Data processing method and device, computer device, and storage medium

Abstract

The application belongs to the technical field of artificial intelligence, and relates to a data processing method and device, computer equipment and a storage medium, comprising: acquiring positioning data of a target vehicle; performing mileage calculation on the positioning data to obtain driving mileage data; if the driving mileage data reaches a threshold of a near-maintenance mileage, an outbound request for providing a dashboard picture is sent to a target customer; receiving a dashboard picture of the target vehicle uploaded by the target customer, and correcting the driving mileage data based on the dashboard picture to obtain target driving mileage data; based on the target driving mileage data, real-time collection of updated driving mileage data and use time data of the target vehicle is performed; if the updated driving mileage data or the use time data meets a maintenance condition in a maintenance rule library, a maintenance reminder information of the target vehicle is generated and pushed to the target customer. The application can be applied to a vehicle maintenance business scene in the field of financial technology, and can effectively improve the accuracy and intelligence of vehicle maintenance reminders.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology and can be applied to the financial technology field, particularly to data processing methods, devices, computer equipment and storage media. Background Technology

[0002] In the operating lease business of auto finance leasing companies within the financial sector, vehicle maintenance management is a crucial link in protecting vehicle asset value and ensuring the smooth operation of the leasing business. Among these, vehicle maintenance reminders, as an important means of vehicle maintenance management, directly impact the effectiveness of vehicle maintenance and the operational efficiency of the leasing company.

[0003] Currently, the generation of vehicle maintenance reminders primarily relies on fixed time thresholds. Specifically, it uses the vehicle's factory-preset maintenance cycle as a fixed standard to send corresponding maintenance reminders to customers. From a data processing perspective, this traditional method has significant problems. It only considers the single dimension of time, failing to comprehensively analyze and process various key data points from actual vehicle use. This method of generating maintenance reminders based on fixed time thresholds cannot adapt to actual vehicle conditions. Because it fails to adequately consider the vehicle's actual usage, inaccurate maintenance reminders occur, resulting in low accuracy and intelligence in maintenance reminder processing.

[0004] For example, in the context of auto finance leasing companies within the financial sector, traditional methods might only provide maintenance reminders every six months as set by the manufacturer. However, adhering to fixed maintenance schedules may fail to detect potential problems in a timely manner, leading to increased vehicle failure rates. Conversely, if the vehicle's mileage is low, fixed-schedule maintenance can result in unnecessary resource waste. This inconsistency in reminders not only affects the efficiency of vehicle asset management but may also reduce the market competitiveness and customer satisfaction of the finance leasing company.

[0005] Therefore, there is an urgent need to provide a dynamic maintenance reminder method based on the actual usage of vehicles, in order to improve the accuracy and intelligence of vehicle maintenance reminders, protect the value of vehicle assets, and enhance the overall efficiency of financial leasing services. Summary of the Invention

[0006] The purpose of this application is to provide a data processing method, apparatus, computer equipment, and storage medium to solve the technical problem that the vehicle maintenance reminder method relying on fixed time thresholds in the operating lease model of traditional financial leasing companies has low accuracy and intelligence in the processing of maintenance reminders.

[0007] Firstly, a data processing method is provided, including: Obtain the location data of the target vehicle; The positioning data is processed to calculate the mileage, and the corresponding driving mileage data is obtained. If the mileage data reaches a preset threshold for approaching maintenance, an outbound call requesting a dashboard image is sent to the target customer; wherein, the target customer is the user of the target vehicle; Receive the dashboard image of the target vehicle uploaded by the target customer, and perform correction processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data; Based on the target mileage data, the updated mileage data and usage time data of the target vehicle are collected in real time. The updated mileage data and usage time data are compared with the maintenance conditions in the preset maintenance rule library; If the updated mileage data or the usage time data meets the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer.

[0008] Secondly, a data processing apparatus is provided, comprising: The first acquisition module is used to acquire the location data of the target vehicle; The calculation module is used to perform mileage calculation processing on the positioning data to obtain the corresponding driving mileage data; The first processing module is used to send an outbound call request to the target customer to provide a dashboard image if the mileage data reaches a preset threshold for approaching maintenance; wherein the target customer is the user of the target vehicle. The calibration module is used to receive the dashboard image of the target vehicle uploaded by the target customer, and to perform calibration processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data. The data acquisition module is used to collect updated mileage data and usage time data of the target vehicle in real time based on the target mileage data; The comparison module is used to compare the updated mileage data and the usage time data with the maintenance conditions in the preset maintenance rule library; The second processing module is used to generate maintenance reminder information corresponding to the target vehicle if the updated mileage data or the usage time data meets the maintenance conditions, and push the maintenance reminder information to the target customer.

[0009] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described data processing method.

[0010] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described data processing method.

[0011] In the above-described data processing method, apparatus, computer equipment, and storage medium, the location data of the target vehicle is first acquired; then, the location data is processed for mileage calculation to obtain corresponding mileage data; if the mileage data reaches a preset near-maintenance mileage threshold, an outbound call request for a dashboard image is sent to the target customer, where the target customer is the user of the target vehicle; subsequently, the dashboard image of the target vehicle uploaded by the target customer is received, and the mileage data is corrected based on the dashboard image to obtain corresponding target mileage data; subsequently, updated mileage data and usage time data of the target vehicle are collected in real time based on the target mileage data; further, the updated mileage data and usage time data are compared with maintenance conditions in a preset maintenance rule library; if the updated mileage data or the usage time data meets the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer. Based on the above automated processing flow, this application obtains mileage data by performing mileage calculation on the acquired target vehicle's location data. Then, when the mileage data is detected to reach a preset near-maintenance mileage threshold, an outbound call request is sent to the target customer to provide a dashboard image. The mileage data is corrected based on the dashboard image of the target vehicle uploaded by the target customer to obtain the corresponding target mileage data. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in the preset maintenance rule library. When the updated mileage data or usage time data is detected to meet the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer. Thus, unlike existing vehicle maintenance reminder methods that rely on fixed time thresholds, this application provides an intelligent processing method that automatically analyzes and processes various key data during actual vehicle use to provide maintenance reminders. By collecting vehicle location data and combining it with dashboard images for correction, target mileage data is obtained. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in a preset maintenance rule library. This achieves automatic and intelligent provision of accurate maintenance reminders, effectively improving the accuracy and intelligence of vehicle maintenance reminders. Attached Figure Description

[0012] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is an exemplary system architecture diagram to which this application can be applied; Figure 2 This is a flowchart of an embodiment of the data processing method according to this application; Figure 3 This is a schematic diagram of the structure of an embodiment of the data processing apparatus according to this application; Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0015] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0017] like Figure 1As shown, system architecture 100 may include terminal device 101, network 102, and server 103. Terminal device 101 may be a laptop 1011, tablet 1012, or mobile phone 1013. Network 102 is used as a medium to provide a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0018] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0019] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to laptops 1011, tablets 1012, or mobile phones 1013, terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, and a desktop computer, etc.

