A vehicle model identification method and device based on big data and electronic equipment

Abstract

The application provides a vehicle type identification method and device based on big data and electronic equipment, and relates to the technical field of data processing. In the method, first vehicle type data and first vehicle feature data sent by a first sensor device are received, the first vehicle type data is vehicle type data of a first vehicle, and the first sensor device is located at an entrance of a highway network; the first vehicle type data and the first vehicle feature data are bound to obtain a first correspondence relationship; second vehicle feature data sent by a second sensor device is received, the second sensor device is located at an exit of the highway network; it is judged whether the second vehicle feature data is consistent with the first vehicle feature data; if it is determined that the second vehicle feature data is consistent with the first vehicle feature data, vehicle type data of a second vehicle is determined as the first vehicle type data according to the first correspondence relationship. The technical scheme provided by the application facilitates the convenience of vehicle type identification.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a vehicle model identification method, device, and electronic device based on big data. Background Technology

[0002] With the elimination of provincial border toll stations, the highway toll system will shift from a closed, networked toll collection model to an open, free-flow model. This free-flow toll collection model places higher demands on the convenient identification of vehicle types.

[0003] Currently, the main technologies for vehicle identification are vehicle identification equipment, weighing equipment, and manual identification at toll booths. However, the excessive number of identification methods prolongs the vehicle identification process and significantly reduces its convenience.

[0004] Therefore, there is an urgent need for a vehicle model identification method, device, and electronic equipment based on big data. Summary of the Invention

[0005] This application provides a vehicle model identification method, device, and electronic device based on big data, which facilitates the improvement of vehicle model identification convenience.

[0006] A first aspect of this application provides a vehicle model identification method based on big data. The method includes: receiving first vehicle model data and first vehicle feature data sent by a first sensor device, wherein the first vehicle model data is vehicle type data of a first vehicle, and the first vehicle feature data includes license plate data of the first vehicle, the first sensor device is located at the entrance of a highway network, and the first vehicle is any one of multiple vehicles passing through the entrance of the highway network; binding the first vehicle model data and the first vehicle feature data to obtain a first correspondence; receiving second vehicle feature data sent by a second sensor device, wherein the second vehicle feature data includes license plate data of a second vehicle, the second sensor device is located at the exit of the highway network, and the second vehicle is any one of multiple vehicles passing through the exit of the highway network; determining whether the second vehicle feature data is consistent with the first vehicle feature data; if it is determined that the second vehicle feature data is consistent with the first vehicle feature data, then determining the vehicle type data of the second vehicle as the first vehicle model data according to the first correspondence.

[0007] By employing the aforementioned technical solution and installing sensor devices at highway network entrances and exits, vehicle characteristic data, including license plate data, can be acquired to identify vehicle identity and type. Binding vehicle model data and vehicle characteristic data allows for comparison of characteristic data to determine vehicle type during subsequent vehicle identification processes. Comparing vehicle characteristic data at entrances and exits determines whether a vehicle has passed through the highway network, enabling the collection of traffic flow and other data. Determining vehicle type allows for the provision of appropriate toll collection services, thus achieving personalized traffic management. Therefore, this approach not only improves the accuracy and efficiency of vehicle identification but also automates vehicle type judgment and identification, reducing the need for manual intervention and enhancing the convenience of vehicle model identification.

[0008] Optionally, vehicle transaction data is acquired, including vehicle type data and vehicle characteristic data; the vehicle transaction data is processed to generate a transaction data index, the data processing including de-identification processing, data cleaning, and data normalization processing; the transaction data index is analyzed to construct a vehicle database.

[0009] By adopting the above technical solutions and acquiring vehicle transaction data, information such as vehicle types, prices, and transaction volumes in the market can be obtained, providing a foundation for subsequent data analysis. De-identification protects personal information, data cleaning removes invalid and erroneous data, and data normalization transforms the data into a unified format, improving data accuracy and reliability while facilitating subsequent data processing and analysis. Analyzing the transaction data index reveals patterns, trends, and changes in vehicle transactions, supporting decision-making. Through the above processing and analysis, a database containing information such as vehicle type, characteristics, and transactions can be constructed, facilitating subsequent data querying and analysis.

[0010] Optionally, receiving the second vehicle feature data sent by the second sensor device specifically includes: determining the time validity of the initial vehicle feature data based on the capture time and transmission time of the second sensor device, wherein the initial vehicle feature data is the vehicle feature data captured by the second sensor device; performing duplicate data removal processing on the initial vehicle feature data based on a preset dimension to obtain corrected vehicle feature data, wherein the preset dimension includes whether the license plate data is the same, whether the collection point is the same, and whether the capture time is the same; acquiring the vehicle image data of the second vehicle; performing multi-scale feature extraction on the vehicle image data, and combining it with the corrected vehicle feature data to obtain the second vehicle feature data.

