Air optimization system for motor vehicle inspection sites based on artificial intelligence

Abstract

The present invention discloses an air optimization system for a motor vehicle inspection site based on artificial intelligence, and relates to the technical field of air optimization. The present invention comprises a personnel monitoring module, a verification module, an adjustment module and a warning module; the personnel monitoring module is used to integrate the original monitoring of the motor vehicle inspection site to monitor personnel activities; the present invention, by integrating existing monitoring and personnel monitoring modules and advanced computer vision technology, realizes real-time monitoring and intelligent analysis of personnel activities in the motor vehicle inspection site. Compared with traditional manual detection and air optimization methods, it can quickly respond to changes in air quality, adjust air optimization measures in time, and significantly improve the efficiency and response speed of air optimization. At the same time, it reduces the installation of cameras, adapts to different inspection sites, and avoids unnecessary losses.

Description

Technical Field

[0001] The present invention relates to the technical field of air optimization, and in particular to an air optimization system for a motor vehicle inspection site based on artificial intelligence. Background Art

[0002] With the rapid development of industrialization and urbanization, the number of motor vehicles has increased dramatically, and the exhaust gas they emit has become one of the important sources of air pollution. As a concentrated vehicle inspection area, the air quality of motor vehicle inspection sites is directly related to the health of inspectors and the sustainability of the environment. Therefore, it is of great significance to optimize the air quality of motor vehicle inspection sites.

[0003] The existing air optimization of motor vehicle inspection sites mainly relies on manual control. Inspectors need to regularly test the air in the site and manually start the air purification equipment according to the test results. This method has many shortcomings, such as low efficiency, long response time, high labor intensity, and difficulty in achieving real-time monitoring and precise control of air quality. In addition, manual control is difficult to adapt to the needs of large-scale and highly dynamic inspection sites, and it is difficult to ensure the stability and reliability of air quality.

[0004] In order to improve the automation and intelligence level of air optimization, some testing sites have begun to try to use automatic identification systems. These systems are usually based on sensor technology and can monitor air quality in real time. However, these automatic identification systems often require sensors to be turned on 24 / 7 to ensure that they can respond to changes in air quality in a timely manner. Although this approach can improve response speed, it also has obvious problems. First, turning on sensors 24 / 7 will lead to a large amount of energy consumption and unnecessary waste of resources. Second, continuously running sensors require regular maintenance and replacement, which increases operating costs. Finally, continuous monitoring may start air purification equipment even when there is no obvious pollution, which not only increases equipment wear and tear, but also reduces the overall efficiency of the air optimization system.

[0005] In addition, most of the air optimization systems in the existing technology lack effective intelligent analysis and decision-making support, and it is difficult to optimize and adjust according to the actual air conditions and personnel activities. For example, when there are few people or no vehicle activities, the system may not be able to determine whether it is necessary to initiate air optimization measures, resulting in waste or shortage of air optimization resources.

[0006] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0007] The purpose of the present invention is to solve the problems of low operating efficiency, high resource consumption and low intelligence level in the existing motor vehicle inspection site air optimization technology, and to propose a motor vehicle inspection site air optimization system based on artificial intelligence.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] The air optimization system for motor vehicle inspection sites based on artificial intelligence includes:

[0010] The personnel monitoring module is used to integrate the existing monitoring of the motor vehicle inspection site to monitor personnel activities. The specific steps are as follows:

[0011] First, obtain the original monitoring positions on the motor vehicle inspection site, determine the functions, performance and limitations of the existing monitoring, and determine whether it is necessary to add additional cameras to the motor vehicle inspection site; then design the integrated architecture, including the integration of hardware and software, and define the interface and communication protocol between the existing monitoring system and the personnel monitoring module;

[0012] Integrate the additional cameras with the existing surveillance cameras to ensure that all cameras are connected to a unified network, realize centralized data transmission and processing, and receive video streams collected by the additional cameras and existing surveillance cameras;

[0013] Computer vision technology is then used in conjunction with pedestrian detection algorithms to analyze in real time whether there is human activity in the video stream. The specific steps are as follows:

[0014] First, key frames are extracted from the video stream, and feature points in the key frames are extracted using image processing technology. Then, a machine learning model is applied to identify people in the image.

