An abnormal parking detection system and its detection method based on 5G recorder patrol inspection

Abstract

The present invention proposes an abnormal parking detection system based on 5G recorder patrol inspection, including a patrol inspection module, a video acquisition module, a GPS position module, an acceleration sensor module, a vehicle detection module, a direction detection module, a vehicle relative direction detection module, a vehicle grid position detection module, a vehicle grid storage module, a 5G end-to-end vehicle position broadcast module, a vehicle anomaly detection module, and a 5G end-to-end patrol inspection planning module; the present invention can accurately detect and determine vehicles in an abnormal parking state and give corresponding warnings; the present invention can identify the specific information of vehicles and can determine the actual state of vehicles with reference to the specific information documents of vehicles, improving the detection accuracy of patrol recorders; the present invention can calculate and provide a suitable patrol route for the device according to the actual situation of parked vehicles in the parking lot, reducing the usage difficulty of the device.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle parking detection, and particularly relates to an abnormal parking detection system based on 5G recorder patrol inspection and a detection method thereof. Background Art

[0002] Mobile computing devices provide the benefit of portability while allowing users to perform various functions including various forms of communication and computing; for example, some mobile devices can access the Internet, execute applications, and provide traditional mobile communication functions; some such mobile computing devices also include a Global Positioning System (GPS) element when used as a vehicle patrol inspection recorder, which can assist the vehicle patrol inspection recorder in determining its current position; through the GPS element, various functions of the vehicle patrol inspection recorder can be further assisted, such as joining a detection system to complete parking detection work, etc.

[0003] However, the existing parking detection systems added to vehicle patrol inspection recorders still have the following problems: 1. They do not have the ability to determine and detect abnormal vehicle parking states, resulting in errors in determining the parking duration of vehicles and the corresponding charging amounts; 2. They do not have corresponding image processing technologies, are unable to identify specific vehicle information, and are unable to determine the specific state of a vehicle with reference to relevant vehicle information; at the same time, the detection accuracy of other detection modules in the system is not fine enough; 3. They are unable to provide a suitable patrol route for the device according to the actual situation of the vehicle, increasing the difficulty of using the device.

[0004] For example, CN106461397A discloses the automatic detection of vehicle parking and position. In one example, a method includes determining that an angular change of a mobile computing device relative to gravity satisfies a threshold change amount based on first motion data, and initiating the storage of second motion data at a first positioning of the mobile computing device in response to determining that the angular change satisfies the threshold change amount, where the first positioning is a reference positioning. The method may further include determining a second positioning of the mobile computing device based on an external signal, where the second positioning is the current positioning of the mobile computing device different from the reference positioning, and determining the reference positioning of the mobile computing device based on the second motion data and the current positioning in response to determining the second positioning of the mobile computing device.

[0005] Although the above technical solution further improves the detection accuracy of the device for vehicle parking states, it still does not have the ability to determine and detect abnormal vehicle parking states, and is unable to complete the detection work well in case of abnormal vehicle parking.

[0006] For example, CN111461026A discloses a vehicle attitude recognition method, a method and device for detecting the state of a vehicle in a parking lot. The vehicle attitude recognition method includes: inputting an image to be recognized into a preset vehicle position detection model to obtain a first position information set and a second position information set, where the first position information set includes at least one vehicle position information, and the second position information set includes at least one vehicle head position information and / or at least one vehicle tail position information, and establishing a first association relationship according to the first position information set and the second position information set, where the first association relationship is used to record the corresponding relationship between each vehicle position information and the part position information. Determining the target part position information of the vehicle to be recognized according to the first association relationship and the vehicle position of the vehicle to be recognized, and determining the attitude information of the vehicle to be recognized according to the target part position information of the vehicle to be recognized, so as to improve the accuracy of determining the vehicle attitude, and thus improve the detection accuracy of the vehicle state in the parking lot.

[0007] The above technical solution adds image processing technology to the detection module, which can further identify the specific attitude of the vehicle and improve the detection accuracy. However, only one detection method of image processing is used in the above technical solution, the detection means of the detection device is relatively single, and the usage scenario is also relatively limited. Moreover, the above technical solution does not have the determination detection for the abnormal parking state of the vehicle. Therefore, there is still a large room for improvement in this technical solution.

[0008] In view of the defects existing in the above-mentioned prior art, there is an urgent need for a detection system and its detection method suitable for a vehicle inspection recorder, which can realize the determination detection for the abnormal parking state of the vehicle, and ensure that the detection accuracy of the recorder is more precise through the cooperation and calculation of multiple detection modules, and judge the parking state of the vehicle more accurately. Summary of the Invention

[0009] The purpose of the present invention is to provide an abnormal parking detection system and its detection method based on 5G recorder inspection, which are used to solve the problems existing in the existing vehicle inspection recorders; the system includes multiple detection modules, multiple detection and calculation means, which can accurately detect and determine the vehicles in the abnormal parking state, and give corresponding warnings to prevent errors in the determination of the parking duration of the vehicle by the relevant parking lot, resulting in incorrect charging; at the same time, the system has corresponding image processing technology, which can identify the specific information of the vehicle and can determine the actual state of the vehicle with the specific information book of the vehicle as a reference, further improving the detection accuracy of the inspection recorder; at the same time, the system also has corresponding inspection planning and inspection modules, which can calculate and provide a suitable inspection route for the equipment according to the actual situation of the parked vehicles in the parking lot, further reducing the usage difficulty of the equipment.

