On-site anti-violation intelligent confirmation system based on mobile platform

Abstract

The invention discloses an on-site anti-violation intelligent confirmation system based on a mobile platform, which belongs to the technical field of electric power safety management, and comprises the steps of acquiring operation data from the mobile platform, establishing a standardized operation plan database, generating execution information based on the operation plan database, and issuing the execution information to corresponding execution units. The execution unit acquires own positioning information, verifies the positioning information and execution information in real time, performs image acquisition on the violation information based on the violation information, synchronizes the violation information to an operation plan database, visually displays the violation information, and synchronizes the violation information to the operation plan database to a mobile platform; according to the invention, digital and intelligent transformation of electric power operation site safety management is realized, a standardized checking process ensures that 35 severe violation paraphrases are executed, the violation rectification closed-loop rate exceeds 95%, and the occurrence probability of safety accidents is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of power safety management technology, specifically to a method and system for on-site intelligent verification of violations based on a mobile platform. Background Technology

[0002] Power operation sites are characterized by complex environments, detailed division of labor, and numerous risk points. Violations of operating procedures are a core contributing factor to safety accidents. With the expansion of the power grid, the number of tasks such as infrastructure construction, maintenance, emergency repairs, and marketing inspections has surged. The traditional "manually-led, experience-driven" on-site anti-violation management model is no longer adequate for the demands of digital transformation, exhibiting the following prominent pain points:

[0003] Marketing, inspection and other comprehensive operation plans are not centrally managed and rely on manual sorting and task allocation without considering the workload and professional matching of personnel, resulting in delayed response to emergency tasks; the execution status of operations lacks real-time tracking, and "stuck" links such as failure to start work on time cannot be alerted in time, resulting in a low plan fulfillment rate.

[0004] Using manual sign-in or single location methods results in poor positioning accuracy and no effective duration constraints, leading to frequent occurrences of phenomena such as "proxy sign-in," "fake sign-in," and "going through the motions," making it impossible to verify the authenticity of managers' attendance and performance of duties.

[0005] Inconsistent inspection standards among different managers and reliance on personal experience lead to a high rate of missed inspections; only core violations are covered, and a standardized list for different professional scenarios has not been formed, resulting in weak ability to identify hidden violations and cross-professional violations.

[0006] Construction companies experience frequent personnel turnover and lack real-time identity verification methods, posing a significant risk of unqualified personnel entering the site; the mobility of subcontractors is difficult to assess, resulting in disordered personnel management.

[0007] Relying on manual vote counting to evaluate the performance of managers is time-consuming, lacks transparency, and suffers from egalitarianism; the evaluation results are not closely linked to rewards, failing to effectively incentivize performance.

[0008] The rectification of violations lacks full-process tracking methods, the progress of rectification is not transparent, there is no warning for overdue rectification, and the problems of "only checking but not rectifying" and "inadequate rectification" are prominent. The closed-loop rate under the traditional model is less than 50%.

[0009] The published document (CN113110243B) discloses an intelligent management and control platform for construction site safety, which has the function of on-site supervision. The solution uses a mobile deployment ball for terminal monitoring, realizing real-time monitoring of violations in electrical operations. Summary of the Invention

[0010] The purpose of this invention is to propose a real-time confirmation system for on-site management of violations by workers, based on existing technology, using a handheld mobile device.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] A mobile platform-based intelligent verification method for on-site traffic violation prevention includes the following steps:

[0013] S1: Obtain work data from the mobile platform and establish a standardized work plan database;

[0014] S2: Based on the job plan database, generate various execution information and distribute it to the corresponding execution units;

[0015] S3: The execution unit obtains its own location information and performs real-time verification between the location information and the execution information;

[0016] S4: Based on the violation information, the execution unit collects images of the violation and synchronizes them to the work plan database;

[0017] S5: Visualize violation information and synchronize it to the work plan database and mobile platform.

[0018] In the above technical solution, the mobile platform sends reminder responses to the execution unit within a set time based on the work plan, and generates corresponding response logs.

[0019] In the above technical solution, when the location information obtained by the execution unit exceeds the location range limit in the execution information, the mobile platform sends an alert response to the execution unit and generates a corresponding response log.

[0020] In the above technical solution, the image acquisition of violation information includes video acquisition of the work site and video acquisition of the workers.

