Identification method for mismatching of vehicle hangers
By installing high-definition cameras on logistics trucks and using artificial intelligence CV recognition technology, the vehicle trajectory is generated and rule comparison is carried out, the problem of identifying the behavior of logistics trucks replacing trailers is solved, and efficient identification and prevention of illegal behavior is achieved.
Patent Information
- Application Number
- CN202411691121.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-04-01
AI Technical Summary
The prior art is difficult to effectively identify and prevent logistics trucks from replacing trailers during transportation, resulting in criminals being able to exchange goods.
By installing a high-definition camera, collect pictures of the tractor and trailer, use artificial intelligence CV to identify license plate numbers and paint numbers, generate vehicle trajectory, judge whether the vehicle trajectory is inconsistent based on rules, and manually handle it in the alarm center.
Real-time tracking and identification of vehicle trailers is realized, the recognition accuracy is improved, the complexity and error rate of manual operations are reduced, and illegal behavior can be discovered and prevented in a timely manner.
Smart Images
Figure CN119741691A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of vehicle identification, and in particular relates to a method for identifying vehicle-hanging mismatch. Background Art
[0002] General heavy-duty commercial logistics trucks (heavy trucks for short) are divided into two parts: tractors and trailers. Usually, during the logistics transportation process, if there are no special circumstances (such as abnormal failure of the tractor), heavy trucks will not replace trailers during transportation. Some criminals replace the goods in the logistics vehicles by replacing trailers during the logistics process. This system continuously tracks the logistics vehicles, detects the inconsistency between the vehicles and trailers in real time, and gives feedback alarms to notify the logistics fleet managers to promptly discover the replacement of trailers (replacing trailers), and promptly detect and stop illegal acts. Summary of the invention
[0003] To achieve the above object, the technical solution of the present invention is as follows: a method for identifying vehicle-hook mismatch, the method comprising the following steps: Step 1, camera data collection; Step 2, license plate number recognition; Step 3, license plate number data processing, generating vehicle trajectory; Step 4: determine whether the vehicle and trailer are inconsistent by comparing the rules; determine whether there is any inconsistency between the vehicle and trailer at each track point of the tractor based on the rules; Step 5: Manual processing by the alarm center; Step 6: Deal with the inconsistency between the vehicle and the suspension.
[0004] As an improvement of the present invention, in step 1, a high-definition camera installed at a specific track point Xi is used to collect a picture of the front of the tractor, a picture of the corresponding rear of the trailer, and a picture of the body of the trailer, and all the pictures are synchronized to the background system.
[0005] As an improvement of the present invention, in step 2, after the background system receives the pictures of the tractor head and the corresponding trailer rear synchronized with the camera, it performs artificial intelligence CV recognition on the tractor license plate number, trailer license plate number and trailer paint number, and records the successfully identified tractor license plate number, trailer license plate number and trailer paint number.
[0006] As an improvement of the present invention, in step 3, based on the license plate number recognition record recorded in step 2, a vehicle driving trajectory record is formed, with the tractor license plate number + trajectory point position + time as the data primary key, and the corresponding trailer license plate number, trailer paint number, tractor front picture, corresponding trailer rear picture, and trailer body picture data are recorded, and these data are sorted around the tractor license plate number as the data object to form a tractor trajectory portrait with the tractor as the main body.
[0007] As an improvement of the present invention, in step 3, the portrait field includes: tractor license plate number, tractor track point time, tractor track point position, whether it is a starting point, tractor picture, corresponding trailer license plate number, trailer paint number, trailer rear picture and trailer body picture, wherein the starting point label is based on specific business rules, identifying a certain time + tractor track point position as the starting point of the tractor.