[0010] To achieve the above object, the present invention proposes an abnormal parking detection system based on 5G recorder patrol, including a patrol module, a video acquisition module, a GPS position module, an acceleration sensor module, a vehicle detection module, a direction detection module, a vehicle relative direction detection module, a vehicle grid position detection module, a vehicle grid storage module, a 5G end-to-end vehicle position broadcast module, a vehicle anomaly detection module, and a 5G end-to-end patrol planning module;

[0011] The patrol module is wirelessly connected to the 5G end-to-end patrol planning module, and the patrol module can receive and store the patrol route transmitted by the 5G end-to-end patrol planning module;

[0012] The GPS position module is wirelessly connected to the patrol module, and the GPS position module can receive the patrol route transmitted by the patrol module;

[0013] The video acquisition module is wirelessly connected to the vehicle detection module, and the vehicle detection module can receive the video frame data transmitted by the video acquisition module;

[0014] The vehicle detection module is wirelessly connected to the vehicle relative direction detection module, and the vehicle relative direction detection module can receive the specific vehicle information data transmitted by the vehicle detection module;

[0015] The direction detection module is wirelessly connected to the acceleration sensor module, and the direction detection module can receive the sensor data of multiple orientations transmitted by the acceleration sensor module; the direction detection module is wirelessly connected to the GPS position module, and the direction detection module can receive the position coordinates of the recorder transmitted by the GPS position module;

[0016] The vehicle grid position detection module is wirelessly connected to the GPS position module, and the vehicle grid position detection module can receive the position coordinates of the recorder transmitted by the GPS position module; the vehicle grid position detection module is wirelessly connected to the direction detection module, and the vehicle grid position detection module can receive the minimum variance estimate value of the movement direction of the recorder transmitted by the direction detection module; the vehicle grid position detection module is wirelessly connected to the vehicle relative direction detection module, and the vehicle grid position detection module can receive the deviation angle, distance and specific vehicle information data of the vehicle relative to the recorder transmitted by the vehicle relative direction detection module;

[0017] The vehicle grid position detection module is wirelessly connected to the vehicle grid storage module, the 5G end-to-end vehicle position broadcast module, and the vehicle anomaly detection module respectively, and the vehicle grid position detection module can sequentially send the vehicle grid number and the specific vehicle information data to the vehicle anomaly detection module, the vehicle grid storage module, and the 5G end-to-end vehicle position broadcast module;

[0018] The vehicle grid storage module is wirelessly connected to the vehicle anomaly detection module, and the vehicle anomaly detection module can receive the vehicle index information transmitted by the vehicle grid storage module;

[0019] The 5G end-to-end vehicle position broadcast module is wirelessly connected to the 5G end-to-end patrol inspection planning module, and the 5G end-to-end patrol inspection planning module can receive the vehicle grid number and vehicle specific information data transmitted by the 5G end-to-end vehicle position broadcast module.

[0020] The present invention realizes an image processing technology through the mutual cooperation of a video acquisition module, a vehicle detection module, and a vehicle relative direction detection module, which can identify the specific information of a vehicle, calculate the deviation angle and distance of the vehicle relative to the recorder, and detect the specific information data of the vehicle, and can determine the actual state of the vehicle with the specific information data of the vehicle as a reference; through the cooperation of multiple modules such as a patrol inspection module, a GPS position module, an acceleration sensor module, and a direction detection module, the present invention can obtain the position coordinates of the recorder and the minimum variance estimate value of the moving direction of the recorder; the vehicle grid position detection module can add the deviation angle of the vehicle relative to the recorder obtained above to the minimum variance estimate value of the moving direction of the recorder to obtain the direction of the vehicle relative to the recorder; and the vehicle grid position detection module calculates the position coordinates of the vehicle according to the position coordinates of the recorder, the direction of the vehicle relative to the recorder, and the distance between the vehicle and the recorder, and generates a specific grid number for the position coordinates of the vehicle; further improving the detection accuracy of the patrol inspection recorder for the parked state of the vehicle.

[0021] The present invention can accurately detect and determine a vehicle in an abnormal parking state through the mutual cooperation of a vehicle grid position detection module, a vehicle grid storage module, and a vehicle anomaly detection module, and give corresponding warnings;

[0022] The present invention can calculate and provide a suitable patrol inspection route for the device according to the actual situation of the parked vehicles in the parking area through the mutual cooperation of a vehicle grid position detection module, a 5G end-to-end vehicle position broadcast module, a 5G end-to-end patrol inspection planning module, and a patrol inspection module, further reducing the use difficulty of the device.

[0023] Preferably, the GPS position module can collect GPS satellite signals and can locate the current position coordinates of the recorder through time difference; the acceleration sensor module can receive the sensor data of three orientations in the three-dimensional space transmitted by the triaxial sensor.

[0024] Preferably, the vehicle specific information data transmitted by the vehicle detection module to the vehicle relative direction detection module includes license plate number, license plate position, license plate size, and vehicle type information;

[0025] The vehicle specific information data transmitted by the vehicle relative direction detection module to the vehicle grid position detection module includes the license plate number and vehicle type information;

[0026] The vehicle specific information data sequentially sent by the vehicle grid position detection module to the vehicle anomaly detection module, vehicle grid storage module, and 5G end-to-end vehicle position broadcast module includes the license plate number and vehicle type information;

[0027] The vehicle specific information data transmitted by the 5G end-to-end vehicle position broadcast module to the 5G end-to-end inspection planning module includes the license plate number and vehicle type information.

[0028] The present invention also proposes a detection method for an abnormal parking detection system based on 5G recorder inspection, including the following steps:

[0029] S1. The inspection module receives the inspection route from the 5G end-to-end inspection planning module; the inspection module stores the inspection route; the inspection module transmits the inspection route to the GPS position module for guiding the inspection personnel to conduct inspections;

[0030] S2. The GPS position module receives the inspection route from the inspection module and conducts direction guidance based on the current position of the recorder; and the GPS position module sends the current position coordinates of the recorder to the vehicle grid position detection module;

[0031] S3. The video acquisition module captures the picture in front of the recorder, generates video frame data and transmits it to the vehicle detection module;

[0032] S4. The vehicle detection module detects the license plate number, license plate position, license plate size, and vehicle type information included in the video frame data;

[0033] S5. The vehicle relative direction detection module detects the deviation angle of the vehicle relative to the recorder;

[0034] S6. The acceleration sensor module sends the sensor data in three directions in the three-dimensional space to the direction detection module;

[0035] S7. The direction detection module detects the movement direction of the recorder;

[0036] S8. The vehicle grid position detection module detects the vehicle grid number;

[0037] S9. The vehicle grid storage module in the cloud center stores the vehicle index information;

[0038] S10. The 5G end-to-end vehicle position broadcast module receives the vehicle grid number, license plate number, and vehicle model information from the vehicle grid position detection module. The 5G end-to-end vehicle position broadcast module uses the device-to-device (D2D) communication technology of 5G to send to other surrounding recorder devices to inspect and recheck whether the vehicle is overdue. The 5G end-to-end vehicle position broadcast module sends the vehicle grid number, license plate number, and vehicle model information to the 5G end-to-end inspection planning module of other recorders.

[0039] S11. The vehicle anomaly detection module detects vehicle anomaly information.