[0011] By employing the above technical solution, comparing the capture time and transmission time of the second sensor device can determine the temporal validity of the initial vehicle feature data, thereby ensuring the accuracy and reliability of the data. By considering multiple dimensions such as whether the license plate data, collection points, and capture times are identical, duplicate initial vehicle feature data can be eliminated, improving data purity. Acquiring vehicle image data from the second vehicle provides richer vehicle information, facilitating more accurate description and identification of vehicle features. Multi-scale feature extraction from the vehicle image data yields more comprehensive and accurate vehicle features, improving the accuracy of vehicle recognition and classification.

[0012] Optionally, after analyzing the transaction data index and constructing a vehicle database, the method further includes: obtaining third vehicle model data based on the transaction data index, wherein the third vehicle model data is vehicle model data participating in billing; determining whether the third vehicle model data is consistent with the corresponding vehicle model data in the vehicle database, wherein the vehicle database stores multiple vehicle model data participating in billing; if the third vehicle model data is inconsistent with the corresponding vehicle model data in the vehicle database, then sending the third vehicle model data to the user equipment, wherein the user equipment is used to audit and correct the third vehicle model data.

[0013] By adopting the above technical solution, the vehicle model data participating in billing, i.e., the third vehicle model data, can be obtained through the transaction data index. By comparing the third vehicle model data with the vehicle model data participating in billing stored in the vehicle database, the correctness of the third vehicle model data can be determined. If the third vehicle model data is inconsistent with the corresponding vehicle model data in the vehicle database, the third vehicle model data is sent to the user equipment. The user equipment can then use this data to audit and correct the third vehicle model data, which helps ensure the accuracy and reliability of the data.

[0014] Optionally, the method further includes: obtaining a re-evaluation request, the re-evaluation request including a preset period; and re-evaluating the vehicle model data in the vehicle database according to the preset period.

[0015] By adopting the above technical solution and obtaining re-evaluation requests, it is possible to identify which vehicle model data needs to be reassessed, ensuring the accuracy and reliability of the data. By setting a preset cycle, vehicle model data can be re-evaluated periodically, ensuring timely updates and accuracy. Re-evaluating vehicle model data in the vehicle database according to the preset cycle can identify and correct erroneous data, while simultaneously adding and updating new vehicle model data, ensuring the integrity and accuracy of the database.

[0016] Optionally, the step of re-evaluating the vehicle model data in the vehicle database according to the preset period specifically includes: obtaining the number of times the target vehicle appears in the highway network within the preset period based on the flow data index; determining the relationship between the number of times the vehicle appears and a preset number threshold; and if the number of times the vehicle appears is greater than or equal to the preset number threshold, then re-evaluating the vehicle model data corresponding to the target vehicle.

[0017] By employing the above technical solution, and by judging the relationship between the frequency of a single vehicle occurrence and a preset threshold, the vehicle model data corresponding to target vehicles whose occurrence frequency exceeds the threshold can be re-evaluated in a targeted manner, rather than blindly re-evaluating all vehicle model data. This improves the efficiency and accuracy of data re-evaluation. Using the frequency of a single vehicle occurrence and the preset threshold for judgment allows for decision-making based on actual data, rather than relying on subjective experience or intuition, making data re-evaluation more objective and accurate. The preset threshold can be adjusted according to actual conditions to adapt to different highway networks and vehicle model data characteristics. This makes the method more flexible and scalable. By periodically re-evaluating vehicle model data whose occurrence frequency exceeds the preset threshold, erroneous data can be identified and corrected in a timely manner, thereby improving data quality and reliability.

[0018] Optionally, the method further includes: obtaining the first single vehicle occurrence count, wherein the first single vehicle occurrence count is the single vehicle occurrence count of any one of the multiple target vehicles; if the first single vehicle occurrence count is greater than the preset period, then clearing the vehicle model data of the target vehicle corresponding to the first single vehicle occurrence count.

[0019] By adopting the above technical solution, and cleaning up vehicle model data corresponding to the number of times a vehicle appears beyond a preset period, expired and no longer needed data can be periodically cleaned up, thus avoiding database redundancy and chaos. After cleaning up expired data, space can be freed up to store new data, ensuring database updates and expansion. Cleaning up expired data prevents it from affecting current data and calculation results, thereby improving data reliability and accuracy. When processing large amounts of data, periodically cleaning up expired data helps manage memory and avoid problems such as memory overflow.