[0020] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0021] It should be noted that the data processing method provided in the embodiments of this application is generally executed by a server / terminal device, and correspondingly, the data processing device is generally located in the server / terminal device.

[0022] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0023] Continue to refer to Figure 2 A flowchart illustrating an embodiment of the data processing method according to this application is shown. The order of steps in the flowchart can be changed, and some steps can be omitted, depending on different needs. The data processing method provided by this application embodiment can be applied to any scenario requiring data processing, and thus can be applied to products in these scenarios, such as vehicle processing in the financial sector. The data processing method includes the following steps: Step S201: Obtain the location data of the target vehicle.

[0024] In this embodiment, the data processing method runs on an electronic device (e.g., Figure 1 The server / terminal device shown can acquire the location data of the target vehicle via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods. The implementing entity of this application is specifically a data processing system, which can be simply referred to as the system. This application can be applied to vehicle maintenance reminder scenarios in the financial leasing business of financial leasing companies in the financial sector.

[0025] Financial leasing companies primarily employ two leasing models: financial leasing and operating leasing. Under the operating leasing model, vehicle ownership remains with the company throughout the lease term, and the lessee only has the right to use the vehicle during the lease period, returning it upon lease termination. This means that maintaining the vehicle's asset value throughout the entire lease cycle is crucial. The vehicle asset belongs to the financial leasing company throughout the lease term, and all maintenance services are financed by the leasing company; the customer is required to cooperate in completing maintenance. According to industry practice, vehicles are accompanied by a maintenance manual at the factory, clearly outlining various maintenance requirements. However, due to lessees' widespread disregard for the manual's contents, a large number of vehicles experience overdue maintenance. If failure to complete maintenance on time leads to potential vehicle usage risks or malfunctions, it will impact the vehicle's residual value after recovery and subsequent leasing business.

[0026] In the context of operating leases, financial leasing companies often install GPS devices in vehicles. These devices calculate mileage based on GPS data, assisting vehicle management personnel in determining when a vehicle has reached its maintenance interval and allowing for manual intervention. Specifically, this can be achieved through onboard GPS modules installed in the target vehicle or aftermarket GPS devices. These devices continuously record the vehicle's latitude and longitude coordinates at different time points at regular intervals, transmitting this location data to a designated storage location to provide foundational data for subsequent processing.

[0027] In addition, in the financial sector, the aforementioned financial leasing companies can be categorized into the following types: 1. Independent Financial Leasing Companies: Characteristics: Independently operated, not specializing in products from any single manufacturer, with a broad business scope including conventional financial leasing and various extended forms. Function: Able to flexibly select suitable vehicles for leasing based on the lessee's needs, and provide professional vehicle maintenance and upkeep services to ensure the vehicles remain in good condition throughout the lease term. Advantages: Possesses rich market experience and a professional asset management team, effectively managing vehicle assets and reducing potential usage risks. 2. Manufacturer Financial Leasing Companies: Characteristics: Affiliated with manufacturers, controlling the exit mechanism for leased assets and the used equipment market, and able to provide customers with services related to leased assets. Function: During the lease term, not only providing financial support, but also responsible for vehicle repair, maintenance, and even technical upgrades to ensure stable vehicle performance. Advantages: Belonging to the same corporate group as the supplier and funding provider, forming a complete industrial chain closed loop and improving the management efficiency of vehicle assets. 3. Retailer Financial Leasing Companies: Characteristics: Affiliated with commercial financial service enterprises, primarily supporting the promotion of certain manufacturers' products, possessing strong financial backing. **Finance and Management of Vehicles:** 1. **Project Finance Leasing Company:** Established for specific large-scale projects, exiting the market upon project completion but capable of continuously developing new projects; flexible structure. **Finance and Management of Vehicles:** Provides professional services for obtaining funding, technology, and equipment in large-scale project leasing, including full-process management of vehicle maintenance and repair. **Advantages:** Possesses a professional project management team and technical support system, ensuring comprehensive and meticulous maintenance of vehicles during the lease term. 2. **Financial Leasing Company:** A non-bank financial institution approved by the State Financial Supervision and Administration Bureau, primarily engaged in financial leasing business; strong financial nature. **Finance and Management of Vehicles:** Utilizes financial advantages to provide lessees with flexible financing solutions and relies on a professional asset management team for full-process tracking and management of leased vehicles. **Advantages:** Possesses strong financial strength and risk management capabilities, ensuring proper maintenance and management of vehicle assets throughout the lease period, reducing asset depreciation risk.

[0028] Step S202: Perform mileage calculation processing on the positioning data to obtain the corresponding driving mileage data.

[0029] In this embodiment, the specific implementation process of performing mileage calculation on the positioning data to obtain the corresponding driving mileage data will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0030] Step S203: If the mileage data reaches a preset threshold for near-maintenance mileage, an outbound call request to provide a dashboard image is sent to the target customer; wherein, the target customer is the user of the target vehicle.

[0031] In this embodiment, the process of generating the aforementioned near-maintenance mileage threshold will be described in further detail in subsequent specific embodiments, and will not be elaborated upon here. The target customer is the user of the target vehicle. When the mileage data continuously monitored by the system reaches the aforementioned near-maintenance mileage threshold, the system will automatically trigger an outbound call task. For example, if the set near-maintenance mileage threshold is 4000km, an outbound call request will be triggered when the GPS-calculated mileage reaches 4000km. Specifically, the system initiates an outbound call to the target customer via voice communication technology, informing the target customer that the vehicle is nearing its maintenance cycle and explicitly requesting the target customer to provide a timestamped image of the dashboard. Furthermore, timely notification of the customer's vehicle's impending maintenance via outbound calls and collection of dashboard images helps provide accurate data for subsequent correction of GPS-calculated mileage, ensuring the accuracy of maintenance reminders.