[0040] S12. The 5G end-to-end inspection planning module receives the vehicle grid number, license plate number, and vehicle model information from the 5G end-to-end vehicle position broadcast module; and re-plans and generates an inspection route to be transmitted to the inspection module. The 5G end-to-end inspection planning module calculates the coordinate range of this area according to the vehicle network number. The network number is split into the x reference value and the y reference value according to the space character, and the coordinates of the lower left vertex are obtained by multiplying them by the grid quantization value Q as x, y, and the coordinates of the upper right vertex are obtained by adding the grid quantization value Q respectively as x’, y’. The 5G end-to-end inspection planning module re-plans and generates an inspection route that passes through the area constructed by the coordinates of the lower left vertex as x, y and the coordinates of the upper right vertex as x’, y’. The 5G end-to-end inspection planning module re-plans and generates an inspection route to send to the inspection module of the recorder.

[0041] Preferably, the step S4 further includes:

[0042] S4.1 The vehicle detection module receives the video frame image transmitted by the video acquisition module.

[0043] S4.2 The vehicle detection module locates the position and size of the license plate according to the YOLO algorithm.

[0044] S4.3 The vehicle detection module extracts the license plate image according to the position and size of the license plate.

[0045] S4.4 The vehicle detection module uses the Hough transform method to horizontally correct the license plate image according to the upper and lower borders of the license plate.

[0046] S4.5 The vehicle detection module uses the Hough transform method to vertically correct the license plate image according to the left and right borders.

[0047] S4.6 The vehicle detection module performs character segmentation on the license plate image according to the character interval characteristics to obtain character images.

[0048] S4.7 The vehicle detection module uses the template matching method to perform character recognition on each character image to obtain the license plate number.

[0049] S4.8 The vehicle detection module expands the image up and down according to the license plate position, matches the vehicle model using the YOLO algorithm, and obtains the vehicle model information of the current vehicle;

[0050] S4.9 The vehicle detection module sends the license plate number, license plate position, license plate size, and vehicle model information to the vehicle relative direction detection module.

[0051] Preferably, the step S5 further includes:

[0052] S5.1 The vehicle relative direction detection module receives the license plate number, license plate position, license plate size, and vehicle model information transmitted by the vehicle detection module;

[0053] S5.2 The vehicle relative direction detection module divides the position coordinates of the license plate by the width of the video frame image, and then multiplies by the current horizontal viewing angle of the recorder to obtain the deviation angle between the vehicle and the recorder;

[0054] S5.3 The vehicle relative direction detection module divides the size of the license plate by the width of the video frame image, and then multiplies by the current horizontal viewing angle of the recorder to obtain the license plate angle. According to the lens focal length of the recorder and the size of the standard license plate, the lens formula is used to calculate the distance between the vehicle and the recorder;

[0055] S5.4 The vehicle relative direction detection module sends the deviation angle between the vehicle and the recorder, the distance between the vehicle and the recorder, the license plate number, and the vehicle model information to the vehicle grid position detection module.

[0056] Preferably, the step S7 further includes:

[0057] S7.1 The direction detection module receives the recorder position coordinates transmitted by the GPS position module, connects them with the previously saved recorder position coordinates to form a vector, and determines the GPS movement direction of the recorder;

[0058] S7.2 The direction detection module receives the sensor data of three orientations in the three-dimensional space transmitted by the acceleration sensor module. The direction detection module judges whether the recorder is worn horizontally or vertically according to the horizontal sensor data; when worn horizontally, the direction detection module takes the z and y direction data for calculation; when worn vertically, the direction detection module takes the x and y direction data for calculation;

[0059] S7.3 The direction detection module obtains the steering vector of the recorder according to the x and y direction data by vector addition. According to the previously saved movement direction of the recorder by the direction detection module, the correction angle of the steering vector is superimposed to obtain the sensor movement direction of the recorder;

[0060] S7.4 The direction detection module takes the GPS movement direction as the calculated value of the Kalman filter;

[0061] The S7.5 direction detection module uses the sensor's movement direction as the state calculation value of the Kalman filter, and finally obtains the minimum variance estimate value of the movement direction of the Kalman filter.

[0062] The S7.6 direction detection module sends the minimum variance estimate value of the movement direction to the vehicle grid position detection module.

[0063] Preferably, the step S8 further includes:

[0064] S8.1 The vehicle grid position detection module receives the position coordinates of the recorder transmitted by the GPS position module.

[0065] S8.2 The vehicle grid position detection module receives the minimum variance estimate value of the movement direction transmitted by the direction detection module.

[0066] S8.3 The vehicle grid position detection module receives the deviation angle of the vehicle relative to the recorder, the distance between the vehicle and the recorder, and the license plate number and vehicle type information transmitted by the vehicle relative direction detection module.

[0067] S8.4 The vehicle grid position detection module adds the minimum variance estimate value of the movement direction to the deviation angle of the vehicle relative to the recorder to obtain the direction of the vehicle relative to the recorder.

[0068] S8.5 The vehicle grid position detection module calculates the position coordinates of the vehicle based on the position coordinates of the recorder, the direction of the vehicle relative to the recorder, and the distance between the vehicle and the recorder.

[0069] S8.6 The vehicle grid position detection module generates a grid number according to the position coordinates of the vehicle and the grid quantization value Q; the grid number is equal to the position abscissa / Q, the position ordinate / Q, and the strings are connected, with adjacent values separated by spaces.

[0070] The vehicle grid position detection module of the recorder sends the vehicle grid number, license plate number, and vehicle type information to the vehicle anomaly detection module, the vehicle grid storage module, and the 5G end-to-end vehicle position broadcast module in sequence.

[0071] Preferably, the step S9 further includes:

[0072] S9.1 The vehicle grid storage module receives the vehicle grid number, license plate number, and vehicle type information from the vehicle grid position detection modules of each recorder.

[0073] S9.2 The vehicle grid storage module establishes an indexing method that can index the corresponding grid number, vehicle type information, and indexing establishment time through the license plate number.

[0074] If the index of the license plate number already exists in the vehicle grid storage module S9.3, then it is judged whether the grid number has changed. If the grid number has changed, the index information is rebuilt; if the grid number has not changed, the index is refreshed for keep-alive.

[0075] The vehicle grid storage module S9.4 establishes an index aging mechanism. If the vehicle grid position detection module has not updated the license plate number after exceeding the time threshold, the license plate number index information, grid number, and vehicle type information stored in the vehicle grid storage module will be cleared.