[0008] As an improvement of the present invention, in step 4, it is determined based on rules whether there is a vehicle-trailer inconsistency at each track point of the tractor, and the rule steps are as follows: Rule step 1, determine the starting point: add a new tractor track image record according to the system input settings in advance; sort the tractor image data synchronized by all cameras based on the tractor track point time. In a single record, if the tractor track point time is pushed forward 24 hours and there is no track point record, then this record is considered as the starting point, and the starting point label is assigned a value of "yes", and the vehicle and trailer are inconsistent. The label is assigned a value of "vehicle and trailer are consistent"; in other cases, the starting point label is assigned a value of "no"; Rule step 2, determine whether the vehicle and trailer are inconsistent: for the tractor trajectory image records synchronized by all cameras, compare them with the tractor trajectory image records at the tractor's starting point to see if the trailer license plate number and trailer paint number in the comparison record are consistent; at the same time, compare them with the tractor trajectory image record at the previous time point to see if the trailer license plate number and trailer paint number in the comparison record are consistent; Rule step 3, when the judgment result of "vehicle-hook inconsistency" appears for the first time in the tractor portrait, an alarm signal is generated, and the data record of the starting point, the data record of a time point after the starting point, the data record of the "vehicle-hook inconsistency" judgment time point, and the data record of a time point before the judgment time point are pushed to the alarm center, and the processing feedback from the alarm center is waited for.
[0009] As an improvement of the present invention, the newly added tractor trajectory portrait record in the rule step 1 includes the tractor license plate number, tractor trajectory point time, tractor trajectory point position, whether it is the starting point, trailer license plate number, trailer paint number, and whether the vehicle and trailer are inconsistent. The label is assigned the value "vehicle and trailer are consistent".
[0010] As an improvement of the present invention, the comparison result rule in the rule step 2 includes: Comparison result rule step 1: if the trailer license plate number and trailer paint number are consistent with the departure point record, it is confirmed as "the vehicle and trailer are consistent"; if they are inconsistent, and the trailer license plate number and trailer paint number are not missing in the departure point record, and the previous time record comparison result is "the vehicle and trailer are consistent", it is defined as "the vehicle and trailer are suspected to be inconsistent"; if they are inconsistent, and the trailer license plate number and trailer paint number are not missing in the departure point record, and the previous time record comparison result is "the vehicle and trailer are suspected to be inconsistent" or "the vehicle and trailer are inconsistent", it is defined as "the vehicle and trailer are inconsistent"; Comparison result rule step 2: if the trailer license plate number and trailer paint number are missing in the departure point record, and the record at the previous time point is the departure point record, the result of the comparison of whether the vehicle and trailer are consistent is "the vehicle and trailer are consistent"; if the record at the previous time point has recorded the vehicle and trailer comparison result as "the vehicle and trailer are inconsistent", the result of the comparison of whether the vehicle and trailer are consistent at the current time point is "the vehicle and trailer are inconsistent"; if the previous time point is not the departure point, it is judged based on the comparison result with the record at the previous time point. If the trailer license plate number and trailer paint number are consistent, and the result of the comparison of whether the vehicle and trailer are consistent recorded at the previous time point is "the vehicle and trailer are consistent", it is confirmed as "the vehicle and trailer are consistent"; if the result of the comparison of whether the vehicle and trailer are consistent recorded at the previous time point is "the vehicle and trailer are suspected to be inconsistent", it is confirmed as "the vehicle and trailer are inconsistent"; For step 3 of the comparison result rule, if the trailer license plate number and the trailer paint number are missing in the departure point record, and the departure point is not the previous time point, then after comparing with the record at the previous time point, if the trailer license plate number and the trailer paint number are inconsistent, and the comparison result at the previous time point is "the vehicle and the trailer are consistent", it is confirmed as "the vehicle and the trailer are suspected to be inconsistent"; if the comparison result at the previous time point is "the vehicle and the trailer are suspected to be inconsistent", then compare with the previous time point again. If the trailer license plate and the paint license plate are still inconsistent, it is defined as "the vehicle and the trailer are inconsistent"; if the trailer license plate and the paint license plate are consistent, it is defined as "the vehicle and the trailer are consistent".
[0011] As an improvement of the present invention, step 5 specifically includes the need for manual secondary confirmation of the alarm when the accuracy of the "vehicle-and-hook inconsistency" judgment does not reach an extremely high accuracy rate. The agent promptly handles the "vehicle-and-hook inconsistency" alarm pushed by the background system in the 7*24 duty monitoring of the alarm center, and compares the starting point with the alarm tractor and trailer photos, and conducts a comprehensive comparison of the tractor number, trailer number, trailer paint number, and trailer body shape to confirm whether the "vehicle-and-hook inconsistency" alarm is accurate. If it is accurate, a "vehicle-and-hook inconsistency" processing work order is initiated, and the corresponding management agency performs business processing.
