A Remote Supervision System for Motor Vehicle Detection

Abstract

The present invention discloses a remote supervision system for motor vehicle detection, which relates to the technical field of motor vehicle detection and includes: a detection data acquisition module, which uses a multi-type sensor array to collect exhaust emission, vehicle speed, braking, lighting, and axle weight data on the detection line and transmits them to the data acquisition unit for caching via a high-speed wired network; a data encryption and transmission module, which encrypts the data with a national cryptographic algorithm and transmits it to the remote supervision center via wireless or VPN network. Digital certificates are used for authentication during transmission; a remote supervision center module, which processes, analyzes, verifies, and checks the data with a high-performance server cluster and stores it in a distributed database; an intelligent analysis and early warning module, which analyzes the data using big data and artificial intelligence algorithms and pushes early warnings in multiple ways in case of anomalies. The present invention improves the supervision efficiency and accuracy. The multi-type sensors and intelligent recognition technology ensure accurate and standardized detection, guarantee data security, and the encrypted transmission and distributed storage are reliable. It can timely warn of risks and optimize the management of detection institutions.

Description

Technical Field

[0001] The present invention relates to the technical field of motor vehicle detection, and particularly to a remote supervision system for motor vehicle detection. Background Art

[0002] With the continuous growth of the motor vehicle population, motor vehicle detection plays a crucial role in ensuring road traffic safety, controlling environmental pollution, etc. The traditional motor vehicle detection supervision mode mainly relies on on-site manual supervision and paper records. This method has many drawbacks and is difficult to meet the needs of modern traffic management.

[0003] In the traditional mode, supervisors need to go to the detection site for supervision, which not only consumes a large amount of human, material and time costs, but also has low supervision efficiency. Due to the subjectivity and limitations of manual supervision, it is difficult to achieve comprehensive and real-time monitoring of the detection process, and violations such as detection cheating and data falsification are likely to occur. For example, some detection agencies may, in order to pursue economic benefits, modify the detection data through improper means in the exhaust emission detection, allowing over-standard vehicles to pass the detection, which causes serious pollution to the environment and endangers public health.

[0004] At the same time, the storage and management of the detected data in paper records are inconvenient, difficult to query and statistically analyze, and have poor data sharing. It cannot provide timely and accurate data support for traffic management departments, which is not conducive to the analysis and decision-making of the overall condition of motor vehicles. In addition, the traditional supervision mode lacks an effective data analysis and early warning mechanism, and it is difficult to timely discover abnormal trends and potential risks in the detection data, and it is impossible to achieve dynamic management and precise control of the quality of motor vehicle detection.

[0005] With the rapid development of information technology, especially the wide application of emerging technologies such as big data, artificial intelligence, and the Internet of Things, new opportunities and solutions are provided for the remote supervision of motor vehicle detection. Using sensor technology can realize the automatic collection and real-time transmission of motor vehicle detection data, improving the accuracy and timeliness of the data; through encryption transmission technology, the security and confidentiality of the data during network transmission can be ensured; with the help of big data analysis and artificial intelligence algorithms, in-depth mining and intelligent analysis of massive detection data can be carried out to timely discover abnormal situations and issue early warnings; high-definition cameras and intelligent image recognition technology can monitor the entire detection process to standardize the operation behavior of detection personnel; and establishing a perfect management module for detection agencies helps to achieve standardized and standardized management of detection agencies and improve the quality of detection services.

[0006] Therefore, it is of great practical significance to develop a remote supervision system for motor vehicle detection based on advanced technologies, which can effectively improve the level of motor vehicle detection supervision, ensure the fairness, accuracy and reliability of motor vehicle detection work, and promote the sustainable development of the transportation industry. Summary of the Invention

[0007] A remote supervision system for motor vehicle detection proposed by the present invention is used to solve the problems mentioned in the above prior art.