[0021] In the above technical solution, facial recognition is used to compare the work personnel with the work data in the mobile platform for secondary verification of violation information.

[0022] In the above technical solution, a point-based evaluation is conducted for the workers corresponding to the violation information:

[0023]

[0024] Where: S is the points evaluation, A is the operational risk level, B is the cumulative number of violations, C is the points deducted for the number of violations found, and D is the coverage ratio of the anti-violation confirmation and execution.

[0025] In the above technical solution, the location information of the execution unit can be automatically acquired in an offline state and verified after establishing a link with the mobile platform.

[0026] This invention also discloses a mobile platform-based on-site intelligent verification system for preventing traffic violations, comprising:

[0027] The first module is used to synchronize data with the system and download job plans;

[0028] The second module is used to acquire real-time location information and verify it against the work plan;

[0029] The third module is used to acquire on-site image information and synchronize it with the work plan through the first module;

[0030] The fourth module manages violation information based on the work plan and generates a visual management window.

[0031] In the above technical solution, the work plan in the first module includes work location information, and the second module includes a positioning verification module, which verifies the work location information by comparing it with the real-time positioning information.

[0032] In the above technical solution, the fourth module includes a visualization module, which is used to visualize the execution of the work plan.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] This invention integrates advanced technologies such as "BeiDou + GPS + WiFi" triple positioning, AI dual intelligent recognition (face + violation image), and quantitative integration model, achieving breakthroughs in key indicators such as positioning accuracy ≤5 meters, face recognition accuracy ≥98.5%, violation recognition accuracy ≥92%, integration model accuracy ≥99%, and rectification closure rate exceeding 95%. It solves the technical bottlenecks in traditional management such as inaccurate positioning, missed detection, and inefficient closure. It also innovatively constructs a full-caliber data access mechanism to achieve seamless integration of multi-source data and improve the comprehensiveness of management.

[0035] This invention is developed based on a mobile platform, eliminating the need for an independent hardware system and reducing development costs by 30%. Through intelligent management, it reduces the workload of manual task sorting, paper records, and post-event statistics, saving an average of 2 person-hours per shift per day. Based on an average annual cost of 120,000 yuan per person, this translates to an annual saving of 158,400 yuan in labor costs per shift. Furthermore, it reduces the rate of serious violations by more than 60%, minimizing accident losses caused by violations and resulting in significant indirect economic benefits.

[0036] This invention enables the digital and intelligent transformation of on-site safety management in power operations. Standardized verification processes ensure the implementation of 35 definitions of serious violations, achieving a violation rectification closure rate of over 95% and significantly reducing the probability of safety accidents. Real-time verification of personnel qualifications eliminates unqualified operations at the source, protecting the personal safety of workers and promoting an overall improvement in safety management within the power industry.

[0037] This invention's system is adaptable to various professional scenarios, including power transmission, substation, distribution, information, and hydropower. The template library supports custom configuration, enabling rapid adaptation to the needs of power companies in different regions and at different levels. Leveraging the versatility of the mobile platform, it can be rapidly deployed and applied throughout the power industry across the province and even the entire country, possessing broad market prospects. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0039] Figure 1 This is a system block diagram of this embodiment;

[0040] Figure 2 This is a flowchart illustrating the process of this embodiment;

[0041] The attached diagram shows the markings and corresponding component names:

[0042] 1 is the first module, 2 is the second module, 3 is the fourth module, and 4 is the third module. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0044] Example 1

[0045] like Figure 1 As shown, the on-site anti-violation intelligent confirmation system in this embodiment is an intelligent management process for on-site construction behavior based on wireless technology, mainly including four aspects, among which:

[0046] Module 1 includes a platform access module and a task allocation module, specifically:

[0047] The platform access module is used to connect with the mobile platform and automatically extract work plan data, work location K-codes, risk levels and participant information from a full range of data sources such as new risk control tools, work fulfillment tools and marketing, inspection, etc., to establish a standardized work plan database and use the MQTT encrypted transmission protocol to ensure data security.

[0048] The task allocation module, based on the work plan database, automatically generates and distributes on-site management tasks to execution units by combining the job responsibilities, regional distribution, workload, and professional matching of management personnel. It supports task priority settings, with urgent tasks being prioritized for allocation to execution units in the corresponding areas. The task distribution response time is ≤5 seconds, and task reminders containing key work information are sent to execution units via the mobile platform.

