Fire operation management system based on artificial intelligence and use method thereof

Abstract

The invention discloses a fire operation management system based on artificial intelligence and a use method thereof, and relates to the field of safety management of building engineering construction. The front end of the system is composed of an AI fire monitoring robot and a WeChat management applet, and the rear end of the system comprises an enterprise end management large screen and a project management platform. After information is input by creating a database, an operator initiates a fire application through an applet, and a license and a disclosure book are generated according to process approval. The AI fire monitoring robot carries out human face recognition before operation, carries out real-time monitoring and early warning by using technologies such as images, organisms and sensors in the process, and carries out continuous monitoring after operation until hidden dangers are eliminated and generates a report. The system effectively solves the problems of tedious flow, difficulty in tracing, lacking in supervision and the like in the traditional fire operation management, achieves the precise management and control of the whole process of the fire operation, remarkably improves the operation safety and management efficiency, and provides an intelligent and efficient safety management guarantee for the fire operation in the fields of building construction and the like.

Description

Technical Field

[0001] The present invention relates to the field of construction engineering construction safety management, and specifically to an artificial intelligence-based hot work management system and a method of using the same. Background Art

[0002] Hot work is frequent and indispensable in many fields, including construction and industrial production. However, traditional hot work management methods have numerous drawbacks. The hot work permit application process is lengthy and cumbersome, often subject to administrative interference and delayed approval processes, seriously affecting work efficiency and hindering project progress. For example, in large-scale construction projects, hot work applications may have to go through multiple departments and wait for approval for several days, stalling construction during this time and resulting in a significant waste of time and manpower. Tracing back to the source of an accident is extremely difficult. Due to a lack of effective records and accurate analysis methods, dangerous situations during the process are often handled improperly, leading to more serious consequences. For example, after a fire accident occurred in some chemical companies, it was difficult to accurately restore the scene of the hot work operation, making it difficult to determine the root cause of the accident and unable to improve management measures in a targeted manner. Process supervision relies heavily on manpower, but project teams have limited manpower. Faced with numerous hot work sites, manual inspections are difficult to fully cover. Furthermore, manual inspections are susceptible to subjective factors, varying levels of responsibility, and unexpected situations, making them less reliable. At some complex construction sites, long intervals between manual inspections make it difficult to monitor the entire hot work process in real time, posing numerous safety risks. Supervisors have a wide range of responsibilities, including professional tasks such as environmental monitoring and emergency response, as well as administrative duties such as work permit inspections and operator qualification verification. During busy construction peak periods, supervisors may be distracted and unable to effectively perform all their duties, increasing the risk of accidents. There are a large number of blind spots in manual inspections, and the information transmission of fire points is poor and asymmetric, which further exacerbates the difficulty of management. Summary of the Invention

[0003] The purpose of the present invention is to provide an artificial intelligence-based hot work management system and a method of using the same to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: an artificial intelligence-based hot work management system, characterized in that: It includes the front-end and back-end. The front-end consists of an AI fire monitoring robot and a WeChat management applet, while the back-end consists of an enterprise management dashboard and a project management platform. The AI ​​fire monitoring robot integrates image recognition, biometrics, sensors, and video surveillance technologies. It uses a combination of thermal imaging and visible light to identify fireworks and fires. Through dual-optical coaxial linkage, it operates on visible light images to view details when a fire is detected. Equipped with Horizon AI chips for computing power and long battery life, it can generate offline voice alarms in offline environments and store alarm information and operation process videos. In networked environments, these information can be uploaded to the management platform in a chronological order. The WeChat management mini-program is used by hot work personnel to initiate work applications, go through the approval process, obtain electronic hot work permits and hot work safety briefing documents, and perform facial recognition verification; The enterprise management screen can view the status of all hot work operations in the enterprise in real time, including the hot work point, number of people working on the hot work, hot work level, hot work location, alarm information and real-time video; The project management platform realizes the approval management, hot work monitoring and hot work recording functions, and can conduct online review of operators and operating processes, and conduct consistency verification based on offline situations.

[0005] Furthermore, the image recognition technology of the AI ​​fire monitoring robot uses a high-definition camera to collect images of the hot work area in real time, and combines it with a deep learning behavior recognition algorithm to accurately identify and classify various behaviors of operators. The algorithm is trained based on a large amount of image data of hot work scenes and can accurately distinguish between normal work behaviors and unsafe behaviors. For example, it can accurately identify whether workers are not wearing safety helmets, not wearing reflective vests, smoking, making phone calls, illegally entering the hot work area, and other violations, with an identification accuracy rate of no less than 95%. Once an unsafe behavior is determined, the voice alarm system is immediately triggered, and the alarm information and the captured image data are synchronously uploaded to the management platform for timely processing and analysis by management personnel, thereby achieving efficient supervision of on-site behaviors of hot work operations. Image recognition technology can also analyze the environment around the ignition point and identify the location and status of flammable materials. If it is detected that the flammable materials are too close to the ignition point or are in an unsafe state, an alarm will be issued in time to remind the operators to take appropriate protective or cleanup measures to ensure the safety of the hot work environment.