[0015] When a person is detected in the image, motion detection is performed on the person, and the movement is monitored by comparing the position changes of pixels in consecutive video frames, and the current frame is compared with the background model to identify the moving object in the foreground;

[0016] When judging that a person is in a moving state, the gait pattern of the person, including step length and step frequency, is used to judge whether the person in the video is walking. At the same time, the body movements of the person in the moving state, including raising hands, bending over, and carrying tools, are identified to judge the activity status of the person in the moving state;

[0017] When it is determined that the person in the video is active, identify whether the person in the video interacts with the vehicle, including opening the door, entering the vehicle, opening the hood and detecting the vehicle, and monitor whether the person uses the detection tool. When it is determined that the person in the video is interacting with the vehicle, generate a verification signal and send it to the verification module;

[0018] A verification module, used to verify the air quality of the motor vehicle inspection site;

[0019] The adjustment module is used to judge the air quality report generated by the verification module and optimize the air at the testing site;

[0020] The warning module is used to warn of abnormalities in the motor vehicle inspection site.

[0021] Furthermore, the specific steps of the verification module for verifying the air quality of the motor vehicle inspection site are as follows:

[0022] After receiving the verification signal, the sensors at the motor vehicle inspection site are activated to detect the air quality. Depending on the type of pollutants to be monitored, the corresponding sensors are activated.

[0023] By activating various sensors to collect real-time monitoring data of air pollutants, including CO, NO x 、SO x , PM, VOC s , CO 2及 O3, and clean the monitoring data received by different sensors to remove outliers and noise, and then convert data of different dimensions to the same scale by standardizing the data;

[0024] Then, based on the national, regional or user-demand air quality standards, the concentration of each pollutant is evaluated to determine whether each pollutant exceeds the standard limit. A weight is assigned to each pollutant based on the degree of impact of the pollutant on health and the environment. The weight setting is determined through expert consultation or literature research.

[0025] Using the formula Calculate the comprehensive air quality index AQI, where I i is the index of the i-th pollutant, W i is the weight of the corresponding pollutant, n is the total number of pollutant types;

[0026] According to the calculated comprehensive air quality index AQI value, it is mapped to the predefined air quality registration intervals, including excellent, good, light pollution, moderate pollution, heavy pollution and severe pollution;

[0027] Analyze the changing trend of pollutant concentration over time and evaluate the dynamic changes of air quality;

[0028] The analysis results are then compiled into an air quality report, which includes real-time data, comprehensive air quality index AQI value, and air quality grade trend analysis;

[0029] Then use visualization tools such as charts and maps to display monitoring results and analyzed data to provide decision support for users.

[0030] Furthermore, the types of pollutants in the motor vehicle inspection site include:

[0031] Carbon monoxide, nitrogen oxides, sulfur oxides, particulate matter, volatile organic compounds, carbon dioxide, ammonia and ozone;

[0032] The corresponding sensors include:

[0033] Non-dispersive infrared sensor: used to detect carbon monoxide and carbon dioxide;

[0034] Electrochemical sensors: used to detect carbon monoxide, nitrogen oxides, sulfur oxides and ammonia;

[0035] Light scattering sensor: used to detect particulate matter;

[0036] Beta ray attenuation sensor: used to detect particulate matter;

[0037] Micro-oscillating balance sensor: used to detect particulate matter;

[0038] Photoionization detector: used to detect volatile organic compounds and some hydrocarbons.

[0039] Furthermore, the specific operation steps of the adjustment module to judge the air quality report generated by the verification module and optimize the air at the detection site are as follows:

[0040] First, obtain real-time or periodic air quality reports in the severe module, analyze the concentration data of various pollutants in the air quality reports, and compare them with the preset air quality standards or thresholds to check whether the concentrations of different pollutants exceed the preset safety thresholds or standards;

[0041] Based on the analysis results, an air optimization plan is developed, including ventilation enhancement, pollutant adsorption and emission control strategies, and then the resources required for the implementation plan are determined, including air purifiers, fans, and chemical neutralizers;

[0042] Automatically start the air optimization equipment through intelligent control, and provide a manual operation interface for operators to manually operate;

[0043] Carry out real-time monitoring of air quality changes after the optimization equipment is started, evaluate the changing trend of pollutant concentrations, and predict future air quality changes;

[0044] When the air quality is gradually improving and reaches the standard, it is obtained whether there is still interaction between personnel and motor vehicles in the motor vehicle inspection site monitored by the personnel monitoring module. When there is still interaction between personnel and motor vehicles, the motor vehicle inspection site is continuously optimized. When there is no interaction between personnel and motor vehicles, the optimization equipment and sensors are turned off, and the optimization and monitoring of the air quality of the motor vehicle inspection site are stopped;

[0045] When the air quality is still declining or not improving after the optimization equipment is started, the optimization plan will be adjusted or added to continuously optimize the air optimization process and measures;

[0046] A detailed air optimization report is then generated, including the measures implemented, evaluation of the effects and recommendations, and all relevant data and reports are archived.