[0008] To achieve the above object, the present invention adopts the following technical solutions: A remote supervision system for motor vehicle detection includes:

[0009] Detection data acquisition module: Adopting a multi-type sensor array, installed on the motor vehicle detection line, used to collect various vehicle detection data, including tail gas emission data, vehicle speed data, braking performance data, light intensity data, axle weight data. The tail gas emission sensor adopts the fusion technology of electrochemical sensor and infrared sensor to detect multiple pollutants simultaneously; the vehicle speed sensor adopts the laser Doppler velocimetry principle; the braking performance sensor is a combination of a pressure sensor and a displacement sensor; the light intensity sensor uses a photodiode array; the axle weight sensor adopts a strain gauge sensor;

[0010] The sensor is connected to the data acquisition unit through a high-speed wired transmission network. The data acquisition unit has a real-time data caching function to capture the dynamic data changes during the vehicle detection process;

[0011] Data encryption and transmission module: Perform real-time encryption processing on the collected detection data, adopt the national encryption algorithm, including the SM4 encryption algorithm to encrypt the data, and the encryption key length is 128 bits;

[0012] The encrypted data is transmitted to the remote supervision center through a wireless communication network or a dedicated virtual private network VPN. The wireless communication module supports multi-band adaptive switching;

[0013] Remote supervision center module: Equipped with a high-performance server cluster, and the server adopts an Intel Xeon series multi-core processor;

[0014] Data parsing and processing software runs on the server. This software decrypts, parses, and verifies the encrypted transmitted data. The parsed data is classified and stored in the database according to vehicle detection items. The database adopts a distributed storage architecture, including the Hadoop distributed file system;

[0015] Intelligent analysis and warning module: Based on big data analysis technology and artificial intelligence algorithms, perform real-time analysis on the detection data stored in the database. Adopt deep learning algorithms, including convolutional neural networks to build an abnormal detection model for vehicle detection data, including normal vehicle detection data and various fault vehicle detection data;

[0016] When abnormal vehicle detection data is detected, the system automatically generates a warning message, and the warning message is pushed to the supervisor's terminal through various methods;

[0017] Inspection process monitoring module: A high-definition camera array is installed on the motor vehicle inspection line, which has night vision and intelligent image recognition functions. It can capture the key links of the vehicle inspection process in real time, including appearance inspection and online inspection, and automatically identify the vehicle model, license plate number, and inspection personnel's operating behavior information through image recognition technology;

[0018] The video data collected by the camera is transmitted to the remote monitoring center in real time through the network, and the supervisor can view the video of the detection process in real time through the monitoring terminal;

[0019] Testing agency management module: establish a testing agency information management database to store the basic information, personnel information, and equipment information of the testing agency. The database has data update and query functions;

[0020] The testing business process of the testing agency is monitored and managed in real time. According to the testing standards and specifications, the testing agency's testing sequence, execution of testing items, timeliness of testing data upload, etc. are automatically checked and compared. When it is found that the testing agency has violated the regulations or does not comply with the testing process, the system automatically records the violation and generates a rectification notice.

[0021] Furthermore, the sensor array in the detection data acquisition module has an automatic calibration function. The calibration process uses a standard source for comparison, and the calibration data is automatically recorded and uploaded to the remote monitoring center.

[0022] Furthermore, during the data transmission process, the data encryption transmission module uses digital certificate technology to authenticate the identity of the data sender and receiver.

[0023] Furthermore, the intelligent analysis and early warning module in the remote supervision center module has self-learning and self-adaptation capabilities, and the model optimization cycle does not exceed 1 month.

[0024] Furthermore, the high-definition camera array in the inspection process monitoring module uses intelligent tracking technology to automatically track the moving position of the vehicle on the inspection line.

[0025] Furthermore, the testing agency management module has the function of automatic review of testing reports. According to the preset review rules and standards, the testing reports uploaded by the testing agency are automatically reviewed. The review content includes the rationality of the test data, the completeness of the test items, and the accuracy of the conclusions. The review time does not exceed 5 minutes, and the review results are automatically fed back to the testing agency. For reports that fail the review, the testing agency needs to modify them and re-upload them for review.