[0020] A second aspect of this application provides a vehicle model identification device based on big data. The device includes an acquisition module and a processing module. The acquisition module receives first vehicle model data and first vehicle feature data sent by a first sensor device. The first vehicle model data is vehicle type data of a first vehicle, and the first vehicle feature data includes license plate data of the first vehicle. The first sensor device is located at the entrance of a highway network, and the first vehicle is any one of multiple vehicles passing through the entrance of the highway network. The processing module binds the first vehicle model data and the first vehicle feature data to obtain a first correspondence. The acquisition module also receives second vehicle feature data sent by a second sensor device. The second vehicle feature data includes license plate data of a second vehicle. The second sensor device is located at the exit of the highway network, and the second vehicle is any one of multiple vehicles passing through the exit of the highway network. The processing module further determines whether the second vehicle feature data is consistent with the first vehicle feature data. If the second vehicle feature data is determined to be consistent with the first vehicle feature data, the processing module determines the vehicle type data of the second vehicle as the first vehicle model data based on the first correspondence.

[0021] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described above.

[0022] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described above.

[0023] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By installing sensor devices at highway entrances and exits, vehicle characteristic data, including license plate data, can be acquired to identify vehicle identity and type. Binding vehicle model data to vehicle characteristic data allows for comparison of characteristic data to determine vehicle type during subsequent vehicle identification processes. Comparing vehicle characteristic data at entrances and exits can determine whether a vehicle has passed through the highway network, enabling the collection of traffic flow and other data. Determining vehicle type allows for the provision of appropriate toll collection services, thus achieving personalized traffic management. Therefore, this approach not only improves the accuracy and efficiency of vehicle identification but also automates vehicle type judgment and identification, reducing the need for manual intervention and enhancing the convenience of vehicle model identification. 2. By comparing the capture time and transmission time of the second sensor device, the temporal validity of the initial vehicle feature data can be determined, thus ensuring the accuracy and reliability of the data. By considering multiple dimensions such as whether the license plate data, collection points, and capture times are identical, duplicate initial vehicle feature data can be eliminated, improving data purity. Acquiring vehicle image data from the second vehicle provides richer vehicle information, facilitating more accurate description and identification of vehicle features. Multi-scale feature extraction from the vehicle image data yields more comprehensive and accurate vehicle features, improving the accuracy of vehicle recognition and classification. 3. The vehicle model data participating in billing, i.e., the third vehicle model data, can be obtained through the transaction data index. By comparing the third vehicle model data with the vehicle model data participating in billing stored in the vehicle database, the correctness of the third vehicle model data can be determined. If the third vehicle model data is inconsistent with the corresponding vehicle model data in the vehicle database, the third vehicle model data is sent to the user equipment. The user equipment can use this data to audit and correct the third vehicle model data, which helps ensure the accuracy and reliability of the data. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a vehicle model identification method based on big data, provided as an embodiment of this application.

[0025] Figure 2 This is a schematic diagram of a vehicle model identification device based on big data, provided in an embodiment of this application.

[0026] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 31. Processor; 32. Communication bus; 33. User interface; 34. Network interface; 35. Memory. Detailed Implementation

[0028] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0029] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0030] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0031] With social progress and technological development, provincial border toll stations have been gradually phased out, and the highway toll system is undergoing a profound transformation. The traditional closed-loop network toll collection model is shifting towards an open, free-flow model. This new toll collection model places higher demands on vehicle type identification, requiring it to be faster, more accurate, and more convenient.

[0032] In traditional toll collection sites, vehicle type identification relies primarily on manual judgment by toll collectors. This method is not only inefficient but also prone to misjudgments due to human error. Furthermore, manual identification is even more difficult for certain special vehicle types, such as container trucks and oversized vehicles. Additionally, the need to stop for transactions undoubtedly increases the risk of traffic congestion, prolonging the vehicle type identification process and significantly reducing convenience.

[0033] To address the aforementioned technical problems, this application provides a vehicle model identification method based on big data, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a vehicle model identification method based on big data, provided as an embodiment of this application. The vehicle model identification method is applied to a server and includes steps S110 to S150, as follows: S110. Receive first vehicle model data and first vehicle feature data sent by the first sensor device. The first vehicle model data is the vehicle type data of the first vehicle, and the first vehicle feature data includes the license plate data of the first vehicle. The first sensor device is located at the entrance of the highway network, and the first vehicle is any one of the multiple vehicles passing through the entrance of the highway network.