[0047] Furthermore, the specific operation steps for issuing an alarm for abnormalities in the motor vehicle inspection site are as follows:

[0048] When the air quality is still in a downward trend or has not improved after the optimization device in the adjustment module is started, a warning signal is generated and sent to the staff terminal;

[0049] The video data obtained from the personnel monitoring module is used to analyze the faces of people at the motor vehicle inspection site, and the identity of the people at the motor vehicle inspection site is determined using facial recognition technology. The terminal number or contact information of the person is obtained by entering the personnel information database in advance, and an alert is sent to the terminal number or contact information of the person to remind the person to wear a mask, and the air optimization equipment is inspected to determine whether there is a fault in the air optimization equipment.

[0050] Furthermore, the warning module is also used to analyze in real time whether there is a risk in the motor vehicle inspection site through the video data obtained from the personnel monitoring module. The specific operation steps are as follows:

[0051] Obtain video data from the monitoring module, extract key frames from the video stream for analysis, identify moving objects in the video, distinguish between stationary and moving objects, and identify motor vehicles and other important targets through image recognition technology;

[0052] Analyze the behavior of vehicles and people, identify risky behaviors, including illegal operations, abnormal driving, and abnormal status of vehicles, and detect risk events, including vehicle collisions and people entering prohibited areas;

[0053] When risky behaviors or risky events are analyzed, an alert is sent to the staff terminal in the motor vehicle inspection site, and the video data of the risky behaviors and events is recorded.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention realizes real-time monitoring and intelligent analysis of personnel activities at motor vehicle inspection sites by integrating existing monitoring and personnel monitoring modules with advanced computer vision technology. Compared with traditional manual inspection and air optimization methods, it can quickly respond to changes in air quality and adjust air optimization measures in a timely manner, significantly improving the efficiency and response speed of air optimization. At the same time, it reduces the installation of cameras, adapts to different inspection sites, and avoids unnecessary losses.

[0056] The present invention intelligently adjusts the working state of sensors and air optimization equipment according to actual monitoring data and personnel activities, avoiding the indiscriminate operation of sensors and air purification equipment around the clock, thereby saving a lot of energy consumption, reducing equipment wear, extending the service life of the equipment, and reducing operation and maintenance costs;

[0057] The present invention uses the warning module to analyze risk behaviors and events in the detection site in real time, and sends warnings to staff in a timely manner, effectively preventing potential safety accidents. In addition, when the air quality is poor, the system can promptly notify personnel to take protective measures, such as wearing masks, thereby protecting the health of detection personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0059] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0062] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0063] like Figure 1 As shown, the air optimization system for motor vehicle inspection sites based on artificial intelligence includes a personnel monitoring module, a verification module, a regulation module and a warning module;

[0064] The personnel monitoring module is used to integrate the existing monitoring of the motor vehicle inspection site to monitor personnel activities;

[0065] First, obtain the original monitoring positions on the motor vehicle inspection site, determine the functions, performance and limitations of the existing monitoring, and determine whether it is necessary to add additional cameras to the motor vehicle inspection site; then design the integrated architecture, including the integration of hardware and software, and define the interface and communication protocol between the existing monitoring system and the personnel monitoring module; integrate the additional cameras with the existing surveillance cameras, ensure that all cameras are connected to a unified network, realize centralized data transmission and processing, and accept the video streams collected by the additional cameras and the existing surveillance cameras;

[0066] Computer vision technology is then used in conjunction with a pedestrian detection algorithm to analyze in real time whether there is human activity in the video stream. The specific steps are as follows: first, key frames are extracted from the video stream, feature points in the key frames are extracted using image processing technology, and machine learning models are applied to identify people in the image. The machine learning model includes a deep learning algorithm; after a person in the image is detected, motion detection is performed on the person, and the motion is monitored by comparing the position changes of pixels in consecutive video frames, and the current frame is compared with the background model to identify moving objects in the foreground; when it is determined that a person is in a moving state, the person's gait pattern, including step length and step frequency, is used to determine whether the person in the video is in a walking state, and at the same time, the body movements of the person in a moving state are identified, including raising hands, bending over, and carrying tools, to determine the activity state of the person in a moving state;

[0067] When it is determined that the person in the video is in an active state, identify whether the person in the video interacts with the vehicle, including opening the door, entering the car, opening the hood and inspecting the vehicle, and monitor whether the person uses detection tools, including lifting the detection instrument or using the detection instrument to contact the motor vehicle. When it is determined that the person in the video is interacting with the vehicle, a verification signal is generated and sent to the verification module.