[0076] The vehicle grid storage module in the cloud center S9.5 provides an index query service for the vehicle anomaly detection module with the license plate number as the search term.

[0077] Preferably, the step S11 further includes:

[0078] The vehicle anomaly detection module S11.1 receives the vehicle grid number, license plate number, and vehicle type information transmitted by the vehicle grid position detection module.

[0079] The vehicle anomaly detection module S11.2 uses the license plate number to query the grid number, vehicle type information, and index establishment time from the vehicle grid storage module in the cloud center.

[0080] If the grid number received by the vehicle anomaly detection module S11.3 is the same as the grid number queried through the vehicle grid storage module index, then it is further judged whether the time difference between the index establishment time and the current time of the recorder exceeds the parking timeout threshold. If it exceeds, the recorder generates an audible and visual alarm and reminds the inspection personnel to handle it.

[0081] If the vehicle type information received by the vehicle anomaly detection module S11.4 is different from the vehicle type information queried through the vehicle grid storage module index, the recorder generates an audible and visual alarm and reminds the inspection personnel to handle it.

[0082] The present invention has the following beneficial effects compared with the prior art:

[0083] 1. The present invention realizes an image processing technology through the mutual cooperation of a video acquisition module, a vehicle detection module, and a vehicle relative direction detection module, which can identify the specific information of a vehicle, calculate the deviation angle and distance of the vehicle relative to the recorder, and detect the specific information data of the vehicle, and can determine the actual state of the vehicle with the specific information data of the vehicle as a reference; the present invention can obtain the position coordinates of the recorder and the minimum variance estimate value of the moving direction of the recorder through the cooperation of multiple modules such as an inspection module, a GPS position module, an acceleration sensor module, and a direction detection module; the vehicle grid position detection module can add the deviation angle of the vehicle relative to the recorder obtained above to the minimum variance estimate value of the moving direction of the recorder to obtain the direction of the vehicle relative to the recorder; and the vehicle grid position detection module calculates the position coordinates of the vehicle according to the position coordinates of the recorder, the direction of the vehicle relative to the recorder, and the distance between the vehicle and the recorder, and generates a specific grid number for the position coordinates of the vehicle; further improving the detection accuracy of the inspection recorder for the parking state of the vehicle.

[0084] Through the mutual cooperation of the vehicle grid position detection module, the vehicle grid storage module, and the vehicle anomaly detection module, the present invention can accurately detect and determine vehicles in an abnormal parking state and give corresponding warnings;

[0085] Through the mutual cooperation of the vehicle grid position detection module, the 5G end-to-end vehicle position broadcast module, the 5G end-to-end inspection planning module, and the inspection module, the present invention can calculate and provide a suitable inspection route for the device according to the actual situation of the parked vehicles in the parking lot, further reducing the use difficulty of the device.

[0086] 2. By planning the multi-recorder inspection route and using the grid position standardization area, the present invention can further check for overtime parking of vehicles in an abnormal parking state, avoiding the high costs caused by setting up large-scale fixed vehicle analysis equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0088] Figure 1 It is a schematic diagram of the connection method and working principle between the various modules in the present invention;

[0089] Among them, the inspection module 1, video acquisition module 2, GPS position module 3, acceleration sensor module 4, vehicle detection module 5, direction detection module 6, vehicle relative direction detection module 7, vehicle grid position detection module 8, vehicle grid storage module 9, 5G end-to-end vehicle position broadcast module 10, vehicle anomaly detection module 11, and 5G end-to-end inspection planning module 12 shown in the figure. Detailed implementation

[0090] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below 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. The components of the embodiments of the present invention usually described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. 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 scope of protection of the present invention.

[0091] As Figure 1 shown, the present invention proposes an abnormal parking detection system based on 5G recorder inspection, including an inspection module 1, a video acquisition module 2, a GPS position module 3, an acceleration sensor module 4, a vehicle detection module 5, a direction detection module 6, a vehicle relative direction detection module 7, a vehicle grid position detection module 8, a vehicle grid storage module 9, a 5G end-to-end vehicle position broadcast module 10, a vehicle anomaly detection module 11, and a 5G end-to-end inspection planning module 12;

[0092] The inspection module 1 is wirelessly connected to the 5G end-to-end inspection planning module 12, and the inspection module 1 can receive and store the inspection route transmitted by the 5G end-to-end inspection planning module 12;

[0093] The GPS position module 3 is wirelessly connected to the inspection module 1, and the GPS position module 3 can receive the inspection route transmitted by the inspection module 1;

[0094] The video acquisition module 2 is wirelessly connected to the vehicle detection module 5, and the vehicle detection module 5 can receive the video frame data transmitted by the video acquisition module 2;

[0095] The vehicle detection module 5 is wirelessly connected to the vehicle relative direction detection module 7, and the vehicle relative direction detection module 7 can receive the specific vehicle information data transmitted by the vehicle detection module 5;

[0096] The direction detection module 6 is wirelessly connected to the acceleration sensor module 4, and the direction detection module 6 can receive sensor data in multiple orientations transmitted by the acceleration sensor module 4; the direction detection module 6 is wirelessly connected to the GPS position module 3, and the direction detection module 6 can receive the position coordinates of the recorder transmitted by the GPS position module 3;

[0097] The vehicle grid position detection module 8 is wirelessly connected to the GPS position module 3, and the vehicle grid position detection module 8 can receive the position coordinates of the recorder transmitted by the GPS position module 3; the vehicle grid position detection module 8 is wirelessly connected to the direction detection module 6, and the vehicle grid position detection module 8 can receive the minimum variance estimate value of the movement direction of the recorder transmitted by the direction detection module 6; the vehicle grid position detection module 8 is wirelessly connected to the vehicle relative direction detection module 7, and the vehicle grid position detection module 8 can receive the deviation angle, distance, and specific information data of the vehicle relative to the recorder transmitted by the vehicle relative direction detection module 7;

[0098] The vehicle grid position detection module 8 is wirelessly connected to the vehicle grid storage module 9, the 5G end-to-end vehicle position broadcast module 10, and the vehicle anomaly detection module 11 respectively, and the vehicle grid position detection module 8 can sequentially send the vehicle grid number and the vehicle specific information data to the vehicle anomaly detection module 11, the vehicle grid storage module 9, and the 5G end-to-end vehicle position broadcast module 10;

[0099] The vehicle grid storage module 9 is wirelessly connected to the vehicle anomaly detection module 11, and the vehicle anomaly detection module 11 can receive the vehicle index information transmitted by the vehicle grid storage module 9;

[0100] The 5G end-to-end vehicle position broadcast module 10 is wirelessly connected to the 5G end-to-end inspection planning module 12, and the 5G end-to-end inspection planning module 12 can receive the vehicle grid number and the vehicle specific information data transmitted by the 5G end-to-end vehicle position broadcast module 10.

