Vehicle test state monitoring method and device, electronic equipment and medium

Abstract

The application relates to the technical field of vehicle informatization management, in particular to a vehicle test state monitoring method and device, electronic equipment and a medium, the method comprising: acquiring running state data and management attribute data of vehicle testing; taking a vehicle identification code as an association primary key, associating and fusing the running state data and the management attribute data of the same vehicle, generating a holographic vehicle object containing vehicle dynamic information and static information, and calculating comprehensive test indexes of the vehicle based on the holographic vehicle object; in response to a user's access request, pushing real-time data containing the comprehensive test indexes to a front-end interactive interface, and monitoring the state of the comprehensive test indexes and performing abnormal traceability analysis through the front-end interactive interface. Thus, the problem of single state monitoring dimension and difficult abnormal traceability caused by the separation of physical running data and business management data in related technologies is solved, and intuitive visual monitoring and accurate abnormal positioning based on comprehensive test indexes are realized.

Description

Technical Field

[0001] This application relates to the field of vehicle information management technology, and in particular to a method, device, electronic equipment and medium for monitoring vehicle test status. Background Technology

[0002] With the rapid development of intelligent connected vehicle technology, vehicles generate massive amounts of operational data (such as driving trajectories, real-time mileage, and online time) during the R&D and testing phase. To effectively manage these test vehicles, automakers typically deploy vehicle networking platforms (T-Box platforms) to monitor vehicle status, and vehicle management systems or office automation systems to manage vehicle borrowing, return, and responsibility assignment. However, existing vehicle test management technologies generally suffer from the following technical problems and shortcomings: (1) The data silo phenomenon is serious and lacks effective integration: the existing vehicle network platform only focuses on the physical status of the vehicle itself (such as whether it is online, fault codes), while the vehicle management system only records administrative information such as the borrower and the department to which it belongs. These two systems usually operate independently and lack a real-time correlation mechanism between the data. When the manager sees that a test vehicle has "insufficient online time" or "failed to meet the test standard" on the big screen, he cannot directly obtain who the current borrower of the vehicle is, and needs to manually query another system, resulting in a delay in information acquisition.

[0003] (2) Single monitoring dimension and lack of comprehensive evaluation indicators: Most existing technologies only monitor a single indicator (e.g., only mileage), and lack comprehensive evaluation methods that combine "online time" and "test task objectives". Managers find it difficult to intuitively judge whether the current test progress of the vehicle meets expectations, and they are also unable to grasp the vehicle test compliance rate of each department from a macro perspective.

[0004] (3) Low efficiency in anomaly tracing: When an abnormal state occurs during the test (such as the vehicle being offline for a long time or the test data not being reported), due to the lack of a visual integrated display interface of "people-vehicle-status", the investigators often need to spend a lot of time exporting data reports for manual comparison, which makes it impossible to quickly locate the responsible person and intervene in a timely manner, affecting the overall progress and efficiency of the test project, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a vehicle testing status monitoring method, device, electronic device, and medium to solve the problems of single status monitoring dimension and difficulty in anomaly tracing caused by the separation of physical operation data and business management data in related technologies, and realizes intuitive and visual monitoring and accurate anomaly location based on comprehensive test indicators.

[0006] The first aspect of this application provides a method for monitoring the test status of a vehicle, including the following steps: Acquire operational status data and management attribute data for vehicle testing; Using the vehicle identification number as the primary key, the operating status data of the same vehicle is associated and fused with the management attribute data to generate a holographic vehicle object containing both dynamic and static information. Based on the holographic vehicle object, the comprehensive test indicators of the vehicle are calculated. In response to a user's access request, real-time data containing the comprehensive test metrics is pushed to the front-end interactive interface, and the status of the comprehensive test metrics and anomaly tracing analysis are performed through the front-end interactive interface.