[0034] Specifically, the first sensor device is installed at the entrance of the highway network to collect data on the first vehicle passing through that entrance. The server receives the first vehicle type data and first vehicle characteristic data sent by the first sensor device. The first vehicle type data refers to the vehicle type data of the first vehicle, such as sedan, bus, and truck. The first vehicle characteristic data includes the license plate data of the first vehicle, such as blue license plate Hebei H4283X. By setting up sensors at the entrance of the highway network, data on various types of vehicles can be collected, including vehicle type and license plate characteristics, thus providing a more comprehensive understanding of the vehicles passing through that entrance. Since the sensor device operates in real time, it can collect and transmit vehicle data in real time, allowing relevant personnel to understand the dynamic information of vehicles in a timely manner. The data collected by the server through the sensors is first-hand data, untouched by human manipulation or modification, and therefore has higher reliability. The server is a server that manages the highway network; it can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center. The server can communicate with user devices via wired or wireless networks.

[0035] S120. Bind the first vehicle model data and the first vehicle feature data to obtain the first correspondence.

[0036] Specifically, after obtaining the first vehicle model data and the first vehicle feature data, the server will bind the first vehicle model data and the first vehicle feature data to obtain the first correspondence. For example, if the first vehicle model data is "passenger vehicle type 1", which refers to a small passenger vehicle, usually a passenger vehicle with 7 seats or less, this type of vehicle is mainly used for urban areas and short-distance travel, and the first vehicle feature data is "blue license plate Hebei H4283X", then the first correspondence obtained is that the vehicle type corresponding to the blue license plate Hebei H4283X is passenger vehicle type 1.

[0037] S130. Receive second vehicle feature data sent by the second sensor device. The second vehicle feature data includes the license plate data of the second vehicle. The second sensor device is located at the exit of the highway network. The second vehicle is any one of the multiple vehicles passing through the exit of the highway network.

[0038] Specifically, a second sensor device is installed at the exit of the highway network to collect data on second vehicles passing through that exit. This is achieved by receiving second vehicle characteristic data transmitted by the second sensor device, including license plate data. By placing sensors at highway network exits, data on vehicles exiting the exit can be collected, making data collection more complete. Since the first and second sensor devices are located at the entrance and exit of the highway network respectively, their collected data can be compared, providing a clearer understanding of vehicle movement within the highway network. The data collected by the first and second sensor devices can corroborate each other, improving data accuracy. For example, if the license plate data collected by the two sensors is inconsistent, further verification of the data's accuracy may be necessary.

[0039] S140. Determine whether the second vehicle feature data is consistent with the first vehicle feature data.

[0040] Specifically, after acquiring the first and second vehicle feature data, the server compares them to check for consistency. This process essentially involves comparing each vehicle passing through the exit one by one. By comparing the vehicle feature data collected by the two sensor devices, data consistency can be detected. If the data is inconsistent, there may be data errors or sensor malfunctions, requiring further processing and repair. Comparing the vehicle feature data collected by the two sensor devices also verifies the accuracy of the data. If the data collected by the two sensor devices is consistent, the accuracy of the data is higher.

[0041] S150. If it is determined that the second vehicle feature data is consistent with the first vehicle feature data, then the vehicle type data of the second vehicle is determined to be the first vehicle model data according to the first correspondence relationship.

[0042] Specifically, by installing sensor devices at highway network entrances and exits, the server can acquire vehicle characteristic data, including license plate data, thereby identifying the vehicle's identity and type. The server binds vehicle model data and vehicle characteristic data, allowing it to determine the vehicle type by comparing the characteristic data during subsequent vehicle identification processes. By comparing vehicle characteristic data at entrances and exits, the server can determine whether a vehicle has passed through the highway network, thus enabling the collection of traffic flow and other data. By determining the vehicle type, the server can provide appropriate toll collection services, thereby achieving personalized traffic management. Therefore, this not only improves the accuracy and efficiency of vehicle identification but also automates vehicle type judgment and identification, reducing the need for manual intervention and facilitating convenient vehicle model identification.

[0043] In one possible implementation, vehicle transaction data is acquired, including vehicle type data and vehicle characteristic data; the vehicle transaction data is processed to generate a transaction data index, the data processing including desensitization, data cleaning, and data normalization; the transaction data index is analyzed to construct a vehicle database.

[0044] Specifically, the server first acquires vehicle transaction data, which refers to the transaction records of vehicles passing through toll stations on the highway network. Next, the server processes the vehicle transaction data to obtain a transaction data index. A transaction data index is a data structure used for fast data querying and retrieval. The transaction data index can locate the primary key. In this embodiment, the transaction data index helps the server quickly find the transaction records of a specific vehicle. A transaction data index can be viewed as a data structure that arranges data in a certain order, containing multiple fields such as timestamp, transaction amount, and transaction type. Through these fields, the server can quickly retrieve and query vehicle transaction data. The advantage of a transaction data index is that it improves the speed and efficiency of data querying, while also allowing for data aggregation and analysis. The vehicle database can include a vehicle model database, for example, vehicle models include passenger vehicles 1, 2, and 3; the vehicle database can also include a vehicle type database, including emergency rescue vehicles, transport vehicles, etc.