[0012] As an improvement of the present invention, step 6 specifically includes that after the management agency performs business processing, the processing results of "normal replacement" and "illegal replacement of trailer" are fed back to the alarm center by the work order processing system to complete the work order processing closed loop; at the same time, the alarm center feeds back the result processing to the background system and adds it to all subsequent consecutive "vehicle and trailer inconsistency" records.
[0013] Compared with the prior art, the beneficial effects of the present invention are: The existing conventional technology requires a lot of manpower to operate. Whether it is the input of the tractor license plate number, trailer license plate number, trailer paint number, or the comparison of whether the front and rear license plate numbers are consistent, manual observation, system input and observation comparison are required. In the present invention, these steps can be automatically processed by the system through the systematic processing method and artificial intelligence CV recognition ability, which improves the overall operation efficiency, improves the work accuracy, and simplifies the overall operation complexity; In practice, a single recognition conclusion of "vehicle-mounted inconsistency" may result in a lower actual recognition rate (e.g., around 90%) due to environmental factors (e.g., momentary pedestrian obstruction, light and shadow problems), equipment factors (e.g., data synchronization is not timely or equipment abnormalities lead to missing synchronized license plate number images), and algorithm problems (e.g., the algorithm's own accuracy rate has an error of 5%, which is enough to account for the error). However, by adding a "suspected inconsistency between vehicle and mounting" link, similar to the integrated learning method, the accuracy rate can be greatly improved to around 99%, meeting the high accuracy requirements in commercial applications. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 This is a flow chart of the vehicle-hanging mismatch identification method in this embodiment; Figure 2 This is a flow chart of the recognition method implemented by the intelligent visual analysis engine, the vehicle-mounted intelligent monitoring system and the business system in this embodiment. DETAILED DESCRIPTION
[0015] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.
[0016] Embodiment: The purpose of this embodiment is to timely locate and discover the inconsistency between the vehicle tractor head and the trailer through the Internet of Things, big data and AI technology, and notify the fleet business manager to promptly stop the occurrence of related illegal and disciplinary incidents. At present, most fleet business managers monitor the inconsistency problem of vehicles and trailers through manual management. For example, a production park monitors and manages all load-carrying vehicles entering the park. When entering the park, the administrator records the license plates of the tractor and trailer. After the load-carrying vehicle completes the factory operation, at the time of leaving the factory, the tractor license plate is compared with the trailer license plate through manual comparison to confirm whether the tractor license plate and the trailer license plate are consistent with the registration when entering the park.
[0017] The above operation can ensure the consistency of the trailer license plate when the load-carrying vehicle enters and leaves the park. However, it cannot solve the problem of illegal elements replacing the trailer inside the park, replacing the original trailer with a substandard load-carrying trailer, and completing the substitution in the unloading process; after completing the illegal operation, the original trailer is replaced to ensure the consistency of the tractor and trailer license plates registered when the vehicle leaves the park and when it enters the park.
[0018] The system support based on this embodiment includes: Intelligent visual analysis engine: It is specially used to handle artificial intelligence CV recognition tasks. After receiving pictures of tractors and trailers, it can identify the license plate number and paint number. It can also perform image comparison after receiving pictures of the appearance of tractors and trailers to compare whether the vehicle body has changed significantly and other visual processing tasks. Intelligent monitoring system for vehicle cargo: Intelligently monitor the cargo on the trailer to confirm whether there are any abnormal cargo problems, among which the inconsistency between the vehicle and the trailer is the most important abnormal problem; Business system: Capture images of loaded trucks through cameras. At the same trajectory point, collect photos of the tractor's license plate and trailer's license plate, and send them to the on-board intelligent monitoring system for further judgment; accept the on-board intelligent monitoring system's judgment on the inconsistency between the vehicle and the trailer, and perform subsequent manual processing.