[0026] Furthermore, a method for applying a motor vehicle detection remote supervision system comprises the following steps:

[0027] Data collection step: Through a multi-type high-precision sensor array installed on the vehicle inspection line, collect various vehicle inspection data as described in claim 1, including exhaust emissions, vehicle speed, braking performance, light intensity, and axle weight data. The sensors collect data in real time and transmit it to the data collection unit;

[0028] Data encryption and transmission step: After the data collection unit receives the sensor data, encrypt the data using the national cryptography algorithm as described in claim 3, and add a digital certificate for identity authentication. Then, transmit the encrypted data to the remote supervision center through a wireless communication network or a dedicated VPN;

[0029] Data parsing and storage step: After the remote supervision center receives the encrypted data, decrypt, parse, and verify it using the data parsing and processing software on the server as described in claim 1. Classify the parsed data according to vehicle inspection items and store it in a distributed database;

[0030] Intelligent analysis and warning step: The intelligent analysis and warning module, based on big data analysis technology and artificial intelligence algorithms as described in claim 4, performs real-time analysis on the inspection data stored in the database, uses a trained anomaly detection model to identify abnormal patterns and potential risks in the data. When an anomaly is detected, generate a warning message and push it to the supervisor's terminal as described in claim 1;

[0031] Inspection process monitoring step: Through a high-definition camera array installed on the inspection line, record the vehicle inspection process video in real time as described in claim 5, use intelligent image recognition technology to automatically identify vehicle and inspector information, transmit the video data to the remote supervision center, and the supervisor can view the monitoring video in real time through the monitoring terminal as described in claim 1;

[0032] Testing agency management step: The testing agency management module, according to claim 6, establishes an information management database for testing agencies, stores and manages relevant information of testing agencies, monitors the business processes of testing agencies in real time, generates rectification notices for violations and tracks the rectification situation, and automatically reviews inspection reports at the same time.

[0033] Furthermore, in the data collection step, the sensor array performs pre-inspection before the vehicle enters the inspection line to check whether the working status of the sensors is normal. If a sensor failure is found, the system automatically alarms and prompts to replace the sensor.

[0034] Furthermore, in the intelligent analysis and warning step, after the warning message is pushed to the supervisor's terminal, the supervisor analyzes the abnormal data through the analysis tools of the remote supervision center, views the data trend, compares historical data, and assists in judging the cause of the anomaly. The analysis tools have data visualization functions, including line charts, bar charts, and scatter plots.

[0035] Furthermore, in the detection agency management step, the detection equipment of the detection agency is regularly remotely spot-checked. The standard sample is detected by remotely controlling the detection equipment to obtain detection data and compare it with the standard value, so as to evaluate the accuracy and reliability of the detection equipment. The spot-check frequency is once a month.

[0036] Compared with the existing technologies, the beneficial effects of the present invention are as follows:

[0037] The detection data is automatically collected by a multi-type high-precision sensor array, avoiding the errors and subjectivity of manual detection. The data collection is comprehensive and highly accurate. For example, the detection accuracy of tail gas emissions can reach ±0.01 ppm, etc. By combining intelligent image recognition technology to monitor the detection process, the vehicle and tester information can be accurately identified to ensure the standardization of the detection process. Multiple detection stations can be monitored in real time, and supervisors do not need to be on-site for supervision, greatly improving the supervision efficiency and effectively preventing detection cheating and data fraud.

[0038] The data encryption transmission adopts the national cryptographic algorithm and digital certificate authentication to ensure the confidentiality, integrity and authenticity of the data, preventing data leakage and tampering. The distributed storage architecture ensures reliable data storage and is convenient for query and statistics, providing strong support for long-term data analysis, helping the traffic management department accurately grasp the overall situation of motor vehicles, and providing a scientific basis for decision-making.

[0039] Based on the intelligent analysis of big data and artificial intelligence algorithms, it can quickly and accurately identify abnormal patterns in detection data, discover potential risks in advance, such as potential safety hazards of vehicles or trends of excessive emissions. The warning information is pushed in a timely manner, enabling supervisors to handle it quickly. The detection agency management module automatically reviews the detection reports, discovers problems in a timely manner and requires rectification to ensure the detection quality.

[0040] An information database of detection agencies is established to achieve all-round management of detection agencies, standardize business processes and improve service quality. Regularly remotely spot-check the detection equipment to ensure the accuracy and reliability of the equipment, prompting detection agencies to improve their technical level and management capabilities, and promoting the healthy development of the entire motor vehicle detection industry.

