Security verification control method and system based on workflow and task scene dynamic binding and storage medium

Abstract

The invention discloses a safety verification control method and system based on workflow and task scene dynamic binding and a storage medium, and belongs to the technical field of industrial internet and safety production informatization. The method comprises the following steps: in response to a circulation request of an electronic workflow instance, acquiring job voucher data and current multi-source task scene data in real time; calculating a matching degree between the data and resources in a verification resource library by utilizing a dynamic association matching model, and generating a target verification resource set; implanting blocking type preposition constraint in circulation control logic of the workflow engine; whether the blocking is removed or not is determined by verifying the space-time environment consistency of the verification effectiveness voucher. According to the method, dynamic strong coupling and environment perception locking of operation permission and safety verification are realized by fusing multi-dimensional environment feature data and a bottom-layer process control technology, and the technical problem that verification content and an operation site environment are disjointed and are easy to avoid in the prior art is solved.

Description

Technical Field

[0001] This application relates to the fields of industrial internet and information technology for safe production, and in particular to a security verification and control method, system and storage medium based on dynamic binding of workflow and task scenarios. Background Technology

[0002] In the safety production management of high-risk industries such as power, chemical, and construction, pre-operation safety verification (such as safety briefing, examination, and qualification review) is a key link in preventing accidents.

[0003] In existing technologies, safety verification systems are typically independent of production operation management systems (workflow systems). That is, operators complete training in an e-learning system and then apply for work permits in another system. This separation introduces significant safety hazards: First, the verification content is static, making it impossible to dynamically adjust the verification focus based on real-time environmental conditions at the work site (such as sudden strong winds or toxic gas leaks); second, verification results are often "passed on the first try," lacking the ability to perceive changes in the environment after verification—for example, operators may pass verification indoors but then perform work outdoors in harsh conditions; finally, existing mobile verification methods are easily circumvented by technical means (such as background processes or scripts for completing training courses), rendering safety briefings ineffective.

[0004] Therefore, there is an urgent need for a security verification and control method that can deeply bind job flow control with real-time task scenarios and has dynamic environmental perception and strong logical interlocking capabilities. Summary of the Invention

[0005] This application provides a security verification control method, system, and storage medium based on dynamic binding of workflow and task scenarios, aiming to solve the technical problems in the prior art such as the disconnect between security verification and the on-site work environment, poor verification timeliness, and ease of circumvention.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] A security verification and control method based on dynamic binding of workflow and task scenarios, the method running in a computing system containing an electronic workflow engine, comprising:

[0008] Step S1: In response to the workflow instance's request for the target node, parse the job voucher data associated with the workflow instance, and collect the current multi-source task scenario data in real time through the external data interface;

[0009] Step S2: Using a pre-set dynamic association matching model, semantic features are extracted from the job voucher data, and the multi-source task scenario data is mapped to environmental risk features. The matching degree between the above features and each verification resource in the verification resource library is calculated, and a target verification resource set is generated based on the matching degree.

[0010] Step S3: In the flow control logic that controls the flow of the workflow instance, activate blocking pre-constraint conditions on the path from the current state to the next business state;

[0011] Step S4: When the workflow instance triggers a flow event, the electronic flow engine verifies the blocking pre-constraint condition; the verification specifically includes: querying whether there is a verification validity certificate associated with the workflow instance, and performing a spatiotemporal environment consistency verification on the verification validity certificate;

[0012] Step S5: If the blocking pre-constraint condition is not met, the electronic workflow engine suspends the process of the workflow instance, locks the status change permission, and sends a loading instruction for the target verification resource set to the front-end interactive interface.

[0013] Step S6: Monitor the verification interaction data of the front-end interactive interface in real time, and generate a new verification validity certificate only after the interaction is completed and the anti-circumvention detection is passed; the electronic circulation engine responds to the generation of the new certificate, resets and re-executes the verification of the blocking pre-constraint condition, and releases the state lock only when the verification is passed.