[0019] Among these three systems, the intelligent visual analysis engine and the vehicle-mounted intelligent monitoring system together constitute the vehicle-hanging inconsistency recognition system, and the business system is responsible for related business operations.
[0020] In the actual operation process, this embodiment uses multiple cameras to identify the license plates of the tractor and trailer, realizes automatic tracking of the system, verifies whether the vehicle and trailer match, and can automatically detect the inconsistency between the vehicle and trailer at the first time, and issue an alarm to the fleet operation manager. The seat customer service will perform unified alarm processing on the unified vehicle operation management platform, and after confirming the illegal and irregular trailer replacement behavior, perform the necessary subsequent vehicle and trailer inconsistency processing operations. The specific product operation process of vehicle and trailer inconsistency is as follows: Figure 1shown.
[0021] Camera data collection: Through the high-definition camera installed at a specific track point Xi, the front picture of the tractor, the corresponding rear picture of the trailer, and the body picture of the trailer are collected, and all the pictures are synchronized to the background system; License plate number recognition: After receiving the images of the tractor head and the corresponding trailer tail synchronized by the camera, the backend system performs artificial intelligence CV (Computer Vision) recognition on the tractor license plate number, trailer license plate number and trailer paint number, and records the successfully recognized tractor license plate number, trailer license plate number and trailer paint number; License plate number data processing, generating vehicle trajectory: Based on the license plate number recognition record recorded in step 2, a vehicle driving trajectory record is formed, with the tractor license plate number + trajectory point location + time as the data primary key, recording the corresponding trailer license plate number, trailer paint number, tractor front picture, corresponding trailer rear picture, trailer body picture and other data. And these data are sorted around the tractor license plate number as the data object to form a tractor trajectory portrait with the tractor as the main body. The portrait fields include: tractor license plate number (primary key), tractor trajectory point time, tractor trajectory point location, whether it is a starting point, tractor picture (link), corresponding trailer license plate number, trailer paint number, trailer rear picture (link), trailer body picture (link), etc. The departure point label is based on specific business rules, identifying a certain time + tractor trajectory point location as the departure point of the tractor.
[0022] Rule comparison to determine whether the vehicle and trailer are inconsistent: Based on the rules, determine whether there is a vehicle and trailer inconsistency at each track point. The rules are designed as follows: Rule step 1, determine the starting point: this can be set in advance according to the system input. For example, a fleet enters the fleet dispatch record and adds a tractor trajectory portrait record: tractor license plate number (primary key), tractor trajectory point time, tractor trajectory point position, whether it is a starting point (yes), trailer license plate number, trailer paint number, and whether the vehicle and trailer are inconsistent. The label is assigned "vehicle and trailer are consistent"; for the tractor portrait data synchronized by all cameras, it is sorted based on the tractor trajectory point time. In a single record, if the tractor trajectory point time is pushed forward 24 hours and there is no trajectory point record, then this record is considered to be the starting point, and the starting point label is assigned "yes", and the vehicle and trailer are inconsistent. The label is assigned "vehicle and trailer are consistent"; in other cases, the starting point label is assigned "no".
[0023] Rule step 2, determine whether the vehicle and trailer are inconsistent: for the tractor trajectory image record synchronized by all cameras, compare it with the tractor trajectory image record at the tractor's starting point to see if the trailer license plate number and trailer paint number in the comparison record are consistent; at the same time, compare it with the tractor trajectory image record at the previous time point to see if the trailer license plate number and trailer paint number in the comparison record are consistent. The rules based on the comparison results are as follows: Comparison result rules step 1), if the trailer license plate number and trailer paint number are consistent with the departure point record, it is confirmed as "vehicle and trailer consistent"; if they are inconsistent, and the trailer license plate number and trailer paint number are not missing in the departure point record, and the previous time record comparison result is "vehicle and trailer consistent", it is defined as "vehicle and trailer suspected inconsistent"; if they are inconsistent, and the trailer license plate number and trailer paint number are not missing in the departure point record, and the previous time record comparison result is "vehicle and trailer suspected inconsistent" or "vehicle and trailer inconsistent", it is defined as "vehicle and trailer inconsistent".