[0007] According to one embodiment of this application, the step of associating and fusing the operating status data of the same vehicle with the management attribute data using the vehicle identification number as the association primary key includes: The management attribute data is loaded into the memory cache, and the corresponding management attribute data is searched in the memory cache using the vehicle identification code in the running status data as the key. The found management attribute data is logically bound to the running status data and time-series aligned to generate the holographic vehicle object.

[0008] According to one embodiment of this application, calculating the comprehensive test indicators of the vehicle based on the holographic vehicle object includes: Based on the mileage information and power-on / off status of the holographic vehicle object, calculate the actual usage time of the vehicle within a preset time period; The vehicle utilization rate is calculated based on the ratio of the actual usage time to the preset working time. The vehicle usage compliance rate is generated based on the comparison between the vehicle usage rate and the preset compliance threshold. The vehicle's online duration is calculated by accumulating the timestamp differences between the power-on and power-off states; The comprehensive test index is obtained based on the vehicle usage rate, the vehicle usage compliance rate, and the online duration.

[0009] According to one embodiment of this application, the comprehensive test index further includes an abnormal state weight value, and the vehicle test state monitoring method further includes: Acquire the vehicle's offline time, mileage zero-growth time, and data reporting interruption time within the preset monitoring window; Configure a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient for the offline duration, the mileage zero-growth duration, and the data reporting interruption duration, respectively; Calculate the first product between the offline duration and the first weight coefficient, the second product between the zero mileage growth duration and the second weight coefficient, and the third product between the data reporting interruption duration and the third weight coefficient. The sum of the first product, the second product, and the third product is used as the abnormal state weight value. When the abnormal state weight value exceeds a preset alarm threshold, an abnormal marker is generated for the vehicle. The abnormal marker is used for differentiated highlighting in the front-end interactive interface.

[0010] According to one embodiment of this application, the step of monitoring the status and analyzing the anomalies of the comprehensive test indicators through the front-end interactive interface includes: The comprehensive test indicators are graphically displayed using the chart component of the front-end interactive interface, generating a monitoring view; In response to a user's click operation on the monitoring screen, the vehicle identification code corresponding to the clicked target object is obtained; Based on the vehicle identification code, obtain the vehicle's full lifecycle status change log, and perform status monitoring and anomaly tracing analysis based on the full lifecycle status change log.

[0011] According to the vehicle test status monitoring method provided in this application, the vehicle identification code is used as the primary key to associate and fuse the operating status data and management attribute data of the same vehicle, generating a holographic vehicle object containing both dynamic and static information. Based on the holographic vehicle object, the comprehensive test indicators of the vehicle are calculated. In response to user access requests, real-time data containing the comprehensive test indicators is pushed to the front-end interactive interface, and the status monitoring and anomaly tracing analysis of the comprehensive test indicators are performed through the front-end interactive interface. This solves the problems of single-dimensional status monitoring and difficulty in anomaly tracing caused by the separation of physical operating data and business management data in related technologies, and realizes intuitive and visual monitoring and accurate anomaly location based on comprehensive test indicators.

[0012] A second aspect of this application provides a vehicle testing status monitoring device, comprising: The acquisition module is used to acquire operational status data and management attribute data for vehicle testing. The processing module is used to associate and fuse the operating status data of the same vehicle with the management attribute data using the vehicle identification code as the association primary key, generate a holographic vehicle object containing dynamic and static information of the vehicle, and calculate the comprehensive test index of the vehicle based on the holographic vehicle object. The monitoring module is used to respond to user access requests by pushing real-time data containing the comprehensive test indicators to the front-end interactive interface, and to perform status monitoring and anomaly tracing analysis on the comprehensive test indicators through the front-end interactive interface.

[0013] According to one embodiment of this application, the processing module is configured to: The management attribute data is loaded into the memory cache, and the corresponding management attribute data is searched in the memory cache using the vehicle identification code in the running status data as the key. The found management attribute data is logically bound to the running status data and time-series aligned to generate the holographic vehicle object.