[0041] It helps to ensure that the motor vehicles on the road have good performance and meet the emission standards, reduce potential traffic accident hazards and environmental pollution, create a safer and more environmentally friendly traffic environment for the public, and improve the public's satisfaction with the traffic management department, having significant social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic block diagram of a remote supervision system for motor vehicle detection proposed by the present invention;

[0043] Figure 2 It is a schematic block diagram of a remote supervision method for motor vehicle detection proposed by the present invention. Detailed implementation manners

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0046] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined. In addition, the terms "installation", "connection" and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. The present invention will be further described in detail below with reference to the accompanying drawings.

[0047] Refer to Figure 1 - Figure 2 : A remote supervision system for motor vehicle detection, comprising:

[0048] Detection Data Acquisition Module: It adopts a multi-type high-precision sensor array, which is installed at key positions on the motor vehicle detection line and is used to collect various vehicle detection data, including but not limited to exhaust emission data, vehicle speed data, braking performance data, light intensity data, axle weight data, etc. The exhaust emission sensor adopts the fusion technology of electrochemical sensor and infrared sensor, and can simultaneously detect various pollutants such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM), etc., with a measurement accuracy of ±0.01 ppm; the vehicle speed sensor adopts the laser Doppler velocimetry principle, and the measurement accuracy is ±0.1 km / h; the braking performance sensor is a combination of a high-precision pressure sensor and a displacement sensor, with a pressure measurement accuracy of ±0.01 MPa and a displacement measurement accuracy of ±0.1 mm; the light intensity sensor uses a photodiode array and can accurately measure the intensity and light pattern of different lights, with a measurement accuracy of ±10 cd; the axle weight sensor adopts a strain gauge sensor, and the measurement accuracy is ±1%.

[0049] The sensors are connected to the data acquisition unit through a high-speed wired transmission network (such as Gigabit Ethernet). The data acquisition unit has a real-time data caching function, and the cache capacity is not less than 1 GB to ensure that data is not lost in case of network failure or abnormal data transmission. The data acquisition frequency is 1 kHz - 10 kHz, which can accurately capture the dynamic data changes during the vehicle detection process.

[0050] Data Encryption Transmission Module: It performs real-time encryption processing on the collected detection data, encrypts the data using the national cryptographic algorithm (such as SM4 encryption algorithm), the encryption key length is 128 bits, and realizes efficient encryption operation through a hardware encryption chip to ensure the confidentiality of data during transmission.

[0051] The encrypted data is transmitted to the remote supervision center through a wireless communication network (such as 5G network) or a dedicated virtual private network (VPN). The wireless communication module supports multi-band adaptive switching to ensure stable transmission in different signal environments. The transmission rate is not less than 100 Mbps, and the network transmission delay does not exceed 50 milliseconds to ensure the real-time nature of data.

[0052] Remote Supervision Center Module: It is equipped with a high-performance server cluster. The server adopts a multi-core processor (such as Intel Xeon series processors, with no less than 16 cores), and the memory capacity is not less than 128 GB, with powerful data processing capabilities, and can simultaneously process a large amount of detection data from multiple detection stations.

[0053] Data parsing and processing software runs on the server. This software can quickly decrypt, parse, and verify the encrypted transmitted data to ensure the integrity and accuracy of the data. The parsed data is classified and stored in the database according to vehicle detection items. The database adopts a distributed storage architecture (such as Hadoop distributed file system), which has high reliability and scalability and can store at least 10 years of detection data history records.

[0054] Intelligent analysis and warning module: Based on big data analysis technology and artificial intelligence algorithms, it conducts real-time analysis on the detection data stored in the database. It uses deep learning algorithms (such as convolutional neural network) to build an abnormal detection model for vehicle detection data. The number of model training samples is not less than 100,000 groups, covering normal vehicle detection data and various types of faulty vehicle detection data. Through learning historical data, it can automatically identify abnormal patterns and potential risks in vehicle detection data.

[0055] Introduce an improved formula For evaluating the abnormal degree of vehicle detection data, where E is the abnormal evaluation value, n is the dimension of the detection data, D i is the actual detection value of the i-th dimension, M i is its normal mean value, w i is the dimension weight, m is the number of detection item categories, p j is the abnormal penalty factor, F j is the item abnormal flag.

