Custom form job license generation method, equipment and storage media

Abstract

This application discloses a method, device, and storage medium for generating custom form work permits, comprising: generating a custom work permit form in response to dragging and dropping form elements in a form design interface; determining target fields based on the field attributes of each form field in the custom work permit form; obtaining field content from a data source based on the target fields and filling the target fields to generate a work permit; generating an approval task based on the work type of the work permit and preset enterprise management regulations; creating an approval task workflow based on the approval task and determining the approval result; and generating a formal work permit based on the work permit upon approval. This application achieves flexible adaptation of work permit forms and approval processes to different work type requirements through visual drag-and-drop dynamic construction of configurable form elements, combined with dynamic / static data fields, and associated with role / organization approval nodes to execute tasks, thereby significantly improving management efficiency.

Description

Technical Field

[0001] This application relates to the construction industry, and in particular to a method, device and storage medium for generating custom form work permits. Background Technology

[0002] In the process of accelerating the transformation of the construction industry towards industrialization and digitalization, the construction of smart construction sites has become an important direction for improving safety management. As a core link in the safety control of construction sites, the level of intelligence of the work permit management system directly determines the site's safety compliance efficiency and production operation quality. Although the current mainstream solutions in the industry have achieved an initial evolution from paper forms to electronic management, their underlying architecture still follows a static management model that predefines fixed form templates and solidifies the permit approval process. This rigid design model is not compatible with the dynamic and scenario-based safety management needs of modern construction sites.

[0003] Specifically, permit creation based on a static management model cannot dynamically reconstruct its form elements according to differentiated scenario characteristics. Furthermore, when temporary changes occur at the work site, the system lacks a real-time form reconstruction mechanism, forcibly using preset templates, leading to a disconnect between risk control measures and actual site conditions. This structural contradiction between static architecture and dynamic needs is particularly acute in special operation permit management. Especially given the significantly different safety requirements for various special operations such as confined space operations and hoisting operations, providing only a uniform, fixed permit form template cannot meet the actual needs of diverse operation types, severely hindering the improvement of construction site safety management efficiency.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device and storage medium for generating custom form work permits, which aims to solve the technical problem that existing construction work permits use fixed form templates and cannot dynamically adapt to the needs of different work types, resulting in low safety management efficiency.

[0006] To achieve the above objectives, this application proposes a method, device, and storage medium for generating custom form work permits. The method includes:

[0007] Responding to drag-and-drop instructions on form elements in the form design interface, generate a custom work permit form;

[0008] Based on the field attributes of each form field in the custom work permit form, determine the target field to be filled;

[0009] The target field is populated by obtaining the corresponding field content from the data source associated with the target field and generating a job permit. The data source includes static data sources and dynamic data sources.

[0010] An approval task is generated based on the work type of the work permit and the preset enterprise management regulations, and an approval task workflow is created based on the approval node corresponding to the approval task. The approval result is determined based on the processing result of the approval task workflow.

[0011] When the approval result is passed, a formal work permit is generated based on the work permit.

[0012] In one embodiment, the custom form job license generation method further includes:

[0013] When a change in operating conditions is detected, the update operation of the form field is triggered. A linkage rule table between the change in operating conditions and the form field is established in advance. The change in operating conditions includes weather warnings, equipment fault alarms, and construction plan adjustment notifications.

[0014] Obtain the field content corresponding to the working condition change event, and update the field content to the component text box of the form field.

[0015] In one embodiment, after the step of obtaining the field content corresponding to the working condition change event and updating the field content to the component text box of the form field, the method further includes:

[0016] Triggered the approval task reorganization operation;

[0017] Determine the change event level of the operating condition change event, and obtain the approval task reorganization strategy corresponding to the change event level;

[0018] The approval tasks for the work permit are restructured using the approval task restructuring strategy.

[0019] In one embodiment, the step of reorganizing the approval task of the work permit through the approval task reorganization strategy includes:

[0020] Identify the responsible party for overseeing the change in operating conditions event, and insert the electronic signature node of the responsible person into the workflow of the approval task corresponding to the approval task;

[0021] Logical bypass processing is performed on adjacent approval nodes of the electronic signature node, and an approval path topology map is generated based on the processing result;

[0022] The approval task workflow is restructured based on the approval path topology.

[0023] In one embodiment, the steps of generating an approval task based on the job type of the work permit and preset enterprise management regulations, creating an approval task workflow based on the approval node corresponding to the approval task, and determining the approval result based on the processing result of the approval task workflow include:

[0024] The enterprise management regulations define the approval requirements based on the job type, and the approval task is generated based on the approval requirements.

[0025] Create a tree diagram of the approval tasks, wherein approval objects and approval processes are added to the tree diagram;

[0026] The approval task workflow is generated based on the tree diagram.

[0027] In one embodiment, before the step of generating a custom work permit form in response to the operation instruction of dragging form elements in the form design interface, the method further includes:

[0028] Collect a set of environmental parameters, which includes meteorological data, geological monitoring data, and equipment operating status data;

[0029] The set of environmental parameters is input into a pre-trained scene feature recognition model, and the risk feature label of the current work scene is output.

[0030] Based on the risk feature tags, a basic form framework is matched in a preset license template library, and the form design interface is generated based on the basic form framework.

[0031] In one embodiment, the step of matching a basic form framework in a preset license template library based on the risk feature tag, and generating the form design interface based on the basic form framework, includes:

[0032] Parse the job type identifier of the risk feature label and activate the field loading channel corresponding to the job type identifier;

[0033] Based on real-time spatial positioning data, retrieve the historical accident case set within the geofence area, and convert the high-frequency risk factors in the historical accident case set into additional verification items;

[0034] The field loading channel and the additional validation items are deployed on the basic form framework to generate the form design interface.