[0014] According to one embodiment of this application, the processing module is configured to: Based on the mileage information and power-on / off status of the holographic vehicle object, calculate the actual usage time of the vehicle within a preset time period; The vehicle utilization rate is calculated based on the ratio of the actual usage time to the preset working time. The vehicle usage compliance rate is generated based on the comparison between the vehicle usage rate and the preset compliance threshold. The vehicle's online duration is calculated by accumulating the timestamp differences between the power-on and power-off states; The comprehensive test index is obtained based on the vehicle usage rate, the vehicle usage compliance rate, and the online duration.

[0015] According to one embodiment of this application, the comprehensive test index further includes an abnormal state weight value, and the vehicle test state monitoring device is further used for: Acquire the vehicle's offline time, mileage zero-growth time, and data reporting interruption time within the preset monitoring window; Configure a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient for the offline duration, the mileage zero-growth duration, and the data reporting interruption duration, respectively; Calculate the first product between the offline duration and the first weight coefficient, the second product between the zero mileage growth duration and the second weight coefficient, and the third product between the data reporting interruption duration and the third weight coefficient. The sum of the first product, the second product, and the third product is used as the abnormal state weight value. When the abnormal state weight value exceeds a preset alarm threshold, an abnormal marker is generated for the vehicle. The abnormal marker is used for differentiated highlighting in the front-end interactive interface.

[0016] According to one embodiment of this application, the monitoring module is used for: The comprehensive test indicators are graphically displayed using the chart component of the front-end interactive interface, generating a monitoring view; In response to a user's click operation on the monitoring screen, the vehicle identification code corresponding to the clicked target object is obtained; Based on the vehicle identification code, obtain the vehicle's full lifecycle status change log, and perform status monitoring and anomaly tracing analysis based on the full lifecycle status change log.

[0017] According to the vehicle test status monitoring device provided in this application embodiment, the vehicle identification code is used as the association primary key to associate and fuse the operating status data and management attribute data of the same vehicle, generating a holographic vehicle object containing both dynamic and static information. Based on the holographic vehicle object, the comprehensive test indicators of the vehicle are calculated. In response to user access requests, real-time data containing the comprehensive test indicators is pushed to the front-end interactive interface, and the status monitoring and anomaly tracing analysis of the comprehensive test indicators are performed through the front-end interactive interface. This solves the problems of single-dimensional status monitoring and difficulty in anomaly tracing caused by the separation of physical operating data and business management data in related technologies, and realizes intuitive and visual monitoring and accurate anomaly location based on comprehensive test indicators.

[0018] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle test status monitoring method as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle test status monitoring method as described in the above embodiments.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle testing status monitoring method provided according to an embodiment of this application; Figure 2 This is an architecture diagram of a vehicle test status monitoring system according to an embodiment of this application; Figure 3 This is a block diagram of a vehicle test status monitoring device according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] To address the issues of severe data silos, limited monitoring dimensions, and low efficiency in anomaly tracing mentioned in the background, this application provides a vehicle testing status monitoring method. This method solves the data silo problem caused by the heterogeneous isolation between vehicle network physical data and enterprise administrative data in the existing vehicle testing management field. It also overcomes the technical bottlenecks of traditional monitoring methods, such as the lack of multi-dimensional comprehensive judgment models and the low efficiency in anomaly tracing when faced with massive amounts of test data. This method is applicable to scenarios such as R&D testing management of intelligent connected vehicles, fleet operation and maintenance monitoring, and vehicle testing performance evaluation.

[0024] To achieve the above objectives, this invention provides a method for end-to-end data fusion and visualization monitoring of vehicle testing based on a microservice architecture. This solution adopts a front-end and back-end separated B / S architecture and constructs a highly cohesive and loosely coupled system architecture through a layered design approach. The specific technical solution is detailed below: (1) Construction of a high-performance backend service system Architecture Design: Based on Java EE enterprise standards, a lightweight microservice ecosystem is built using the Spring Boot auto-configuration framework. Leveraging its dependency injection and aspect-oriented programming features, business logic is decoupled from the underlying infrastructure, ensuring high availability and scalability of the system.