[0024] Comparison result rule step 2), if the trailer license plate number and trailer paint number are missing in the departure point record, and the record at the previous time point is the departure point record, then the vehicle-trailer consistency comparison result is "vehicle-trailer consistency"; if the vehicle-trailer comparison result recorded at the previous time point is "vehicle-trailer inconsistency", then the vehicle-trailer consistency comparison result at the current time point is "vehicle-trailer inconsistency"; if the previous time point is not the departure point, it is judged based on the comparison result with the previous time point record. If the trailer license plate number and trailer paint number are consistent, and the vehicle-trailer consistency comparison result recorded at the previous time point is "vehicle-trailer consistency", it is confirmed as "vehicle-trailer consistency"; if the vehicle-trailer consistency comparison result recorded at the previous time point is "vehicle-trailer suspected inconsistency", it is confirmed as "vehicle-trailer inconsistency".
[0025] Comparison result rule step 3), if the trailer license plate number and trailer paint number are missing in the departure point record, and it is not the departure point at the previous time point, then after comparing with the record at the previous time point, the trailer license plate number and trailer paint number are inconsistent, and the comparison result at the previous time point is "vehicle and trailer are consistent", then it is confirmed as "vehicle and trailer are suspected to be inconsistent"; if the comparison result at the previous time point is "vehicle and trailer are suspected to be inconsistent", then compare with the previous time point again, if the trailer license plate and the paint license plate are still inconsistent, it is defined as "vehicle and trailer are inconsistent"; if the trailer license plate and the paint license plate are consistent, it is defined as "vehicle and trailer are consistent".
[0026] Rule step 3, when the judgment result of "vehicle-hook inconsistency" appears for the first time in the tractor portrait, an alarm signal is generated, and the data record of the starting point, the data record of a time point after the starting point, the data record of the "vehicle-hook inconsistency" judgment time point, and the data record of a time point before the judgment time point are pushed to the alarm center, and the processing feedback from the alarm center is waited for.
[0027] Manual processing at the alarm center: When the accuracy of the "vehicle-trailer inconsistency" judgment does not reach a very high accuracy (such as more than 99%), manual secondary confirmation of the alarm is required to avoid resource waste and adverse effects caused by invalid processing actions after issuing false alarms. The seat staff will promptly handle the "vehicle-trailer inconsistency" alarm pushed by the background system through the 7*24 duty monitoring of the alarm center, and conduct a comprehensive comparison of the tractor number, trailer number, trailer paint number, and trailer body shape by comparing the starting point with the alarm tractor and trailer photos, to confirm whether the "vehicle-trailer inconsistency" alarm is accurate. If it is accurate, a "vehicle-trailer inconsistency" processing work order will be initiated, and the corresponding management agency will handle the business and hold accountable.
[0028] Handling of vehicle-trailer inconsistency issues: After the management agency processes the business, the work order processing system will feed back the processing results such as "normal replacement" and "illegal replacement of trailer" to the alarm center to complete the work order processing closed loop; at the same time, the alarm center will feed back the processing results to the background system and add it to all subsequent consecutive "vehicle-trailer inconsistency" records.
[0029] In this embodiment, the current product integrates artificial intelligence CV processing capabilities, which can intelligently identify the tractor license plate number, trailer license plate number, and trailer paint number. Some existing products are manually transcribing the tractor license plate number, trailer license plate number, and trailer paint number, but the efficiency is very low, and it is impossible to efficiently complete online management; some products have the ability to automatically identify the tractor license plate number and trailer license plate number through CV processing, and can also efficiently complete the system synchronization and online management after automatic license plate recognition, but there is no mature recognition solution for the trailer paint number industry. This patent improves the accuracy of paint recognition to more than 95% through the CV recognition algorithm training of a large number of rear photos, which can achieve commercialization.
