneous task tree generation algorithm and ant colony optimization strategy, dynamically decompose tasks and optimize the dispatch path, combine regional, responsibility, load multi-dimensional accurate matching executor, support local cache and incremental transmission in offline environment, guarantee task reach rate and execution efficiency;

[0031] 2. Intelligent collaboration of audit process: intercept low-level errors through basic logic verification, rely on multi-level progressive audit mechanism of community / township / county, etc. to ensure data authenticity, compliance and standardization, combine blockchain storage technology to realize full-process operation trace and penetration traceability, improve audit efficiency and responsibility traceability ability;

[0032] 3. Intelligent and efficient data integration: integrate multi-source data with special fields as unique identifiers, such as building a global population theme library with ID card number as unique identifier, supporting dynamic update and version traceability; automatically associate historical fields (such as name, residence address) through intelligent filling engine, reduce repeated input, sensitive data authorized access, form high-value government data assets. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0034] The present application will be further described below in conjunction with the drawings.

[0035] Figure 1 The flowchart of the embodiment 1, a government task dynamic dispatching and multi-level auditing method. DETAILED DESCRIPTION

[0036] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and can implement it, but the embodiments are not as a limitation on the present application, and the technical features in the embodiments and the embodiments can be combined with each other without conflict.

[0037] The present application provides a government task dynamic dispatching and multi-level auditing method and system, which is used to solve the technical problem of how to realize task dynamic decomposition, hierarchical intelligent auditing and multi-source data deep fusion of government task management method.

[0038] Embodiment 1:

[0039] The application discloses a government affair task dynamic dispatching and multistage auditing method.

[0040] Step S100 government affair task management: creating a government affair task, defining attributes of the government affair task, and adopting a heterogeneous task tree generation method to disassemble the government affair task to form multistage subtasks.

[0041] When the attributes of the government affair task are defined, a structured task description language JSON is adopted to define the attributes of the government affair task, the attributes of the government affair task include a task type, a jurisdictional area, an associated data source and a priority, and a dispatching network of "vertical level penetration + horizontal area coverage" is constructed.

[0042] In specific operation, an original task is created through a county-level platform, the task type, the associated data source and the priority are defined, the task is disassembled to form multistage subtasks based on the jurisdictional area, the responsibility authority and the historical task load by adopting a heterogeneous task tree generation algorithm.

[0043] Step S200 task dispatching optimization: calculating an optimal dispatching path based on an improved ant colony algorithm, and dispatching the subtasks to an execution end based on the optimal dispatching path.

[0044] The improved ant colony algorithm parameters include a pheromone evaporation coefficient (ρ=0.6), a heuristic factor (α=1.2) and an expectation factor (β=2.1), and the dispatching path is dynamically adjusted based on the ant colony algorithm parameters to balance the load.

[0045] Step S300 task execution: after the execution end receives the subtask, historical data is associated through an intelligent filling engine, and data collection and data submission operations are performed.

[0046] Step S400 task auditing: performing basic logic verification on the submitted data, and performing multistage auditing on the data passing the basic logic verification according to predetermined multistage auditing rules, and performing rejection correction when any link in the multistage auditing process is rejected.

[0047] The basic logic verification is performed on the submitted data, identity card number bits, date format, numerical range and mandatory field verification are performed on the data, and abnormal data is intercepted; when the data passing the basic logic verification is audited according to the predetermined multistage auditing rules, a two-person review mechanism is adopted at each auditing stage, the auditing operation is recorded by using a block chain storage technology, the block header includes a task ID, a timestamp and an auditor digital signature. For example, the basic logic verification includes identity card number regular expression verification (18 digits), age range constraint (0-150 years old) and date format compliance detection. When the multistage progressive auditing is performed, multistage auditing such as community / village level, town level and county level is sequentially performed, and rejection correction is performed when any link is rejected.

[0048] Step S500 data storage: for the data passed by the audit, store the data to the global database, the data tables in the global database are associated through the unique identification field, the global database supports dynamic updating and intelligent filling of data, and the data related operators, timestamps and modification traces are written into the blockchain.

[0049] In specific operation, for the data passed by the audit, the data related operators, timestamps and modification traces are recorded, and an unalterable blockchain evidence is generated. When the data is stored to the database, the identity card number is used as the unique identifier to associate multi-table data, and a global population theme library is constructed. For example, the unique identifier is used as the key, and cross-system association is realized through field mapping rules (such as “name-public security household library” and “insurance state-medical insurance system”).