[0032] Step S204: Receive the dashboard image of the target vehicle uploaded by the target customer, and perform correction processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data.

[0033] In this embodiment, target customers can take photos and upload images of the target vehicle's dashboard through the APP or mini-program provided by the financial leasing company, or they can send the dashboard images to the operations staff, who will then collect them and upload them to the system through the backend.

[0034] The specific implementation process of correcting the mileage data based on the dashboard image to obtain the corresponding target mileage data will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0035] Step S205: Based on the target mileage data, collect the updated mileage data and usage time data of the target vehicle in real time.

[0036] In this embodiment, the corrected target mileage data is used as a baseline, and the daily incremental mileage of the target vehicle's GPS data collected subsequently is added to this baseline to obtain the corrected vehicle mileage updated mileage data. For example, if the incremental mileage calculated by GPS on the second day is 100km, then the corrected mileage is 4600 + 100 = 4700km. Simultaneously, the system collects the target vehicle's usage time data in real time. Specifically, it records the start time of vehicle use and calculates the difference between the current time and the start time to obtain the usage time.

[0037] Step S206: Compare the updated mileage data and usage time data with the maintenance conditions in the preset maintenance rule library.

[0038] In this embodiment, the collected updated mileage and usage time data can be compared with the maintenance conditions in a preset maintenance rule library. The maintenance rule library stores maintenance rules for different vehicle models, such as requiring maintenance every 10,000 kilometers or every 6 months for the target vehicle. The system automatically determines whether the target vehicle meets these maintenance conditions.

[0039] Step S207: If the updated mileage data or the usage time data meets the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer.

[0040] In this embodiment, the above-mentioned updated mileage data or usage time data meeting the maintenance conditions means that at least one of the updated mileage data and usage time data meets the maintenance conditions.

[0041] Furthermore, the specific implementation process of generating maintenance reminder information corresponding to the target vehicle and pushing the maintenance reminder information to the target customer will be described in more detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0042] This application first acquires the location data of the target vehicle; then, it performs mileage calculation processing on the location data to obtain the corresponding mileage data; if the mileage data reaches a preset near-maintenance mileage threshold, it sends an outbound call request to the target customer to provide a dashboard image; wherein, the target customer is the user of the target vehicle; then, it receives the dashboard image of the target vehicle uploaded by the target customer, and performs correction processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data; subsequently, it collects updated mileage data and usage time data of the target vehicle in real time based on the target mileage data; further, it compares the updated mileage data and usage time data with maintenance conditions in a preset maintenance rule library; if the updated mileage data or the usage time data meets the maintenance conditions, it generates maintenance reminder information corresponding to the target vehicle and pushes the maintenance reminder information to the target customer. Based on the above automated processing flow, this application obtains mileage data by performing mileage calculation on the acquired target vehicle's location data. Then, when the mileage data is detected to reach a preset near-maintenance mileage threshold, an outbound call request is sent to the target customer to provide a dashboard image. The mileage data is corrected based on the dashboard image of the target vehicle uploaded by the target customer to obtain the corresponding target mileage data. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in the preset maintenance rule library. When the updated mileage data or usage time data is detected to meet the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer. Thus, unlike existing vehicle maintenance reminder methods that rely on fixed time thresholds, this application provides an intelligent processing method that automatically analyzes and processes various key data during actual vehicle use to provide maintenance reminders. By collecting vehicle location data and combining it with dashboard images for correction, target mileage data is obtained. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in a preset maintenance rule library. This achieves automatic and intelligent provision of accurate maintenance reminders, effectively improving the accuracy and intelligence of vehicle maintenance reminders.

[0043] In some alternative implementations, step S202 includes the following steps: The positioning data is cleaned to obtain the corresponding target positioning data.

[0044] In this embodiment, the specific implementation process of cleaning the positioning data to obtain the corresponding target positioning data will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0045] Invoke the preset distance calculation strategy.

[0046] In this embodiment, the distance calculation strategy includes: based on the cleaned trajectory information, calculating the distance between adjacent positioning points sequentially according to the distance calculation method between latitude and longitude coordinates. Specifically, the calculation method can use the Haversian formula. Assuming the Earth is a perfect sphere, given the latitude and longitude coordinates (lat1, lon1) and (lat2, lon2) of two points, after converting the latitude and longitude to radians, the distance is calculated using the formula... , , The distance between adjacent positioning points is calculated using the formula (where R is the Earth's radius, approximately 6371 kilometers, and d is the distance between the two points). These distances are then summed to determine the vehicle's mileage.

[0047] The target positioning data is processed by mileage calculation based on the distance calculation strategy to obtain the corresponding calculation results.

[0048] In this embodiment, the mileage of the target positioning data can be calculated based on the strategy content of the distance calculation strategy, and the calculated result can be used as the driving mileage data of the target vehicle.

[0049] The calculation result is used as the driving mileage data.

[0050] This application cleans the positioning data to obtain corresponding target positioning data; then it calls a preset distance calculation strategy; subsequently, it performs mileage calculation on the target positioning data based on the distance calculation strategy to obtain the corresponding calculation result; and finally, it uses the calculation result as the driving mileage data. Based on the above processing flow, this application cleans the positioning data to obtain target positioning data, then performs mileage calculation on the target positioning data based on the use of a distance calculation strategy, and uses the calculated result as the driving mileage data of the target vehicle. This enables efficient and accurate mileage calculation processing of the target vehicle's positioning data, improves the processing efficiency of mileage calculation, and ensures the accuracy of the obtained driving mileage data.

[0051] In some optional implementations of this embodiment, the step of cleaning the positioning data to obtain the corresponding target positioning data includes the following steps: The location data is classified based on a preset anomaly identification model to obtain the corresponding anomaly identification results.

[0052] In this embodiment, a pre-trained anomaly detection model can be used to comprehensively examine the collected positioning data. Due to signal interference, equipment malfunctions, or other reasons, anomalies may occur, such as the positioning point deviating significantly from the actual driving route.