[0068] The verification module is used to verify the air quality of the motor vehicle inspection site;

[0069] After receiving the verification signal, the sensors at the motor vehicle inspection site are started to detect the air quality. According to the type of pollutants to be monitored, the corresponding sensors are started. The types of pollutants at the motor vehicle inspection site include: carbon monoxide, which is produced by incomplete combustion and is one of the main pollutants in motor vehicle exhaust; nitrogen oxides, including nitrogen monoxide and nitrogen dioxide, which are harmful to the environment and human health; sulfur oxides, including sulfur dioxide, which are produced by the combustion of sulfur-containing fuels; particulate matter, including PM10, i.e. particulate matter with a diameter less than or equal to 10 microns, and PM2.5, i.e. fine particulate matter with a diameter less than or equal to 2.5 microns; volatile organic compounds, including a variety of harmful gases, such as benzene, toluene, xylene, etc.; carbon dioxide: emissions are also an important indicator for monitoring; ammonia: when urea solution is used as an exhaust treatment agent, ammonia emissions will be detected; ozone: under the ultraviolet irradiation of the motor vehicle inspection site, nitrogen oxides and volatile organic compounds will undergo photochemical reactions to generate ozone;

[0070] The corresponding sensors include non-dispersive infrared (NDIR) sensors: used to detect carbon monoxide (CO) and carbon dioxide (CO2); electrochemical sensors: used to detect carbon monoxide (CO), nitrogen oxides (NO x ), sulfur oxides (SO x ) and ammonia (NH3); light scattering sensor: used to detect particulate matter (PM); beta ray attenuation sensor: used to detect particulate matter (PM); micro-oscillating balance sensor: used to detect particulate matter (PM); photoionization detector (PID): used to detect volatile organic compounds (VOC s ) and some hydrocarbons (HC);

[0071] By activating various sensors to collect real-time monitoring data of air pollutants, including CO, NO x 、SO x , PM, VOC s , CO2, O3, etc., and clean the monitoring data received by different sensors to remove outliers and noise, and then convert data of different dimensions to the same scale by standardizing the data; then evaluate the concentration of each pollutant according to the air quality standards of the country, region or user needs, determine whether each pollutant exceeds the standard limit, and then assign weights to each pollutant according to the degree of impact of the pollutant on health and the environment. The weight setting can also be determined through expert consultation or literature research;

[0072] Using the formula Calculate the comprehensive air quality index AQI, where Ii is the index of the i-th pollutant, W i is the weight of the corresponding pollutant, n is the total number of pollutant types;

[0073] According to the calculated comprehensive air quality index AQI value, it is mapped to the predefined air quality registration intervals, including excellent, good, light pollution, moderate pollution, heavy pollution and severe pollution; and the trend of pollutant concentration changes over time is analyzed to evaluate the dynamic changes in air quality; the analysis results are then organized into an air quality report, which includes real-time data, comprehensive air quality index AQI value, and air quality grade trend analysis; the monitoring results and analyzed data are displayed using visualization tools such as charts and maps to provide decision support for users.

[0074] The adjustment module is used to judge the air quality report generated by the verification module and optimize the air at the testing site;

[0075] First, obtain real-time or regular air quality reports in the severe module, analyze the pollutant concentration data in the report, and compare them with the preset air quality standards or thresholds; check whether the concentrations of different pollutants exceed the preset safety thresholds or standards; then formulate air optimization plans based on the analysis results, including ventilation enhancement, pollutant adsorption and emission control strategies, and then determine the resources required for the implementation plan, including air purifiers, fans, chemical neutralizers, etc.; automatically start the air optimization equipment through intelligent control, and provide a manual operation interface for operators to manually operate;

[0076] Carry out real-time monitoring of air quality changes after the optimization equipment is started, evaluate the changing trend of pollutant concentrations, and predict future air quality changes; when the air quality is in a trend of gradual optimization and reaches the standard, obtain whether there is still interaction between personnel and motor vehicles in the motor vehicle inspection site monitored by the personnel monitoring module. When there is still interaction between personnel and motor vehicles, continue to optimize the motor vehicle inspection site; when there is no interaction between personnel and motor vehicles, turn off the optimization equipment and sensors, and stop optimizing and monitoring the air quality of the motor vehicle inspection site; when the optimization equipment is started, if the air quality is still in a trend of gradual decline or no improvement, adjust or add to the optimization plan, and continuously optimize the air optimization process and measures; then generate a detailed air optimization report, including the measures implemented, the evaluation of the effect and suggestions, and archive all relevant data and reports for future reference.

