Safety self-inspection mirror system

Abstract

The invention provides a safety self-inspection mirror system, and belongs to the technical field of industrial safety management, and the system comprises a multi-mode identity verification system which is used for carrying out the identity verification of a worker based on face recognition, RFID reading and voice recognition; the safety equipment detection system is used for detecting whether a worker wears safety equipment or not and whether the worker wears the safety equipment correctly or not in real time; the non-contact multi-point body temperature monitoring system is used for simultaneously scanning and monitoring the temperature of a plurality of parts of the human body of the worker and calculating the body temperature of the worker according to the temperature of the plurality of parts of the human body by adopting an artificial intelligence algorithm; and the self-adaptive real-time safety prompt system is used for dynamically generating personalized safety prompts according to the identities, the working properties, the areas where the workers are located and the current safety conditions of the workers. According to the scheme, the safety in the high-risk working environment can be comprehensively managed.

Description

Technical Field

[0001] The present application relates to the technical field of industrial safety management, and in particular to a safety self-inspection mirror system. Background Art

[0002] In high-risk working environments such as thermal power plants, ensuring the safety of workers is of vital importance. In existing technologies, traditional safety self-inspection mirrors only provide visual feedback, which cannot meet the high requirements of safety management in modern industrial environments and have certain limitations.

[0003] Therefore, how to comprehensively manage safety in high-risk work environments remains an unresolved issue. Summary of the invention

[0004] The purpose of the present application is to provide a safety self-inspection mirror system, which can solve the problem that safety management in high-risk working environments in the prior art has certain limitations.

[0005] In a first aspect, an embodiment of the present application provides a safety self-inspection mirror system, the system comprising: Multimodal authentication system for authenticating workers based on facial recognition, RFID reading, and voice recognition; Safety equipment detection system, used to detect in real time whether the staff are wearing safety equipment and whether they are wearing it correctly; The non-contact multi-point body temperature monitoring system is used to scan and monitor the temperature of multiple parts of the staff's body at the same time, and uses artificial intelligence algorithms to calculate the staff's body temperature based on the temperatures of multiple parts of the body; The adaptive real-time safety reminder system is used to dynamically generate personalized safety reminders based on the worker's identity, nature of work, area and current safety conditions.

[0006] In a possible implementation of the first aspect, identity verification of a staff member based on face recognition, RFID reading, and voice recognition includes: It uses deep learning algorithms to recognize faces under different lighting conditions, reads various ID cards in various frequency bands based on RFID, and uses natural language processing technology to recognize at least one dialect and accent.

[0007] In a possible implementation manner of the first aspect, the system further includes: The intelligent environmental perception and hazard warning system is used to integrate multiple environmental sensors, monitor the working environment in real time based on each environmental sensor, use machine learning algorithms to identify potential dangerous situations, issue warnings, learn and predict environmental change patterns, and make warnings in advance.

[0008] In a possible implementation of the first aspect, the environmental sensors include: a gas concentration sensor, a noise sensor, and a light sensor; the potential dangerous situations include: leakage of harmful gases and excessive noise.

[0009] In a possible implementation manner of the first aspect, the system further includes: The blockchain-based security data management system is used to store all security inspection records and environmental monitoring data in encrypted form on the blockchain.

[0010] In a possible implementation manner of the first aspect, the system further includes: The augmented reality safety training system is used to integrate AR technology, overlay virtual safety guidance information on the display screen, simulate various dangerous situations, and provide an immersive safety training experience.

[0011] In a possible implementation manner of the first aspect, the system further includes: The intelligent voice interaction system uses natural language processing technology to support all-round voice interaction, understand the context, support multi-round dialogue, recognize special voice commands in emergency situations, and respond.

[0012] In a possible implementation manner of the first aspect, the system further includes: The dynamic safety scoring and incentive system uses machine learning algorithms to score the safety behavior of each staff member in real time and dynamically adjust the staff member's safety level based on the score.

[0013] In a possible implementation manner of the first aspect, the system further includes: The predictive maintenance system is used to predict worker illnesses and safety equipment safety hazards based on data from the safety equipment detection system and the non-contact multi-point temperature monitoring system using a deep learning algorithm.

[0014] In a possible implementation of the first aspect, based on data from a safety equipment detection system and a non-contact multi-point body temperature monitoring system, a deep learning algorithm is used to predict worker diseases and safety hazards of safety equipment, including: Based on the body temperature of staff calculated by the non-contact multi-point body temperature monitoring system, a deep learning algorithm is used to analyze the temperature change trend and predict mass diseases; Based on the usage data of safety equipment obtained by the safety equipment detection system, a deep learning algorithm is used to analyze the usage frequency and status of the safety equipment to predict the safety hazards of the safety equipment.