[0049] The second module 2 includes a positioning verification module, which integrates a triple positioning unit of "BeiDou + GPS + WiFi" to obtain the real-time location information of the management personnel's execution unit. The real-time location information is compared with the K-code location corresponding to the work plan to determine the status of the execution unit's arrival and arrival. The positioning accuracy is ≤5 meters. If the position deviation exceeds 100 meters, the sign-in confirmation cannot be completed. A minimum supervision time threshold of 30 minutes is set. Offline positioning data caching is supported. The verification results are automatically uploaded after the network is restored. When the positioning error is between 5 and 100 meters, a secondary positioning reminder and on-site image upload manual review mechanism are triggered.

[0050] In this embodiment, the platform access module relies on the unified security system of the mobile platform, manages sensitive data with hierarchical permissions, retains operation logs for more than 6 months for traceability, has a data extraction accuracy rate of ≥99.8%, a synchronization delay of ≤3 seconds, and prohibits local caching of sensitive data.

[0051] Module 4 of the third module includes a multi-professional scenario violation confirmation module, an AI intelligent control module, and a violation closed-loop management module, specifically:

[0052] Multi-professional scenario anti-violation confirmation module: Decodes the definitions of 35 serious violations, the "Electric Power Safety Work Regulations" and various professional special management specifications, and constructs a standardized violation confirmation template library covering 13 typical work scenarios in 5 major professions: power transmission, substation, distribution, information, and hydropower. Each scenario is configured with core violation points and extended inspection items, forming an anti-violation "on-site answer sheet"; provides scenario point selection and adaptation function, supports joint signature confirmation of inspection results by patrol personnel and work leaders, supports recording and verification results in multiple forms such as text, images, and videos, and mandates the uploading of supporting images for violations. The template library supports dynamic updates.

[0053] In this embodiment, the multi-professional scenario anti-violation confirmation template library supports custom configuration. Construction units can submit template customization applications according to special operation needs, which will be included in the template library after review. The system can display high-frequency violation reminders by type according to the violation behaviors that the provincial and municipal companies have recently focused on.

[0054] The AI-powered intelligent management module integrates three core functions: facial recognition, intelligent image recognition, and qualification verification, achieving a dual intelligent upgrade in personnel management and violation identification. The facial recognition unit supports automatic parsing of photos from pre-shift meetings of subcontracting teams, completing personnel identity verification within 1 second with an accuracy rate of ≥98.5%, and can store over 1000 data entries offline. The intelligent image recognition unit uses deep learning to analyze the characteristics of 15 typical violation images and real-time analysis of uploaded images to assist in identifying hidden violations such as not wearing safety helmets and working at heights without protection, achieving an accuracy rate of ≥92%. The qualification verification unit accesses data from the mobile platform's qualification management system in real-time, automatically verifying personnel's certificate status and validity, and immediately issuing warnings for unqualified personnel, eliminating the risk of unqualified operations at the source.

[0055] In this embodiment, the face recognition unit of the AI ​​intelligent management module can pre-store more than 1,000 face recognition data without the user's awareness when offline, and automatically synchronize and verify them after the network is restored, thus solving the network connection problem; the violation image intelligent recognition unit supports real-time analysis of 15 typical violation images in 5 major fields, with a recognition accuracy of ≥92%.

[0056] Violation Closed-Loop Management Module: Constructs a closed-loop mechanism for the entire process of "identification-dispatch-rectification-verification-archiving". Identified violations are automatically associated with the violation file management module, generating rectification tasks that include violation descriptions, supporting materials, rectification requirements, and deadlines. It supports uploading images of the rectification process, real-time feedback from the person in charge of rectification, and an automatic early warning function for overdue rectification. It tracks the rectification implementation process and verifies the results, forming a closed-loop process. Major violations are automatically upgraded to early warnings and pushed to higher-level regulatory departments.

[0057] Module 4.3 includes a quantitative scoring evaluation module and a visualization display module, specifically:

[0058] Quantitative Points Evaluation Module: An innovative (A+BC)×D quantitative points model is constructed, where A represents the risk level score (weight 30%), B represents the violation investigation and punishment score (weight 25%), C represents the violation deduction score (weight 25%), and D represents the unit's anti-violation confirmation coverage rate coefficient (weight 20%). Points calculation automatically incorporates multi-dimensional data such as task execution rate, on-the-job performance quality, number of violations discovered, and rectification closure effect, with a model accuracy ≥99%. Points results are displayed dynamically in real time and automatically synchronized to the human resources performance appraisal system.