[0053] For some complex scenarios, anomaly identification may not be accurate enough. For example, in complex urban environments, positioning deviations caused by factors such as tall buildings and signal reflections may prevent traditional anomaly identification methods from effectively distinguishing between normal fluctuations and anomalous data. Machine learning algorithms can learn from the features of a large amount of normal and anomalous GPS data to more accurately identify and eliminate anomalous positioning data.

[0054] The model construction process of the aforementioned anomaly detection model includes: 1) Data collection: Collecting a large amount of vehicle GPS positioning data, covering various driving scenarios, including urban roads, highways, mountain roads, and different weather and signal conditions. Simultaneously, manually labeling this data to identify obviously abnormal data, which will serve as both training and testing datasets. The data is then preprocessed, including normalizing latitude and longitude coordinates to ensure their values ​​fall within a certain range, facilitating processing by the machine learning model; and extracting other relevant information for each location point, such as timestamps, speed, and direction, as features. 2) Model selection and training: Selecting a suitable machine learning model, such as Support Vector Machine (SVM), Random Forest, or Long Short-Term Memory (LSTM) networks in deep learning. SVM and Random Forest are suitable for processing structured data and can classify quickly and effectively; LSTM can handle time-series data, better capturing the temporal correlation of GPS data. The selected model is then trained using the labeled training dataset. During training, the model parameters are continuously adjusted to minimize classification error. For example, for SVM models, adjust the kernel function parameters and penalty coefficients; for random forest models, adjust the number and depth of trees; for LSTM models, adjust the hidden layer size and learning rate, etc. 3) Model Evaluation and Optimization. Evaluate the trained model using a test dataset, calculating metrics such as accuracy, recall, and F1 score. If the model's performance does not meet requirements, analyze the problems, such as overfitting or underfitting. Then optimize the model based on the evaluation results. For overfitting, regularization methods, increasing the amount of training data, or reducing model complexity can be used; for underfitting, model complexity can be increased, more features introduced, or model parameters adjusted, thus using the optimized model as a trained anomaly detection model.

[0055] Furthermore, the trained anomaly detection model is integrated into the system. For newly acquired GPS data, such as the location data mentioned above, its features are first extracted, and then input into the anomaly detection model for classification to determine whether the location data is anomalous, generating corresponding anomaly detection results. The anomaly detection results include whether the data is anomalous or normal.

[0056] Abnormal location data is determined from the location data based on the anomaly identification results.

[0057] In this embodiment, the above-mentioned abnormal location data refers to the data in the above-mentioned location data whose abnormal identification result is abnormal data.

[0058] Abnormal location data in the location data is removed based on a preset filtering algorithm to obtain the corresponding processed data.

[0059] In this embodiment, the filtering algorithm described above can specifically employ the Kalman filter algorithm. This algorithm is based on the system's state equation and observation equation, estimating the system's true state through two steps: prediction and update. When processing GPS positioning data, the vehicle's position, speed, etc., are taken as the system state. Using the state estimate from the previous moment and the observation value at the current moment, a more accurate state estimate for the current moment is calculated, thereby removing abnormal positioning points and generating accurate vehicle trajectory information, i.e., processing the data.

[0060] The processed data is used as the target location data.

[0061] This application classifies the positioning data based on a preset anomaly detection model to obtain corresponding anomaly detection results; then, based on the anomaly detection results, it identifies abnormal positioning data from the positioning data; subsequently, it removes the abnormal positioning data from the positioning data using a preset filtering algorithm to obtain corresponding processed data; finally, it uses the processed data as the target positioning data. Based on the above processing flow, this application classifies positioning data using an anomaly detection model to obtain anomaly detection results, and identifies abnormal positioning data from the positioning data based on these results. By utilizing machine learning algorithms, it can learn a large number of normal and abnormal GPS data features, effectively distinguishing between normal fluctuations and abnormal data, and thus accurately identifying abnormal positioning data in the positioning data. Subsequently, by using a filtering algorithm to remove the abnormal positioning data from the positioning data, accurate target positioning data can be effectively generated.

[0062] In some alternative implementations, step S204 includes the following steps: Obtain the preset data recognition strategy.

[0063] In this embodiment, the above data recognition strategy may specifically employ OCR (Optical Character Recognition) technology.

[0064] The dashboard image is processed based on the data recognition strategy to identify the mileage value on the dashboard.

[0065] In this embodiment, OCR (Optical Character Recognition) technology is used to extract vehicle mileage data from the dashboard image. OCR technology uses image processing and pattern recognition algorithms to convert the numerical information in the dashboard image into processable text data. Specifically, the dashboard image is first preprocessed, including grayscale conversion, binarization, and noise reduction, to improve image quality. Then, feature extraction algorithms are used to identify the features of the numbers, which are then matched with a preset numerical template to identify the mileage numbers on the dashboard and used as the corresponding mileage values.

[0066] The mileage data is corrected based on the mileage value to obtain corrected mileage data.

[0067] In this embodiment, the specific implementation process of correcting the mileage data based on the mileage value to obtain the corrected mileage data will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0068] The corrected mileage data is used as the target mileage data.

[0069] This application obtains a preset data recognition strategy; then, based on the data recognition strategy, it performs recognition processing on the dashboard image to identify the mileage value on the dashboard; subsequently, it performs correction processing on the mileage data based on the mileage value to obtain corrected mileage data; finally, it uses the corrected mileage data as the target mileage data. Based on the above processing flow, this application, by recognizing the dashboard image based on a data recognition strategy, and then correcting the mileage data based on the identified mileage value on the dashboard, and using the corrected mileage data as the corresponding target mileage data, can achieve intelligent correction of the generated mileage data, solving the problem of possible errors in positioning data, making the final vehicle mileage data more accurate, and providing a reliable basis for subsequent matching processing of the maintenance rule base.

[0070] In some optional implementations, the step of correcting the mileage data based on the mileage value to obtain corrected mileage data includes the following steps: Based on the mileage values ​​and the driving mileage data, a corresponding target graph structure data is constructed.

[0071] In this embodiment, the processing method in the subsequent data preparation and graph construction steps can be referred to to perform graph structure construction processing on the above mileage values ​​and the driving mileage data to generate the corresponding target graph structure data.

[0072] Invoke the preset mileage prediction model.