[0025] Heterogeneous data fusion engine: To address the issue of inconsistent formats between vehicle physical data (time-series data) and administrative data (relational data), a data cleaning and mapping module was constructed.

[0026] Leveraging the powerful BaseMapper interface and LambdaQueryWrapper dynamic query builder of the MyBatis-Plus persistence layer framework, seamless aggregation of multi-source data is achieved. The system uses the vehicle identification number as the primary key index and, through memory-level mapping, deeply associates discrete "online status" and "mileage increment" with static "borrower" and "department organizational structure," encapsulating them into a standardized, unified business view object.

[0027] Intelligent indicator calculation service: The built-in business logic processing layer, based on the fused full data, uses streaming computing algorithms to derive high-level metrics in real time, including "single vehicle test pass rate", "vehicle resource utilization rate" and "abnormal state weight value".

[0028] The API interface is designed in a RESTful style and supports lightweight data transmission in JSON format, ensuring low latency and high throughput for front-end and back-end interactions.

[0029] (2) Construction of front-end responsive visual interactive interface Architecture Design: Developed based on the Vue.js progressive framework and the MVVM (Model-View-ViewModel) pattern. Virtual DOM technology improves page rendering performance, and a two-way data binding mechanism enables real-time synchronization between the data model and the view layer.

[0030] Dynamic visualization rendering engine: Integrate with enterprise-grade data visualization libraries (such as ECharts) to build multi-dimensional monitoring dashboards. The front-end uses Axios interceptors to encapsulate asynchronous requests, establishing long connections or heartbeat polling mechanisms to pull backend metrics in real time.

[0031] The dry two-dimensional table data is transformed into vehicle usage trend line charts, trend Gantt charts, and relationship topology diagrams, which intuitively display the vehicle testing progress and resource usage.

[0032] Interactive drill-up and traceability components: Develop a generic details drawer component and modal interaction box. When a user triggers a click event, the front end sends a precise query to the back end via route parameters. Leveraging Vue's component-based reusability, the system instantly renders the vehicle's full lifecycle log and the profile of the current responsible person, achieving rapid "what you see is what you get" anomaly accountability.

[0033] (3) Data-driven end-to-end processing flow The core workflow of the system of this invention includes the following closed-loop steps: Step 1 (Data Awareness): The system backend triggers multi-threaded data collection tasks through a timed scheduling framework to capture vehicle network terminal streaming data and administrative system static data in parallel, ensuring the timeliness of the data.

[0034] Step 2 (Integration Processing): After entering the business logic layer, the system automatically executes data cleaning strategies (duplicate removal and completion) and performs efficient joint queries through MyBatis-Plus to logically bind the three elements of "people, vehicles, and events" in memory.

[0035] Step 3 (Service Encapsulation): Serialize the processed aggregated data into a JSON object and respond through the RESTful interface layer to complete the data encapsulation and output on the server side.

[0036] Step 4 (Visualization): The Vue frontend receives data updates and uses the responsive design principle to drive the view layer to redraw. The visualization components automatically refresh the chart metrics, and managers can conduct interactive analysis and decision-making based on the real-time data dashboard.

[0037] The vehicle test status monitoring method, apparatus, electronic device, and medium of this application are described below with reference to the accompanying drawings.

[0038] Specifically, Figure 1 This is a flowchart illustrating a vehicle testing status monitoring method provided in an embodiment of this application.

[0039] like Figure 1 As shown, the vehicle test status monitoring method includes the following steps: In step S101, the vehicle test operation status data and management attribute data are obtained.

[0040] Among them, operational status data refers to the dynamic physical data generated in real time during vehicle testing and operation, such as vehicle identification number (VIN), timestamp, location information, mileage information, and power-on / off status. Management attribute data refers to static file data related to vehicle administration, such as VIN, borrower's name, department, and usage type.