[0006] Furthermore, the biometric recognition technology of the AI ​​fire monitoring robot can accurately verify the identity of the operator by collecting facial image information before the hot work is carried out, using advanced facial recognition algorithms to compare it with pre-stored personnel identity information. The facial recognition algorithm adopts multimodal feature fusion technology, comprehensively considering multi-dimensional features such as facial texture, facial shape, and facial depth information, effectively improving recognition accuracy and anti-interference ability, ensuring that only authorized personnel can carry out hot work and eliminating the situation where unlicensed personnel can work on the job. Biometric technology is deeply integrated with the personnel positioning management system to continuously track the location information of operators during the operation. Combined with the setting of the hot work area, if an operator is found to have left his post or entered an unauthorized area without authorization, a voice alarm will be issued immediately and the abnormal location information will be uploaded to the management platform to ensure that the activities of personnel during the hot work are safe and controllable.

[0007] Furthermore, the AI ​​fire monitoring robot's sensor technology includes built-in high-precision position sensors and environmental sensors. The position sensors utilize a combination of ultra-wideband (UWB) positioning technology and inertial navigation technology to accurately determine the location of personnel and equipment involved in hot work in real time, even in complex construction environments, with centimeter-level accuracy. By comparing the location with the pre-set safe operating area, the robot can promptly detect any out-of-bounds behavior by personnel or equipment and issue an alarm. Environmental sensors can monitor environmental parameters such as temperature, humidity, smoke concentration, and harmful gas concentration in the fire area in real time. When abnormal temperature rises close to the danger threshold, smoke concentration exceeds the standard, or harmful gas leaks are detected, the alarm mechanism is quickly activated and the environmental data is uploaded to the management platform synchronously, providing managers with comprehensive environmental safety information so that they can take timely response measures to prevent fires and poisoning accidents.

[0008] Furthermore, the hot work status viewing function of the enterprise management screen is characterized by: displaying real-time status information of all hot work within the enterprise in the form of visual charts and maps; the geographical distribution of each hot work point is clearly marked on the map interface, and different hot work statuses are distinguished by different colored icons. Clicking the icon can view detailed hot work information, including the list of operators, hot work permit number, operation start time, and expected end time; The data statistics charts conduct in-depth analysis and display of relevant data on hot work, such as the number of hot work operations by time dimension, the distribution ratio of operations with different hot work levels, and the hot work frequency ranking of each project; at the same time, the system updates alarm information in real time and prompts managers to pay attention to emergency situations through eye-catching pop-ups and sounds, so that managers can fully, intuitively and timely grasp the overall situation of the company's hot work, providing strong data support for decision-making.

[0009] Furthermore, the project management platform's hot work monitoring function is characterized by: establishing a stable communication connection with the AI ​​fire monitoring robot to receive real-time hot work site video streams and various monitoring data collected by the robot; using intelligent video analysis algorithms to process video images in real time, and being able to automatically identify key information such as flame characteristics, personnel activity trajectories, and equipment operating status within the hot work area; For flame monitoring, the combination of thermal imaging technology and image recognition technology can not only quickly detect open flames, but also accurately identify small flames or hidden combustion, and judge the fire risk level based on parameters such as flame size and temperature change trend; in terms of personnel monitoring, the changes in the number of people in the hot fire area can be counted, and whether the operating personnel are performing operations in accordance with the prescribed operating procedures can be monitored. If any violations or abnormal gatherings of people are found, an alarm will be issued immediately and the relevant responsible persons will be notified, realizing all-round and intelligent monitoring of the hot fire operation site.

[0010] A method for using a hot work management system based on artificial intelligence comprises the following steps: S1. Create a database for the hot work management system and enter project information, personnel information, and authority settings; S2. Hot work personnel initiate a hot work application through the WeChat mini-program on their mobile phone, entering detailed information such as the hot work location, hot work level, personnel and number of personnel, and a picture of the hot work point; S3. Hot work applications are reviewed and approved according to the process of applicant, team leader, professional engineer, safety and fire supervisor, subcontractor project manager, general contractor safety and fire supervisor, and general contractor project manager. After approval, an electronic hot work permit and hot work safety briefing document will be automatically generated. S4. Operators collect hot work tools and AI fire monitoring robots. After arriving at the work site, they set up the AI ​​fire monitoring robots. Before hot work, operators undergo facial recognition. After passing the recognition, they can start the hot work. S5. During hot work operations, AI fire monitoring robots and a back-end management platform provide real-time early warning and control of hot work operations. Monitoring includes unexpected flames, illegal operations, personnel absence, equipment status, and firefighting equipment availability. Once an abnormality is detected, a voice alarm is immediately issued and information is pushed to the management platform. S6. After the hot work operation, the hot work area will be continuously monitored until the hidden dangers are eliminated, and a hot work report for this hot work operation will be automatically generated to achieve a closed-loop management of the entire process. At the same time, the operation process data will be used to evaluate and manage the operators, providing a reference basis for subsequent operations.