[0030] The current product has greatly improved the actual recognition accuracy through rule design processing: adding the "suspected inconsistency between vehicle and hanging" link between "consistent between vehicle and hanging" and "inconsistent between vehicle and hanging" can effectively avoid errors caused by various environmental factors and operation processes. Assuming that there is no "suspected inconsistency between vehicle and hanging" intermediate link, the judgment accuracy of "inconsistent between vehicle and hanging" is P1=90%. After adding "suspected inconsistency between vehicle and hanging", the accuracy can reach P2=1-(1-P1)*(1-P1)=1-(1-90%)*(1-90%)=99%. This method of improving the overall recognition accuracy based on independent judgment of two consecutive observation results is also well implemented in practice. For example, at some work points, such as unloading through weighbridges and platforms, the observation point camera completes 2 or more observations and shooting of the target vehicle within a time interval of 30 seconds, and synchronizes it to the background system as two different records.
[0031] It should be noted that the above content only illustrates the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications all fall within the protection scope of the claims of the present invention.
Claims
1. A method for identifying vehicle-hanging mismatch, characterized in that: The method comprises the following steps: Step 1, camera data collection; Step 2, license plate number recognition; Step 3, license plate number data processing, generating vehicle trajectory; Step 4: determine whether the vehicle and trailer are inconsistent by comparing the rules; determine whether there is any inconsistency between the vehicle and trailer at each track point of the tractor based on the rules; Step 5: Manual processing by the alarm center; Step 6: Deal with the inconsistency between the vehicle and the suspension.
2. The method for identifying vehicle-hook mismatch according to claim 1, characterized in that: In step 1, a high-definition camera installed at a specific track point Xi is used to collect a picture of the front of the tractor, a picture of the corresponding rear of the trailer, and a picture of the body of the trailer, and all the pictures are synchronized to the background system.
3. The method for identifying vehicle-hook mismatch according to claim 1, characterized in that: In step 2, after the background system receives the images of the tractor head and the corresponding trailer tail synchronized by the camera, it performs artificial intelligence CV recognition on the tractor license plate number, trailer license plate number and trailer paint number, and records the successfully recognized tractor license plate number, trailer license plate number and trailer paint number.
4. The method for identifying vehicle-hook mismatch according to claim 1, characterized in that: In step 3, based on the license plate number recognition record recorded in step 2, a vehicle driving trajectory record is formed, with the tractor license plate number + trajectory point position + time as the data primary key, and the corresponding trailer license plate number, trailer paint number, tractor front picture, corresponding trailer rear picture, trailer body picture data are recorded, and these data are sorted around the tractor license plate number as the data object to form a tractor trajectory portrait with the tractor as the main body.
5. The method for identifying vehicle-hook mismatch according to claim 4, characterized in that: In step 3, the portrait field includes: tractor license plate number, tractor track point time, tractor track point position, whether it is a starting point, tractor picture, corresponding trailer license plate number, trailer paint number, trailer rear picture and trailer body picture, among which the starting point label is based on specific business rules, identifying a certain time + tractor track point position as the starting point of the tractor.
6. The method for identifying vehicle-hook mismatch according to claim 1, characterized in that: In step 4, it is determined based on the rules whether there is a vehicle-trailer inconsistency at each track point of the tractor, and the rule steps are as follows: Rule step 1, determine the starting point: add a new tractor track image record according to the system input settings in advance; sort the tractor image data synchronized by all cameras based on the tractor track point time. In a single record, if the tractor track point time is pushed forward 24 hours and there is no track point record, then this record is considered as the starting point, and the starting point label is assigned a value of "yes", and the vehicle and trailer are inconsistent. The label is assigned a value of "vehicle and trailer are consistent"; in other cases, the starting point label is assigned a value of "no"; Rule step 2, determine whether the vehicle and trailer are inconsistent: for the tractor trajectory image records synchronized by all cameras, compare them with the tractor trajectory image records at the tractor's starting point to see if the trailer license plate number and trailer paint number in the comparison record are consistent; at the same time, compare them with the tractor trajectory image record at the previous time point to see if the trailer license plate number and trailer paint number in the comparison record are consistent; Rule step 3, when the judgment result of "vehicle-hook inconsistency" appears for the first time in the tractor portrait, an alarm signal is generated, and the data record of the starting point, the data record of a time point after the starting point, the data record of the "vehicle-hook inconsistency" judgment time point, and the data record of a time point before the judgment time point are pushed to the alarm center, and the processing feedback from the alarm center is waited for.