[0041] In step S102, the vehicle identification code is used as the primary key to associate and fuse the operating status data and management attribute data of the same vehicle to generate a holographic vehicle object containing dynamic and static information of the vehicle. Based on the holographic vehicle object, the comprehensive test index of the vehicle is calculated.

[0042] Furthermore, in some embodiments, the vehicle identification code is used as the association key to associate and fuse the operating status data and management attribute data of the same vehicle, including: loading the management attribute data into the memory cache, using the vehicle identification code in the operating status data as the key to search for the corresponding management attribute data in the memory cache; logically binding the searched management attribute data with the operating status data and performing time-series alignment processing to generate a holographic vehicle object.

[0043] Specifically, when the system starts up or refreshes periodically, the backend service uses the data extraction module to read the management attribute data of all registered test vehicles in batches from the vehicle management system (i.e., the relational database that stores management attribute data), and uses the vehicle identification code as the key to preload this data into the memory cache, thus building a mapping table residing in the server memory.

[0044] After obtaining the latest operational status data of a vehicle from the vehicle-to-everything (V2X) platform via a heartbeat polling mechanism, the data fusion engine first parses the vehicle identification number (VIN) field in the data packet. Using this VIN as the lookup key, it performs an exact match search in the memory cache mapping table to retrieve static file information such as the borrower and department to which the vehicle belongs. After logically binding the retrieved management attribute data with the operational status data, the fusion engine further performs time-series alignment processing on the timestamps of the two data sources.

[0045] For example, when there is a discrepancy of several seconds between the reporting timestamp of the operation status data (e.g., 14:30:25) and the most recent update timestamp of the management attribute data (e.g., 14:30:20), the management attribute data is associated with the timestamp of the operation status data to form a unified time baseline. At the same time, for occasional missing fields in the operation status data (e.g., temporary loss of GPS signal), the location information in the previous valid record of the vehicle can be used to fill in the missing fields.

[0046] After the above-mentioned time-series alignment and missing data processing, the data is encapsulated into a standardized holographic vehicle object containing dynamic physical information and static archive information. This object resides in memory in a structured data format (such as key-value pairs or object mappings), providing a one-stop data source for subsequent comprehensive index calculations and front-end visualization. When the back-end service processes any vehicle query request initiated by the front-end, it can directly extract all the required information from this holographic vehicle object without having to access the original database again for cross-table join queries.

[0047] Furthermore, in some embodiments, based on the holographic vehicle object, the comprehensive test index of the vehicle is calculated, including: calculating the actual usage time of the vehicle within a preset time period based on the mileage information and power-on / off status of the holographic vehicle object; calculating the vehicle utilization rate based on the ratio of the actual usage time to the preset working time; generating the vehicle utilization compliance rate based on the comparison result of the vehicle utilization rate and the preset compliance threshold; calculating the vehicle online time based on the cumulative difference of the timestamps of the power-on / off status; and obtaining the comprehensive test index based on the vehicle utilization rate, the vehicle utilization compliance rate, and the online time.

[0048] Specifically, using the Vehicle Identification Number (VIN) as an index, the index calculation engine extracts mileage information and the timestamp sequence of power-on and power-off states from the holographic vehicle object. First, it calculates the actual usage time of the vehicle within a preset time period. For example, using the past 7 natural days as a statistical window, the system filters out all event records of "power-on" and "power-off" state switching within the window, calculates the time span from "power-on" to "power-off" for each segment, and adds up these spans to obtain the cumulative actual usage hours of the vehicle within 7 days.

[0049] Furthermore, the system obtains the preset working hours corresponding to the statistical window, and divides the cumulative actual usage hours by the preset working hours. For example, if a vehicle has accumulated 28 hours of actual usage within 7 days, the usage rate is 28 ÷ 40 = 70%. After obtaining the usage rate, it is further compared with a preset compliance threshold, for example, if the threshold is set to 50%. Since 70% ≥ 50%, the system determines that the vehicle is "compliant" and generates the corresponding compliance mark; if the usage rate is lower than 50%, it is determined to be "non-compliant".