[0045] Data processing includes anonymization, data cleaning, and data normalization. Anonymization removes sensitive data such as license plate numbers and vehicle owner names to protect personal privacy and data security. Data cleaning removes invalid, erroneous, or incomplete data to ensure data quality and accuracy. Data normalization converts data in different formats or units into a unified format or unit to facilitate subsequent data analysis and processing. After generating a transaction data index, the index is analyzed to construct a vehicle database. In this embodiment, the vehicle database may include vehicle type data, vehicle characteristic data, and other relevant data obtained through analysis, such as vehicle transaction frequency, transaction time, and transaction location.

[0046] In one possible implementation, receiving the second vehicle feature data sent by the second sensor device specifically includes: determining the time validity of the initial vehicle feature data based on the capture time and transmission time of the second sensor device, wherein the initial vehicle feature data is the vehicle feature data captured by the second sensor device; performing duplicate data removal processing on the initial vehicle feature data based on preset dimensions to obtain corrected vehicle feature data, wherein the preset dimensions include whether the license plate data is the same, whether the collection point is the same, and whether the capture time is the same; acquiring the vehicle image data of the second vehicle; performing multi-scale feature extraction on the vehicle image data and combining it with the corrected vehicle feature data to obtain the second vehicle feature data.

[0047] Specifically, the server performs a time validity check on the initial vehicle feature data based on the capture and transmission times of the second sensor device. The initial vehicle feature data refers to the vehicle feature data captured by the second sensor device. This step ensures the timeliness of the data and excludes outdated or invalid data. The server then performs duplicate data removal on the initial vehicle feature data based on preset dimensions to obtain corrected vehicle feature data. These preset dimensions include whether the license plate data, collection points, and capture times are identical. This step removes duplicate data and improves data quality. Next, the server acquires the vehicle image data of the second vehicle. This step aims to obtain more intuitive and specific vehicle feature information and performs multi-scale feature extraction on the vehicle image data. Combined with the corrected vehicle feature data, this yields the second vehicle feature data. Multi-scale feature extraction refers to processing and analyzing image data from different scales or angles to extract more comprehensive and accurate vehicle feature information. This step transforms the image data into more representative feature data for subsequent applications and analysis.

[0048] In one possible implementation, after analyzing the transaction data index and constructing the vehicle database, the method further includes: obtaining third vehicle model data based on the transaction data index, wherein the third vehicle model data is the vehicle model data participating in billing; determining whether the third vehicle model data is consistent with the corresponding vehicle model data in the vehicle database, wherein the vehicle database stores multiple vehicle model data participating in billing; if the third vehicle model data is inconsistent with the corresponding vehicle model data in the vehicle database, then sending the third vehicle model data to the user equipment, wherein the user equipment is used to audit and correct the third vehicle model data.

[0049] Specifically, the server retrieves the third vehicle model data based on the transaction data index. This step can be achieved by querying the transaction data index, as it may contain data on the vehicle models involved in billing. Next, the vehicle database stores multiple vehicle model data for billing purposes. The server compares this data to existing data in the database to determine if the third vehicle model data matches. If the third vehicle model data does not match the corresponding data in the database, it is sent to the user's device. This step allows the user's device to audit and correct the third vehicle model data. The user's device can be a specific computer or mobile device, allowing the user to audit and correct the data. Therefore, by comparing the third vehicle model data with the corresponding data in the database, data accuracy is ensured, avoiding problems caused by data inconsistencies. If an inconsistency is found, the data can be promptly sent to the user's device for auditing and correction, ensuring timeliness and accuracy. The combined use of the transaction data index and the vehicle database automates data retrieval and comparison, reducing the cost and error rate of manual operations.

[0050] The user equipment refers to the equipment used by the management personnel of the highway network toll collection system. Types of user equipment include, but are not limited to: Android devices, Apple's iOS devices, personal computers (PCs), World Wide Web (WWW) devices, virtual reality (VR) devices, and augmented reality (AR) devices. In this embodiment, the user equipment is preferably a computer.

[0051] In one possible implementation, a reassessment request is obtained, the reassessment request including a preset period; and the vehicle model data in the vehicle database is reassessed according to the preset period.