[0073] In this embodiment, the construction process of the above-mentioned mileage prediction model includes: 1) Data preparation and graph construction. Collect historical vehicle data, including GPS-calculated mileage, instrument mileage, and corresponding time information. Treat the data at each time point as a node in the graph, and the node features include GPS mileage, instrument mileage, time, etc. Construct edges between nodes based on time order and data similarity. For example, establish edges between nodes at adjacent time points, and the weight of the edges can be determined based on the similarity of the features of the two nodes (such as the similarity of mileage change trends). 2) Graph neural network model selection and construction. Select a suitable graph neural network architecture, such as a graph convolutional network (GCN). GCN can aggregate and update features of nodes in the graph and mine deep relationships between nodes through multi-layer propagation. Construct a GCN model, with the input being the constructed graph structure data. After multi-layer graph convolution operations, the features of each node are continuously fused with the information of surrounding nodes to obtain a more comprehensive and accurate feature representation. In the last layer of the model, add a fully connected layer to output the fused mileage data. 3) Model training. Divide the historical data into training set, validation set, and test set. The constructed graph neural network model is trained using a training set, and a loss function, such as the mean squared error loss function, is defined to measure the difference between the fused mileage output by the model and the true value. Backpropagation algorithm and an optimizer (such as the Adam optimizer) are used to update the model parameters, continuously reducing the value of the loss function. During training, a validation set is used to monitor the model's performance to prevent overfitting. Training is stopped when the model's performance on the validation set no longer improves. 4) Model Evaluation and Optimization. The trained model is evaluated using a test set, and metrics such as accuracy and mean squared error on the mileage fusion task are calculated. If the model's performance does not meet the requirements, the problems with the model are analyzed, such as whether it handles certain special driving modes (such as prolonged idling or frequent start-stop) poorly. Based on the evaluation results, the model is optimized by adjusting the structure of the graph neural network (such as increasing or decreasing the number of graph convolutional layers), changing the edge weight calculation method, adjusting the number of neurons in fully connected layers, etc., until a mileage prediction model that meets the construction requirements is obtained.

[0074] The trained mileage prediction model can fully utilize the inherent relationship between instrument mileage and GPS-measured vehicle mileage, as well as information from historical data. Both instrument mileage and GPS mileage reflect the vehicle's driving status, and there is a certain correlation between them. Graph neural networks can process graph-structured data and uncover complex relationships between data points. They can be used to fuse instrument mileage and GPS mileage data to improve the accuracy and reliability of mileage data.

[0075] The target map structure data is predicted based on the mileage prediction model to generate corresponding prediction results.

[0076] In this embodiment, the generated target graph structure data can be input into a trained mileage prediction model, which will output fused and accurate mileage data for subsequent maintenance rule matching and other operations. Simultaneously, based on the prediction results of the mileage prediction model, vehicle maintenance schedules can be planned in advance, improving the system's intelligence level.

[0077] The driving mileage data is corrected to the predicted result to obtain the corrected driving mileage data.

[0078] In this embodiment, the original GPS-calculated vehicle mileage data can be corrected to the predicted result by using the prediction result output by the mileage prediction model as the absolute benchmark. For example, if the GPS-calculated mileage is 4500km, while the predicted result is 4600km, then the GPS-calculated mileage will be corrected to 4600km.

[0079] Alternatively, other methods can be used to correct the mileage data of the target vehicle. For example, the odometer reading can be used as an absolute reference to correct the mileage calculated by the GPS to the odometer reading. For instance, if the GPS mileage is 4500km and the odometer reading is 4800km, then the GPS mileage will be corrected to 4800km.

[0080] This application constructs a corresponding target map structure data based on the mileage value and the driving mileage data; then calls a preset mileage prediction model; subsequently, it performs prediction processing on the target map structure data based on the mileage prediction model to generate a corresponding prediction result; and finally, it corrects the driving mileage data to the prediction result to obtain the corrected driving mileage data. Based on the above processing flow, this application constructs target map structure data based on mileage value and driving mileage data, then performs prediction processing on the target map structure data based on the mileage prediction model to generate a prediction result, and then corrects the driving mileage data to the prediction result to obtain the corrected driving mileage data. This allows for intelligent and accurate correction of driving mileage data, effectively solving the problem of potential errors in positioning data, making vehicle driving mileage data more accurate, and providing a reliable basis for subsequent maintenance rule matching.

[0081] In some optional implementations of this embodiment, step S207 includes the following steps: Obtain vehicle information, maintenance items, and estimated maintenance time corresponding to the target vehicle.

[0082] In this embodiment, the vehicle information may include license plate number, VIN code, etc. Maintenance rules matching the updated mileage data of the target vehicle can be obtained, and information such as matching maintenance items, estimated maintenance time, and cost budget can be retrieved from these rules. For example, if the vehicle's mileage reaches 10,000 kilometers and the usage time has not exceeded 6 months, corresponding maintenance items, such as changing engine oil and oil filter, are matched, and the required time and cost are estimated as the corresponding estimated maintenance time and cost budget.

[0083] Based on the vehicle information, the maintenance items, and the estimated maintenance time, a maintenance reminder message corresponding to the target vehicle is generated.

[0084] In this embodiment, maintenance reminder information corresponding to the target vehicle can be generated by filling the aforementioned vehicle information, maintenance items, and estimated maintenance time into a preset maintenance reminder information template. The template content of the maintenance reminder information template can be written and generated according to the actual vehicle maintenance reminder needs.

[0085] Get the preset push notification method.

[0086] In this embodiment, the selection of the above-mentioned push method is not specifically limited, and can be determined according to the actual business needs. For example, any one or more of the following methods can be used: email, SMS, APP push, etc.

[0087] Based on the aforementioned push method, the maintenance reminder information will be sent to the target customer.

[0088] In this embodiment, the generated maintenance reminder information can be sent to the communication terminal of the target customer according to the selected push method.