[0056] When abnormal vehicle detection data is detected, that is, when the E value meets the warning condition, E≥T1, where T1 is the mild warning threshold, the system automatically generates a warning message. The warning message includes vehicle information (license plate number, vehicle identification number, etc.), detection items, abnormal data values, warning levels (divided into mild, moderate, and severe), etc. The warning message is pushed to the supervisor's terminal through multiple methods, such as SMS push, mobile APP push, email push, etc., and the push time does not exceed 10 seconds to ensure that the supervisor can obtain the warning message in time and take corresponding measures.

[0057] Detection process monitoring module: Install a high-definition camera array on the motor vehicle detection line. The camera resolution is not less than 1080p, the frame rate is 25fps, and it has night vision function and intelligent image recognition function. It can capture key links (such as appearance inspection, on-line detection, etc.) during the vehicle detection process in real time, and automatically identify information such as vehicle models, license plate numbers, and inspection personnel's operation behaviors through image recognition technology. The image recognition accuracy is not less than 95%.

[0058] The video data collected by the camera is transmitted to the remote supervision center in real time through the network. Supervisors can view the video of the detection process in real time through the monitoring terminal. The monitoring terminal supports functions such as multi-screen display, video playback, and screenshot. Supervisors can switch to view the video images of different detection workstations at any time according to needs. The video transmission delay does not exceed 100 milliseconds, ensuring the real-time and effectiveness of monitoring.

[0059] Testing agency management module: Establish a database for managing testing agency information, storing the basic information of testing agencies (such as agency name, address, legal person, qualification certificates, etc.), personnel information (list of testing personnel, qualification certificates, training records, etc.), and equipment information (list of testing equipment, calibration records, maintenance records, etc.). The database has data update and query functions, and supervisors can query and update relevant information of testing agencies at any time;

[0060] Monitor and manage the testing business processes of testing agencies in real time. Automatically check and compare the testing sequence, implementation of testing items, and timeliness of uploading testing data of testing agencies according to testing standards and specifications. When it is found that a testing agency has violated regulations or does not conform to the testing process, the system automatically records the violation behavior and generates a rectification notice. The rectification notice is automatically sent to the terminal of the testing agency management personnel through the system, requiring the testing agency to rectify within a time limit and track the rectification situation to ensure that the testing agency conducts testing work strictly in accordance with the specifications;

[0061] Specifically: Introduce the formula , where C represents the comprehensive compliance evaluation value of the testing agency, used to measure the overall compliance degree of the testing agency; k represents the key link category of the testing business process, k = 1 represents the testing sequence, k = 2 represents the implementation of testing items, k = 3 represents the timeliness of uploading testing data; a k represents the weight of the kth key link category in the compliance evaluation; Q k represents the compliance score corresponding to the kth key link category; l represents the violation type category; b l represents the penalty coefficient of the lth violation type in the comprehensive evaluation; P l represents the number of times the lth violation type occurs within a certain period of time;

[0062] By establishing a database for managing testing agency information, the system can obtain detailed information on various aspects of testing agencies, providing basic data for evaluating their compliance.

[0063] For the monitoring and management of the inspection business process, when evaluating the compliance of the inspection sequence, if the inspection agency conducts inspections in strict accordance with the specified sequence, then Q1 = 10; if there are some cases of reversed sequence, etc., corresponding scores shall be given according to the actual situation. In terms of the implementation of inspection items, if all items are accurately implemented, Q2 = 10; if there are missed inspections or misinspections, points shall be deducted according to the severity of the problem. For the timeliness of uploading inspection data, if it is uploaded on time, Q3 = 10, and points shall be deducted correspondingly for delayed or incorrect uploads.

[0064] The system calculates the C value according to the above formula. If C is lower than the threshold, it is determined that the inspection agency has violated regulations or does not conform to the inspection process. At this time, the system automatically records the violations, such as detecting violations of the inspection sequence (P1 + 1), missed inspections of inspection items (P2 + 1), or delays in uploading inspection data (P3 + 1), etc., and increases the negative impact on the comprehensive evaluation value through the latter part of the formula according to the type and number of violations.