[0035] In one embodiment, after the step of generating a formal work permit based on the work permit when the approval result is passed, the method further includes:

[0036] The form data information of the formal work permit is integrated, and the integration result is used to generate a structured work report. The form data information includes form data, approval records, and attachments.

[0037] A preset report template is matched according to the job type of the formal work permit, and a formal work permit report is generated based on the structured job report and the preset report template.

[0038] In addition, to achieve the above objectives, this application also proposes a custom form job permit generation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the custom form job permit generation method described above.

[0039] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the custom form job license generation method described above.

[0040] One or more technical solutions proposed in this application have at least the following technical effects:

[0041] The technical solution of this application generates a custom work permit form in response to dragging and dropping form elements in the form design interface; it determines the target fields to be filled based on the field attributes of each form field in the custom work permit form; it obtains the corresponding field content based on the data source associated with the target field and fills the target field to generate the work permit, wherein the data source includes static data sources and dynamic data sources; it generates an approval task based on the work type of the work permit and preset enterprise management regulations, and creates an approval task workflow based on the approval node corresponding to the approval task, and determines the approval result based on the processing result of the approval task workflow; when the approval result is passed, it generates a formal work permit based on the work permit. This application achieves flexible adaptation of the work permit form and approval process to different work type requirements by visually dragging and dropping configurable form elements, combining dynamic / static data fields, and associating them with role / organization approval nodes to execute tasks, thereby significantly improving management efficiency. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating an embodiment of the custom form job license generation method provided in this application;

[0045] Figure 2 A flowchart illustrating Embodiment Two of the method for generating a custom form job license in this application;

[0046] Figure 3 A flowchart illustrating Embodiment 3 of the custom form job permit generation method of the present invention;

[0047] Figure 4 A flowchart illustrating Embodiment 4 of the custom form job license generation method of the present invention;

[0048] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the custom form job license generation method in this application embodiment.

[0049] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0051] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The main solution of this application embodiment is as follows: A custom work permit form is generated in response to the drag-and-drop operation instruction of form elements in the form design interface; the target field to be filled is determined according to the field attributes of each form field in the custom work permit form; the corresponding field content is obtained based on the data source associated with the target field to fill the target field, thereby generating a work permit, wherein the data source includes static data sources and dynamic data sources; an approval task is generated according to the work type of the work permit and preset enterprise management regulations, and an approval task workflow is created according to the approval node corresponding to the approval task, and the approval result is determined based on the processing result of the approval task workflow; when the approval result is passed, a formal work permit is generated based on the work permit.

[0053] This application provides a solution that uses visual drag-and-drop to dynamically construct configurable form elements, combines dynamic / static data fields, and associates them with role / organization approval nodes to execute tasks. This enables work permit forms and approval processes to flexibly adapt to the needs of different work types, thereby significantly improving management efficiency.

[0054] Based on this, this application provides a method for generating a custom form job permit, referring to... Figure 1 , Figure 1 The flowchart of Embodiment 1 of the method for generating a custom form job permit provided in this application is shown below. The method for generating a custom form job permit includes steps S10-S40, as follows:

[0055] Step S10: Responding to the drag-and-drop operation command of form elements in the form design interface, a custom work permit form is generated;

[0056] In this embodiment, on the form design page, the user drags and drops form elements according to the current construction requirements to create a construction work permit form. The drag-and-drop operation triggers a visual form building engine, thereby constructing the work permit form. The form elements are essentially pre-defined standardized input controls, and the technical entities are web component libraries carrying construction metadata. Each component includes a UI rendering template and data validation logic. When creating the form based on the drag-and-drop operation, data is collected based on the data and functionality involved in the drag-and-drop action. Specifically, the drag-and-drop operation includes three technical actions: element selection, displacement trajectory recording, and release positioning. When the user selects a specific form element (such as a date picker or equipment number input box) from the interface material library, the element type code and initial coordinates are captured simultaneously. During movement, the cursor offset is calculated in real time to drive the element preview image to follow. At the moment of release, a collision detection algorithm determines the landing area, and the corresponding element node is dynamically inserted into the canvas document object model. The drag-and-drop process relies on the HTML5 drag-and-drop application interface to achieve cross-browser compatibility, ensuring that construction safety supervisors can complete form design without coding skills.

[0057] Furthermore, when generating a custom form based on the drag-and-drop operation, the generated form is essentially a simple structural layout achieved by dragging and dropping form elements. This custom form then serves as a structured data container. Specifically, the generation of this work permit form must comply with construction safety regulations. Specifically, the custom work permit form automatically incorporates construction safety standard clauses during generation; for example, hot work forms are required to include an oxygen concentration detection field, and confined space work requires a gas monitoring and recording area. Additionally, a preset rule engine is activated by the construction type tag. This preset rule engine is linked to the clause database of the State Administration of Work Safety and automatically verifies the completeness of the required fields in the custom work permit form and indicates any missing elements. The final generated form structure is stored in JSON-Schema format, containing metadata such as field keys, data types, and validation rules, establishing a technical contract for subsequent field filling. Specifically, the custom work permit is represented as a structured form, including form fields and text boxes for filling in the field content.

[0058] Step S20: Determine the target field to be filled based on the field attributes of each form field in the custom work permit form;

[0059] Based on the generated custom job license, and based on the field attributes contained in the custom job license, the target fields that need to be filled in are determined by parsing the field attributes; specifically, the field attribute parsing adopts a metadata-driven strategy, and the field attributes are essentially a set of control instructions based on field behavior, represented as a set of key-value pairs stored in the field metadata node of the form configuration.

[0060] Specifically, the field attributes include two categories: static attributes and dynamic associated attributes, represented as static data sources and dynamic data sources. The static attributes of the static data source define the basic characteristics of the field (e.g., the data type of the field name "Guardian's Signature" is a handwritten signature image). The dynamic attributes of the dynamic data source mark the rules for obtaining field values ​​(e.g., the "Work Area" field is associated with a geographic information system interface). Specifically, the parsing process of the dynamic data source involves traversing the form's JSON-Schema tree structure and identifying fields that need external filling through attribute markers. For example, fields marked "auto_fill_source=ERP" are automatically added to the target field set.