[0059] The visualization module utilizes the ECharts charting engine to construct a multi-dimensional visualization interface, presenting real-time core data such as task allocation progress, on-duty attendance statistics, violation distribution (by profession / scenario / region), personnel movement trajectories, points ranking, and rectification closure progress. It supports generating custom reports by daily / weekly / monthly / yearly dimensions, as well as by region and profession, and automatically identifies and alerts to abnormal information such as overdue work starts and high-frequency violations. Data visualization assists management in accurately identifying regulatory weaknesses, providing data support for safety management decisions.

[0060] Example 2

[0061] like Figure 2 As shown, this embodiment, based on Embodiment 1, provides an on-site intelligent verification method for preventing traffic violations. The specific steps include:

[0062] Step S1: Full-Scope Data Synchronization Phase: The system establishes an encrypted communication link with the mobile platform through the platform access module. It automatically extracts full-scope data, including new risk control work plans, marketing inspection tasks, and maintenance work orders, using the MQTT protocol. The data includes work name, location K-code, risk level, construction unit, list of participating personnel, and qualification numbers. After standardization, deduplication, and missing data completion, the extracted data is stored in a MySQL distributed database, forming a standardized work plan database, ensuring real-time data updates with a synchronization delay of ≤3 seconds.

[0063] Step S2: Intelligent Task Allocation Phase: The task allocation module utilizes a three-dimensional algorithm based on "load-specialty-region," combining data from the work plan database with management personnel information (job responsibilities, jurisdiction, current unfinished tasks, professional qualifications) to automatically generate a personalized task list. The system prioritizes tasks according to "urgent + high risk > urgent + medium risk > high risk + routine," and pushes these tasks to management personnel's handheld terminals via a mobile platform. The push notification includes task details, risk warnings, navigation links, and a confirmation list preview. Upon receiving the task, the terminal automatically reports its status, and the system records the task reception time and processing progress. A second reminder is sent 10 minutes after a task has not been received.

[0064] Step S3: Accurate On-Duty Verification Phase: After arriving on-site, management personnel open the handheld terminal APP. The positioning verification module automatically activates triple positioning (BeiDou + GPS + WiFi), collects latitude and longitude coordinates, converts them into K-codes, and compares them with the work plan K-code: ① Distance ≤ 5 meters: Directly displays "On-duty" and records the time; ② 5-100 meters: A secondary positioning reminder pops up, requiring the upload of photos containing the work scene and personnel. After review and approval by the back-end management personnel, on-duty status is confirmed; ③ > 100 meters: Prompts "Location mismatch, unable to check in." The system simultaneously starts timing. When the cumulative supervision time is less than 30 minutes, the APP pops up a message "Insufficient duty time, please continue supervision," and the on-duty status is synchronized to the visualization module in real time. In offline scenarios, the positioning data is temporarily stored locally on the terminal and automatically uploaded and verified after the network is restored.

[0065] Step S4: Multi-Scenario Violation Confirmation Stage: Managers select the current work specialty (e.g., power transmission) and sub-scenario (e.g., line maintenance) in the APP. The system automatically loads the corresponding standardized anti-violation "on-site questionnaire." Managers conduct on-site verification item by item against the checklist. For compliant items, click "Pass" and record the verification time. When a violation is found, click "Violation," fill in a detailed description of the violation, and take photos or videos of the violation as evidence. After uploading, both the manager and the work supervisor must sign for confirmation. The system simultaneously displays recent high-frequency violation alerts for this scenario to assist in focused verification. The AI ​​image recognition unit analyzes the uploaded images in real time. If a hidden violation is identified (e.g., personnel not wearing safety helmets), a pop-up window prompts the manager for secondary confirmation.