[0050] Step S600 dynamic updating and reuse: when the execution end executes data collection again, the intelligent filling engine is triggered based on the identifier of the data, the existing fields in the global library are associated through the intelligent filling engine, the filling content is matched according to the collection scene, and data updating and reuse are realized.

[0051] When the existing fields in the global library are associated through the intelligent filling engine and the filling content is matched according to the collection scene, for sensitive fields, the display needs to be authorized. For example, the intelligent filling engine associates the existing fields (such as name and household address) in the population library through the identity card number, and the sensitive fields (such as bank card number) need to be authorized before display.

[0052] Based on the method and Figure 1 The method realizes intelligent management of the whole process of government affairs tasks through task dynamic disassembly and distribution, intelligent multi-level review and multi-source data integration and storage mechanism. Specific examples are as follows:

[0053] Dynamic disassembly and distribution of government affairs tasks: based on multi-dimensional attributes such as jurisdictional area, responsibility and authority and historical task load, the task intelligent disassembly is realized by using a heterogeneous task tree generation algorithm. The task attributes are defined by using a structured task description language (such as JSON), including task type, associated data source, priority and the like, and a distribution network of “vertical level penetration + horizontal area coverage” is constructed. For example, after a dangerous house investigation task is created at the county level, the system automatically disassembles it into town-level sub-tasks, and according to the grid employee jurisdictional community, historical task processing efficiency and other parameters, the specific executors are distributed again. The improved ant colony optimization algorithm is used to dynamically avoid overload nodes, and the pheromone volatilization mechanism and path optimization strategy are combined to ensure the optimal task distribution path. The task state is realized by using a lightweight communication protocol (MQTT) to realize multi-end real-time synchronization, supporting local caching and incremental transmission in a network interruption environment, and ensuring the consistency of task progress at county, town and community levels.

[0054] Intelligent multi-level task review: Design multi-level review routing rule library, through basic logic verification and multi-level progressive review mechanism, to ensure that the government task data from collection to storage is compliant, true and standardized. When the mobile terminal submits data, the system first performs basic logic verification, including identity card number verification (18 digits), date format compliance, age range (0-150 years old), mandatory field integrity, etc. If a logical error is detected (such as missing or incorrect format of the identity card number), the system will locate the abnormal field in real time and block the submission, and prompt the grid member to correct it on site. After passing the verification, the data will enter the multi-level review process of community / village level, town level, county level, etc. The community / village level review focuses on data authenticity (such as whether the person exists, whether the address matches), the town level review checks business compliance (such as whether the subsidy standard meets the policy), and the county level final review checks data standardization (such as whether the field naming and coding rules meet the provincial standards). Each level of review adopts a double-check mechanism, and if any reviewer rejects, the task will be returned to the previous step. During the review process, the system tracks the task status through a unique serial number, and records the operator, timestamp and modification trail of key operations (reject, modify, pass) using blockchain storage technology, forming an unalterable audit chain. For example, after the "lone elderly living information" submitted by the grid member is verified by the community for actual living conditions, audited by the town for subsidy eligibility, and checked by the county for data coding, the final data is stamped with a three-level electronic seal and archived.

[0055] Multi-source data integration and dynamic update: The multi-source task data (such as population census table, subsidy application table, etc.) that passes the review is integrated and associated through a unique identifier field (such as ID number) to form a large and complete global database (such as population theme library), and supports dynamic updating and intelligent filling. The task data that passes the review is associated across tables through field mapping rules (such as "name - public security household registry", "insurance status - medical insurance system"). The system uses a dynamic update mechanism, and new data is standardized and cleaned according to the versioning strategy. When the mobile terminal collects data again, entering the ID number can trigger the intelligent filling engine to automatically associate the stored fields in the population library (such as name, household address, historical subsidy records), dynamically match and fill the content according to the collection scenario, and display sensitive fields (such as bank card number) after authorization, thereby realizing intelligent and efficient reuse of data. Through continuous accumulation and updating, the database gradually enriches to form high-value government data assets, providing unified data support for grassroots governance, policy making and public services.

[0056] Embodiment 2

[0057] The application discloses a government task dynamic assignment and multi-level review system, which comprises a government task management module, a task assignment optimization module, a task execution module, a task review module, a data storage module, and a dynamic update and reuse module.

[0058] The government affair task management module is configured to create a government affair task, define attributes of the government affair task, and decompose the government affair task into multiple levels of sub-tasks using a heterogeneous task tree generation method.