[0015] In the present application, a multimodal identity authentication system is used to authenticate the identity of staff members based on face recognition, RFID reading and voice recognition; a safety equipment detection system is used to detect in real time whether the staff members are wearing safety equipment and whether they are wearing it correctly; a non-contact multi-point body temperature monitoring system is used to simultaneously scan and monitor the temperature of multiple parts of the staff member's body, and use an artificial intelligence algorithm to calculate the body temperature of the staff member based on the temperatures of multiple parts of the body; an adaptive real-time safety prompt system is used to dynamically generate personalized safety prompts based on the identity of the staff member, the nature of his or her work, the area where he or she is located, and the current safety status.

[0016] This application solution can comprehensively manage safety in high-risk working environments and is highly easy to use and practical.

[0017] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 It is a schematic diagram of the overall structure of the safety self-inspection mirror system provided in an embodiment of the present application; Figure 2 It is a specific structural diagram of the safety self-inspection mirror system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0021] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or photovoltaic components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, photovoltaic components and / or combinations thereof.

[0022] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0023] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0025] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0027] In high-risk working environments such as thermal power plants, ensuring the safety of workers is of vital importance. In existing technologies, traditional safety self-inspection mirrors only provide visual feedback, which cannot meet the high requirements of safety management in modern industrial environments and have certain limitations.

[0028] Therefore, how to comprehensively manage safety in high-risk work environments remains an unresolved issue.

[0029] In response to the above-mentioned defects, the embodiments of the present application provide a safety self-inspection mirror system, a multi-modal identity authentication system for authenticating staff members based on face recognition, RFID reading and voice recognition; a safety equipment detection system for detecting in real time whether staff members are wearing safety equipment and whether they are wearing them correctly; a non-contact multi-point body temperature monitoring system for simultaneously scanning and monitoring the temperature of multiple parts of the staff member's body, and using an artificial intelligence algorithm to calculate the body temperature of the staff member based on the temperatures of multiple parts of the human body; an adaptive real-time safety prompt system for dynamically generating personalized safety prompts based on the staff member's identity, nature of work, area and current safety status.

[0030] This application solution can comprehensively manage safety in high-risk working environments and is highly easy to use and practical.

[0031] The specific process implemented by this application is introduced below through specific embodiments.

[0032] See also Figure 1 , Figure 1 Schematic diagram of the overall structure of the safety self-checking mirror system provided in the embodiment of the present application. Figure 1 As shown in FIG. 1 , the safety self-inspection mirror system includes: a multi-modal identity authentication system, a safety equipment detection system, a non-contact multi-point body temperature monitoring system, and an adaptive real-time safety prompt system. The safety self-inspection mirror system is suitable for high-risk working environments such as thermal power plants.

[0033] Multimodal identity verification system, used to verify the identity of staff members based on face recognition, RFID reading, and voice recognition. Safety equipment detection system, used to detect in real time whether staff members are wearing safety equipment and whether they are wearing it correctly. Non-contact multi-point body temperature monitoring system, used to scan and monitor the temperature of multiple parts of the staff member's body at the same time, and use artificial intelligence algorithms to calculate the body temperature of staff members based on the temperature of multiple parts of the body.

[0034] The adaptive real-time safety reminder system is used to dynamically generate personalized safety reminders based on the worker's identity, nature of work, area and current safety conditions.

[0035] According to one embodiment of the present application, identity verification of staff members is performed based on face recognition, RFID reading, and voice recognition, including: It uses deep learning algorithms to recognize faces under different lighting conditions, reads various ID cards in various frequency bands based on RFID, and uses natural language processing technology to recognize at least one dialect and accent.

[0036] In some embodiments, the multimodal authentication system combines three authentication methods: face recognition, RFID (Radio Frequency Identification) reading, and voice recognition. Face recognition technology uses deep learning algorithms to accurately recognize faces under different lighting conditions. RFID reading supports multiple frequency bands and is compatible with various ID cards. Voice recognition uses natural language processing technology to recognize multiple dialects and accents. This multimodal authentication greatly improves the security and reliability of the system and effectively prevents identity fraud.

[0037] In some embodiments, the safety equipment detection system uses advanced computer vision technology to detect in real time whether workers are wearing safety helmets, protective glasses, gloves, safety shoes and other equipment correctly. The safety equipment detection system can not only identify the presence of equipment, but also determine whether it is worn correctly. For example, it can detect whether the safety helmet is fastened tightly and whether the protective glasses cover the eye area.

[0038] In addition, the safety equipment detection system can automatically adjust the type of safety equipment that needs to be detected according to the requirements of different work areas.