[0101] The present invention also proposes a detection method for an abnormal parking detection system based on 5G recorder inspection, including the following steps: S1. The inspection module 1 receives the inspection route of the 5G end-to-end inspection planning module 12; the inspection module 1 stores the inspection route; the inspection module 1 transmits the inspection route to the GPS position module 3;

[0102] S2. The GPS position module 3 receives the inspection route of the inspection module 1 and performs direction guidance according to the current position of the recorder; and the GPS position module 3 sends the current position coordinates of the recorder to the vehicle grid position detection module 8;

[0103] S3. The video acquisition module 2 acquires the image in front of the recorder, generates video frame data and transmits it to the vehicle detection module 5;

[0104] S4. The vehicle detection module 5 detects the license plate number, license plate position, license plate size and vehicle type information contained in the video frame data;

[0105] S5. The vehicle relative direction detection module 7 detects the deviation angle of the vehicle relative to the recorder;

[0106] S6. The acceleration sensor module 4 sends the sensor data in three directions in the three-dimensional space to the direction detection module 6;

[0107] S7. The direction detection module 6 detects the moving direction of the recorder;

[0108] S8. The vehicle grid position detection module 8 detects the vehicle grid number;

[0109] S9. The vehicle grid storage module 9 in the cloud center stores the vehicle index information;

[0110] S10. The 5G end-to-end vehicle position broadcast module 10 receives the vehicle grid number, license plate number and vehicle type information from the vehicle grid position detection module 8; the 5G end-to-end vehicle position broadcast module 10 sends to other surrounding recorder devices through the device-to-device (D2D) communication technology of 5G to check whether the vehicle is overdue; the 5G end-to-end vehicle position broadcast module 10 sends the vehicle grid number, license plate number and vehicle type information to the 5G end-to-end patrol planning module 12 of other recorders.

[0111] S11. The vehicle anomaly detection module 11 detects vehicle anomaly information;

[0112] S12. The 5G end-to-end patrol planning module 12 receives the vehicle grid number, license plate number and vehicle type information from the 5G end-to-end vehicle position broadcast module 10, and re-plans and generates a patrol route to transmit to the patrol module 1. The 5G end-to-end patrol planning module 12 calculates the coordinate range of the area according to the vehicle network number. The network number is split into the x reference value and the y reference value according to the space character, and multiplied by the grid quantization value Q respectively to obtain the coordinates of the lower left vertex as x, y, and added with the grid quantization value Q respectively to obtain the coordinates of the upper right vertex as x', y'. The 5G end-to-end patrol planning module 12 re-plans and generates a patrol route to pass through the area constructed by the coordinates of the lower left vertex as x, y and the coordinates of the upper right vertex as x', y'; the 5G end-to-end patrol planning module 12 re-plans and generates a patrol route to send to the patrol module 1 of the recorder.

[0113] Embodiment 1 (Regarding how the present invention further improves the detection accuracy of the patrol recorder for the vehicle parking state):

[0114] The patrol inspection module 1 of the present invention receives the patrol inspection route of the 5G end-to-end patrol inspection planning module 12 and stores it. Subsequently, the patrol inspection module 1 transmits the patrol inspection line to the GPS position module 3;

[0115] After receiving the patrol inspection line from the patrol inspection module 1, the GPS position module 3 conducts direction guidance based on the current position of the recorder, and the GPS position module 3 sends the current position coordinates of the recorder to the vehicle grid position detection module 8;

[0116] The video acquisition module 2 acquires the picture in front of the recorder, generates video frame data and transmits it to the vehicle detection module 5;

[0117] The vehicle detection module 5 receives the video frame data transmitted by the video acquisition module 2, and detects and reads the specific vehicle information contained in the video frame data. The vehicle detection module 5 locates the position and size of the license plate according to the YOLO algorithm. The vehicle detection module 5 extracts the license plate image according to the position and size of the license plate. The vehicle detection module 5 uses the Hough transform method to horizontally correct the license plate image according to the upper and lower borders of the license plate, the vehicle detection module 5 uses the Hough transform method to vertically correct the license plate image according to the left and right borders, the vehicle detection module 5 segments the license plate image into character images according to the character interval characteristics, the vehicle detection module 5 uses the template matching method to perform character recognition on each character image to obtain the license plate number, the vehicle detection module 5 expands the image vertically and horizontally according to the license plate position, matches the vehicle type using the YOLO algorithm, and obtains the vehicle type information of the current vehicle. The vehicle detection module 5 sends the license plate number, license plate position, license plate size, and vehicle type information to the vehicle relative direction detection module 7;

[0118] The vehicle relative direction detection module 7 receives the license plate number, license plate position, license plate size, and vehicle type information transmitted by the vehicle detection module 5. The vehicle relative direction detection module 7 divides the position coordinates of the license plate by the width of the video frame image, and then multiplies by the current horizontal viewing angle of the recorder to obtain the deviation angle between the vehicle and the recorder. The vehicle relative direction detection module 7 divides the size of the license plate by the width of the video frame image, and then multiplies by the current horizontal viewing angle of the recorder to obtain the license plate angle. According to the lens focal length of the recorder and other lenses and the size of the standard license plate, the vehicle-to-recorder distance is calculated using the lens calculation formula. The vehicle relative direction detection module 7 sends the deviation angle of the vehicle relative to the recorder, the distance between the vehicle and the recorder, the license plate number, and the vehicle type information to the vehicle grid position detection module 8;

[0119] The acceleration sensor module 4 sends the sensor data in three directions in the three-dimensional space to the direction detection module 6;