[0011] Furthermore, the hot work permit application function of the WeChat management applet provides a simple and intuitive user interface. When applying, the operator needs to fill in detailed hot work information in sequence according to the system prompts, including but not limited to the specific location description of the hot work, the hot work level, the estimated operation time, the operator's name and certificate number, a description of the surrounding environment of the hot work point, and upload a clear picture of the hot work point; The system automatically verifies the integrity and compliance of input information. If any information is missing or does not comply with regulations, the operator will be promptly prompted to supplement or correct it. After the application is submitted, the system will automatically transfer it to the relevant management personnel according to the preset approval process, and display the application processing progress in real time, making it convenient for operators to track and inquire, ensuring that the hot work permit application process is efficient, accurate and transparent.

[0012] Furthermore, the WeChat management mini-program has an approval function. Managers at all levels can log in to the mini-program through their own independent accounts and view a list of hot work applications awaiting approval. The approval interface displays detailed application information and related images, and managers review applications based on hot work safety regulations and actual on-site conditions. During the approval process, comments and suggestions can be added, and electronic signatures are supported to ensure traceability of the approval process. The approval process strictly follows the preset hierarchical order. When a certain level of approval fails, the system automatically returns the application to the applicant and clearly marks the reason for the return. The applicant can resubmit the application after making modifications based on the feedback. At the same time, the system counts and monitors the approval time. If an approval link is not processed beyond the specified time, a reminder notification will be automatically sent to the superior management to ensure the timeliness of the approval process and prevent the construction progress from being affected by approval delays.

[0013] Furthermore, the facial recognition verification function of the WeChat management applet uses the mobile phone camera to capture the operator's facial image before the operator performs the hot work. It adopts the same facial recognition algorithm core technology as the AI ​​fire monitoring robot to ensure the accuracy and consistency of the verification. During the verification process, the system conducts real-time quality assessment of the collected image. If the image is blurred, the lighting is insufficient, or there are occlusions and other factors that affect recognition, the operator will be automatically prompted to retake the image. To improve security, facial recognition verification supports multi-factor authentication methods, which can be combined with the operator's device fingerprint recognition or dynamic password verification; only when facial recognition is passed and other auxiliary authentication methods are also successfully verified, the mini program allows the operator to enter the hot work operation interface, and synchronizes the verification results to the AI ​​fire monitoring robot and management platform, to achieve multiple protections for the operator's identity and prevent security risks such as identity impersonation.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The AI-based hot work management system of this invention has significant benefits in many areas. In terms of efficiency improvements, it enables digital management of the entire hot work process. Hot work permit applications and approvals are completed online, significantly shortening the processing cycle and reducing administrative delays. This allows workers to quickly obtain work permits, improving construction efficiency and accelerating project progress. For example, in a municipal engineering project, the processing time for hot work permits was reduced from an average of five days to less than one day, significantly improving the overall construction pace.

[0015] In terms of safety, the AI ​​fire monitoring robot utilizes various advanced technologies to monitor hot work operations in real time, promptly and accurately detecting safety hazards such as accidental flames and illegal operations, issuing warnings and effectively preventing accidents. Its fire and smoke recognition technology, combining thermal imaging with visible light, can reliably detect fires even in complex environments such as smoke and strong light, mitigating fire risks. After implementation at a metal processing company, the incidence of hot work accidents was reduced by 80%, effectively ensuring the safety of personnel and property.

[0016] At the management optimization level, the system automatically generates fire reports, fully recording process data for easy traceability and analysis, providing a scientific basis for management decisions and promoting continuous improvement of management processes. Companies can use this data to summarize experience and strengthen targeted safety training and management measures.

[0017] In personnel management, accurate facial recognition and operator behavior monitoring ensure that operators meet compliance requirements and conduct standards, improve safety awareness and work quality, and provide objective data for personnel assessment and evaluation, helping to optimize human resource allocation. In construction companies, statistical analysis of operator violations and specialized training have significantly reduced the number of violations and improved overall operational standards.