[0061] To ensure the correctness of the form fields in the custom work permit form, the target fields are intelligently deduplicated and prioritized. Specifically, the target fields serve as a set of mounting points for the data to be filled, essentially a field task queue built in memory. Each task in the field task queue includes a field path, data source binding information, and a filling status flag. When multiple fields are associated with the same data source, a merging optimization is initiated: for example, when both "Work Supervisor" and "Safety Responsible Person" are associated with the personnel management system, they are automatically merged into a single data query. Simultaneously, the filling order is sorted according to the construction process dependencies. Specifically, basic work information (such as work number) can be filled first, followed by equipment parameters (such as crane tonnage), and finally, derived fields dependent on the aforementioned fields (such as risk level assessment) can be generated. This sorting algorithm is implemented based on a directed graph topological sorting of field dependencies, avoiding circular dependencies during the filling process.

[0062] Step S30: Obtain the corresponding field content based on the data source associated with the target field and populate the target field to generate a job permit. The data source includes a static data source and a dynamic data source.

[0063] Based on the identified target fields, the corresponding field content is obtained from the data source associated with the target fields. During this process, a dual-mode data source collaboratively populates the data to achieve the fusion of static and dynamic data. The static data source points to a persistent storage system (such as an equipment ledger table in a relational database), extracting constant data according to field rules using a structured query language. The dynamic data source connects to a real-time service interface (such as an IoT sensor data bus), using a publish-subscribe model to obtain instantaneous values. A data source adapter factory is established; for example, static data calls a JDBC connector to execute pre-compiled queries, while dynamic data listens for device status events via a WebSocket channel. For instance, when populating the "ambient temperature" field of a high-temperature work permit, the temperature threshold standard (static) in the database and real-time sensor feedback data (dynamic) are simultaneously obtained.

[0064] Furthermore, the integrity of the work permit form is ensured through exception handling. As shown above, based on the limitation of obtaining dynamic field content from the data source, when the data source is abnormal, a backup data source is first tried (such as switching to a cached copy when the main database fails); default value generation rules can also be applied (such as filling in a "pending confirmation" mark and triggering approval for supplementary entry when personnel information is missing); finally, cross-system data compensation is initiated, and the enterprise service bus is used to call the interface of the related system to complete the data. All fill operations are recorded in atomic transaction logs, supporting rollback and retry at any step. After the custom work permit form is filled, a work permit is generated, which is converted to PDF / A format for archiving and includes a digital watermark and metadata fingerprint for tamper-proofing.

[0065] Step S40: Generate an approval task based on the job type of the work permit and the preset enterprise management regulations, create an approval task workflow based on the approval node corresponding to the approval task, and determine the approval result based on the processing result of the approval task workflow;

[0066] The generated work permits are approved, specifically through a work approval rule engine that dynamically parses enterprise management regulations. These preset enterprise management regulations are stored as an executable rule set. Upon inputting the work type and equipment parameters, an approval task is output. This approval task is essentially a work item carrying approval context, including a permit summary, a list of approvers, and a message body indicating urgency. When performing rule matching based on the preset enterprise management regulations for the work type, the Rete algorithm is used to optimize performance and improve retrieval efficiency. Furthermore, for work types with special scenarios, fuzzy matching is initiated, such as automatically adding a fire department co-signature node for "hot work within a 30-meter radius of a storage tank".

[0067] Based on the generated approval tasks, an approval task workflow is created. This workflow is essentially an approval process controller, technically implemented as a BPMN 2.0 standard workflow engine instance, including a process definition file and a runtime execution context. Specifically, the creation of the approval workflow enables intelligent node routing. Furthermore, the approval task workflow is constructed based on a finite state machine model, where each approval node includes a status identifier (e.g., "Pending approval by safety supervisor"), transition conditions (e.g., "Agreed and risk level < 3"), and timeout handling strategies (e.g., automatic escalation if not approved within 24 hours) with corresponding rule constraints.

[0068] Approval tasks are processed based on the created workflow. The workflow engine loads templates for each task type and instantiates them; for example, a gas detection and review node is automatically inserted for confined space operations. Inter-node routing supports conditional branching, dynamically inserting an expert review stage when a "high-risk process" is detected. The approval process status of the workflow is persisted to a workflow instance library, supporting breakpoint resumption.

[0069] Furthermore, the specific process of generating a corresponding approval task workflow based on the approval task includes the steps of generating an approval task according to the work type of the work permit and preset enterprise management regulations, creating an approval task workflow according to the approval node corresponding to the approval task, and determining the approval result based on the processing result of the approval task workflow, including:

[0070] The enterprise management regulations define the approval requirements based on the job type, and the approval task is generated based on the approval requirements.

[0071] Create a tree diagram of the approval tasks, wherein approval objects and approval processes are added to the tree diagram;

[0072] The approval task workflow is generated based on the tree diagram.

[0073] In this embodiment, the process of determining approval requirements based on job type is achieved through technical transformation by parsing the natural language clauses of enterprise management regulations. This parsing relies on a rule-based semantic engine. Specifically, when the job type is marked as "work at height," the semantic engine automatically extracts clauses such as "more than 15 meters requires approval from two guardians" and "involving cross-operations requires countersignature from the regional head" from the regulations. Specifically, a named entity recognition model is used to locate key constraint items (such as trigger words like "exceeds" and "requires"), and then dependency parsing is used to construct a mapping rule between "job conditions" and "approval actions." For example, after recognizing the syntactic association between the condition phrase "lifting weight ≥ 100 tons" and the action phrase "submit to chief engineer for approval," a structured approval rule tuple is generated. This transformation process essentially converts textual regulations into machine-executable decision logic, ensuring that the generated approval tasks strictly conform to the enterprise's regulations.