[0066] Step S5: AI Intelligent Management Phase: Managers use the APP's camera to collect facial images of all personnel on site, including construction workers and subcontractors. Group photos from pre-shift meetings can be uploaded in batches and automatically analyzed. The AI ​​facial recognition unit compares the collected facial features with the personnel information database (synchronized from the mobile platform), completing identity verification within one second and displaying the personnel's name, qualification certificate number, and validity period. The qualification verification unit simultaneously accesses the qualification system data on the mobile platform to check certificate status. For personnel without qualifications, with expired certificates, or whose qualifications do not match the job type, the APP immediately displays a red alert, records the personnel information, and pushes it to the manager. Simultaneously, the system automatically analyzes the on-site images uploaded during the operation, identifies typical violations, and links them to a confirmation list, prompting the manager for verification.

[0067] Step S6: Closed-Loop Violation Handling Phase: Confirmed violation information is automatically synchronized to the closed-loop violation management module. The system classifies violations according to the "Interim Provisions on the Investigation and Management of Hidden Dangers in Power Safety Accidents," generates rectification task orders with QR codes, and pushes them to the responsible unit leader via the mobile platform. After logging into the system to view the task, the responsible party organizes rectification and uploads rectification process videos in real time. After rectification is completed, a verification application is submitted. Upon receiving the application, the management personnel review the rectification effect on-site or through video. If it is qualified, it is marked as "rectification completed" and archived; if it is unqualified, it is returned with the reason noted, requiring rectification again. If rectification is not completed within the time limit, the system will push a warning every 2 hours, and if it is more than 3 days overdue, it will be included in the monthly safety report.

[0068] Step S7: Quantification and Visualization Stage: The points evaluation module automatically calculates points using the (A+BC)×D model. A is a base score based on the risk level of the task (30 points for high risk, 20 points for medium risk, and 10 points for low risk). B is accumulated based on the number of violations detected (5 points per violation). C is deducted points for each detected violation (50 points for serious violations, 20 points for minor violations, and 5 points for slight violations). D is based on the unit's anti-violation confirmation coverage rate (1.2 for 100% coverage, 1.0 for 80%-99%, 0.8 for 60%-79%, and 0.5 for <60%). The points results are updated in real-time to the visualization module, generating individual point rankings and departmental point statistics charts. The visualization module also displays other core data, generates multi-dimensional reports, automatically alerts for anomalies, and reports can be exported to Excel for archiving. Point data is synchronized to the performance appraisal system.

[0069] Example 3

[0070] Developed on a mobile platform, the system adopts a B / S architecture. The backend is developed using Java and the Spring Boot microservice framework, with a MySQL 8.0 distributed cluster database. The frontend uses the Vue.js 3.0 framework combined with the ECharts charting engine to build the visualization interface. The system is deployed on the State Grid Dazhou Power Supply Company's cloud server cluster, and data interaction is achieved through the mobile platform's standard API interfaces. The deployment architecture complies with the State Grid's Level 3 Information Security Protection Requirements.

[0071] After deployment, a comprehensive three-month testing period was conducted, covering five power supply stations and three types of professional operation scenarios in a certain district. The specific test results are as follows:

[0072] Data synchronization test: The communication stability with the mobile platform was tested for 30 consecutive days. 120-150 full-scope work plans were extracted daily. The data extraction accuracy was 100%. The average synchronization delay was 2.1 seconds. There were no data loss or mismatches.

[0073] Location verification test: Ten typical work scenarios, including mountainous areas, urban areas, and basements, were selected. Each scenario was tested 20 times. The location error was ≤5 meters. For scenarios with a deviation of 5-100 meters, the manual verification pass rate was 98%. For scenarios with a deviation >100 meters, check-in could not be completed. The K-code matching rate was 100%.

[0074] AI Functionality Testing: Facial images of 150 construction workers were collected to build a test library. The face recognition accuracy rate was 98.7%, with an average response time of 0.8 seconds. After storing 1,000 data entries offline and restoring the network, the synchronous verification success rate was 100%. Fifty images from each of 15 typical types of violations were selected for testing, and the image recognition accuracy rate was 92.3%.

[0075] Integral model test: Based on one month of historical work data (task execution rate, number of violations discovered, etc.) of 5 managers, the scores were calculated using the model of this invention and manual evaluation respectively. The two were consistent with 99.2%, and the model accuracy met the requirements.

[0076] Closed-loop management test: Simulated input of 50 violations of different levels, rectification closed-loop rate of 100%, serious violations 24-hour rectification completion rate of 96%, general violations 72-hour completion rate of 98%, and overdue warning accuracy rate of 100%.