[0014] Optionally, the flow control logic is constructed based on a finite state machine (FSM) model; in the state transition table of the finite state machine, the blocking pre-constraint is configured as a guard condition for the target transition path; when the guard condition returns a false value, the state machine kernel refuses to execute the state transition instruction.

[0015] Optionally, the spatiotemporal environment consistency verification includes: extracting an environmental data snapshot at the time the voucher was generated; calculating the difference feature value between the current multi-source task scenario data and the snapshot; and determining that the verification fails if the difference feature value exceeds the risk jump threshold.

[0016] The beneficial effects of this application are as follows:

[0017] 1. Through a dynamic correlation matching model, the verification content is dynamically adjusted in real time according to the job content and environmental risks.

[0018] 2. By utilizing the guard condition mechanism of finite state machines, security verification is made a rigid prerequisite for workflow, thus eliminating management loopholes.

[0019] 3. It innovatively introduces spatiotemporal environment consistency verification, which effectively addresses the risks brought about by environmental changes by comparing the differences between the environmental snapshots when the voucher is generated and when it is used, and prevents invalid vouchers from being used illegally.

[0020] 4. Through multi-dimensional anti-evasion detection, the authenticity of the verification process is ensured. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the method provided in this application embodiment;

[0022] Figure 2 This is a logical diagram of the dynamic association matching model in the embodiments of this application;

[0023] Figure 3 This is a timing diagram of the detection evasion measures in the embodiments of this application;

[0024] Figure 4 This is a system structure block diagram provided for an embodiment of this application. Detailed Implementation

[0025] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0026] Example 1: Basic Process Control in Substation Maintenance Operations

[0027] This embodiment is applied to the substation maintenance scenario in the power industry. The system is deployed on a private cloud server, and the client is a handheld operating terminal (PDA).

[0028] 1. Work Permit Parsing and Matching: The worker initiates a workflow application for "110kV main transformer maintenance" on their PDA. The system backend receives the request and extracts the keywords "high voltage," "transformer," and "climbing" using NLP algorithms. Simultaneously, the system obtains local weather data (current weather: sunny, wind force: level 2) via API. Dynamic association matching model calculations reveal that although the work permit belongs to the "high voltage" category, due to the involvement of "climbing" and favorable weather data, the system automatically reduces the weight of the "severe weather protection" category and pushes "fall from height protection" and "high voltage electric shock first aid" as the target verification resource set to the frontend.

[0029] 2. State Machine Control: The process engine manages this work order based on a Finite State Machine (FSM). The current state is "Pending Permission," and the next state is "Permission to Start." The engine activates a blocking pre-constraint (GuardCondition) on the migration path. At this time, the work order status is locked, and the "Start Confirm" button on the PDA is grayed out and disabled.

[0030] 3. Verification and Unlocking: The operator clicks the pop-up verification window to complete the video learning and exam. After successful verification, the server generates a verification validity credential. The FSM engine detects the credential generation, re-verifies the guard conditions, and the conditions return true. The FSM performs a state transition, the work order changes to "Permit to Start Work," and the "Start Work Confirmation" button on the PDA becomes clickable.

[0031] This embodiment achieves "rigid control" by embedding security verification into the state transition path of the FSM and utilizing guard conditions. This differs from the loose coupling achieved by existing technologies that rely solely on hyperlink jumps, eliminating the possibility of starting work without verification from the underlying logic.

[0032] Example 2: Dynamic Environmental Interlocking in Hot Work Operations in Petrochemical Industries

[0033] This embodiment is applied to outdoor welding and hot work operations in a chemical plant area, and focuses on demonstrating the "spatiotemporal environment consistency verification" function.

[0034] 1. Initial Verification and Credential Generation: The operator completed the safety training for hot work in the break room (indoors) at 09:00 AM. The system generated a verification credential (Token_A) and encapsulated an environmental snapshot: timestamp 09:00, GPS coordinates as break room coordinates (Loc_Room), ambient wind speed 0 m / s (indoors), and combustible gas concentration 0 ppm.