[0039] In some embodiments, the non-contact multi-point temperature monitoring system uses an infrared array sensor that can simultaneously scan the temperature of multiple parts of the human body, such as the forehead, neck, wrist, etc. Combined with artificial intelligence algorithms, the body temperature can be calculated quickly and accurately, and the influence of interference factors such as ambient temperature can be automatically eliminated. This non-contact multi-point temperature measurement method greatly improves the accuracy and efficiency of temperature measurement, while reducing the risk of cross-infection.

[0040] In some embodiments, the adaptive real-time safety prompt system dynamically generates personalized safety prompts based on the user's identity, job nature, location, and current safety conditions. Using natural language generation technology, the prompt content is both professional and easy to understand.

[0041] The adaptive real-time safety reminder system can also continuously optimize the reminder content and method based on user responses and historical data to improve the effectiveness of the reminder. For example, for employees who often ignore a certain safety rule, the frequency and intensity of related reminders can be increased.

[0042] See also Figure 2 , Figure 2 Schematic diagram of the specific structure of the safety self-checking mirror system provided in the embodiment of the present application. Figure 2 As shown, the safety self-inspection mirror system also includes an intelligent environment perception and danger warning system.

[0043] The intelligent environmental perception and hazard warning system is used to integrate multiple environmental sensors, monitor the working environment in real time based on each environmental sensor, use machine learning algorithms to identify potential dangerous situations, issue warnings, learn and predict environmental change patterns, and make warnings in advance.

[0044] According to one embodiment of the present application, the environmental sensors include: a gas concentration sensor, a noise sensor, and a light sensor; potential dangerous situations include: leakage of harmful gases and excessive noise.

[0045] In some embodiments, the intelligent environment perception and hazard warning system integrates multiple environmental sensors, such as gas concentration sensors, noise sensors, light sensors, etc., to monitor the working environment in real time. Combined with machine learning algorithms, it can identify potential dangerous situations, such as harmful gas leakage, excessive noise, etc., and issue warnings in a timely manner.

[0046] The intelligent environmental perception and hazard warning system can also learn and predict environmental change patterns and give early warnings, such as predicting possible equipment failures.

[0047] Please continue to see Figure 2 According to one embodiment of the present application, the safety self-inspection mirror system also includes a blockchain-based safety data management system. The blockchain-based safety data management system is used to store all safety inspection records and environmental monitoring data in encrypted form on the blockchain.

[0048] In some embodiments, the blockchain-based security data management system applies blockchain technology to security data management. All security inspection records, environmental monitoring data, etc. are stored in encrypted form on the blockchain to ensure that the data cannot be tampered with and is traceable. This not only improves the reliability of the data, but also provides a solid legal basis for security management.

[0049] The blockchain-based security data management system supports smart contracts and can automatically execute security rewards and penalties.

[0050] Please continue to see Figure 2 According to one embodiment of the present application, the safety self-inspection mirror system also includes an augmented reality safety training system. The augmented reality safety training system is used to integrate AR (Augmented Reality) technology, superimpose virtual safety guidance information on the display screen, simulate various dangerous situations, and provide an immersive safety training experience.

[0051] In some embodiments, the augmented reality safety training system integrates AR technology to overlay virtual safety guidance information on the display screen. For example, when it is detected that the user's safety equipment is not worn correctly, the correct wearing method is intuitively displayed through AR technology. The augmented reality safety training system can also simulate various dangerous situations, provide an immersive safety training experience, and greatly improve the training effect.

[0052] Please continue to see Figure 2 According to one embodiment of the present application, the safety self-inspection mirror system also includes an intelligent voice interaction system. The intelligent voice interaction system is used to adopt natural language processing technology, support all-round voice interaction, understand context, support multiple rounds of dialogue, recognize special voice commands in emergency situations, and respond.

[0053] In some embodiments, the intelligent voice interaction system uses advanced natural language processing technology to support all-round voice interaction. Users can query safety information, report safety hazards, request assistance, etc. through voice commands. The intelligent voice interaction system can understand the context, support multiple rounds of dialogue, and can recognize special voice commands in emergency situations and respond in a timely manner.

[0054] Please continue to see Figure 2 According to one embodiment of the present application, the safety self-inspection mirror system also includes a dynamic safety scoring and incentive system. The dynamic safety scoring and incentive system is used to use a machine learning algorithm to score the safety behavior of each staff member in real time and dynamically adjust the safety level of the staff member according to the score.