[0050] Simultaneously, the system calculates the vehicle's online duration by cumulatively adding up the timestamp differences between power-on and power-off states. This metric reflects the total duration for which the vehicle connects to the vehicle-to-everything (V2X) platform and maintains data transmission. Finally, the system encapsulates the three calculated results—vehicle utilization rate, vehicle utilization compliance rate, and online duration—to form a comprehensive test metric for the vehicle. This comprehensive metric is output in a structured data format for subsequent push and display to the user's front-end interface. For example, on a monitoring screen, vehicles that meet the standards are highlighted in green, while vehicles that do not meet the standards are highlighted in red. The system also displays the specific utilization rate and online duration statistics for each vehicle, providing managers with an intuitive basis for evaluating the vehicle's test status.

[0051] In step S103, in response to the user's access request, real-time data containing comprehensive test indicators is pushed to the front-end interactive interface, and the status of comprehensive test indicators and anomaly tracing analysis are performed through the front-end interactive interface.

[0052] Furthermore, in some embodiments, the status monitoring and anomaly tracing analysis of comprehensive test indicators are performed through a front-end interactive interface, including: graphically displaying the comprehensive test indicators through the chart component of the front-end interactive interface to generate a monitoring view; responding to the user's click operation on the monitoring screen view to obtain the vehicle identification code corresponding to the clicked target object; obtaining the vehicle's full life cycle status change log based on the vehicle identification code, and performing status monitoring and anomaly tracing analysis based on the full life cycle status change log.

[0053] In the front-end interactive interface, the system integrates an enterprise-level data visualization chart component library (such as ECharts) to graphically render the comprehensive test indicators pushed from the back-end, generating a multi-dimensional monitoring view. This monitoring view intuitively displays the comprehensive test indicators of each vehicle in the form of vehicle usage trend line charts, vehicle compliance rate statistical bar charts, and resource usage distribution maps. Managers can view key information such as the usage rate, compliance rate, and online time of all vehicles under test in a single interface, achieving macro-level monitoring of the test status.

[0054] When administrators discover suspicious situations in the monitoring view, such as low usage, abnormal online time, or a "not meeting standards" status for a vehicle, they can directly click on the corresponding graphical display element (e.g., data points, bars, or vehicle cards in a table). The front-end interaction component captures this click event, parses and extracts the corresponding vehicle identification number (VIN) from the clicked target object, and then uses this VIN as a query parameter to send a detailed data retrieval request to the backend via a RESTful API interface. Upon receiving the request, the backend uses the VIN as an index to retrieve and aggregate the vehicle's full lifecycle status change log from the database. This log can include time-series data such as timestamps of every power-on / off event since the vehicle was put into testing, mileage increment records for each time period, offline event records, records of changes in responsible personnel, and historical compliance judgment results.

[0055] After obtaining the above log data, further targeted status monitoring and anomaly tracing analysis are performed based on the log content: On the one hand, the historical status change logs are presented in a structured way in the details display component in the form of a timeline or list, to help managers trace the complete operating trajectory of the vehicle before and after the anomaly occurred; on the other hand, the responsible person information in the current vehicle management attribute data is associated, and the current borrower and department of the vehicle are displayed synchronously in the details view, so that managers can directly penetrate from "macro-indicator anomalies" to "micro-log details" and locate the specific responsible person, reducing the time cost of manual investigation.

[0056] Furthermore, in some embodiments, the comprehensive test index also includes an abnormal state weight value. The vehicle test state monitoring method further includes: obtaining the vehicle's offline duration, mileage zero growth duration, and data reporting interruption duration within a preset monitoring window; configuring a first weight coefficient, a second weight coefficient, and a third weight coefficient for the offline duration, mileage zero growth duration, and data reporting interruption duration, respectively; calculating a first product between the offline duration and the first weight coefficient, a second product between the mileage zero growth duration and the second weight coefficient, and a third product between the data reporting interruption duration and the third weight coefficient, and using the sum of the first product, the second product, and the third product as the abnormal state weight value; when the abnormal state weight value exceeds a preset alarm threshold, generating an abnormal marker for the vehicle, which is used for differentiated highlighting in the front-end interactive interface.