[0052] Specifically, first, the server receives a re-evaluation request. This request may originate from various sources, such as user devices, other systems, or scheduled tasks. The re-evaluation request typically includes a preset period, indicating the frequency or period at which data re-evaluation is required. Next, the server re-evaluates the vehicle model data in the vehicle database according to the preset period. This step involves re-evaluating and verifying the vehicle model data in the database, including data cleaning, correction, and normalization. This process ensures the accuracy, integrity, and consistency of the vehicle model data in the database. This data re-evaluation mechanism can periodically check and correct data to ensure accuracy. Furthermore, the preset period allows control over the frequency of data re-evaluation to meet different business needs and data quality requirements.

[0053] In one possible implementation, the vehicle model data in the vehicle database is re-evaluated according to a preset period. Specifically, this includes: obtaining the number of times a target vehicle appears in the highway network within a preset period based on the flow data index; determining the relationship between the number of times a vehicle appears and a preset threshold; and if the number of times a vehicle appears is greater than or equal to the preset threshold, then the vehicle model data corresponding to the target vehicle is re-evaluated.

[0054] Specifically, the server uses a transaction data index to obtain the number of times a target vehicle appears on the highway network within a preset period. The transaction data index contains records of vehicle appearances on the highway network; by querying and calculating, the number of times the target vehicle appears within the preset period can be obtained. The preset frequency threshold is a pre-defined threshold for the number of times a vehicle appears, which can be adjusted according to actual business needs. If the number of times a vehicle appears is greater than or equal to the preset frequency threshold, the vehicle model data corresponding to the target vehicle is re-evaluated. This means that if the number of times a target vehicle appears exceeds the preset threshold within the preset period, the vehicle's data needs to be re-evaluated and verified to ensure data accuracy. This data re-evaluation mechanism allows for focused re-evaluation of vehicle data that appears frequently within the preset period, ensuring the accuracy of this data. Simultaneously, the preset frequency threshold controls the scope of data requiring re-evaluation, avoiding redundant evaluation of all vehicle data, thereby improving data processing efficiency. Therefore, by setting a preset frequency threshold, vehicle data with high frequency can be prioritized for inspection and correction, ensuring the accuracy of this data. Furthermore, it allows for control over the scope of data requiring reassessment, avoiding redundant evaluations of all vehicle data and thus improving data processing efficiency. Regular data reassessment ensures the accuracy of vehicle model data in the vehicle database, preventing data errors or inconsistencies from impacting business operations.

[0055] In one possible implementation, the first vehicle occurrence count is obtained, which is the occurrence count of any one of the multiple target vehicles; if the first vehicle occurrence count is greater than a preset period, the vehicle model data of the target vehicle corresponding to the first vehicle occurrence count is cleared.

[0056] Specifically, the server obtains the occurrence count of the first vehicle, which refers to the number of times any one of multiple target vehicles appears. This first vehicle occurrence count is obtained through previous steps. If the first vehicle occurrence count exceeds a preset period, then the vehicle model data corresponding to that first vehicle occurrence count needs to be cleaned. This means that within the preset period, if the occurrence count of a certain vehicle exceeds a preset threshold, then the data for that vehicle needs to be cleaned and deleted to avoid data redundancy and inaccuracy. This data cleanup mechanism ensures the real-time nature and accuracy of the data, preventing data redundancy and inaccuracy from affecting subsequent data processing and analysis. Furthermore, the preset period allows control over the scope and timing of data cleanup to meet different business needs and data quality requirements.

[0057] Therefore, by cleaning up vehicle model data corresponding to the number of times a vehicle appears beyond a preset period, expired and no longer needed data can be periodically cleaned up, thus avoiding database redundancy and chaos. Cleaning up expired data frees up space to store new data, ensuring database updates and expansion. Cleaning up expired data prevents it from affecting current data and calculation results, thereby improving data reliability and accuracy. When processing large amounts of data, periodically cleaning up expired data helps manage memory and avoid problems such as memory overflow.

[0058] This application also provides a vehicle model identification device based on big data, referring to... Figure 2 , Figure 2 This is a schematic diagram of a vehicle model identification device based on big data, provided in an embodiment of this application. The vehicle model identification device is a server, which includes an acquisition module 21 and a processing module 22. The acquisition module 21 receives first vehicle model data and first vehicle feature data sent by a first sensor device. The first vehicle model data is the vehicle type data of the first vehicle, and the first vehicle feature data includes the license plate data of the first vehicle. The first sensor device is located at the entrance of a highway network, and the first vehicle is any one of multiple vehicles passing through the entrance of the highway network. The processing module 22 binds the first vehicle model data and the first vehicle feature data to obtain a first correspondence. The acquisition module 21 also receives second vehicle feature data sent by a second sensor device. The second vehicle feature data includes the license plate data of the second vehicle. The second sensor device is located at the exit of the highway network, and the second vehicle is any one of multiple vehicles passing through the exit of the highway network. The processing module 22 further determines whether the second vehicle feature data is consistent with the first vehicle feature data. If the processing module 22 determines that the second vehicle feature data is consistent with the first vehicle feature data, it then determines the vehicle type data of the second vehicle as the first vehicle model data based on the first correspondence.