[0089] This application obtains vehicle information, maintenance items, and estimated maintenance time corresponding to the target vehicle; then generates maintenance reminder information corresponding to the target vehicle based on the vehicle information, maintenance items, and estimated maintenance time; subsequently, it obtains a preset push method; and then sends the maintenance reminder information to the target customer based on the push method. Based on the above processing flow, when this application detects that updated mileage data or usage time data meets maintenance conditions, i.e., the target vehicle meets maintenance conditions, it generates maintenance reminder information corresponding to the target vehicle based on the obtained vehicle information, maintenance items, and estimated maintenance time, and then sends the maintenance reminder information to the target customer based on the obtained push method. This enables timely reminders to the target customer, ensuring that the target vehicle receives timely maintenance.

[0090] In some optional implementations of this embodiment, before step S203, the electronic device may further perform the following steps: Obtain the vehicle model information of the target vehicle.

[0091] In this embodiment, the vehicle model information can be obtained by acquiring the vehicle model of the target vehicle.

[0092] Based on the vehicle model information, obtain the maintenance mileage requirements corresponding to the target vehicle.

[0093] In this embodiment, after the vehicle rental commences or after the last maintenance, the maintenance interval and mileage information for each maintenance reminder batch can be determined based on the vehicle maintenance manual recommendations for each vehicle model. The lifespan and replacement cycle of components such as the engine and oil filter vary across different vehicle models; for example, some models recommend maintenance every six months or one year, with maintenance intervals of 5000km or 10000km. Therefore, the required maintenance mileage information matching the target vehicle can be found by consulting the vehicle maintenance manual corresponding to the target vehicle's model information.

[0094] The maintenance mileage requirement information is processed based on a preset threshold generation strategy to generate corresponding threshold data.

[0095] In this embodiment, the threshold generation strategy includes setting a certain trigger threshold based on the determined maintenance mileage requirement information. For example, for a maintenance cycle of 5000km (maintenance mileage requirement information), considering the need to remind customers to prepare for maintenance in advance, the trigger threshold (i.e., the near-maintenance mileage threshold) is set to 4000km. Furthermore, the threshold generation process for the maintenance mileage requirement information can be performed based on the threshold generation strategy, and the generated threshold data can be used as the corresponding near-maintenance mileage threshold.

[0096] The threshold data is used as the threshold for the nearest maintenance mileage.

[0097] This application obtains the vehicle model information of the target vehicle; then, based on the vehicle model information, it obtains the maintenance mileage requirement information corresponding to the target vehicle; subsequently, it performs threshold generation processing on the maintenance mileage requirement information based on a preset threshold generation strategy to generate corresponding threshold data; and finally, it uses the threshold data as the near-maintenance mileage threshold. Based on the above processing flow, this application obtains the maintenance mileage requirement information corresponding to the target vehicle using the vehicle model information, then performs threshold generation processing on the maintenance mileage requirement information based on the threshold generation strategy, and uses the generated threshold data as the near-maintenance mileage threshold, thereby realizing the automatic and intelligent generation processing of the near-maintenance mileage threshold. Setting the near-maintenance cycle threshold is to detect in advance that the vehicle is about to reach the maintenance time or mileage, so as to trigger subsequent reminders and operation processes in a timely manner, ensuring that the vehicle can be maintained on time, extending the vehicle's service life, and ensuring vehicle performance.

[0098] In some optional implementations, the system also includes a GPS data anomaly handling mechanism. The specific implementation process includes: 1) Anomaly monitoring: The system continuously monitors the collection of GPS data. A monitoring cycle is set, for example, checking GPS data every 24 hours. If GPS data anomalies are detected for three consecutive days, anomalies include no data updates (i.e., no GPS positioning information received during these three days); or data that is clearly unreasonable, such as the vehicle traveling an extremely long distance in a short period, exceeding the normal vehicle speed and distance range. 2) Task sending: When the system detects GPS data anomalies, it sends a task to operations personnel through the system's internal message push mechanism. The task requires operations personnel to notify customers to upload photos of the instrument panel mileage daily to ensure accurate vehicle mileage information and avoid inaccurate maintenance reminders due to GPS data anomalies.

[0099] The GPS data anomaly handling mechanism is designed to address potential problems with GPS devices and ensure that accurate vehicle mileage can still be obtained through other means when GPS data is unreliable, thus maintaining the normal operation of the maintenance reminder system.

[0100] In some optional implementations, the system also provides a function to follow up on vehicle maintenance procedures. The specific implementation process includes: 1. Maintenance Service Outlet Dispatch. 1) Request Reception: After the user confirms the maintenance request, the system sends the request information to the selected maintenance outlet. This information includes vehicle information (license plate number, VIN code, etc.), scheduled maintenance time, and maintenance items. The maintenance outlet obtains this information through a dedicated receiving system or interface. 2) Dispatch Confirmation: After receiving the dispatch information, the maintenance outlet reviews it. It determines whether the scheduled arrival time is feasible and whether the maintenance items can be completed. If everything is correct, the system sends a successful appointment notification to both the customer and the leasing company's operations personnel, informing both parties of the specific maintenance appointment information, such as arrival time, maintenance items, and outlet address.

[0101] Among them, the maintenance outlet dispatching process connects customer maintenance needs with outlet services, ensuring that customers can go to the designated outlet for maintenance at the scheduled time, thereby improving the organization and efficiency of maintenance services.

[0102] 2. Customer In-Store Maintenance. 1) Vehicle Reception Record: Upon arrival, the staff at the service center records basic information such as the vehicle's VIN, license plate number, and arrival time. This information can be recorded manually or entered using a dedicated vehicle reception system to ensure accuracy and traceability. 2) Maintenance Service: The service center performs maintenance services on the customer's vehicle according to the scheduled items. If additional maintenance services are required during the process, such as replacement or repair of other vehicle parts discovered during inspection, the customer must sign a service list confirming the additional services. The service list details the contents and prices of any additional services to ensure the customer's right to know about the maintenance content and to avoid future disputes.

[0103] The customer's in-store maintenance process is the actual execution of maintenance services. Through accurate vehicle reception records and standardized maintenance service procedures, the quality of maintenance services and the rights and interests of customers are guaranteed.

[0104] 3. Complete maintenance and return the settlement statement. 1) Settlement Statement Upload: After maintenance, the service center staff records the maintenance process items through the service center's app and uploads the settlement statement. The settlement statement includes vehicle information (license plate number, VIN code, etc.), maintenance time, service center information, maintenance item list, amount, and actual odometer reading. Ensure the settlement statement information is complete and accurate. 2) Information Comparison: The system uses OCR to recognize various information in the settlement statement and compares it with the original dispatch information. For example, it compares whether the vehicle information is consistent, whether the maintenance items were completed according to the dispatch requirements, and whether the amount is reasonable. After confirming that the vehicle information and maintenance items are correct, the settlement statement information is stored in the system as part of the maintenance record for easy future retrieval and statistics.