[0059] When defining the attributes of the government affair task, a structured task description language JSON is used to define the attributes of the government affair task, and the attributes of the government affair task include a task type, a jurisdictional area, a related data source, and a priority, and a "vertical level penetration + horizontal area coverage" distribution network is constructed.

[0060] In a specific operation, an original task is created through a county-level platform, a task type, a related data source, and a priority are defined, and a heterogeneous task tree generation algorithm is used to decompose the task based on a jurisdictional area, a responsibility authority, and a historical task load to form multiple levels of sub-tasks.

[0061] The task distribution optimization module is configured to calculate an optimal distribution path based on an improved ant colony algorithm and distribute the sub-tasks to an execution end based on the optimal distribution path.

[0062] The parameters of the improved ant colony algorithm include a pheromone evaporation coefficient (p = 0.6), a heuristic factor (a = 1.2), and an expectation factor (b = 2.1), and the distribution path is dynamically adjusted based on the parameters of the ant colony algorithm to balance the load.

[0063] The task execution module is configured to associate historical data through an intelligent filling engine after the execution end receives the sub-tasks, and perform data collection and data submission operations.

[0064] The task review module is configured to perform basic logical verification on the submitted data, and perform multi-level review on the data that passes the basic logical verification according to predetermined multi-level review rules, and perform a rejection correction if any link in the multi-level review process is rejected.

[0065] The basic logical verification is performed on the submitted data, and the data is verified for an ID number bit, a date format, a numerical range, and a required field to intercept abnormal data; when the multi-level review is performed on the data that passes the basic logical verification according to the predetermined multi-level review rules, a double-check mechanism is used for each level of review, the review operation is recorded by a blockchain storage technology, the block header includes a task ID, a timestamp, and an auditor digital signature. For example, the basic logical verification includes a regular expression verification (18 digits) of an ID number, an age range constraint (0-150 years old), and a date format compliance detection. When the multi-level progressive review is performed, multi-level review such as community / village level, town level, and county level is sequentially performed, and a rejection correction is performed if any link is rejected.

[0066] The data storage module is configured to perform the following: for data that passes the review, store the data to a global database, associate the data tables in the global database through a unique identification field, support dynamic updating and intelligent filling of the data in the global database, and write the data-related operators, timestamps, and modification traces into a blockchain.

[0067] In particular, for data that passes the review, record the data-related operators, timestamps, and modification traces, and generate an unalterable blockchain record. When the data is stored to the database, associate the multi-table data with the unique identification of the ID card, construct a global population theme library, and for example, through the field mapping rules (such as "name-public security household library" and "insurance state-health insurance system"), realize cross-system association based on the unique identification as the key.

[0068] The dynamic updating and reuse module is configured to perform the following: when the execution end performs data collection again, trigger the intelligent filling engine based on the identifier of the data, associate the existing fields in the global library through the intelligent filling engine, match and fill the content according to the collection scenario, and realize data updating and reuse.

[0069] When associating the existing fields in the global library through the intelligent filling engine and matching and filling the content according to the collection scenario, for sensitive fields, the sensitive fields need to be displayed after authorization. For example, the intelligent filling engine associates the existing fields (such as name and household address) in the population library through the ID card number, and the sensitive fields (such as bank card number) need to be displayed after authorization.

[0070] The system disclosed in the embodiment can perform the method disclosed in the embodiment 1 to realize dynamic assignment and multi-level review of government affairs tasks.

[0071] The above describes the method and system for dynamic assignment and multi-level review of government affairs tasks in detail. The principles and implementation modes of the present application are described by using specific examples. The above description of the embodiments is only used to help understand the method and core idea of the present application. For those skilled in the art, the specific implementation modes and application ranges can be changed according to the idea of the present application. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for dynamic dispatch and multi-level review of government tasks, characterized in that: The steps include: Government task management: Create government tasks, define their attributes, and use heterogeneous task tree generation methods to decompose government tasks into multi-level subtasks; Task dispatch optimization: Calculate the optimal dispatch path based on the improved ant colony algorithm, and dispatch subtasks to the execution end based on the optimal dispatch path; Task execution: After receiving the subtask, the execution end associates historical data through the intelligent filling engine and performs data collection and data submission operations; Task review: Perform basic logic verification on the submitted data, and conduct multi-level review on the data that passes the basic logic verification according to the predetermined multi-level review rules. If any link in the multi-level review process is rejected, the rejection correction will be executed; Data storage: For data that passes the review, the data will be stored in the global database. Data tables in the global database are linked through unique identification fields. The global database supports dynamic data updates and intelligent filling, and the operator, timestamp, and modification traces related to the data are written into the blockchain. Dynamic update and reuse: When the execution end performs data collection again, the intelligent fill engine is triggered based on the data identifier. The intelligent fill engine associates the existing fields in the global database and matches the fill content according to the collection scenario to achieve data update and reuse.