[0065] Then the system generates a rectification notice and automatically sends it to the terminal of the inspection agency's management personnel through the system. The notice clearly points out the violations and the evaluation results calculated according to the formula, and requires the inspection agency to rectify within a time limit. During the rectification period, the system continuously tracks the rectification situation. When re-evaluating, the C value is recalculated according to the implementation of the rectified business process. If the C value reaches the qualified standard, it is regarded as qualified rectification; if it is still unqualified, further supervision measures shall be strengthened, such as increasing the sampling frequency, etc., to ensure that the inspection agency conducts inspections strictly in accordance with the specifications and guarantee the standardized and orderly operation of the motor vehicle inspection market.

[0066] In the present invention, the sensor array in the inspection data acquisition module has an automatic calibration function and automatically performs calibration every certain period (such as 1 hour). The calibration process uses a standard source for comparison to ensure the long-term stability of the sensor measurement accuracy. The calibration data is automatically recorded and uploaded to the remote supervision center.

[0067] In the present invention, during the data transmission process, the data encryption transmission module uses digital certificate technology to authenticate the identities of the data sender and receiver to ensure the security and reliability of data transmission. The digital certificate is issued by an authoritative certification agency, and the certificate validity period is 1 year. The certificate update process is automatic and does not affect the normal operation of the system.

[0068] In the present invention, the intelligent analysis and early warning module in the remote supervision center module has self-learning and adaptive capabilities, and can continuously optimize the anomaly detection model according to new inspection data to improve the accuracy and timeliness of early warning. The model optimization period does not exceed 1 month, and the model performance improvement after each optimization is not less than 10%.

[0069] In the present invention, the high-definition camera array in the detection process monitoring module adopts intelligent tracking technology, which can automatically track the moving position of the vehicle on the detection line, ensure that the vehicle is always at the center of the monitoring screen, improve the monitoring effect, and the intelligent tracking error does not exceed ±0.5 meters.

[0070] In the present invention, the detection agency management module has an automatic detection report review function. According to the preset review rules and standards, it automatically reviews the detection reports uploaded by the detection agency. The review content includes the rationality of detection data, the integrity of detection items, the accuracy of conclusions, etc. The review time does not exceed 5 minutes, and the review results are automatically fed back to the detection agency. For reports that fail the review, the detection agency needs to modify and re-upload them for review.

[0071] In the present invention, a remote supervision method for motor vehicle detection is also disclosed, including the following steps:

[0072] Data acquisition step: Through a multi-type high-precision sensor array installed on the motor vehicle detection line, various vehicle detection data are collected, including exhaust emissions, vehicle speed, braking performance, light intensity, axle weight, etc. The sensors collect data in real time and transmit it to the data acquisition unit.

[0073] Data encryption and transmission step: After the data acquisition unit receives the sensor data, it encrypts the data using the national cryptographic algorithm as described in claim 3 and adds a digital certificate for identity authentication, and then transmits the encrypted data to the remote supervision center through a wireless communication network or a dedicated VPN to ensure the confidentiality, integrity, and authenticity of the data during transmission.

[0074] Data parsing and storage step: After the remote supervision center receives the encrypted data, it decrypts, parses, and verifies it using the data parsing and processing software on the server as described in claim 1, and stores the parsed data in a distributed database classified by vehicle detection items to establish a complete vehicle detection data file.

[0075] Intelligent analysis and warning step: The intelligent analysis and warning module, as described in claim 4, based on big data analysis technology and artificial intelligence algorithms, performs real-time analysis on the detection data stored in the database, uses the trained anomaly detection model to identify abnormal patterns and potential risks in the data, and when anomalies are detected, generates warning information and pushes it to the supervisor's terminal to notify the supervisor to take measures in a timely manner.

[0076] Detection process monitoring step: Through the high-definition camera array installed on the detection line, the video of the vehicle detection process is captured in real time, and the vehicle and inspector information are automatically identified using intelligent image recognition technology. The video data is transmitted to the remote supervision center, and the supervisor can view the monitoring video in real time through the monitoring terminal to ensure the standardization and accuracy of the detection process.