[0055] In some embodiments, the dynamic safety scoring and incentive system uses machine learning algorithms to score each employee's safety behavior in real time. The scoring criteria include: wearing of safety equipment, compliance with safety rules, participation in safety training, etc. The dynamic safety scoring and incentive system dynamically adjusts the employee's safety level based on the score, and combines it with the company's reward and punishment mechanism to effectively motivate employees to improve their safety awareness.

[0056] Please continue to see Figure 2 According to one embodiment of the present application, the safety self-inspection mirror system also includes a predictive maintenance system. The predictive maintenance system is used to predict the illness of the staff and the safety hazards of the safety equipment by using a deep learning algorithm based on the data of the safety equipment detection system and the non-contact multi-point body temperature monitoring system.

[0057] According to one embodiment of the present application, based on the data of the safety equipment detection system and the non-contact multi-point body temperature monitoring system, a deep learning algorithm is used to predict the diseases of workers and the safety hazards of safety equipment, including: Based on the body temperature of the staff calculated by the non-contact multi-point body temperature monitoring system, a deep learning algorithm is used to analyze the temperature change trend and predict group diseases. Based on the usage data of safety equipment obtained by the safety equipment detection system, a deep learning algorithm is used to analyze the usage frequency and status of safety equipment and predict the safety hazards of safety equipment.

[0058] In some embodiments, the intelligent predictive maintenance system uses a large amount of data collected from the safety self-inspection mirror, combined with a deep learning algorithm, to predict potential safety hazards of the equipment. For example, by analyzing the temperature change trend of employees, it can predict the possible occurrence of mass diseases; by analyzing the frequency of use and status of safety equipment, it can predict when it needs to be replaced or maintained. This kind of predictive maintenance greatly improves the initiative of safety management and has a certain degree of foresight.

[0059] According to one implementation of the present application, the safety self-inspection mirror system is mainly composed of a hardware layer, a system software layer, an application layer and a data layer.

[0060] Hardware layer: including perception module (HD camera, temperature sensor, RFID reader, microphone), interaction module (HD display, speaker), communication module (Wi-Fi, 4G / 5G), processing module (embedded processor), power module and shell module. System software layer: using embedded operating system, equipped with various device drivers and middleware.

[0061] Application layer: includes core function modules (image processing, data processing, voice processing), business logic modules (identity authentication, security check, temperature monitoring, real-time prompts, data recording), system management modules and user interface modules. Data layer: includes local data storage and cloud data storage, which are used to store and manage various types of data.

[0062] Security layer: spans all layers, including data encryption, access control, and security auditing functions. Communication layer: responsible for local communication, remote communication, and cloud interface.

[0063] In some embodiments, identity verification includes: the system double-verifies the user's identity through face recognition and RFID reading. The user stands in front of the mirror, the camera captures the facial image, and the RFID reads the ID card information. The system compares the collected information with the database to confirm the identity.

[0064] In some embodiments, the safety equipment check includes: the camera captures the user's full body image, and the image processing module analyzes whether the necessary equipment such as helmets, gloves, and safety shoes are correctly worn. If it is found that the necessary equipment is not worn correctly, the system prompts the user through the display screen and voice.

[0065] In some embodiments, body temperature monitoring includes: when a user stands in a specified position, a temperature sensor automatically detects body temperature. If the body temperature is abnormal, the system issues an alarm and notifies the management staff. Real-time prompts include: the system provides personalized safety prompts and precautions through display screens and voice according to the user's identity and work area.

[0066] In some embodiments, data logging includes: the system records detailed information of each inspection, including user identity, inspection time, body temperature, safety equipment status, etc., and uploads it to cloud storage. Remote management includes: managers can monitor the system operation status in real time through a remote terminal, view statistical data, and perform necessary system configuration.

[0067] In some embodiments, data analysis includes: the system uses historical data stored in the cloud to predict potential safety hazards through big data analysis technology to provide support for safety management decisions.

[0068] According to one embodiment of the present application, the method for using the safety self-inspection mirror system includes: Installation: Install the system at the entrance of the work area to ensure network connection and power supply. Configuration: The administrator performs initial settings through the system interface or remote terminal, including user information entry, safety rule setting, etc. Daily use: Before entering the work area, the staff stands in front of the mirror to complete identity authentication and safety checks. Maintenance: Regularly check the system hardware status, update the software version, and back up important data.

[0069] In the safety self-inspection mirror system provided in the embodiment of the present application, a multimodal identity authentication system is used to authenticate the identity of the staff based on face recognition, RFID reading and voice recognition; a safety equipment detection system is used to detect in real time whether the staff is wearing safety equipment and whether it is worn correctly; a non-contact multi-point body temperature monitoring system is used to simultaneously scan and monitor the temperature of multiple parts of the staff's body, and use an artificial intelligence algorithm to calculate the body temperature of the staff according to the temperature of multiple parts of the body; an adaptive real-time safety prompt system is used to dynamically generate personalized safety prompts based on the identity of the staff, the nature of their work, the area they are in, and the current safety status.