7. The method for identifying vehicle-hook mismatch according to claim 6, characterized in that: The newly added tractor trajectory portrait record in the rule step 1 includes the tractor license plate number, tractor trajectory point time, tractor trajectory point position, whether it is a starting point, trailer license plate number, trailer paint number, and whether the vehicle and trailer are inconsistent. The label is assigned the value "vehicle and trailer are consistent".
8. The method for identifying vehicle-hook mismatch according to claim 6, characterized in that: The comparison result rules in the rule step 2 include: Comparison result rule step 1: if the trailer license plate number and trailer paint number are consistent with the departure point record, it is confirmed as "vehicle and trailer consistent"; if they are inconsistent, and the trailer license plate number and trailer paint number are not missing in the departure point record, and the previous time record comparison result is "vehicle and trailer consistent", it is defined as "vehicle and trailer suspected inconsistent"; if they are inconsistent, and the trailer license plate number and trailer paint number are not missing in the departure point record, and the previous time record comparison result is "vehicle and trailer suspected inconsistent" or "vehicle and trailer inconsistent", it is defined as "vehicle and trailer inconsistent"; Comparison result rule step 2: if the trailer license plate number and trailer paint number are missing in the departure point record, and the record at the previous time point is the departure point record, the result of the comparison of whether the vehicle and trailer are consistent is "vehicle and trailer consistent"; if the record at the previous time point has recorded the result of the comparison of the vehicle and trailer as "vehicle and trailer inconsistent", the result of the comparison of whether the vehicle and trailer are consistent at the current time point is "vehicle and trailer inconsistent"; if the previous time point is not the departure point, it is judged based on the comparison result with the record at the previous time point. If the trailer license plate number and trailer paint number are consistent, and the result of the comparison of whether the vehicle and trailer are consistent recorded at the previous time point is "vehicle and trailer consistent", it is confirmed as "vehicle and trailer consistent"; if the result of the comparison of whether the vehicle and trailer are consistent recorded at the previous time point is "vehicle and trailer suspected to be inconsistent", it is confirmed as "vehicle and trailer inconsistent"; For step 3 of the comparison result rule, if the trailer license plate number and the trailer paint number are missing in the departure point record, and the departure point is not the previous time point, then after comparing with the record at the previous time point, if the trailer license plate number and the trailer paint number are inconsistent, and the comparison result at the previous time point is "the vehicle and the trailer are consistent", it is confirmed as "the vehicle and the trailer are suspected to be inconsistent"; if the comparison result at the previous time point is "the vehicle and the trailer are suspected to be inconsistent", then compare with the previous time point again. If the trailer license plate and the paint license plate are still inconsistent, it is defined as "the vehicle and the trailer are inconsistent"; if the trailer license plate and the paint license plate are consistent, it is defined as "the vehicle and the trailer are consistent".
9. The method for identifying vehicle-hook mismatch according to claim 1, characterized in that: The step 5 specifically includes that when the accuracy of the "vehicle-and-trailer inconsistency" judgment does not reach a very high accuracy rate, manual secondary confirmation of the alarm is required. The agent promptly handles the "vehicle-and-trailer inconsistency" alarm pushed by the background system in the 7*24 duty monitoring of the alarm center, and compares the starting point with the alarm tractor and trailer photos, and conducts a comprehensive comparison of the tractor number, trailer number, trailer paint number, and trailer body shape to confirm whether the "vehicle-and-trailer inconsistency" alarm is accurate. If it is accurate, a "vehicle-and-trailer inconsistency" processing work order is initiated, and the corresponding management agency performs business processing.
10. The method for identifying vehicle-hook mismatch according to claim 1, characterized in that: The step 6 specifically includes that after the management agency performs business processing, the processing results of "normal replacement" and "illegal replacement of trailer" are fed back to the alarm center by the work order processing system to complete the work order processing closed loop; at the same time, the alarm center feeds back the result processing to the background system and adds it to all subsequent consecutive "vehicle and trailer inconsistency" records.