[0077] Steps for managing the testing agency: Establish a database for managing the information of the testing agency to store and manage relevant information of the testing agency, monitor the business process of the testing agency in real time, generate a rectification notice for violations and track the rectification situation, and automatically review the test reports to ensure the work quality of the testing agency.

[0078] In the data acquisition step of the present invention, the sensor array performs pre-detection before the vehicle enters the detection line to check whether the working status of the sensors is normal. If a sensor failure is detected, the system automatically alarms and prompts to replace the sensor to ensure the accuracy of the collected data.

[0079] In the intelligent analysis and early warning step of the present invention, after the early warning information is pushed to the supervisor's terminal, the supervisor can further deeply analyze the abnormal data through the analysis tools of the remote supervision center, view data trends, compare historical data, etc., to assist in judging the cause of the abnormality. The analysis tools have data visualization functions such as line charts, bar charts, and scatter plots, which are convenient for supervisors to intuitively view the data characteristics.

[0080] In the testing agency management step of the present invention, the testing equipment of the testing agency is regularly remotely sampled. The standard sample testing is carried out by remotely controlling the testing equipment to obtain the test data and compare it with the standard value to evaluate the accuracy and reliability of the testing equipment. The sampling frequency is once a month. For testing agencies whose equipment accuracy does not meet the requirements, they are required to suspend their testing business and perform equipment calibration or maintenance.

[0081] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A motor vehicle inspection remote supervision system, characterized in that: include: Detection data acquisition module: It uses a multi-type sensor array and is installed on the motor vehicle inspection line to collect various vehicle inspection data, including exhaust emission data, vehicle speed data, braking performance data, light intensity data, and axle weight data. The exhaust emission sensor uses electrochemical sensor and infrared sensor fusion technology to detect multiple pollutants at the same time; the vehicle speed sensor uses the laser Doppler speed measurement principle; the braking performance sensor is a combination of a pressure sensor and a displacement sensor; the light intensity sensor uses a photodiode array; and the axle weight sensor uses a strain gauge sensor; The sensor and data acquisition unit are connected via a high-speed wired transmission network. The data acquisition unit has a real-time data cache function to capture dynamic data changes during vehicle detection. Data encryption transmission module: performs real-time encryption processing on the collected detection data, adopts national encryption algorithm, including SM4 encryption algorithm to encrypt the data, and the encryption key length is 128 bits; The encrypted data is transmitted to the remote monitoring center via a wireless communication network or a dedicated virtual private network VPN. The wireless communication module supports multi-band adaptive switching; Remote monitoring center module: equipped with a high-performance server cluster, the server uses Intel Xeon series multi-core processors; The data analysis and processing software runs on the server, which decrypts, analyzes and verifies the encrypted data. The analyzed data is stored in the database according to the vehicle inspection items. The database adopts a distributed storage architecture, including the Hadoop distributed file system. Intelligent analysis and early warning module: Based on big data analysis technology and artificial intelligence algorithms, it conducts real-time analysis of the detection data stored in the database, and uses deep learning algorithms, including convolutional neural networks, to build an abnormal detection model for vehicle detection data, including normal vehicle detection data and various types of faulty vehicle detection data; When abnormal vehicle detection data is detected, the system automatically generates warning information, which is pushed to the supervisor's terminal in a variety of ways; Inspection process monitoring module: A high-definition camera array is installed on the motor vehicle inspection line, which has night vision and intelligent image recognition functions. It can capture the key links of the vehicle inspection process in real time, including appearance inspection and online inspection, and automatically identify the vehicle model, license plate number, and inspection personnel's operating behavior information through image recognition technology; The video data collected by the camera is transmitted to the remote monitoring center in real time through the network, and the supervisor can view the video of the detection process in real time through the monitoring terminal; Testing agency management module: establish a testing agency information management database to store the basic information, personnel information, and equipment information of the testing agency. The database has data update and query functions; The testing business process of the testing agency is monitored and managed in real time. According to the testing standards and specifications, the testing agency's testing sequence, execution of testing items, timeliness of testing data upload, etc. are automatically checked and compared. When it is found that the testing agency has violated the regulations or does not comply with the testing process, the system automatically records the violation and generates a rectification notice.