[0018] In terms of cost control, this system reduces manual supervision and mitigates the significant losses caused by accidents, thereby improving corporate economic efficiency and competitiveness. For large industrial enterprises, this system can save significant annual costs for manual inspections and potential accident compensation, enhance their sustainable development capabilities, and comprehensively improve their hot work management, laying a solid foundation for safe production and efficient operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flowchart of a method for using the hot work management system based on artificial intelligence. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 The present invention provides a technical solution: an implementation method of a hot work management system based on artificial intelligence and its use method: The fire operation management system mainly consists of a front-end and a back-end. The front-end includes an AI fire monitoring robot and a WeChat management applet, while the back-end covers the enterprise management dashboard and project management platform.

[0022] In terms of hardware, the AI ​​fire monitoring robot utilizes a high-performance processor as its core computing unit and is equipped with a high-resolution visible light camera and a thermal imaging camera, which are linked via a sophisticated mechanical structure to achieve dual-optical coaxial linkage. For example, the use of an industrial-grade image sensor ensures clear images in all lighting conditions. Its housing is constructed of durable, fireproof, explosion-proof materials, such as aluminum alloy with special fireproofing treatment, to withstand complex and harsh hot work environments. Furthermore, its built-in high-capacity battery and efficient power management module support long-term continuous operation, with a battery life of over 14 hours. It also automatically issues a warning when the battery is low.

[0023] In terms of software, both the front-end and back-end are developed based on stable and reliable operating systems, such as Linux. A distributed architecture is employed, utilizing message queues for efficient communication and data transmission between modules, ensuring high concurrency and stability. The database uses a highly scalable and high-performance relational database, such as MySQL or PostgreSQL, to store various data, including project information, personnel information, hot work records, and alarm information.

[0024] An image recognition model was built using a deep learning framework. First, a large number of image samples containing various behaviors and objects in hot work scenarios were collected, covering correct and incorrect worker postures, different types of hot work equipment, and flammable materials in the surrounding environment, to train the model. During the training process, the model parameters were continuously adjusted, the model's loss function was optimized, and recognition accuracy was improved. Feature extraction from the image was performed using a convolutional neural network, and then classification and judgment were performed using a fully connected layer. For the behavior recognition of workers not wearing safety helmets, the model can accurately identify the worker's head area and make judgments based on features such as the shape and color of the safety helmet. Once the helmet is not worn, the robot's voice alarm module is immediately triggered, issuing a voice prompt of "Not wearing a safety helmet, please correct it immediately." The relevant images and alarm information are then uploaded to the management platform via the wireless network.

[0025] Advanced facial recognition algorithm libraries, such as ArcFace or FaceNet, are used to extract and compare features from the operator's facial image. Before hot work begins, the operator stands at a designated location within the robot. After the camera captures the facial image, the algorithm converts it into a feature vector and calculates its similarity with the feature vectors of authorized personnel stored in a database. If the similarity exceeds a set threshold, authentication is considered successful, and the robot announces, "Authentication successful, hot work permitted." Otherwise, an alarm is issued, "Authentication failed, hot work prohibited," and the management staff is notified. Furthermore, integrated with the personnel location management system, indoor positioning technology is used to track the operator's location in real time. If the operator leaves the designated hot work area, the robot quickly detects and issues an alert, preventing any unforeseen circumstances.

[0026] The position sensor combines a UWB positioning chip with an inertial measurement unit (IMU). The UWB positioning chip transmits and receives ultra-wideband pulse signals to accurately calculate the distance to surrounding base stations, thereby determining its own position. The IMU measures the robot's acceleration and angular velocity, supplementing and correcting this position information to achieve high-precision positioning. For example, within large industrial plants, even in the presence of electromagnetic interference, the robot's position accuracy can be controlled to within centimeters. Environmental sensors utilize high-precision temperature sensors, smoke sensors, and hazardous gas sensors. The temperature sensor uses a platinum resistance thermometer or thermocouple sensor, which can quickly and accurately measure temperature changes in active fire areas. The smoke sensor uses light scattering or ion smoke sensing technology to detect smoke concentration. The hazardous gas sensor monitors common harmful gases and, upon detecting abnormal environmental parameters, triggers an alarm and uploads the data to the management platform.

[0027] In the development of the mini-program, the user interface was constructed using front-end technologies such as HTML5, CSS3, and JavaScript. After opening the mini-program, workers enter the hot work permit application page and use a form component to fill in information such as the hot work location, hot work level, workers and number of workers, and an image of the hot work point. Before submitting the application, the mini-program performs front-end validation on the input information, such as checking that required fields are complete and that the image format is correct. After submission, the application data is sent to the back-end server via HTTP. The back-end server assigns the application to the corresponding management account according to the pre-set approval process and updates the application status in real time in the mini-program for easy review by the worker.