[0120] The direction detection module 6 receives the recorder position coordinates transmitted by the GPS position module 3, connects them with the previously saved recorder position coordinates to form a vector, and determines the GPS movement direction of the recorder. The direction detection module 6 receives the sensor data of three orientations in the three-dimensional space transmitted by the acceleration sensor module 4. The direction detection module 6 judges whether the recorder is worn horizontally or vertically according to the horizontal sensor data. When worn horizontally, the direction detection module 6 takes the z and y direction data for calculation. When worn vertically, the direction detection module 6 takes the x and y direction data for calculation. The direction detection module 6 obtains the steering vector of the recorder and other objects according to the x and y direction data by vector addition. According to the previously saved recorder movement direction of the direction detection module 6, the correction angle of the steering vector is superimposed to obtain the sensor movement direction of the recorder. The direction detection module 6 takes the GPS movement direction as the calculated value of the Kalman filter. The direction detection module 6 takes the sensor movement direction as the state calculated value of the Kalman filter, and finally obtains the minimum variance estimated value of the movement direction of the Kalman filter. The direction detection module 6 sends the minimum variance estimated value of the movement direction to the vehicle grid position detection module 8;

[0121] The vehicle grid position detection module 8 receives the position coordinates of the recorder transmitted by the GPS position module 3. The vehicle grid position detection module 8 receives the minimum variance estimated value of the movement direction transmitted by the direction detection module 6. The vehicle grid position detection module 8 receives the deviation angle of the vehicle relative to the recorder, the distance between the vehicle and the recorder, and the license plate number and vehicle type information transmitted by the vehicle relative direction detection module 7. The vehicle grid position detection module 8 adds the minimum variance estimated value of the movement direction to the deviation angle of the vehicle relative to the recorder to obtain the direction of the vehicle relative to the recorder. The vehicle grid position detection module 8 calculates the position coordinates of the vehicle according to the position coordinates of the recorder, the direction of the vehicle relative to the recorder, and the distance between the vehicle and the recorder. The vehicle grid position detection module 8 generates a grid number according to the position coordinates of the vehicle, according to the grid quantization value Q; the grid number is equal to the position abscissa / Q, the position ordinate / Q, and the strings are connected, and adjacent values are separated by spaces.

[0122] Embodiment 2 (regarding how the present invention accurately detects and determines a vehicle in an abnormal parking state):

[0123] The vehicle grid position detection module 8 of the present invention sends the vehicle grid number, license plate number, and vehicle type information to the vehicle anomaly detection module 11 and the vehicle grid storage module 9;

[0124] The vehicle grid storage module 9 receives the vehicle grid number, license plate number, and vehicle model information of the vehicle grid position detection module 8 of each recorder. The vehicle grid storage module 9 establishes an indexing method that can index to the corresponding grid number, vehicle model information, and indexing establishment time through the license plate number. If the index for the license plate number already exists in the vehicle grid storage module 9, it determines whether the grid number has changed. If the grid number has changed, it reconstructs the index information. If the grid number has not changed, it performs a keep-alive refresh of the index. The vehicle grid storage module 9 establishes an index aging mechanism. If the license plate number has not been updated by the vehicle grid position detection module 8 after exceeding the time threshold, the license plate number index information, grid number, and vehicle model information stored in the vehicle grid storage module 9 will be cleared. The vehicle grid storage module 9 in the cloud center provides an index query service for the vehicle anomaly detection module 11 with the license plate number as the search term;

[0125] The vehicle anomaly detection module 11 receives the vehicle grid number, license plate number, and vehicle model information transmitted by the vehicle grid position detection module 8. The vehicle anomaly detection module 11 uses the license plate number to index and query the grid number, vehicle model information, and indexing establishment time from the vehicle grid storage module 9 in the cloud center. If the grid number received by the vehicle anomaly detection module 11 from the vehicle grid position detection module 8 is the same as the grid number queried through the vehicle grid storage module 9, it continues to determine whether the time difference between the indexing establishment time and the current time of the recorder exceeds the parking timeout threshold. If it exceeds, the recorder generates an audible and visual alarm. If the vehicle model information received by the vehicle anomaly detection module 11 from the vehicle grid position detection module 8 is different from the vehicle model information queried through the vehicle grid storage module 9, the recorder generates an audible and visual alarm.

[0126] Embodiment 3 (Regarding how the present invention calculates and provides a suitable inspection route for the device):

[0127] The vehicle grid position detection module 8 of the present invention sends the vehicle grid number, license plate number, and vehicle model information to the 5G end-to-end vehicle position broadcast module 10;

[0128] The 5G end-to-end vehicle position broadcast module 10 receives the vehicle grid number, license plate number, and vehicle model information of the vehicle grid position detection module 8. The 5G end-to-end vehicle position broadcast module 10 uses the 5G device-to-device (D2D) communication technology to send to other surrounding recorder devices for inspection and recheck whether the vehicle is overtime. The 5G end-to-end vehicle position broadcast module 10 sends the vehicle grid number, license plate number, and vehicle model information to the 5G end-to-end inspection planning module 12 of other recorders;

[0129] The 5G end-to-end inspection planning module 12 receives the vehicle grid number, license plate number, and vehicle model information from the 5G end-to-end vehicle position broadcasting module 10, and re-plans and generates an inspection route for transmission to the inspection module 1. The 5G end-to-end inspection planning module 12 calculates the coordinate range of the area based on the vehicle network number. The network number is split into the x reference value and the y reference value according to the space character, and the coordinates of the lower left vertex are obtained by multiplying them by the grid quantization value Q as x, y, and the coordinates of the upper right vertex are obtained by adding the grid quantization value Q respectively as x', y'. The 5G end-to-end inspection planning module 12 re-plans and generates an inspection route that passes through the area constructed by the coordinates of the lower left vertex x, y and the upper right vertex x', y'. The 5G end-to-end inspection planning module 12 re-plans and generates an inspection route and sends it to the inspection module 1 of the recorder.

[0130] Certainly, the above are only specific embodiments of the present invention and do not limit the scope of implementation of the present invention. Any equivalent changes or modifications made according to the structure, features, and principles described in the scope of the patent application of the present invention should be included in the scope of the patent application of the present invention.