[0035] 2. Site Transfer Request: At 09:30 AM, the operator arrives at the outdoor work site (Loc_Site) and clicks "Start Welding". The process engine intercepts this request and performs a spatiotemporal environment consistency check. The system obtains multi-source task scenario data for the current moment: current time 09:30, current GPS coordinates of the work site (Loc_Site), current wind speed 8.5 m / s (sudden strong wind, exceeding the safety threshold).

[0036] 3. Difference Feature Calculation and Blocking: The system calculates the differences. Displacement determination: The distance between Loc_Room and Loc_Site is 500 meters. Risk jump determination: The difference feature value between the wind speed in the environmental snapshot (0 m / s) and the current wind speed (8.5 m / s) indicates that the on-site environment has jumped from "safe" to "high risk (strong wind and fire)".

[0037] 4. Judgment Result: Although Token_A is within its validity period, the system determines that Token_A is invalid due to a sudden change in the environmental risk level. The system refuses the state transition, and the PDA displays a warning: "Excessive wind speed detected on site. The original verification certificate is invalid. Please re-confirm the hot work safety in windy weather."

[0038] The core of this embodiment lies in the introduction of a differential comparison mechanism between the "environmental snapshot" and the "real-time environment." This solves the technical problem that "static verification" in the prior art cannot cope with "dynamic risks." Through spatiotemporal environment consistency verification, the system can dynamically sense risk jumps and promptly invalidate expired verification credentials.

[0039] Example 3: Mobile Anti-Cheating Control in Construction Scenarios

[0040] This embodiment is applied to a mobile APP at a construction site, focusing on demonstrating the detection avoidance logic.

[0041] 1. Establish connection: Workers open the safety briefing video for "scaffolding dismantling" on their mobile phones. The server establishes a WebSocket long connection with the APP and enables heartbeat detection.

[0042] 2. Abnormal behavior detection logic:

[0043] Scenario A (Fast-forwarding cheating): A worker attempts to drag the progress bar. The interaction data reported by the app shows the playback progress jumps directly from 00:10 to 05:00. The server calculates: Progress increment (290 seconds) / Time interval (1 second) = 290x speed. This operation rate exceeds the preset physiological limit range (0.5-2.0 times), the server determines it as cheating, and forcibly resets the progress.

[0044] Scenario B (Background Operation): The worker switches the app to the background. The app's reported window focus status indicator changes to False (background). The server detects that this state persists for more than 10 seconds, immediately sends a pause command, and stops the timer.

[0045] Scenario C (Network Disconnection Bypass): Workers attempt to shut down the network to circumvent detection. If the server does not receive a connection heartbeat packet within 3 heartbeat cycles (e.g., 15 seconds), it determines that the connection is interrupted and automatically discards the currently incomplete verification session.

[0046] 3. Result processing: The server will only generate a verification certificate when the video ends, provided that the playback progress is continuous, the speed is normal, the window focus is always in the foreground, and the heartbeat packets are continuous.

[0047] This embodiment constructs a multi-dimensional anti-avoidance fence by comprehensively analyzing interaction rate, window focus, and heartbeat packet continuity. This detection method, based on underlying communication protocols and behavioral characteristics, has higher security than simple front-end script detection and effectively prevents technical cheating.