[0028] Managers at all levels log in to the WeChat mini-program via their mobile phones or computers and enter the approval page. The approval page displays a list of applications awaiting approval. Clicking on each application reveals detailed information and images. Managers review the application based on hot work safety regulations and actual on-site conditions, choosing to agree or disagree on the page and entering their approval opinions. The approval process updates data by calling the back-end interface and triggering a message notification to inform the next-level approver or applicant of the approval result. If the application is rejected, the mini-program automatically returns the application to the applicant and displays the reason for the return on the applicant interface. The applicant can then submit the application again after making modifications. The entire approval process strictly follows the preset hierarchical order to ensure the standardization and rigor of the approval process.

[0029] When a worker prepares to perform a hot work operation, the mini program activates the phone's camera to capture a facial image. Using the WeChat mini program's image processing API, the captured image is pre-processed, including cropping, grayscaling, and normalization, to improve image quality. The processed image data is then sent to the backend server, which uses the AI ​​fire monitoring robot's facial recognition algorithm for verification. The verification result is returned to the mini program, which then determines whether to allow the worker to access the hot work interface. If verification fails, the mini program prompts the worker to re-verify or contact management for resolution.

[0030] The enterprise-side management screen establishes a connection with the back-end server through a network communication protocol, and regularly queries and obtains the latest hot work data from the database, including hot work point location information, operator information, hot work level, alarm information, etc. Utilize data visualization libraries such as Echarts or D3.js to process and visualize the data. In terms of map display, by calling the map API, the location of the hot work point is marked on the map, and different color icons are used to distinguish according to the operation status. For data statistical charts, such as hot work operation frequency statistical bar charts and hot work level distribution pie charts, the data is grouped and counted according to the time dimension (day, week, month, year), and the chart display is updated in real time, so that managers can intuitively understand the overall situation of the company's hot work.

[0031] The management screen maintains real-time communication with the AI ​​fire monitoring robot and the project management platform, receiving live video streams and alarm information uploaded by the robot. A video player component plays live footage of the fire operation on the screen, highlighting alarm information. When an alarm is received, the screen emits a loud siren through the audio player and displays a pop-up window with alarm details, displaying the alarm type, time, and location, prompting management to address it promptly. Managers can click on the alarm to view relevant video clips and detailed data, enabling them to make informed decisions.

[0032] In developing the project management platform, we employed a back-end development language combined with a web framework to construct an approval management module. After receiving hot work applications submitted via the WeChat management mini-program, the platform processes them according to a pre-set approval process. This approval process is implemented through a workflow engine, allowing for flexible definition of approval nodes, approvers, and approval conditions. During the approval process, the platform automatically records approval history and comments for easy traceability and query. Furthermore, the platform integrates with the company's internal personnel and project management systems to obtain relevant personnel and project information, ensuring the accuracy and effectiveness of approvals.

[0033] The project management platform establishes a data connection with the AI ​​fire monitoring robot, receiving real-time data and video streams from the hot work site collected by the robot. Video surveillance software decodes and plays the video streams, enabling real-time monitoring of the hot work site. The platform stores and analyzes the collected data, such as the duration of the hot work, flame temperature curves, and operator frequency, and generates corresponding reports and charts. When an anomaly is detected, the platform automatically triggers an alarm notification, notifying relevant management and operators via text message, email, or internal messaging, ensuring timely action to address the risk.

[0034] Operators submit a hot work application through the WeChat management mini-program, filling in the required details and uploading a picture of the fire site. After submitting the application, the system follows a pre-set approval process, with managers at all levels reviewing it within a specified timeframe. Upon approval, operators receive their hot work tools and the AI ​​fire monitoring robot. Upon arriving at the work site, they position the robot appropriately and perform initial setup to ensure proper operation. Operators then perform facial recognition verification in front of the robot. Upon successful verification, the system unlocks hot work access.

[0035] During hot work operations, the AI ​​fire monitoring robot utilizes its image recognition and sensor technologies to monitor the site in real time. Upon detecting unexpected flames, illegal operations, or abnormal environmental parameters, it immediately issues a voice alert and uploads the alert information to the management platform. Upon receiving the alert, the management platform promptly notifies relevant managers and operators. Managers can view on-site video and detailed data via the enterprise management dashboard or project management platform, providing remote guidance or arranging on-site personnel. The robot also continuously records video and data during the operation, providing a basis for subsequent analysis and tracing.

[0036] After the hot work is completed, the operator applies for the end of the hot work through the WeChat management mini-program. The system notifies the administrator for review. After the administrator confirms that there are no safety hazards in the hot work area, they approve the end of the hot work. At this point, the AI ​​fire monitoring robot continues to monitor the hot work area, continuously monitoring environmental parameters for a specified period of time (e.g., 30 minutes) to ensure that no fire hazards remain. The system automatically generates a hot work report for this hot work, including the operation time, operator, hot work point status, alarm information, and handling results. This report is stored in the database for subsequent query and statistical analysis.