[0131] Finally, it should be noted that: the above embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments or can easily think of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An abnormal parking detection system based on 5G recorder patrol, characterized in that, It includes an inspection module (1), a video acquisition module (2), a GPS location module (3), an acceleration sensor module (4), a vehicle detection module (5), a direction detection module (6), a vehicle relative direction detection module (7), a vehicle grid location detection module (8), a vehicle grid storage module (9), a 5G end-to-end vehicle location broadcast module (10), a vehicle anomaly detection module (11), and a 5G end-to-end inspection planning module (12); The inspection module (1) is wirelessly connected to the 5G end-to-end inspection planning module (12), and the inspection module (1) can receive and store the inspection route transmitted by the 5G end-to-end inspection planning module (12); The GPS location module (3) is wirelessly connected to the inspection module (1), and the GPS location module (3) can receive the inspection route transmitted by the inspection module (1); The video acquisition module (2) is wirelessly connected to the vehicle detection module (5), and the vehicle detection module (5) can receive the video frame data transmitted by the video acquisition module (2); The vehicle detection module (5) is wirelessly connected to the vehicle relative direction detection module (7), and the vehicle relative direction detection module (7) can receive the specific vehicle information data transmitted by the vehicle detection module (5); The direction detection module (6) is wirelessly connected to the acceleration sensor module (4), and the direction detection module (6) can receive the sensor data of multiple orientations transmitted by the acceleration sensor module (4); The direction detection module (6) is wirelessly connected to the GPS location module (3), and the direction detection module (6) can receive the position coordinates of the recorder transmitted by the GPS location module (3); The vehicle grid location detection module (8) is wirelessly connected to the GPS location module (3), and the vehicle grid location detection module (8) can receive the position coordinates of the recorder transmitted by the GPS location module (3); The vehicle grid location detection module (8) is wirelessly connected to the direction detection module (6), and the vehicle grid location detection module (8) can receive the minimum variance estimate value of the movement direction of the recorder transmitted by the direction detection module (6); The vehicle grid location detection module (8) is wirelessly connected to the vehicle relative direction detection module (7), and the vehicle grid location detection module (8) can receive the deviation angle, distance between the vehicle and the recorder, and the specific vehicle information data transmitted by the vehicle relative direction detection module (7); The vehicle grid location detection module (8) is wirelessly connected to the vehicle grid storage module (9), the 5G end-to-end vehicle location broadcast module (10), and the vehicle anomaly detection module (11) respectively, and the vehicle grid location detection module (8) can sequentially send the vehicle grid number and the specific vehicle information data to the vehicle anomaly detection module (11), the vehicle grid storage module (9), and the 5G end-to-end vehicle location broadcast module (10); The vehicle grid storage module (9) is wirelessly connected to the vehicle anomaly detection module (11), and the vehicle anomaly detection module (11) can receive the vehicle index information transmitted by the vehicle grid storage module (9); The 5G end-to-end vehicle position broadcast module (10) is wirelessly connected to the 5G end-to-end inspection planning module (12), and the 5G end-to-end inspection planning module (12) can receive the vehicle grid number and vehicle specific information data transmitted by the 5G end-to-end vehicle position broadcast module (10).

2. The abnormal parking detection system based on 5G recorder patrol according to claim 1, characterized in that, The GPS position module (3) can collect GPS satellite signals and can locate the current position coordinates through a time difference locator; the acceleration sensor module (4) can receive sensor data in three directions in the three-dimensional space transmitted by a three-axis sensor.

3. The abnormal parking detection system based on 5G recorder patrol according to claim 1, wherein The vehicle specific information data transmitted by the vehicle detection module (5) to the vehicle relative direction detection module (7) includes license plate number, license plate position, license plate size, and vehicle type information. The vehicle specific information data transmitted by the vehicle relative direction detection module (7) to the vehicle grid position detection module (8) includes license plate number and vehicle type information. The vehicle specific information data sequentially sent by the vehicle grid position detection module (8) to the vehicle anomaly detection module (11), the vehicle grid storage module (9), and the 5G end-to-end vehicle position broadcast module (10) includes license plate number and vehicle type information. The vehicle specific information data transmitted by the 5G end-to-end vehicle position broadcast module (10) to the 5G end-to-end inspection planning module (12) includes license plate number and vehicle type information.

4. A detection method for an abnormal parking detection system based on a 5G recorder patrol inspection according to any one of claims 1-3, characterized in that, It includes the following steps: S1. The inspection module (1) receives the inspection route of the 5G end-to-end inspection planning module (12). The inspection module (1) stores the inspection route; the inspection module (1) transmits the inspection route to the GPS position module (3). S2. The GPS position module (3) receives the inspection route of the inspection module (1) and conducts direction guidance based on the current position of the recorder; and the GPS position module (3) sends the current position coordinates of the recorder to the vehicle grid position detection module (8). S3. The video acquisition module (2) acquires the picture in front of the recorder, generates video frame data and transmits it to the vehicle detection module (5). S4. The vehicle detection module (5) detects the license plate number, license plate position, license plate size, and vehicle type information contained in the video frame data. S5. The vehicle relative direction detection module (7) detects the deviation angle of the vehicle relative to the recorder. S6. The acceleration sensor module (4) sends the sensor data in three directions in the three-dimensional space to the direction detection module (6). S7. The direction detection module (6) detects the movement direction of the recorder. S8. The vehicle grid position detection module (8) detects the vehicle grid number. S9. The vehicle grid storage module (9) in the cloud center stores vehicle index information. S10. The 5G end-to-end vehicle position broadcast module (10) receives the vehicle grid number, license plate number, and vehicle type information of the vehicle grid position detection module (8). S11. The vehicle anomaly detection module (11) detects vehicle anomaly information. S12. The 5G end-to-end inspection planning module (12) receives the vehicle grid number, license plate number, and vehicle type information of the 5G end-to-end vehicle position broadcast module (10); and re-plans and generates an inspection route and transmits it to the inspection module (1).

5. The detection method of an abnormal parking detection system based on 5G recorder patrol according to claim 4, characterized in that, The step S4 further includes: S4.1 The vehicle detection module (5) receives the video frame images transmitted by the video acquisition module (2); S4.2 The vehicle detection module (5) locates the position and size of the license plate according to the YOLO algorithm; S4.3 The vehicle detection module (5) extracts the license plate image according to the position and size of the license plate; S4.4 The vehicle detection module (5) horizontally corrects the license plate image according to the upper and lower borders of the license plate using the Hough transform method; S4.5 The vehicle detection module (5) vertically corrects the license plate image according to the left and right borders using the Hough transform method; S4.6 The vehicle detection module (5) segments the license plate image into character images according to the interval characteristics between characters; S4.7 The vehicle detection module (5) uses the template matching method to identify each character image to obtain the license plate number; S4.8 The vehicle detection module (5) expands the image vertically and horizontally according to the license plate position, matches the vehicle type using the YOLO algorithm, and obtains the vehicle type information of the current vehicle; S4.9 The vehicle detection module (5) sends the license plate number, license plate position, license plate size, and vehicle type information to the vehicle relative direction detection module (7).