[0059] In one possible implementation, the acquisition module 21 acquires vehicle transaction data, which includes vehicle type data and vehicle characteristic data; the processing module 22 processes the vehicle transaction data to generate a transaction data index, and the data processing includes desensitization processing, data cleaning, and data normalization processing; the processing module 22 analyzes the transaction data index to construct a vehicle database.

[0060] In one possible implementation, the acquisition module 21 receives second vehicle feature data sent by the second sensor device, specifically including: the processing module 22 performs a time validity judgment on the initial vehicle feature data based on the capture time and transmission time of the second sensor device, wherein the initial vehicle feature data is the vehicle feature data captured by the second sensor device; the processing module 22 performs duplicate data removal processing on the initial vehicle feature data based on preset dimensions to obtain corrected vehicle feature data, wherein the preset dimensions include whether the license plate data is the same, whether the collection point is the same, and whether the capture time is the same; the processing module 22 acquires vehicle image data of the second vehicle; the processing module 22 performs multi-scale feature extraction on the vehicle image data and combines it with the corrected vehicle feature data to obtain the second vehicle feature data.

[0061] In one possible implementation, after the processing module 22 analyzes the transaction data index and constructs the vehicle database, the method further includes: the processing module 22 obtains third vehicle model data based on the transaction data index, the third vehicle model data being the vehicle model data participating in billing; determining whether the third vehicle model data is consistent with the corresponding vehicle model data in the vehicle database, the vehicle database storing multiple vehicle model data participating in billing; if the third vehicle model data is inconsistent with the corresponding vehicle model data in the vehicle database, the processing module 22 sends the third vehicle model data to the user equipment, the user equipment being used to audit and correct the third vehicle model data.

[0062] In one possible implementation, the acquisition module 21 acquires a re-evaluation request, which includes a preset period; the processing module 22 performs a data re-evaluation on the vehicle model data in the vehicle database according to the preset period.

[0063] In one possible implementation, the processing module 22 re-evaluates the vehicle model data in the vehicle database according to a preset period. Specifically, the processing module 22 obtains the number of times the target vehicle appears in the highway network within the preset period based on the flow data index; the processing module 22 determines the relationship between the number of times the vehicle appears and a preset threshold; if the number of times the vehicle appears is greater than or equal to the preset threshold, the processing module 22 re-evaluates the vehicle model data corresponding to the target vehicle.

[0064] In one possible implementation, the acquisition module 21 acquires the first single vehicle occurrence count, which is the single vehicle occurrence count of any one of the multiple target vehicles; if the first single vehicle occurrence count is greater than a preset period, the processing module 22 cleans up the vehicle model data of the target vehicle corresponding to the first single vehicle occurrence count.

[0065] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0066] This application also provides an electronic device, with reference to... Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 31, at least one network interface 34, a user interface 33, a memory 35, and at least one communication bus 32.

[0067] The communication bus 32 is used to enable communication between these components.

[0068] The user interface 33 may include a display screen and a camera. Optionally, the user interface 33 may also include a standard wired interface and a wireless interface.

[0069] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0070] The processor 31 may include one or more processing cores. The processor 31 connects to various parts of the server via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in the memory 35, and calling data stored in the memory 35 to perform various server functions and process data. Optionally, the processor 31 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 31 and may be implemented as a separate chip.

[0071] The memory 35 may include random access memory (RAM) or read-only memory. Optionally, the memory 35 may include a non-transitory computer-readable storage medium. The memory 35 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 35 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 35 may also be at least one storage device located remotely from the aforementioned processor 31. Figure 3 As shown, the memory 35, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a vehicle model identification method based on big data.