[0037] Regular hardware inspections are performed on the AI ​​fire monitoring robot, including camera cleaning, battery performance testing, and sensor calibration. For the camera, professional cleaning tools and reagents are used to ensure the lens surface is free of dust and dirt to ensure image quality. Battery testing can be performed using specialized battery testing equipment to check battery capacity, charge and discharge performance, and other indicators, ensuring timely replacement of aging or damaged batteries. Sensor calibration follows the manufacturer's calibration methods and tools to ensure the accuracy of sensor measurement data. The robot's exterior is also inspected and maintained, repairing any damage and ensuring its fire and explosion resistance.

[0038] A software version management system is established to monitor security vulnerabilities and functional requirements of front-end and back-end software in real time. When security vulnerabilities are discovered, security patches are promptly released and repaired through automatic updates or manual installation. For functional upgrades, such as AI algorithm optimizations and user interface improvements, after thorough testing, upgrade notifications are pushed to users to guide them through the upgrade process. During the upgrade process, data backup and recovery mechanisms are implemented to ensure user data security and prevent data loss or corruption.

[0039] The hot work operation management system of the present invention can be integrated with other management systems of the enterprise, such as the safety management system, the project progress management system, the personnel training management system, etc. Data sharing and interaction are achieved through data interfaces. After integration with the safety management system, the safety incidents and hidden danger information during the hot work operation can be synchronized to the safety management system, which is convenient for unified safety analysis and management decision-making; when integrated with the project progress management system, the overall progress of the project can be dynamically adjusted and optimized according to the progress and situation of the hot work operation; when integrated with the personnel training management system, training plans can be formulated in a targeted manner according to the performance and violations of the hot work personnel, so as to improve the safety awareness and operational skills of the personnel. During the integration process, relevant data standards and specifications are followed to ensure the consistency and accuracy of the data, so as to achieve the integration and coordination of enterprise management.

[0040] At large-scale construction sites, AI fire monitoring robots are strategically deployed based on the division of construction areas and the distribution of hot work locations. For example, robots are deployed in key locations such as hot work areas on the floors of high-rise buildings, hot work locations near tower cranes, and on-site processing workshops. Before hot work begins, construction teams apply for a hot work permit through the WeChat management mini-program, detailing the hot work location, hot work content, and worker information. Once approved, workers bring the robots to the work site for setup and facial recognition verification. During the hot work process, the robots monitor the work site in real time. If they detect workers not wearing safety belts during high-altitude hot work or flammable materials not cleared near the hot work area, they immediately issue an alert and upload the information to the management platform. Managers can monitor the on-site situation through the enterprise management dashboard or project management platform and promptly notify site safety management personnel to address the situation, ensuring the safe conduct of hot work while minimizing disruptions to construction progress.

[0041] In chemical production workshops, the management of hot work is crucial due to the presence of flammable and explosive chemicals. An AI-powered fire monitoring robot provides 24-hour continuous monitoring of hot work areas within the workshop. Before starting a hot work operation, workshop operators apply for hot work through the WeChat management mini-program. In addition to general information, they must provide detailed information about the chemicals involved, potential chemical reactions, and appropriate safety measures. Once approved, operators conduct hot work under the robot's supervision. The robot utilizes its high-precision sensor technology to monitor workshop environmental parameters such as temperature and hazardous gas concentrations, as well as flame characteristics, in real time. If it detects a hazardous gas leak, an unexpected flame, or an operator's misuse of the device, it immediately activates an emergency alarm, liaising with the workshop's ventilation and fire extinguishing systems. Emergency measures are then implemented to ensure safe and stable operation of the production workshop, prevent fires and explosions, and minimize production losses and environmental pollution risks.

[0042] In municipal engineering projects such as municipal pipeline maintenance and road construction, hot work often takes place outdoors, subject to complex weather and geographical conditions. AI fire monitoring robots require excellent adaptability and stability. Before starting a hot work, construction teams apply for hot work through the WeChat management mini-program, specifying information such as the construction location, construction time, and estimated duration. During the hot work, the robot uses its image recognition and positioning technologies to monitor the site. For example, if it detects construction workers performing hot work without setting up warning signs, or if the hot work area is near gas pipelines or other municipal pipelines, it will promptly issue an alert and notify the on-site construction manager to make corrective actions. Furthermore, through integration with the municipal engineering management department's information system, hot work information is promptly reported, facilitating oversight and coordination, ensuring the smooth progress of municipal projects, protecting the lives and property of citizens, and safeguarding the normal operation of the city.