[0074] Specifically, based on the parsed approval requirements, a tree graph is constructed to visualize the approval logic. The tree graph for creating approval tasks adopts a directed acyclic graph (DAG) data structure. Specifically, the root node can be the applicant for the work permit, and branch nodes expand according to the approval hierarchy. Furthermore, each approval object node contains three elements: approval role (e.g., safety director), approval action (e.g., review plan), and decision threshold (e.g., risk value < 5). The connecting edges between nodes represent the approval process sequence; ordinary sequential approvals are represented by unidirectional arrows, while parallel approvals create a multi-branch structure. The tree graph construction process incorporates a logical disambiguation algorithm. When a skip clause based on an approval node is detected, a condition judgment node is automatically inserted, and alternative paths are generated. The tree graph becomes the topological blueprint of the approval process, supporting lineage tracing and impact analysis of any node.

[0075] In the process of generating the approval task workflow from the tree graph, the nodes of the approval objects in the workflow depend on the matching of the tree graph. Specifically, the process of adding an approval object calls the enterprise personnel profiling system, which can search for matching personnel in the organizational chart based on the approval role tag (such as "Electrical Professional Head"). The specific matching algorithm comprehensively considers factors such as the validity of qualification certificates (such as special operations examiner certificate), current task load (such as the number of pending tasks < three), and physical proximity (such as within a 500-meter construction radius). When multiple people meet the conditions, an optimal election is initiated, for example, selecting the engineer with the highest approval rate in the last 72 hours. The personnel matching results are written to the node attribute pool, and a pending message is generated and pushed to the approver's terminal simultaneously. This process can avoid the arbitrariness of manually designating approval subjects and improve the accuracy of task distribution.

[0076] Furthermore, the generation of the approval task workflow follows the principle of minimizing waiting time. When generating the approval process based on the tree graph, the longest approval chain (e.g., "team application → regional review → expert review → director approval") is first identified and set as the main process; non-critical nodes (e.g., document clerk filing) are executed asynchronously in parallel. The estimated time for approval nodes is automatically calculated by the process engine (e.g., expert review by default is four hours), and reminder nodes are dynamically inserted when the risk of cumulative timeout is detected. In special scenarios, process folding optimization is initiated, such as merging three consecutive read-only filing nodes into a batch signing task. The final generated process instance conforms to the BPMN 2.0 standard and can be deployed to any workflow engine for execution.

[0077] The work permit is approved according to the approval task workflow generated above, and the approval results of the approval task workflow are monitored in real time.

[0078] Step S50: When the approval result is approved, a formal work permit is generated based on the work permit.

[0079] Based on the approval results of the generated approval task workflow, the approval results are verified using a digital signature chain mechanism. Specifically, when the workflow engine pushes the approval approval event for the approval task workflow, it first verifies the completeness of the approval process (e.g., whether all required nodes have signed); secondly, it checks the validity of the approver's permissions (e.g., the chief engineer's approval must match the validity period of the qualification certificate); finally, it triggers the blockchain notarization service to write the approval record into the distributed ledger. Any verification failure freezes the license generation process and triggers an audit alarm. After the approval approval event is verified as successful, the approval task workflow is determined to have passed.

[0080] Furthermore, a formal work permit is generated using the relevant data information from the work permit. This involves activating the permit factory service after verification, merging the approval comments appendix into the original permit document, and embedding a QR code containing the approval serial number. Geographical validity is generated by associating with electronic fence data, for example, valid only in the No. 3 unit area of ​​the factory; a dynamic watermark is added to display a real-time countdown to the validity period. The final formal work permit is published in encrypted PDF format and simultaneously pushed to the mobile terminals of operators and the central monitoring screen, achieving simultaneous effectiveness across multiple terminals. Specifically, the formal work permit, as a legally valid electronic certificate, conforms to the signature document package standards of the State Cryptography Administration and integrates timestamp services and digital certificates.

[0081] This embodiment enables flexible customization of work permit forms, improving their adaptability to different work types. By acquiring dynamic data fields in real time, the accuracy and timeliness of work permit data are enhanced. The dynamic approval mechanism improves approval efficiency and ensures that high-risk operations are fully reviewed. Overall, this solution improves the intelligence level of work permit management and effectively supports the safety management needs of smart construction sites.

[0082] Further reference Figure 2 , Figure 2 The flowchart of Embodiment 2 of the method for generating a custom form job permit provided in this application is shown below. The method for generating a custom form job permit includes steps S60-S100, as follows:

[0083] Step S60: When a working condition change event is detected, the update operation of the form field is triggered. Here, a linkage rule table between the working condition change event and the form field is established in advance. The working condition change event includes weather warning, equipment fault alarm and construction plan adjustment notification.

[0084] Step S70: Obtain the field content corresponding to the working condition change event, and update the field content to the component text box of the form field.

[0085] Step S80 triggers the approval task reorganization operation;

[0086] Step S90: Determine the change event level of the working condition change event and obtain the approval task reorganization strategy corresponding to the change event level;

[0087] Step S100: Reorganize the approval task of the work permit through the approval task reorganization strategy.

[0088] In this embodiment, the working condition change event is detected through a full-element perception network. Specifically, the capture of the working condition change event is realized relying on a distributed event bus, and the distributed event bus is docked with three types of data sources, namely, the early warning interface of the meteorological department, the equipment predictive maintenance system, and the engineering change management system. Specifically, when a red high wind warning information is issued, the event encoder converts it into a standard event object, including structured data such as an event type label "meteorological warning", an influence range coordinate set, and an effective time window; the equipment vibration value exceeding the limit alarm carries the equipment number, fault code, and emergency level mark; the construction plan adjustment event needs to analyze the process change points and safety measure revision items in the change notice. After all event objects are de-duplicated and merged, they are injected into the rule matching engine to form the real-time situation awareness ability of the construction environment, providing an event-driven basis for subsequent linkage updates.