[0105] Among these measures, completing the maintenance settlement form and comparing the information ensures the accuracy of maintenance service settlement and the completeness of maintenance records, providing strong support for the overall maintenance management of vehicles.

[0106] 4. Record completed instrument mileage and correct GPS mileage. 1) Mileage Recording: The system identifies and records the instrument mileage data upon completion of maintenance. Mileage information is either recognized by OCR in the invoice or manually entered into the system by service center staff to ensure accurate recording. 2) Mileage Correction: Using the completed instrument mileage as a baseline, the GPS mileage is corrected again. For example, if the GPS calculates the current mileage as 12,000 km, while the completed instrument mileage is 12,500 km, the GPS mileage is corrected to 12,500 km. This ensures consistency between the GPS mileage and the actual instrument mileage, allowing for more accurate determination of the next maintenance cycle. Simultaneously, a task to collect instrument mileage is triggered when the next threshold is reached, forming a closed loop in data processing and ensuring the entire maintenance reminder system always operates based on accurate mileage data.

[0107] Recording the completed instrument mileage and calibrating the GPS mileage calculation is the final step in the entire process and a crucial step in ensuring the accuracy of the system data. By continuously calibrating the mileage data, the accuracy of maintenance reminders is improved, and the vehicle's service life is extended.

[0108] In some alternative implementations, the user information obtained is subject to user consent and complies with relevant laws and policies.

[0109] Furthermore, any software tools or components not belonging to our company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0110] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0111] It should be emphasized that, to further ensure the privacy and security of the mileage data, the aforementioned mileage data can also be stored in a blockchain node.

[0112] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0113] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0115] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0116] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data processing apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0117] like Figure 3 As shown, the data processing device 300 described in this embodiment includes: a first acquisition module 301, a calculation module 302, a first processing module 303, a correction module 304, a data acquisition module, a comparison module 306, and a second processing module 307. Wherein: The first acquisition module 301 is used to acquire the location data of the target vehicle; The calculation module 302 is used to perform mileage calculation processing on the positioning data to obtain the corresponding driving mileage data; The first processing module 303 is used to send an outbound call request to the target customer to provide a dashboard image if the mileage data reaches a preset near-maintenance mileage threshold; wherein the target customer is the user of the target vehicle. The correction module 304 is used to receive the dashboard image of the target vehicle uploaded by the target customer, and to perform correction processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data. The acquisition module 305 is used to collect updated mileage data and usage time data of the target vehicle in real time based on the target mileage data; The comparison module 306 is used to compare the updated mileage data and the usage time data with the maintenance conditions in the preset maintenance rule library; The second processing module 307 is used to generate maintenance reminder information corresponding to the target vehicle if the updated mileage data or the usage time data meets the maintenance conditions, and push the maintenance reminder information to the target customer.

[0118] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0119] In some optional implementations of this embodiment, the measurement module 302 includes: The cleaning submodule is used to clean the positioning data to obtain the corresponding target positioning data. Call the submodule to invoke the preset distance calculation strategy; The calculation submodule is used to perform mileage calculation processing on the target positioning data based on the distance calculation strategy to obtain the corresponding calculation results; The first determining submodule is used to use the calculation result as the driving mileage data.

[0120] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0121] In some optional implementations of this embodiment, the cleaning submodule includes: The classification unit is used to classify the location data based on a preset anomaly recognition model to obtain the corresponding anomaly recognition result. The first determining unit is used to determine abnormal location data from the location data based on the abnormal identification result; The removal unit is used to remove abnormal positioning data from the positioning data based on a preset filtering algorithm to obtain the corresponding processed data. The second determining unit is used to use the processed data as the target positioning data.

[0122] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0123] In some optional implementations of this embodiment, the correction module 304 includes: The first acquisition submodule is used to acquire a preset data recognition strategy; The recognition submodule is used to perform recognition processing on the dashboard image based on the data recognition strategy in order to identify the mileage value on the dashboard. The correction submodule is used to correct the driving mileage data based on the mileage value to obtain the corrected driving mileage data. The second determining submodule is used to use the corrected driving mileage data as the target driving mileage data.

[0124] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0125] In some optional implementations of this embodiment, the correction submodule includes: The construction unit is used to construct corresponding target map structure data based on the mileage value and the driving mileage data; The calling unit is used to call a preset mileage prediction model; The prediction unit is used to perform prediction processing on the target map structure data based on the mileage prediction model and generate corresponding prediction results. The third determining unit is used to correct the driving mileage data to the prediction result, thereby obtaining the corrected driving mileage data.

[0126] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here. In some optional implementations of this embodiment, the second processing module 307 includes: The second acquisition submodule is used to acquire vehicle information, maintenance items and estimated maintenance time corresponding to the target vehicle; A generation submodule is used to generate maintenance reminder information corresponding to the target vehicle based on the vehicle information, the maintenance items, and the estimated maintenance time. The third acquisition submodule is used to acquire preset push methods; The sending submodule is used to send the maintenance reminder information to the target customer based on the push method.

[0127] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0128] In some optional implementations of this embodiment, the data processing apparatus further includes: The second acquisition module is used to acquire the vehicle model information of the target vehicle; The third acquisition module is used to acquire maintenance mileage requirement information corresponding to the target vehicle based on the vehicle model information; The generation module is used to perform threshold generation processing on the maintenance mileage requirement information based on a preset threshold generation strategy, and generate corresponding threshold data. The determination module is used to use the threshold data as the near-maintenance mileage threshold.

[0129] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here. To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed] for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0130] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0131] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0132] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for data processing methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0133] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the data processing method.