[0070] This application solution can comprehensively manage safety in high-risk working environments and is highly easy to use and practical.

[0071] This application solution integrates high-definition display screens, cameras, multiple sensors and communication modules, and realizes automated identity authentication and security checks through functions such as face recognition, safety equipment detection, and body temperature monitoring. This application solution also has functions such as real-time prompts, data recording and analysis, which greatly improves security management efficiency, enhances security awareness, and effectively prevents safety hazards.

[0072] This application scheme adopts a modular design, has good scalability and maintainability, and provides an innovative solution for industrial safety management.

[0073] This application solution improves security management efficiency: Automated identity authentication and security checks greatly improve security management efficiency. Enhance security awareness: Real-time prompts and feedback help improve employees' security awareness. Prevent potential safety hazards: Through data analysis, potential safety hazards can be predicted and prevented. Provide traceability: Detailed data records provide a reliable traceability basis for security management. Flexible and scalable: The modular design makes the system easy to upgrade and expand to meet the needs of different working environments.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A safety self-inspection mirror system, characterized in that: The system comprises: Multimodal authentication system for authenticating workers based on facial recognition, RFID reading, and voice recognition; Safety equipment detection system, used to detect in real time whether the staff are wearing safety equipment and whether they are wearing it correctly; The non-contact multi-point body temperature monitoring system is used to scan and monitor the temperature of multiple parts of the staff's body at the same time, and uses artificial intelligence algorithms to calculate the staff's body temperature based on the temperatures of multiple parts of the body; The adaptive real-time safety reminder system is used to dynamically generate personalized safety reminders based on the worker's identity, nature of work, area and current safety conditions.

2. The safety self-inspection mirror system according to claim 1, characterized in that: Authentication of staff based on face recognition, RFID reading and voice recognition, including: It uses deep learning algorithms to recognize faces under different lighting conditions, reads various ID cards in various frequency bands based on RFID, and uses natural language processing technology to recognize at least one dialect and accent.

3. The safety self-inspection mirror system according to claim 1, characterized in that: The system further comprises: The intelligent environmental perception and hazard warning system is used to integrate multiple environmental sensors, monitor the working environment in real time based on each environmental sensor, use machine learning algorithms to identify potential dangerous situations, issue warnings, learn and predict environmental change patterns, and make warnings in advance.

4. The safety self-inspection mirror system according to claim 3, characterized in that: The environmental sensors include: gas concentration sensors, noise sensors and light sensors; potential dangerous situations include: leakage of harmful gases and excessive noise.

5. The safety self-inspection mirror system according to claim 1, characterized in that: The system further comprises: The blockchain-based security data management system is used to store all security inspection records and environmental monitoring data in encrypted form on the blockchain.

6. The safety self-inspection mirror system according to claim 1, characterized in that: The system further comprises: The augmented reality safety training system is used to integrate AR technology, overlay virtual safety guidance information on the display screen, simulate various dangerous situations, and provide an immersive safety training experience.

7. The safety self-inspection mirror system according to claim 1, characterized in that: The system further comprises: The intelligent voice interaction system uses natural language processing technology to support all-round voice interaction, understand the context, support multi-round dialogue, recognize special voice commands in emergency situations, and respond.

8. The safety self-inspection mirror system according to claim 1, characterized in that: The system further comprises: The dynamic safety scoring and incentive system uses machine learning algorithms to score the safety behavior of each staff member in real time and dynamically adjust the staff member's safety level based on the score.

9. The safety self-inspection mirror system according to any one of claims 1 to 8, characterized in that: The system further comprises: The predictive maintenance system is used to predict the diseases of the staff and the safety hazards of the safety equipment by using a deep learning algorithm based on the data of the safety equipment detection system and the non-contact multi-point body temperature monitoring system.

10. The safety self-inspection mirror system according to claim 9, characterized in that: According to the data of the safety equipment detection system and the non-contact multi-point body temperature monitoring system, a deep learning algorithm is used to predict the illness of the staff and the safety hazards of the safety equipment, including: According to the body temperature of the staff calculated by the non-contact multi-point body temperature monitoring system, the deep learning algorithm is used to analyze the temperature change trend and predict mass diseases; Based on the usage data of the safety equipment acquired by the safety equipment detection system, the deep learning algorithm is used to analyze the usage frequency and status of the safety equipment to predict the safety hazards of the safety equipment.

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