[0089] The multi-dimensional response logic is constructed through a linkage rule table. The pre-established linkage rule table is stored in a dual mode of a relational database and a graph database. Specifically, in the linkage rule table, the basic matching rules (such as "wind speed warning > 10 levels" associated with the "high-altitude operation protection measures" field) are recorded, and the graph database stores the dependency network between fields (such as the "equipment shutdown" event needs to synchronously update the "energy isolation" and "tagging and locking" fields). And each record in the linkage rule table contains a five-tuple represented by an event feature fingerprint, a target field path, an update value calculation expression, an effective condition, and a version number.

[0090] The corresponding form fields are found in the linkage rule table with the working condition change event. Specifically, a fuzzy reasoning mechanism is introduced in this rule matching process. For example, when an event of "excessive gas in a confined space" is received, not only the update of the directly associated "gas concentration" field is triggered, but also the inspection item of "emergency ventilation equipment" needs to be added through the dependency network derivation.

[0091] Specifically, after determining the form fields, the field content generated by the working condition change event is obtained, and the form content of the form fields is updated with the field content. Specifically, precise content replacement needs to be realized based on this field update operation. The process of obtaining the field content corresponding to the working condition change event executes an intelligent value-taking strategy. Specifically, for meteorological warning events, the geographic fence engine is called to calculate the maximum wind force value of the construction coordinate point in the next two hours and fill it into the "ambient wind force" field; for equipment failure events, the knowledge graph is queried to obtain the description of the standard disposal measures, such as the preset text of "stop the pump and cool it down, then复测 the temperature" corresponding to "pump bearing overheating"; for construction plan adjustments, the revised paragraph numbers of the change document need to be extracted and located to the corresponding chapter of the permit form for incremental update. Technologically, the target text box is located through the document object model, and the difference comparison algorithm is used to only update the changed part, avoiding the interface flicker caused by full-scale refreshing.

[0092] When updating the field content corresponding to the work condition change event to the corresponding form field, this is achieved by filling the component text box with the field content, and refreshing the component text box in real time after filling, thus ensuring visual consistency. The operation of updating the field content to the component text box is implemented through the collaboration of the data binding layer and the rendering layer. Specifically, the data binding layer injects the new value into the form data model, triggering the model listener to send a dirty data marker to the rendering layer; the rendering layer starts the difference comparison engine, calculates the change nodes of the document object model, and performs a minimal update. For example, when the "Working Height" is adjusted from 30 meters to 50 meters, only the height input box and the associated risk level warning label are redrawn. For offline scenarios on mobile devices, an operation log replay mechanism is used to ensure that the results are updated synchronously after the network is restored, avoiding data version splitting.

[0093] Furthermore, the event level of the operational condition change event is assessed by initiating a tiered response mechanism. This event level determination process employs a risk impact matrix algorithm. In this matrix, the horizontal axis represents event severity (e.g., equipment failure is categorized into three levels: functional degradation, partial shutdown, and systemic paralysis), and the vertical axis represents the sensitivity of the construction phase (e.g., earthwork excavation period, structural hoisting period, etc.). The intersection of the matrix defines the event level (Level 1 is low impact and can be handled with some leeway; Level 3 requires emergency shutdown). The specific assessment process incorporates artificial intelligence-assisted decision-making, such as analyzing historical records of similar events to generate level recommendations. The final level result is written to the level label field of the event object, driving subsequent approval task reorganization strategies.

[0094] The approval task reorganization strategy enables dynamic process adaptation. When obtaining the approval task reorganization strategy corresponding to the change event level, the corresponding preset template is activated according to the level: Level 1 events initiate the "Approval Chain Addition" strategy, inserting a review node (such as adding a safety supervisor confirmation step) at the end of the original process; Level 2 events trigger the "Parallel Countersigning" strategy, simultaneously initiating a review request to relevant professional departments; Level 3 events execute the "Process Interruption Reset" strategy, terminating all current approval tasks, rebuilding the custom license form according to the new license template, and performing the entire approval process based on the custom license form. The strategy engine automatically analyzes the scope of the change's impact. If it only involves adjustments to construction time, the original approver is retained; if the process route is changed, a technical expert node is automatically added.

[0095] The implementation of the approval task reorganization strategy ensures the integrity of approval transactions. Specifically, when reorganizing approval tasks through the strategy, a transaction compensation mechanism is established to guarantee consistency. First, a snapshot is created to save the current progress; then, node addition, deletion, and modification operations are performed according to the strategy (e.g., removing expired equipment inspection nodes); finally, approval relationship calibration is initiated, notifying affected approvers to update their pending items. For nodes that have already been approved, a voting weight inheritance algorithm is used: if the original approver is still in the countersigning list, their historical approval conclusions automatically inherit validity. The reorganized new process instance is bound to the changed license version to ensure that the approval basis strictly corresponds to the actual construction.

[0096] Specifically, the step of reorganizing the approval task of the work permit through the approval task reorganization strategy includes:

[0097] Identify the responsible party for overseeing the change in operating conditions event, and insert the electronic signature node of the responsible person into the approval task workflow corresponding to the approval task;

[0098] Logical bypass processing is performed on adjacent approval nodes of the electronic signature node, and an approval path topology map is generated based on the processing result;

[0099] The approval task workflow is restructured based on the approval path topology.

[0100] In this embodiment, the regulatory responsibility entity for the operational condition change event is intelligently identified by an organizational responsibility mapping engine. The determination of the regulatory responsibility entity for the operational condition change event is achieved by activating the enterprise responsibility matrix library by parsing the event type and scope of impact. Specifically, when an equipment fault alarm carries the "special equipment" tag, it automatically associates the dual regulatory entities of the equipment management department and the safety supervision department; for meteorological warning events, it matches the local emergency management responsible person based on the coordinates of the construction area. This matching process introduces a fuzzy attribution algorithm, automatically tracing the distribution of responsible entities in similar historical cases for new events, and selecting the regulatory department with the highest frequency of occurrence as the primary responsible party. The identification result is written as the event responsibility object to the electronic signature node in the approval task workflow corresponding to the approval task. The specific content written includes the responsible person's job title, contact information, and electronic signature key index, providing a target anchor point for subsequent node insertion.