[0134] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0135] Compared with the prior art, the embodiments of this application have the following beneficial effects: In this embodiment, the application obtains mileage data by performing mileage calculation on the acquired location data of the target vehicle. Then, when the mileage data is detected to reach a preset near-maintenance mileage threshold, an outbound call request to provide a dashboard image is sent to the target customer. The mileage data is corrected based on the dashboard image of the target vehicle uploaded by the target customer to obtain the corresponding target mileage data. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in the preset maintenance rule library. When the updated mileage data or usage time data is detected to meet the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer. Thus, unlike existing vehicle maintenance reminder methods that rely on fixed time thresholds, this application provides an intelligent processing method that automatically analyzes and processes various key data during actual vehicle use to provide maintenance reminders. By collecting vehicle location data and combining it with dashboard images for correction, target mileage data is obtained. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in a preset maintenance rule library. This achieves automatic and intelligent provision of accurate maintenance reminders, effectively improving the accuracy and intelligence of vehicle maintenance reminders.

[0136] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the data processing method described above.

[0137] Compared with the prior art, the embodiments of this application have the following main advantages: In this embodiment, the application obtains mileage data by performing mileage calculation on the acquired location data of the target vehicle. Then, when the mileage data is detected to reach a preset near-maintenance mileage threshold, an outbound call request to provide a dashboard image is sent to the target customer. The mileage data is corrected based on the dashboard image of the target vehicle uploaded by the target customer to obtain the corresponding target mileage data. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in the preset maintenance rule library. When the updated mileage data or usage time data is detected to meet the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer. Thus, unlike existing vehicle maintenance reminder methods that rely on fixed time thresholds, this application provides an intelligent processing method that automatically analyzes and processes various key data during actual vehicle use to provide maintenance reminders. By collecting vehicle location data and combining it with dashboard images for correction, target mileage data is obtained. Subsequently, the updated mileage data and usage time data of the target vehicle collected in real time based on the target mileage data are compared with the maintenance conditions in a preset maintenance rule library. This achieves automatic and intelligent provision of accurate maintenance reminders, effectively improving the accuracy and intelligence of vehicle maintenance reminders.

[0138] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0139] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

[0140] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

Claims

1. A data processing method, characterized in that, Includes the following steps: Obtain the location data of the target vehicle; The positioning data is processed to calculate the mileage, and the corresponding driving mileage data is obtained. If the mileage data reaches a preset threshold for approaching maintenance, an outbound call requesting a dashboard image is sent to the target customer; wherein, the target customer is the user of the target vehicle; Receive the dashboard image of the target vehicle uploaded by the target customer, and perform correction processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data; Based on the target mileage data, the updated mileage data and usage time data of the target vehicle are collected in real time. The updated mileage data and usage time data are compared with the maintenance conditions in the preset maintenance rule library; If the updated mileage data or the usage time data meets the maintenance conditions, a maintenance reminder message corresponding to the target vehicle is generated and pushed to the target customer.

2. The data processing method according to claim 1, characterized in that, The step of performing mileage calculation processing on the positioning data to obtain the corresponding driving mileage data specifically includes: The positioning data is cleaned to obtain the corresponding target positioning data; Invoke the preset distance calculation strategy; Based on the distance calculation strategy, the target positioning data is processed for mileage calculation to obtain the corresponding calculation results; The calculation result is used as the driving mileage data.

3. The data processing method according to claim 2, characterized in that, The step of cleaning the positioning data to obtain the corresponding target positioning data specifically includes: The location data is classified based on a preset anomaly detection model to obtain the corresponding anomaly detection results. Abnormal location data is determined from the location data based on the anomaly identification results; Abnormal location data in the location data is removed based on a preset filtering algorithm to obtain the corresponding processed data; The processed data is used as the target location data.

4. The data processing method according to claim 1, characterized in that, The step of correcting the mileage data based on the dashboard image to obtain the corresponding target mileage data specifically includes: Obtain the preset data recognition strategy; The dashboard image is processed based on the data recognition strategy to identify the mileage value on the dashboard. The driving mileage data is corrected based on the mileage value to obtain the corrected driving mileage data. The corrected mileage data is used as the target mileage data.

5. The data processing method according to claim 4, characterized in that, The step of correcting the mileage data based on the mileage value to obtain corrected mileage data specifically includes: Construct a corresponding target map structure data based on the mileage values ​​and the driving mileage data; Invoke the preset mileage prediction model; Based on the mileage prediction model, the target map structure data is predicted and processed to generate corresponding prediction results; The driving mileage data is corrected to the predicted result to obtain the corrected driving mileage data.

6. The data processing method according to claim 1, characterized in that, The step of generating maintenance reminder information corresponding to the target vehicle and pushing the maintenance reminder information to the target customer specifically includes: Obtain vehicle information, maintenance items, and estimated maintenance time corresponding to the target vehicle; Based on the vehicle information, the maintenance items, and the estimated maintenance time, a maintenance reminder message corresponding to the target vehicle is generated. Get the preset push notification method; Based on the aforementioned push method, the maintenance reminder information will be sent to the target customer.

7. The data processing method according to claim 1, characterized in that, Before the step of sending an outbound call requesting a dashboard image to the target customer if the mileage data reaches a preset near-maintenance mileage threshold, the method further includes: Obtain the vehicle model information of the target vehicle; Based on the vehicle model information, obtain the maintenance mileage requirement information corresponding to the target vehicle; The maintenance mileage requirement information is processed based on a preset threshold generation strategy to generate corresponding threshold data. The threshold data is used as the threshold for the nearest maintenance mileage.

8. A data processing apparatus, characterized in that, include: The first acquisition module is used to acquire the location data of the target vehicle; The calculation module is used to perform mileage calculation processing on the positioning data to obtain the corresponding driving mileage data; The first processing module is used to send an outbound call request to the target customer to provide a dashboard image if the mileage data reaches a preset threshold for approaching maintenance; wherein the target customer is the user of the target vehicle. The calibration module is used to receive the dashboard image of the target vehicle uploaded by the target customer, and to perform calibration processing on the mileage data based on the dashboard image to obtain the corresponding target mileage data. The data acquisition module is used to collect updated mileage data and usage time data of the target vehicle in real time based on the target mileage data; The comparison module is used to compare the updated mileage data and the usage time data with the maintenance conditions in the preset maintenance rule library; The second processing module is used to generate maintenance reminder information corresponding to the target vehicle if the updated mileage data or the usage time data meets the maintenance conditions, and push the maintenance reminder information to the target customer.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data processing method as described in any one of claims 1 to 7.

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