2. The method for dynamic dispatch and multi-level review of government affairs tasks according to claim 1 is characterized in that: When defining the attributes of government affairs tasks, the structured task description language JSON is used to define the attributes of government affairs tasks. The attributes of government affairs tasks include task type, jurisdiction, associated data source and priority.

3. The method for dynamic dispatch and multi-level review of government affairs tasks according to claim 1 is characterized in that: The parameters of the improved ant colony algorithm include pheromone volatility coefficient, heuristic factor and expectation factor. The dispatch path is dynamically adjusted based on the ant colony algorithm parameters to balance the load.

4. The method for dynamic dispatch and multi-level review of government affairs tasks according to claim 1 is characterized in that: When using the smart fill engine to associate existing fields in the global library and match fill content based on the collection scenario, sensitive fields must be authorized before they can be displayed.

5. The method for dynamic dispatch and multi-level review of government affairs tasks according to claim 1 is characterized in that: Perform basic logic verification on submitted data, including verification of ID number digits, date format, value range, and required fields, to intercept abnormal data; When conducting multi-level audits on data that has passed basic logical verification according to predetermined multi-level audit rules, a two-person review mechanism is adopted for each level of audit. The audit operations are recorded through blockchain evidence storage technology, and the block header contains the task ID, timestamp and auditor's digital signature.

6. A system for dynamic distribution and multi-level review of government tasks, characterized by: It includes government task management module, task dispatch optimization module, task execution module, task review module, data storage module and dynamic update and reuse module; The government affairs task management module is used to perform the following: create government affairs tasks, define the attributes of government affairs tasks, and use the heterogeneous task tree generation method to disassemble government affairs tasks into multi-level subtasks; The task dispatch optimization module is used to perform the following: calculate the optimal dispatch path based on the improved ant colony algorithm, and dispatch subtasks to the execution end based on the optimal dispatch path; The task execution module is used to perform the following operations: after receiving the subtask, the execution end associates historical data through the intelligent filling engine and performs data collection and data submission operations; The task review module is used to perform the following: perform basic logic verification on the submitted data, and conduct multi-level review of the data that passes the basic logic verification according to the predetermined multi-level review rules. If any link in the multi-level review process is rejected, the rejection correction will be executed; The data storage module is used to perform the following operations: For data that passes the review, the data is stored in the global database. Data tables in the global database are linked through unique identification fields. The global database supports dynamic data updates and intelligent filling. The operator, timestamp, and modification traces related to the data are written into the blockchain. The dynamic update and reuse module is used to perform the following: When the execution end performs data collection again, the intelligent filling engine is triggered based on the data identifier. The intelligent filling engine associates the existing fields in the global library and matches the filling content according to the collection scenario to achieve data update and reuse.

7. The system for dynamic dispatching and multi-level review of government affairs tasks according to claim 6 is characterized in that: When defining the attributes of government tasks, government task management is used to define the attributes of government tasks using the structured task description language JSON. The attributes of government tasks include task type, jurisdiction, associated data source, and priority.

8. The system for dynamic dispatching and multi-level review of government affairs tasks according to claim 6 is characterized in that: The parameters of the improved ant colony algorithm include pheromone volatility coefficient, heuristic factor and expectation factor. The dispatch path is dynamically adjusted based on the ant colony algorithm parameters to balance the load.

9. The system for dynamic dispatching and multi-level review of government affairs tasks according to claim 6 is characterized in that: When using the smart fill engine to associate existing fields in the global library and match fill content based on the collection scenario, sensitive fields must be authorized before they can be displayed.

10. The system for dynamic dispatching and multi-level review of government affairs tasks according to claim 6 is characterized in that: Perform basic logic verification on the submitted data. The task review module is used to perform verification on the number of digits in the ID card number, date format, value range, and required fields, and intercept abnormal data. When performing multi-level audits on data that has passed basic logical verification according to predetermined multi-level audit rules, the task audit module is used to perform the following: each level of audit adopts a two-person review mechanism, and the audit operation is recorded through blockchain evidence storage technology. The block header contains the task ID, timestamp and auditor's digital signature.

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