[0072] exist Figure 3In the electronic device shown, the user interface 33 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 31 can be used to call an application program stored in the memory 35 that is a big data-based vehicle model discrimination method. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0073] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0074] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0075] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0078] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0079] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0080] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A vehicle model identification method based on big data, characterized in that, The method includes: The system receives first vehicle model data and first vehicle feature data sent by a first sensor device. The first vehicle model data is the vehicle type data of the first vehicle, and the first vehicle feature data includes the license plate data of the first vehicle. The first sensor device is located at the entrance of the highway network, and the first vehicle is any one of multiple vehicles passing through the entrance of the highway network. The first vehicle model data and the first vehicle feature data are bound together to obtain a first correspondence; The system receives second vehicle feature data sent by a second sensor device, the second vehicle feature data including the license plate data of the second vehicle, the second sensor device being located at the exit of the highway network, and the second vehicle being any one of a plurality of vehicles passing through the exit of the highway network; The receipt of the second vehicle feature data sent by the second sensor device specifically includes: Based on the capture time and transmission time of the second sensor device, the time validity of the initial vehicle feature data is determined, wherein the initial vehicle feature data is the vehicle feature data captured by the second sensor device; Based on preset dimensions, duplicate data removal is performed on the initial vehicle feature data to obtain corrected vehicle feature data. The preset dimensions include whether the license plate data is the same, whether the collection point is the same, and whether the capture time is the same. Obtain vehicle image data of the second vehicle; Multi-scale feature extraction is performed on the vehicle image data, and combined with the corrected vehicle feature data to obtain the second vehicle feature data. Determine whether the second vehicle feature data is consistent with the first vehicle feature data; If it is determined that the second vehicle feature data is consistent with the first vehicle feature data, then the vehicle type data of the second vehicle is determined to be the first vehicle model data according to the first correspondence relationship; The method further includes: Acquire vehicle transaction data, which includes vehicle type data and vehicle characteristic data; The vehicle transaction data is processed to generate a transaction data index. The data processing includes desensitization, data cleaning, and data normalization. The flow data index is analyzed to construct a vehicle database; Based on the transaction data index, obtain the third vehicle model data, which is the vehicle model data participating in the billing process; Determine whether the third vehicle model data is consistent with the corresponding vehicle model data in the vehicle database, wherein the vehicle database stores multiple vehicle model data that participate in the billing process; If the third vehicle model data is inconsistent with the corresponding vehicle model data in the vehicle database, the third vehicle model data is sent to the user equipment, which is used to check and correct the third vehicle model data.

2. The vehicle model identification method based on big data according to claim 1, characterized in that, The method further includes: Obtain a reassessment request, wherein the reassessment request includes a preset period; The vehicle model data in the vehicle database is re-evaluated according to the preset cycle.

3. The vehicle model identification method based on big data according to claim 2, characterized in that, The step of re-evaluating the vehicle model data in the vehicle database according to the preset period specifically includes: Based on the flow data index, obtain the number of times the target vehicle appears in the highway network within the preset period; Determine the relationship between the number of times the bicycle appears and a preset threshold number of occurrences; If the number of times a single vehicle appears is greater than or equal to a preset threshold, the vehicle model data corresponding to the target vehicle will be re-evaluated.

4. The vehicle model identification method based on big data according to claim 3, characterized in that, The method further includes: Get the first single vehicle appearance count, where the first single vehicle appearance count is the single vehicle appearance count of any one of the multiple target vehicles; If the number of times the first vehicle appears is greater than the preset period, then the vehicle model data of the target vehicle corresponding to the number of times the first vehicle appears is cleared.

5. A vehicle model identification device based on big data, applied to a vehicle model identification method based on big data as described in any one of claims 1-4, characterized in that, The vehicle model identification device includes an acquisition module (21) and a processing module (22), wherein, The acquisition module (21) is used to receive first vehicle model data and first vehicle feature data sent by the first sensor device. The first vehicle model data is the vehicle type data of the first vehicle, and the first vehicle feature data includes the license plate data of the first vehicle. The first sensor device is located at the entrance of the highway network, and the first vehicle is any one of the multiple vehicles passing through the entrance of the highway network. The processing module (22) is used to bind the first vehicle model data and the first vehicle feature data to obtain a first correspondence relationship; The acquisition module (21) is also used to receive second vehicle feature data sent by the second sensor device. The second vehicle feature data includes the license plate data of the second vehicle. The second sensor device is located at the exit of the highway network. The second vehicle is any one of the multiple vehicles passing through the exit of the highway network. The processing module (22) is also used to determine whether the second vehicle feature data is consistent with the first vehicle feature data; The processing module (22) is further configured to determine the vehicle type data of the second vehicle as the first vehicle model data based on the first correspondence relationship if it is determined that the second vehicle feature data is consistent with the first vehicle feature data.

6. An electronic device, characterized in that, The electronic device includes a processor (31), a memory (35), a user interface (33), and a network interface (34). The memory (35) is used to store instructions. The user interface (33) and the network interface (34) are both used to communicate with other devices. The processor (31) is used to execute the instructions stored in the memory (35) to cause the electronic device to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 4.

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