[0101] Specifically, the dynamic injection of electronic signature nodes is to achieve precise reinforcement of the approval chain. When inserting electronic signature nodes into the approval task workflow, a non-intrusive instrumentation technique is used. By locating the approval nodes related to the responsible entity in the original process (such as the review stage of the safety supervision department), a signature slot is inserted in their subsequent position. This node configuration needs to carry change context awareness capabilities to achieve the purpose of automatically pre-filling event summaries (such as "review of additional windproof measures due to strong wind warning"), associated attachments (such as wind speed monitoring curves), and signature opinion templates. In practice, signature action hooks are injected through the node extension interface of the workflow engine to ensure seamless compatibility between the new node and the original process state machine. Each signature node generates an independent transaction log, supporting atomic rollback operations.

[0102] Based on the inserted electronic signature nodes, optimal connectivity of the approval path is ensured through logical bypassing. When logically bypassing adjacent approval nodes of an electronic signature node, a topology reconstruction engine is activated. Based on the activated topology reconstruction engine, the position of the newly added node in the approval chain is determined. If the new node is in a serial approval chain, the original direct successor connection is automatically disconnected, and a new link of "predecessor node → signature node → original successor node" is established. If the adjacent nodes are in a parallel co-signing group, a bidirectional association channel is created between the signature node and each parallel branch. Specifically, the bypassing process introduces a path conflict detection mechanism. For example, if a circular dependency is found between a signature node and an existing node, a conditional judgment gateway is automatically inserted to split the process. The final generated approval path topology graph is presented as a weighted directed graph, with edge weights representing the approval transmission time between nodes, providing a visual analysis basis for process optimization.

[0103] New process instances are generated through dynamic compilation of the approval path topology map. Specifically, when creating a new approval task workflow based on the topology map, a three-stage compilation is implemented. First, the node relationships are compiled into standard BPMN 2.0 elements (such as converting approval nodes into user task nodes), and the task allocation strategy is optimized, configuring a dedicated processing queue for newly added approval nodes (such as prioritizing the scheduling of urgent change approval tasks); finally, a process monitoring probe is injected to track the processing time of approval nodes in real time.

[0104] In this embodiment, the new approval task workflow generated based on the approval task reorganization strategy can automatically inherit the valid parts of the original process approval results (such as completed environmental review conclusions), and only re-approval is required for nodes within the change impact domain. The entire process ensures the continuity of approval criteria through version snapshot comparison, eliminating regulatory gaps.

[0105] Further, see Figure 3 , Figure 3 This is a flowchart illustrating Embodiment 3 of the custom form job license generation method of the present invention. Figure 3The process includes steps S110-S130. Specifically, before the step of generating a custom work permit form in response to the operation instruction of dragging and dropping form elements in the form design interface, the process further includes:

[0106] Step S110: Collect a set of environmental parameters, which includes meteorological data, geological monitoring data, and equipment operating status data;

[0107] Step S120: Input the set of environmental parameters into the pre-trained scene feature recognition model and output the risk feature label of the current work scene;

[0108] Step S130: Match a basic form framework in a preset license template library based on the risk feature tags, and generate the form design interface based on the basic form framework.

[0109] In this embodiment, a set of environmental parameters is collected by constructing a panoramic perception base. Specifically, this collection process achieves heterogeneous data fusion through an IoT central gateway. Specifically, meteorological data originates from the regional subscription interface of the national early warning information release system, capturing wind speed, precipitation, and lightning warning signals within a ten-kilometer radius of the construction coordinate point in real time; geological monitoring data is obtained from slope radar sensors and rock mass displacement monitors, with a sampling frequency of five times per second and transmission latency controlled within five hundred milliseconds; equipment operating status data is obtained by parsing the bus messages of the engineering machinery control system, extracting key operating indicators such as hydraulic pressure and engine speed. The heterogeneous data described above flows through a spatiotemporal alignment engine for processing in the form of a data stream. During processing, the data stream of the heterogeneous data is uniformly tagged with location labels and timestamps, forming an environmental parameter snapshot with spatial topological relationships.

[0110] A scene feature recognition model is used to achieve dimensionality reduction mapping of risk dimensions. This model is essentially an intelligent converter from environmental parameters to risk features, and its architecture is created using a multimodal fusion neural network. Specifically, meteorological data is input into a convolutional branch to extract storm pattern features, geological data is input into a time-series branch to identify stratum deformation trends, and equipment data is analyzed through a fully connected layer to analyze abnormal vibration spectra. The output layer of the scene feature recognition model connects to a risk feature label dictionary, compressing high-dimensional features into discrete risk feature labels (such as readable labels like "high risk of strong winds," "slope instability warning," and "equipment overload"). Specifically, the training data pre-trained based on the scene feature recognition model originates from the replay of environmental parameters from historical major engineering accident cases, and the sample diversity is expanded through adversarial generative networks. The scene feature recognition model is deployed using dedicated integrated circuit hardware acceleration, thereby improving the stability of single inference time.

[0111] When matching the basic form framework based on the risk feature tags, the system first searches the preset template library for templates that perfectly match the tags (e.g., the "Confined Space" tag matches the confined space operation form). If no perfect match is found, semantic association retrieval is initiated; for example, the "Slope Instability" tag can be associated with the "Deep Foundation Pit Operation" template. A weighted voting mechanism is introduced into the matching process: when multiple tags coexist (e.g., both "Strong Wind" and "High-Altitude Operation" appear simultaneously), the template with the highest number of votes is selected as the basic framework.