[0077] The warning module is used to warn of abnormalities in the motor vehicle inspection site;

[0078] When the air quality is still in a downward trend or has not improved after the optimization equipment in the adjustment module is started, a warning signal is generated and sent to the staff terminal, and the faces of the personnel at the motor vehicle inspection site are analyzed through the video data obtained in the personnel monitoring module. The identity of the personnel at the motor vehicle inspection site is determined through face recognition technology, and the terminal number or contact information of the person is obtained by entering the personnel information database in advance, and the personnel are reminded to wear a mask, and the air optimization equipment is inspected and repaired to determine whether there is a fault in the air optimization equipment;

[0079] Then, the video data obtained from the personnel monitoring module is used to analyze in real time whether there are risks in the motor vehicle inspection site. The specific steps are as follows:

[0080] Acquire video data from the monitoring module, extract key frames from the video stream for analysis, identify moving objects in the video, distinguish between stationary and moving objects, and identify motor vehicles and other important targets through image recognition technology; analyze the behavior of motor vehicles and personnel, identify risky behaviors, including illegal operations, abnormal driving, and abnormal status of motor vehicles, and detect risky events, including vehicle collisions and people entering prohibited areas; when risky behaviors or risky events are analyzed, send warnings to the staff terminals in the motor vehicle inspection site, and record video data of risky behaviors and events.