Claims

1. An artificial intelligence-based hot work management system, characterized by: It includes the front-end and back-end. The front-end consists of an AI fire monitoring robot and a WeChat management applet, while the back-end consists of an enterprise management screen and a project management platform. The AI ​​fire monitoring robot integrates image recognition, biometrics, sensors, and video surveillance technologies. It uses a combination of thermal imaging and visible light to identify fireworks and fires. Through dual-optical coaxial linkage, it operates on visible light images to view details when a fire is detected. Equipped with Horizon AI chips for computing power and long battery life, it can generate offline voice alarms in offline environments and store alarm information and operation process videos. In networked environments, these information can be uploaded to the management platform in a chronological order. The WeChat management mini-program is used by hot work personnel to initiate work applications, go through the approval process, obtain electronic hot work permits and hot work safety briefing documents, and perform facial recognition verification; The enterprise management screen can view the status of all hot work operations in the enterprise in real time, including the hot work point, number of people working on the hot work, hot work level, hot work location, alarm information and real-time video; The project management platform realizes the approval management, hot work monitoring and hot work recording functions, and can conduct online review of operators and operating processes, and conduct consistency verification based on offline situations.

2. A method for using the artificial intelligence-based hot work management system according to claim 1, characterized in that: The following steps are involved: S1. Create a database for the hot work management system and enter project information, personnel information, and authority settings; S2. Hot work personnel initiate a hot work application through the WeChat mini-program on their mobile phone, entering detailed information such as the hot work location, hot work level, personnel and number of personnel, and a picture of the hot work point; S3. Hot work applications are reviewed and approved according to the process of applicant, team leader, professional engineer, safety and fire supervisor, subcontractor project manager, general contractor safety and fire supervisor, and general contractor project manager. After approval, an electronic hot work permit and hot work safety briefing document will be automatically generated. S4. Operators collect hot work tools and AI fire monitoring robots. After arriving at the work site, they set up the AI ​​fire monitoring robots. Before hot work, operators undergo facial recognition. After passing the recognition, they can start the hot work. S5. During hot work operations, AI fire monitoring robots and a back-end management platform provide real-time early warning and control of hot work operations. Monitoring includes unexpected flames, illegal operations, personnel absence, equipment status, and firefighting equipment availability. Once an abnormality is detected, a voice alarm is immediately issued and information is pushed to the management platform. S6. After the hot work operation, the hot work area will be continuously monitored until the hidden dangers are eliminated, and a hot work report for this hot work operation will be automatically generated to achieve a closed-loop management of the entire process. At the same time, the operation process data will be used to evaluate and manage the operators, providing a reference basis for subsequent operations.

3. The artificial intelligence-based hot work management system according to claim 1, characterized in that: The AI ​​fire monitoring robot's image recognition technology uses a high-definition camera to collect real-time images of the fire area and combines it with a deep learning behavior recognition algorithm to accurately identify and classify various behaviors of operators. This algorithm, trained on a large amount of image data from hot work scenes, can accurately distinguish between normal and unsafe behaviors. For example, it can accurately identify whether a worker is not wearing a hard hat or reflective vest, smoking, making a phone call, or illegally entering a hot work area, with an accuracy rate of at least 95%. Once an unsafe behavior is determined, a voice alarm system is immediately triggered, and the alarm information and captured image data are simultaneously uploaded to the management platform for timely processing and analysis by management personnel, achieving efficient supervision of behavior at the hot work site. Image recognition technology can also analyze the environment around the ignition point and identify the location and status of flammable materials. If it is detected that the flammable materials are too close to the ignition point or are in an unsafe state, an alarm will be issued in time to remind the operators to take appropriate protective or cleanup measures to ensure the safety of the hot work environment.

4. The artificial intelligence-based hot work management system according to claim 1, characterized in that: The AI ​​fire monitoring robot's biometric recognition technology collects facial images of operators before any hot work begins, then uses advanced facial recognition algorithms to compare these images with pre-stored identification information to achieve accurate identity verification. This facial recognition algorithm uses multimodal feature fusion technology, comprehensively considering multi-dimensional features such as facial texture, facial shape, and facial depth information, effectively improving recognition accuracy and anti-interference capabilities, ensuring that only authorized personnel can perform hot work operations and preventing unlicensed personnel from working. Biometric technology is deeply integrated with the personnel positioning management system to continuously track the location information of operators during the operation. Combined with the setting of the hot work area, if an operator is found to have left his post or entered an unauthorized area without authorization, a voice alarm will be issued immediately and the abnormal location information will be uploaded to the management platform to ensure that the activities of personnel during the hot work are safe and controllable.

5. The artificial intelligence-based hot work management system according to claim 1 is characterized in that: The AI ​​fire monitoring robot's sensor technology includes built-in high-precision position sensors and environmental sensors. The position sensors utilize a combination of ultra-wideband (UWB) positioning technology and inertial navigation technology to accurately determine the location of personnel and equipment involved in hot work in real time, even in complex construction environments, with centimeter-level accuracy. By comparing this location with the pre-set safe operating area, the robot can promptly detect any out-of-bounds activity by personnel or equipment and issue an alarm. Environmental sensors can monitor environmental parameters such as temperature, humidity, smoke concentration, and harmful gas concentration in the fire area in real time. When abnormal temperature rises close to the danger threshold, smoke concentration exceeds the standard, or harmful gas leaks are detected, the alarm mechanism is quickly activated and the environmental data is uploaded to the management platform synchronously, providing managers with comprehensive environmental safety information so that they can take timely response measures to prevent fires and poisoning accidents.