[0112] When generating the form design interface based on the basic form framework, the associated field groups are automatically activated according to risk characteristic tags (such as the "Strong Wind High Risk" tag pre-loading wind speed monitoring area and windproofing measures fields), while non-core fields remain optional. The interface layout can adopt a responsive grid system, rendering fields hierarchically according to their importance: high-risk fields are displayed prominently at the top, while reference fields are collapsed. Simultaneously, a real-time environmental parameter display component is injected (such as embedding a displacement change curve in the sidebar of the geological form), enabling real-time linkage between the design process and the on-site situation. The final generated design interface becomes a risk-adaptive creative space, significantly reducing the complexity of form configuration.

[0113] Furthermore, the step of matching a basic form framework in a preset license template library based on the risk feature tags, and generating the form design interface based on the basic form framework, includes:

[0114] Parse the job type identifier of the risk feature label and activate the field loading channel corresponding to the job type identifier;

[0115] Based on real-time spatial positioning data, retrieve the historical accident case set within the geofence area, and convert the high-frequency risk factors in the historical accident case set into additional verification items;

[0116] The field loading channel and the additional validation items are deployed on the basic form framework to generate the form design interface.

[0117] When parsing the work type identifier of risk feature tags, semantic feature decoupling is employed. Specifically, when a tag contains keywords such as "restricted space" or "hot work," the core work type code (e.g., "CONFINED_SPACE") is automatically extracted. This core work type code activates a preset field loading channel. This field loading channel is essentially a set of binding relationships between field groups and data sources. For example, the high-altitude work channel is associated with the fall protection equipment inspection field group, and the channel has built-in field loading priority rules (e.g., required fields are rendered first). Specifically, a registry mechanism manages the channel configuration to ensure that the field combination strategy is switched in real time when the identifier changes.

[0118] Specifically, when retrieving historical accident case sets based on geofence boundaries, a spatial-temporal dual-dimensional retrieval model is established. This model is created by defining a circular fence with a 10-kilometer radius centered on the construction site, and retrospectively analyzing accident reports from the same period (same quarter, same lunar phase) within a preset historical timeframe. The direct causal chains (such as "flash explosion due to failure to detect combustible gas") are extracted from the accident investigation reports using a case analysis engine, and high-frequency risk factors (such as "missing gas detection" occurring in 70% of cases) are identified using a clustering algorithm.

[0119] When deploying field loading channels and additional validation items to the basic form framework, the field groups are arranged according to the workflow order at the spatial layout layer (e.g., environmental detection before indoor operations); at the data binding layer, real-time data interfaces are pre-configured for new validation items (e.g., combustible gas detection directly connects to the sensor data stream); and at the interaction logic layer, dynamic validation triggers are injected (e.g., locking the form submit button when the detection value exceeds the limit). The deployment process employs a version compatibility strategy: when a new validation item conflicts with an existing field, field reconstruction is automatically initiated (e.g., merging "oxygen detection" and "toxic gas detection" into a joint monitoring area). The final generated design interface becomes a visual defense platform that carries historical experience and real-time risks.

[0120] Further, see Figure 4 , Figure 4 This is a flowchart illustrating Embodiment 4 of the custom form job license generation method of the present invention. Figure 4 The process includes steps S140-S150. Specifically, after the step of generating a formal work permit based on the work permit when the approval result is passed, the process further includes:

[0121] Step S140: Integrate the form data information of the work permit, and generate a structured work report based on the integration result. The form data information includes form data, approval records, and attachments.

[0122] Step S150: Match a preset report template to the job type of the job permit, and generate a job permit report based on the structured job report and the preset report template.

[0123] In this embodiment, intelligent integration of form data achieves full-element archiving. Specifically, this is achieved by integrating form data from formal work permits. During this process, the main form data channel extracts key-value pairs of user-filled fields (e.g., "Work height = 30 meters"), automatically filtering out redundant fields that are not enabled. The approval record channel captures the decision-making process in the approval flow (e.g., "The safety director requested supplementary protection plans on a certain day and time"), retaining electronic signatures and timestamp metadata. The attachment channel packages uploaded unstructured files such as construction drawings and inspection reports, extracting key text and creating an index using optical character recognition technology. The integration process employs an atomic transaction mechanism to ensure data consistency; any missing sub-item freezes the archiving process and triggers a data compensation request. The resulting structured work report becomes a complete digital archive of the permit's lifecycle, supporting traceability and auditing of any field.

[0124] When matching report templates to achieve scenario-based output, a two-factor weighting algorithm is adopted. The primary factor of the two-factor weighting algorithm is the job type identifier (e.g., "confined space operation" directly matches template number CT-003), and the secondary factor is the industry characteristic label (e.g., adding a chapter on emergency response to leaks in the petrochemical industry). The matching engine calls the template feature vector library and recommends the optimal template through cosine similarity calculation. When the similarity is less than 70%, cross-industry template retrieval is initiated (e.g., adapting the nuclear power industry's closed operation template to the chemical industry scenario). When generating formal documents based on structured job reports and preset templates, a layered rendering operation is performed. In the basic layer of the layered rendering operation, structured data is injected into the template placeholder area (e.g., filling the "job supervisor" field value into the corresponding position in the report), and unit labels are automatically added to numerical fields (e.g., "thirty meters" is rendered as "30m"). In the enhancement layer, a visual timeline is generated for key approval nodes (e.g., drawing a full-process timeline diagram of "application-countersigning-approval"), and safety warning icons are embedded next to high-risk operation fields. In the interaction layer, a verification function is bound to the electronic signature area (clicking the signature can retrieve blockchain evidence information). The rendering process employs a differential update strategy, refreshing only the affected areas when data changes, ensuring efficient generation of reports spanning thousands of pages. The final output, the official work permit report, is a PDF / document compliant with ISO 19005 standards, integrating a machine-readable metadata layer.