6. The detection method of an abnormal parking detection system based on 5G recorder patrol according to claim 4, characterized in that, The step S5 further includes: S5.1 The vehicle relative direction detection module (7) receives the license plate number, license plate position, license plate size, and vehicle type information transmitted by the vehicle detection module (5); S5.2 The vehicle relative direction detection module (7) divides the position coordinates of the license plate by the width of the video frame image, and then multiplies by the current horizontal viewing angle of the recorder to obtain the deviation angle between the vehicle and the recorder; S5.3 The vehicle relative direction detection module (7) divides the size of the license plate by the width of the video frame image, and then multiplies by the current horizontal viewing angle of the recorder to obtain the license plate angle. According to the lens focal length of the recorder and the size of the standard license plate, the lens formula is used to calculate the distance between the vehicle and the recorder; S5.4 The vehicle relative direction detection module (7) sends the deviation angle of the vehicle relative to the recorder, the distance between the vehicle and the recorder, the license plate number, and the vehicle type information to the vehicle grid position detection module (8).

7. The detection method of an abnormal parking detection system based on 5G recorder patrol according to claim 4, characterized in that, The step S7 further includes: S7.1 The direction detection module (6) receives the recorder position coordinates transmitted by the GPS position module (3), connects them with the previously saved recorder position coordinates to form a vector, and determines the GPS movement direction of the recorder; S7.2 The direction detection module (6) receives the sensor data of three orientations in the three-dimensional space transmitted by the acceleration sensor module (4). The direction detection module (6) judges whether the recorder is worn horizontally or vertically according to the horizontal sensor data; when worn horizontally, the direction detection module (6) takes the z and y direction data for calculation; when worn vertically, the direction detection module (6) takes the x and y direction data for calculation; S7.3 The direction detection module (6) obtains the steering vector of the recorder according to the x and y direction data by vector addition. According to the previously saved movement direction of the recorder by the direction detection module (6), the correction angle of the steering vector is superimposed to obtain the sensor movement direction of the recorder; S7.4 The direction detection module (6) uses the GPS movement direction as the calculated value of the Kalman filter; The S7.5 direction detection module (6) uses the sensor's movement direction as the state calculation value of the Kalman filter, and finally obtains the minimum variance estimated value of the movement direction of the Kalman filter; The S7.6 direction detection module (6) sends the minimum variance estimated value of the movement direction to the vehicle grid position detection module (8).

8. The detection method of an abnormal parking detection system based on 5G recorder patrol according to claim 4, characterized in that, The step S8 further includes: S8.1 The vehicle grid position detection module (8) receives the position coordinates of the recorder transmitted by the GPS position module (3); S8.2 The vehicle grid position detection module (8) receives the minimum variance estimated value of the movement direction transmitted by the direction detection module (6); S8.3 The vehicle grid position detection module (8) receives the deviation angle of the vehicle relative to the recorder, the distance between the vehicle and the recorder, and the license plate number and vehicle type information transmitted by the vehicle relative direction detection module (7); S8.4 The vehicle grid position detection module (8) adds the minimum variance estimated value of the movement direction to the deviation angle of the vehicle relative to the recorder to obtain the direction of the vehicle relative to the recorder; S8.5 The vehicle grid position detection module (8) calculates the position coordinates of the vehicle based on the position coordinates of the recorder, the direction of the vehicle relative to the recorder, and the distance between the vehicle and the recorder; S8.6 The vehicle grid position detection module (8) generates a grid number according to the position coordinates of the vehicle. According to the grid quantization value Q; the grid number is equal to the position abscissa / Q, the position ordinate / Q, and the strings are connected, and adjacent values are separated by spaces; S8.7 The vehicle grid position detection module (8) of the recorder sends the vehicle grid number, license plate number, and vehicle type information to the vehicle anomaly detection module (11), the vehicle grid storage module (9), and the 5G end-to-end vehicle position broadcast module (10) in sequence.

9. The detection method of an abnormal parking detection system based on 5G recorder patrol according to claim 4, characterized in that, The step S9 further includes: S9.1 The vehicle grid storage module (9) receives the vehicle grid number, license plate number, and vehicle type information from the vehicle grid position detection module (8) of each recorder; S9.2 The vehicle grid storage module (9) establishes an indexing method that can index the corresponding grid number, vehicle type information, and indexing establishment time through the license plate number; S9.3 If the index of the license plate number already exists in the vehicle grid storage module (9), it is judged whether the grid number has changed. If the grid number has changed, the index information is rebuilt; if the grid number has not changed, the index is refreshed for keeping alive; S9.4 The vehicle grid storage module (9) establishes an index aging mechanism. If the vehicle grid position detection module (8) has not updated the license plate number after exceeding the time threshold, the license plate number index information, grid number, and vehicle type information stored in the vehicle grid storage module (9) will be cleared; S9.5 The vehicle grid storage module (9) in the cloud center provides an index query service for the vehicle anomaly detection module (11) with the license plate number as the search term.

10. The detection method of an abnormal parking detection system based on 5G recorder patrol according to claim 4, characterized in that, The step S11 further includes: S11.1 The vehicle anomaly detection module (11) receives the vehicle grid number, license plate number, and vehicle type information transmitted by the vehicle grid position detection module (8); S11.2 The vehicle anomaly detection module (11) uses the license plate number to index and query the grid number, vehicle model information, and the time when the index is established from the vehicle grid storage module (9) in the cloud center; S11.3 If the grid number transmitted by the vehicle grid position detection module (8) received by the vehicle anomaly detection module (11) is the same as the grid number queried through indexing by the vehicle grid storage module (9), then continue to determine whether the time difference between the time when the index is established and the current time of the recorder exceeds the parking timeout threshold. If it exceeds, the recorder generates an audible and visual alarm; S11.4 If the vehicle model information transmitted by the vehicle grid position detection module (8) received by the vehicle anomaly detection module (11) is different from the vehicle model information queried through indexing by the vehicle grid storage module (9), then the recorder generates an audible and visual alarm.

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