[0077] Example 4

[0078] Based on Embodiment 3, this embodiment manifests itself in practical applications as follows:

[0079] From June to November 2025, a large-scale pilot application was carried out in five power supply stations in a certain city, covering four professional categories: power transmission, substation, distribution, and information, 23 typical operation scenarios, 12 management personnel, and 8 construction units. A total of 320 operation tasks were covered. The specific application results are as follows:

[0080] Task allocation efficiency: The system automatically allocates 320 tasks with a response time of ≤5 seconds, with no missed or incorrect assignments. The task plan fulfillment rate has increased from 82% in the traditional mode to 98%.

[0081] On-duty monitoring results: A total of 480 on-duty checks were conducted, with positioning errors all ≤5 meters. Three cases of false on-duty behavior were discovered and corrected through secondary verification, effectively preventing absenteeism and achieving 100% authenticity of on-duty performance.

[0082] Violation identification capability: Through standardized lists and AI-assisted identification, a total of 42 violations were detected (8 serious, 20 minor, and 14 slight), which is 50% more efficient than the traditional method (28 violations in the same period), and the proportion of hidden violations identified increased from 15% to 40%.

[0083] Personnel management effectiveness: A total of 650 on-site personnel were verified, identifying 7 unqualified personnel and 4 personnel with expired qualifications, issuing 11 warnings, and increasing the on-site personnel management compliance rate from 85% to 99%.

[0084] Results of closed-loop management: All 42 violations have been rectified, achieving a 100% closure rate. The rectification rate for serious violations within 24 hours was 93.8%, and the rectification rate for general violations within 72 hours was 95%. There were no cases of overdue rectification.

[0085] Cost-benefit analysis: The average daily working hours of the 12-management team were reduced from 8.5 hours to 6.2 hours. The team saved RMB 79,200 in labor costs in six months. The rate of serious violations decreased by 62% year-on-year, and no safety accidents caused by violations occurred.

[0086] Pilot application results show that this system and method significantly improve the efficiency and quality of on-site anti-violation management, solve the core pain points of traditional management models, and are ready for large-scale promotion and application.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A mobile platform based live anti-violation intelligent confirmation method, characterized in that The method comprises the following steps: S1: obtaining job data from a mobile platform, and establishing a standardized job plan database; S2: generating each execution information based on the job plan database, and distributing it to the corresponding execution unit; S3: the execution unit obtains its own positioning information, and performs real-time verification on the positioning information and the execution information; S4: based on the violation information, the execution unit performs image acquisition, and synchronizes to the job plan database; S5: visualizing the violation information, and synchronizing to the job plan database to the mobile platform.

2. The method of claim 1, wherein The mobile platform distributes a reminder response to the execution unit within a set time based on the job plan, and produces a corresponding response log.

3. The method of claim 2, wherein: When the positioning information obtained by the execution unit exceeds the position range limit in the execution information, the mobile platform distributes a reminder response to the execution unit, and produces a corresponding response log.

4. The method of claim 1, wherein The image acquisition of the violation information includes video acquisition of the job site and video acquisition of the job personnel.

5. The method of claim 4, wherein The job personnel are compared with the job data in the mobile platform based on face recognition, which is used for secondary verification of the violation information.

6. The method of claim 1, wherein The job personnel corresponding to the violation information are evaluated by points: ， Wherein: S is the point evaluation, A is the job risk level, B is the number of violations, C is the number of violations, and D is the anti-violation confirmation execution coverage ratio.

7. The method of claim 1, wherein The positioning information of the execution unit can be automatically obtained in an offline state, and the verification can be completed after establishing a link with the mobile platform.

8. A mobile platform based live anti-violation intelligent confirmation system, characterized in that Comprising: The first module is used for synchronizing with the system data, and downloading the job plan; The second module is used for obtaining real-time location information, and verifying with the job plan; The third module is used for obtaining on-site image information, and synchronizing with the job plan through the first module; The fourth module is based on the job plan to confirm the management of violation information, and generates a visual management window.

9. The system of claim 8, wherein: The job plan in the first module includes job location information, the second module includes a positioning verification module, and the positioning verification module verifies the real-time positioning information with the job location information.

10. The mobile platform-based live anti-violation intelligent confirmation system according to claim 8, characterized in that The fourth module includes a visual display module for visualizing the execution of the job plan.

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