[0125] This embodiment achieves automated integration and structured output of work permit data and approval process information, effectively solving the data fragmentation problem caused by traditional approval systems and avoiding the risk of information omissions when manually compiling reports. Through an intelligent matching mechanism between preset templates and work types, it ensures that the generated work permit reports can automatically adapt to the format specifications of different special operations, significantly improving the compliance and traceability of safety supervision documents.

[0126] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the custom form operation license generation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0127] This application provides a custom form job license generation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute the custom form job license generation method in Embodiment 1 above.

[0128] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the custom form job license generation device of this application. The custom form job license generation device in this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), etc., and fixed terminals such as desktop computers. Figure 5 The custom form job license generation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0129] like Figure 5As shown, the custom form job license generation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the custom form job license generation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the custom form job license generation device to communicate wirelessly or wiredly with other devices to exchange data. Although a custom form job license generation device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0130] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0131] The custom form work permit generation device provided in this application, employing the custom form work permit generation method described in the above embodiments, solves the technical problem that existing house subdivision methods rely on two-dimensional drawings and manual verification, failing to identify building structural risks in real time, resulting in low subdivision efficiency and significant safety hazards. Compared with the prior art, the beneficial effects of the custom form work permit generation device provided in this application are the same as those of the custom form work permit generation method provided in the above embodiments, and other technical features in this custom form work permit generation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0132] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0133] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0134] This application provides a storage medium, which is a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the custom form job license generation method in the above embodiments.

[0135] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), or any suitable combination thereof.

[0136] The aforementioned computer-readable storage medium may be included in the custom form job license generation device; or it may exist independently and not be assembled into the custom form job license generation device.

[0137] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the custom form job license generation device, enable the custom form job license generation device to implement the technical content of the custom form job license generation method embodiment shown above.

[0138] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0140] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0141] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned custom form work permit generation method. This solves the technical problem that existing house partitioning methods rely on two-dimensional drawings and manual verification, making it impossible to identify building structural risks in real time, resulting in low partitioning efficiency and significant safety hazards. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the custom form work permit generation method provided in the above embodiments, and will not be elaborated upon here.

Claims

1. A method for generating a custom form job permit, characterized in that, Includes the following steps: Responding to drag-and-drop instructions on form elements in the form design interface, generate a custom work permit form; Based on the field attributes of each form field in the custom work permit form, determine the target field to be filled; The target field is populated by obtaining the corresponding field content from the data source associated with the target field and generating a job permit. The data source includes static data sources and dynamic data sources. An approval task is generated based on the work type of the work permit and the preset enterprise management regulations, and an approval task workflow is created based on the approval node corresponding to the approval task. The approval result is determined based on the processing result of the approval task workflow. When the approval result is passed, a formal work permit is generated based on the work permit; The form data information of the formal work permit is integrated, and the integration result is used to generate a structured work report. The form data information includes form data, approval records, and attachments. A preset report template is matched according to the job type of the formal work permit, and a formal work permit report is generated based on the structured job report and the preset report template; Prior to the step of generating a custom work permit form in response to dragging form elements in the form design interface, the method further includes: Collect a set of environmental parameters, which includes meteorological data, geological monitoring data, and equipment operating status data; The set of environmental parameters is input into a pre-trained scene feature recognition model, and the risk feature label of the current work scene is output. Based on the risk feature tags, a basic form framework is matched in a preset license template library, and the form design interface is generated based on the basic form framework; The step of matching a basic form framework in a preset license template library based on the risk feature tags, and generating the form design interface based on the basic form framework, includes: Parse the job type identifier of the risk feature label and activate the field loading channel corresponding to the job type identifier; Based on real-time spatial positioning data, retrieve the historical accident case set within the geofence area, and convert the high-frequency risk factors in the historical accident case set into additional verification items; The field loading channel and the additional validation items are deployed on the basic form framework to generate the form design interface.

2. The method for generating a custom form job license as described in claim 1, characterized in that, The method for generating custom form job licenses also includes: When a change in operating conditions is detected, the update operation of the form field is triggered. A linkage rule table between the change in operating conditions and the form field is established in advance. The change in operating conditions includes weather warnings, equipment fault alarms, and construction plan adjustment notifications. Obtain the field content corresponding to the working condition change event, and update the field content to the component text box of the form field.

3. The method for generating a custom form job license as described in claim 2, characterized in that, After the step of obtaining the field content corresponding to the working condition change event and updating the field content to the component text box of the form field, the method further includes: Triggered the approval task reorganization operation; Determine the change event level of the operating condition change event, and obtain the approval task reorganization strategy corresponding to the change event level; The approval tasks for the work permit are restructured using the approval task restructuring strategy.

4. The method for generating a custom form job license as described in claim 3, characterized in that, The step of reorganizing the approval tasks of the work permit through the approval task reorganization strategy includes: Identify the responsible party for overseeing the change in operating conditions event, and insert the electronic signature node of the responsible person into the workflow of the approval task corresponding to the approval task; Logical bypass processing is performed on adjacent approval nodes of the electronic signature node, and an approval path topology map is generated based on the processing result; The approval task workflow is restructured based on the approval path topology.

5. The method for generating a custom form job license as described in claim 1, characterized in that, The steps of generating an approval task based on the job type of the work permit and preset enterprise management regulations, creating an approval task workflow based on the approval node corresponding to the approval task, and determining the approval result based on the processing result of the approval task workflow include: The enterprise management regulations define the approval requirements based on the job type, and the approval task is generated based on the approval requirements. Create a tree diagram of the approval tasks, wherein approval objects and approval processes are added to the tree diagram; The approval task workflow is generated based on the tree diagram.

6. A custom form job permit generation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the custom form job license generation method as described in any one of claims 1 to 5.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the custom form job license generation method as described in any one of claims 1 to 5.

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