[0081] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The air optimization system for motor vehicle inspection sites based on artificial intelligence is characterized by: include: The personnel monitoring module is used to integrate the existing monitoring of the motor vehicle inspection site to monitor personnel activities. The specific steps are as follows: First, obtain the original monitoring positions on the motor vehicle inspection site, determine the functions, performance and limitations of the existing monitoring, and determine whether it is necessary to add additional cameras to the motor vehicle inspection site; then design the integrated architecture, including the integration of hardware and software, and define the interface and communication protocol between the existing monitoring system and the personnel monitoring module; Integrate the additional cameras with the existing surveillance cameras to ensure that all cameras are connected to a unified network, realize centralized data transmission and processing, and receive video streams collected by the additional cameras and existing surveillance cameras; Computer vision technology is then used in conjunction with pedestrian detection algorithms to analyze in real time whether there is human activity in the video stream. The specific steps are as follows: First, key frames are extracted from the video stream, and feature points in the key frames are extracted using image processing technology. Then, a machine learning model is applied to identify people in the image. When a person is detected in the image, motion detection is performed on the person, and the movement is monitored by comparing the position changes of pixels in consecutive video frames, and the current frame is compared with the background model to identify the moving object in the foreground; When judging that a person is in a moving state, the gait pattern of the person, including step length and step frequency, is used to judge whether the person in the video is walking. At the same time, the body movements of the person in the moving state, including raising hands, bending over, and carrying tools, are identified to judge the activity status of the person in the moving state; When it is determined that the person in the video is active, identify whether the person in the video interacts with the vehicle, including opening the door, entering the vehicle, opening the hood and detecting the vehicle, and monitor whether the person uses the detection tool. When it is determined that the person in the video is interacting with the vehicle, generate a verification signal and send it to the verification module; The verification module is used to verify the air quality of the motor vehicle inspection site. The specific steps are as follows: After receiving the verification signal, the sensors at the motor vehicle inspection site are activated to detect the air quality. Depending on the type of pollutants to be monitored, the corresponding sensors are activated. By activating various sensors to collect real-time monitoring data of air pollutants, including CO, NO x 、SO x , PM, VOC s , CO 2及 O3, and clean the monitoring data received by different sensors to remove outliers and noise, and then convert data of different dimensions to the same scale by standardizing the data; Then, based on the national, regional or user-demand air quality standards, the concentration of each pollutant is evaluated to determine whether each pollutant exceeds the standard limit. A weight is assigned to each pollutant based on the degree of impact of the pollutant on health and the environment. The weight setting is determined through expert consultation or literature research. Using the formula Calculate the comprehensive air quality index AQI, where I i is the index of the i-th pollutant, W i is the weight of the corresponding pollutant, n is the total number of pollutant types; According to the calculated comprehensive air quality index AQI value, it is mapped to the predefined air quality registration intervals, including excellent, good, light pollution, moderate pollution, heavy pollution and severe pollution; Analyze the changing trend of pollutant concentration over time and evaluate the dynamic changes of air quality; The analysis results are then compiled into an air quality report, which includes real-time data, comprehensive air quality index AQI value, and air quality grade trend analysis; Then use visualization tools such as charts and maps to display monitoring results and analyzed data to provide decision support for users; The adjustment module is used to judge the air quality report generated by the verification module and optimize the air quality of the test site. The specific process is as follows: First, obtain real-time or periodic air quality reports in the severe module, analyze the concentration data of various pollutants in the air quality reports, and compare them with the preset air quality standards or thresholds to check whether the concentrations of different pollutants exceed the preset safety thresholds or standards; Based on the analysis results, an air optimization plan is developed, including ventilation enhancement, pollutant adsorption and emission control strategies, and then the resources required for the implementation plan are determined, including air purifiers, fans, and chemical neutralizers; Automatically start the air optimization equipment through intelligent control, and provide a manual operation interface for operators to manually operate; Carry out real-time monitoring of air quality changes after the optimization equipment is started, evaluate the changing trend of pollutant concentrations, and predict future air quality changes; When the air quality is gradually improving and reaches the standard, it is obtained whether there is still interaction between personnel and motor vehicles in the motor vehicle inspection site monitored by the personnel monitoring module. When there is still interaction between personnel and motor vehicles, the motor vehicle inspection site is continuously optimized. When there is no interaction between personnel and motor vehicles, the optimization equipment and sensors are turned off, and the optimization and monitoring of the air quality of the motor vehicle inspection site are stopped; When the air quality is still declining or not improving after the optimization equipment is started, the optimization plan will be adjusted or added to continuously optimize the air optimization process and measures; Then generate a detailed air optimization report, including the implemented measures, effect evaluation and suggestions, and archive all relevant data and reports; The warning module is used to warn of abnormalities in the motor vehicle inspection site. The specific steps are as follows: When the air quality is still in a downward trend or has not improved after the optimization device in the adjustment module is started, a warning signal is generated and sent to the staff terminal; The video data obtained from the personnel monitoring module is used to analyze the faces of people at the motor vehicle inspection site, and the identity of the people at the motor vehicle inspection site is determined using facial recognition technology. The terminal number or contact information of the person is obtained by entering the personnel information database in advance, and an alert is sent to the terminal number or contact information of the person to remind the person to wear a mask, and the air optimization equipment is inspected to determine whether there is a fault in the air optimization equipment.

2. The artificial intelligence-based air optimization system for motor vehicle inspection sites according to claim 1 is characterized in that: The types of pollutants in the motor vehicle inspection site include: Carbon monoxide, nitrogen oxides, sulfur oxides, particulate matter, volatile organic compounds, carbon dioxide, ammonia and ozone; The corresponding sensors include: Non-dispersive infrared sensor: used to detect carbon monoxide and carbon dioxide; Electrochemical sensors: used to detect carbon monoxide, nitrogen oxides, sulfur oxides and ammonia; Light scattering sensor: used to detect particulate matter; Beta ray attenuation sensor: used to detect particulate matter; Micro-oscillating balance sensor: used to detect particulate matter; Photoionization detector: used to detect volatile organic compounds and some hydrocarbons.

3. The artificial intelligence-based air optimization system for motor vehicle inspection sites according to claim 1 is characterized in that: The warning module is also used to analyze in real time whether there is a risk in the motor vehicle inspection site through the video data obtained from the personnel monitoring module. The specific operation steps are as follows: Obtain video data from the monitoring module, extract key frames from the video stream for analysis, identify moving objects in the video, distinguish between stationary and moving objects, and identify motor vehicles and other important targets through image recognition technology; Analyze the behavior of vehicles and people, identify risky behaviors, including illegal operations, abnormal driving, and abnormal status of vehicles, and detect risk events, including vehicle collisions and people entering prohibited areas; When risky behaviors or risky events are analyzed, an alert is sent to the staff terminal in the motor vehicle inspection site, and the video data of the risky behaviors and events is recorded.

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