[0048] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A security verification control method based on dynamic binding of workflow and task scenario, characterized in that, The method is run in a computing system comprising an electronic workflow engine, comprising: Step S1: in response to a request for a target node for an electronic workflow instance, parse the job credential data associated with the workflow instance, and collect real-time multi-source task scene data through an external data interface; Step S2: using a pre-set dynamic correlation matching model, performing semantic feature extraction on the job credential data, and mapping the multi-source task scene data into environmental risk features, calculating the matching degree of the above features with each verification resource in the verification resource library, and generating a target verification resource set according to the matching degree; Step S3: in the flow control logic for controlling the flow of the workflow instance, activating a blocking precondition on the path from the current state to the next business state; Step S4: when the workflow instance triggers a flow event, the electronic flow engine checks the blocking precondition; the checking specifically includes: querying whether there is a verification validity credential associated with the workflow instance, and performing spatiotemporal environment consistency check on the verification validity credential; Step S5: if the blocking precondition is not met, the electronic flow engine suspends the process of the workflow instance, locks the state change authority, and sends a loading instruction to the front-end interactive interface for the target verification resource set; Step S6: real-time monitoring of the verification interaction behavior data of the front-end interactive interface, only after the interaction is completed and the anti-evasion detection is passed, a new verification validity credential is generated; the electronic flow engine responds to the generation of the new credential, resets and re-executes the verification of the blocking precondition, and only when the verification is passed, the state lock is released.

2. The method of claim 1, wherein, The flow control logic is constructed based on a finite state machine (FSM) model; The step S3 specifically includes: in the state transition table of the finite state machine, the blocking precondition is configured as a guard condition (Guard Condition) of the target migration path; The step S5 specifically includes: when the guard condition returns a false value, the state machine kernel refuses to execute the state migration instruction, and forcibly marks the current state as a verification blocking state.

3. The method of claim 1, wherein, The spatiotemporal environment consistency check on the verification validity credential in step S4 specifically includes: Parse the verification validity credential, extract the environmental data snapshot and timestamp encapsulated when it is generated; Obtain the multi-source task scene data at the current time; Calculate the difference feature value between the multi-source task scene data at the current time and the environmental data snapshot through numerical difference or vector distance algorithm; If the difference feature value exceeds the preset risk jump threshold, or the difference between the timestamp and the current time exceeds the validity period threshold, it is determined that the spatiotemporal environment consistency check fails, and the validity of the verification validity credential is cancelled.

4. The method of claim 3, wherein, The multi-source task scene data includes meteorological sensing data, geographic spatial coordinate data, and device operation telemetry data; The calculation logic of the difference feature value includes: When the displacement distance of the geographic spatial coordinate data is detected to exceed the preset radius, it is determined that the difference feature value is out of limit; Or, when the change rate of meteorological sensing data or equipment operation telemetry data indicates that the environmental risk level jumps, it is determined that the difference characteristic value is out of limit.

5. The method of claim 1, wherein, The anti-evasion detection in step S6 includes: The service end and the front-end interaction interface establish a real-time bidirectional communication connection, receive the interaction behavior data, window focus state identifier and connection heartbeat packet reported by the front end according to the preset frequency; The service end executes the abnormal behavior judgment logic: if the operation rate calculated based on the interaction behavior data exceeds the preset physiological limit interval, or the window focus state identifier shows an inactive state, or the loss rate of the connection heartbeat packet exceeds the preset threshold, it is determined that the anti-evasion detection fails, and the generation of the verification validity certificate is refused.

6. The method of claim 1, wherein, The dynamic correlation matching model in step S2 performs the following processing: Based on the natural language processing algorithm, the unstructured text in the job certificate is converted into a text vector, and the cosine similarity between it and the verification resource feature vector is calculated; The environmental risk weighting logic is executed: when the environmental risk features in the multi-source task scene data meet the preset trigger condition, a risk weighting coefficient is generated, and the matching degree of the verification resource with the corresponding environmental protection attribute is weighted and amplified by using the coefficient, so that the matching degree result is prior to the calculation result based on the cosine similarity only.

7. A security verification control system based on dynamic binding of workflow and task scenarios, characterized in that, It includes: The data perception module is used to obtain job certificate data and multi-source task scene data through an API interface; The calculation matching module is used to run a dynamic correlation matching model to generate a target verification resource set; The process control engine is configured with state flow control logic for executing the verification of blocking preconditions, and locking and releasing the workflow state; The interaction verification module is used to execute the life cycle management and anti-evasion detection of the verification validity certificate; The above modules are connected through a bus or a network to cooperatively implement the method of any one of claims 1-6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-6.

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