2. The motor vehicle inspection remote supervision system according to claim 1 is characterized in that: The sensor array in the detection data acquisition module has an automatic calibration function. The calibration process uses a standard source for comparison, and the calibration data is automatically recorded and uploaded to the remote monitoring center.

3. The motor vehicle inspection remote supervision system according to claim 1, characterized in that: During the data transmission process, the data encryption transmission module uses digital certificate technology to authenticate the sender and receiver of the data.

4. The motor vehicle inspection remote supervision system according to claim 1, characterized in that: The intelligent analysis and early warning module in the remote supervision center module has self-learning and self-adaptation capabilities, and the model optimization cycle does not exceed 1 month.

5. The motor vehicle inspection remote supervision system according to claim 1, characterized in that: The high-definition camera array in the inspection process monitoring module uses intelligent tracking technology to automatically track the movement of the vehicle on the inspection line.

6. The motor vehicle inspection remote supervision system according to claim 1, characterized in that: The testing agency management module has the function of automatic review of testing reports. According to the preset review rules and standards, it automatically reviews the testing reports uploaded by the testing agencies. The review content includes the rationality of the test data, the completeness of the test items, and the accuracy of the conclusions. The review time does not exceed 5 minutes, and the review results are automatically fed back to the testing agency. For reports that fail the review, the testing agency needs to modify them and re-upload them for review.

7. A method for applying the motor vehicle inspection remote supervision system according to claims 1-6, characterized in that: The following steps are involved: Data collection step: by installing a multi-type high-precision sensor array on the motor vehicle inspection line, various vehicle inspection data are collected according to claim 1, including exhaust emissions, vehicle speed, braking performance, light intensity, and axle weight data. The sensor collects data in real time and transmits it to the data acquisition unit; Data encryption and transmission step: after receiving the sensor data, the data acquisition unit encrypts the data using the national secret algorithm as described in claim 3, adds a digital certificate for identity authentication, and then transmits the encrypted data to the remote monitoring center via a wireless communication network or a dedicated VPN; Data parsing and storage step: after receiving the encrypted data, the remote monitoring center uses the data parsing and processing software on the server to decrypt, parse and verify the data according to claim 1, and stores the parsed data in a distributed database according to the classification of vehicle inspection items; Intelligent analysis and early warning step: The intelligent analysis and early warning module performs real-time analysis on the detection data stored in the database based on big data analysis technology and artificial intelligence algorithms as described in claim 4, and uses the trained anomaly detection model to identify abnormal patterns and potential risks in the data. When an abnormality is detected, it generates early warning information as described in claim 1 and pushes it to the supervisor terminal; Inspection process monitoring step: using a high-definition camera array installed on the inspection line to shoot a video of the vehicle inspection process in real time according to claim 5, using intelligent image recognition technology to automatically identify vehicle and inspection personnel information, and transmitting video data to a remote monitoring center, and the supervisory personnel viewing the monitoring video in real time through the monitoring terminal according to claim 1; Testing agency management step: The testing agency management module establishes a testing agency information management database as described in claim 6, stores and manages testing agency related information, monitors the testing agency business process in real time, generates rectification notices for violations and tracks rectification status, and automatically reviews the testing report.

8. The motor vehicle inspection remote supervision method according to claim 7, characterized in that: In the data collection step, the sensor array performs a pre-detection before the vehicle enters the detection line to check whether the sensor is working normally. If a sensor fault is found, the system automatically alarms and prompts to replace the sensor.

9. The motor vehicle inspection remote supervision method according to claim 7, characterized in that: In the intelligent analysis and early warning step, after the early warning information is pushed to the supervisor's terminal, the supervisor uses the analysis tool of the remote supervision center to analyze the abnormal data, view data trends, compare historical data, and assist in determining the cause of the abnormality. The analysis tool has data visualization functions, including line graphs, bar graphs, and scatter graphs.

10. The motor vehicle inspection remote supervision method according to claim 7, characterized in that: In the testing agency management steps, regular remote spot checks are carried out on the testing equipment of the testing agency. Standard sample tests are carried out through remote control of the testing equipment. The test data is obtained and compared with the standard values ​​to evaluate the accuracy and reliability of the testing equipment. The frequency of spot checks is once a month.

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