6. The method for using the hot work management system based on artificial intelligence according to claim 2, characterized in that: The hot work permit application function of the WeChat management applet provides a simple and intuitive user interface. When applying, the operator needs to fill in detailed hot work information in sequence according to the system prompts, including but not limited to the specific location description of the hot work, the hot work level, the estimated operation time, the operator's name and certificate number, a description of the surrounding environment of the hot work point, and upload a clear picture of the hot work point; The system automatically verifies the integrity and compliance of input information. If any information is missing or does not comply with regulations, the operator will be promptly prompted to supplement or correct it. After the application is submitted, the system will automatically transfer it to the relevant management personnel according to the preset approval process, and display the application processing progress in real time, making it convenient for operators to track and inquire, ensuring that the hot work permit application process is efficient, accurate and transparent.

7. The method for using the artificial intelligence-based hot work management system according to claim 2, characterized in that: The WeChat management mini-program has an approval function. Managers at all levels can log in to the mini-program through their own independent accounts and view a list of hot work applications awaiting approval. The approval interface displays detailed application information and related images, and managers review applications based on hot work safety regulations and actual on-site conditions. During the approval process, comments and suggestions can be added, and electronic signatures are supported to ensure traceability of the approval process. The approval process strictly follows the preset hierarchical order. When a certain level of approval fails, the system automatically returns the application to the applicant and clearly marks the reason for the return. The applicant can resubmit the application after making modifications based on the feedback. At the same time, the system counts and monitors the approval time. If an approval link is not processed beyond the specified time, a reminder notification will be automatically sent to the superior management to ensure the timeliness of the approval process and prevent the construction progress from being affected by approval delays.

8. The method for using the hot work management system based on artificial intelligence according to claim 2, characterized in that: The facial recognition verification function of the WeChat management applet uses the mobile phone camera to capture the operator's facial image before the operator performs hot work. It uses the same facial recognition algorithm core technology as the AI ​​fire monitoring robot to ensure the accuracy and consistency of the verification. During the verification process, the system conducts real-time quality assessment of the collected image. If the image is blurry, the lighting is insufficient, or there are occlusions that affect recognition, the operator will be automatically prompted to retake the image. To improve security, facial recognition verification supports multi-factor authentication methods, which can be combined with the operator's device fingerprint recognition or dynamic password verification; only when facial recognition is passed and other auxiliary authentication methods are also successfully verified, the mini program allows the operator to enter the hot work operation interface, and synchronizes the verification results to the AI ​​fire monitoring robot and management platform, to achieve multiple protections for the operator's identity and prevent security risks such as identity impersonation.

9. The artificial intelligence-based hot work management system according to claim 1, characterized in that: The hot work status viewing function of the enterprise management screen displays real-time status information of all hot work within the enterprise in the form of visual charts and maps. The geographical distribution of each hot work point is clearly marked on the map interface, and different hot work statuses are distinguished by different colored icons. Clicking an icon can view detailed hot work information, including the list of operators, hot work permit number, operation start time, and expected end time. The data statistics charts conduct in-depth analysis and display of relevant data on hot work, such as the number of hot work operations by time dimension, the distribution ratio of operations with different hot work levels, and the hot work frequency ranking of each project; at the same time, the system updates alarm information in real time and prompts managers to pay attention to emergency situations through eye-catching pop-ups and sounds, so that managers can fully, intuitively and timely grasp the overall situation of the company's hot work, providing strong data support for decision-making.

10. The artificial intelligence-based hot work management system according to claim 1, characterized in that: The project management platform's hot work monitoring function establishes a stable communication connection with the AI ​​fire monitoring robot, receiving real-time video streams and various monitoring data collected by the robot from the hot work site. It uses intelligent video analysis algorithms to process video images in real time, automatically identifying key information such as flame characteristics, personnel activity trajectories, and equipment operating status within the hot work area. For flame monitoring, the combination of thermal imaging technology and image recognition technology can not only quickly detect open flames, but also accurately identify small flames or hidden flames, and determine the fire risk level based on parameters such as flame size and temperature change trends; In terms of personnel monitoring, the system can count the changes in the number of people in the hot work area and monitor whether the workers are performing their work in accordance with the prescribed operating procedures. If any violations or abnormal gatherings of people are found, an alarm will be issued immediately and the relevant responsible persons will be notified, thus realizing all-round and intelligent monitoring of the hot work site.

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