[0057] Specifically, a preset monitoring window is defined with the current moment as the endpoint, for example, using the past 24 hours as the lookback interval. Within this window, the following three types of abnormal behavior parameters are collected for each vehicle under test: offline time, which is the cumulative time during which the vehicle is disconnected from the vehicle-to-everything (V2X) platform and no data is reported; zero mileage growth time, which is the continuous time during which the vehicle remains online but the odometer reading does not change positively; and data reporting interruption time, which is the cumulative time during which the vehicle is online but periodic data reporting is interrupted or has gaps. These three parameters characterize the degree of abnormality of the vehicle from three independent dimensions: connection stability, activity level, and data integrity.

[0058] Furthermore, corresponding weighting coefficients are configured for offline duration, mileage zero-growth duration, and data reporting interruption duration. Since offline status is considered the most severe abnormal state, the first weighting coefficient can be set to the highest value; mileage zero-growth is the next most severe, the second weighting coefficient is in the middle; and the impact of data reporting interruption is relatively small, so the third weighting coefficient is set to the lowest value. For ease of explanation, an exemplary set of weighting coefficients is used here: the first weighting coefficient for offline duration is set to 0.5, the second weighting coefficient for mileage zero-growth duration is set to 0.3, and the third weighting coefficient for data reporting interruption duration is set to 0.2, with the sum of all weighting coefficients equal to 1. The system calculates the first product of offline duration and the first weighting coefficient, the second product of mileage zero-growth duration and the second weighting coefficient, and the third product of data reporting interruption duration and the third weighting coefficient, and then adds these three products to obtain an abnormal state weight value that comprehensively reflects the current degree of abnormality of the vehicle.

[0059] After calculating the abnormal state weight value, it is compared with a preset alarm threshold. If the value exceeds the alarm threshold, the system determines that the vehicle is currently in an abnormal state and generates a corresponding abnormal marker. This marker is then bound to the vehicle identification number and pushed to the front-end interactive interface. When rendering the monitoring view, the front-end interface highlights each vehicle differently based on its abnormal marker status. For example, vehicle cards or data entries with abnormal markers are rendered with a red background and an alarm icon, while vehicles without abnormalities are displayed in normal green or blue. Through the quantitative calculation and visual alarm mechanism of the abnormal state weight value, managers can identify vehicles with potential problems on the monitoring screen and quickly trigger targeted drill-down tracing operations under the guidance of the abnormal markers, thereby transforming passive post-event investigation into proactive abnormal warnings and timely intervention.

[0060] To help those skilled in the art to better understand the vehicle test status monitoring method of this application, the following explanation is provided with specific examples.

[0061] Example 1: System Overall Architecture like Figure 2As shown in the figure, this embodiment provides a vehicle testing end-to-end data fusion and visualization monitoring system based on a microservice architecture. The system is deployed on a cloud server cluster, uses Docker containerization technology for service encapsulation, implements reverse proxy and load balancing through Nginx, and utilizes Jenkins for automated continuous integration and deployment (CI / CD).

[0062] The specific architecture layers and operating logic of the system are as follows: (1) Client interaction layer Deployment method: It runs in the user's web browser environment and does not require the installation of a local client.

[0063] Technical implementation: A single-page application (SPA) is built based on the Vue.js front-end framework.

[0064] Functional Logic: Responsible for building a visual monitoring dashboard and interactive interface. It initiates data requests to the backend via the HTTP communication protocol and utilizes asynchronous data binding technology to render the vehicle status returned by the backend to the page's DOM nodes in real time, enabling the sending of user interaction commands and the dynamic display of data.

[0065] (2) The backend service layer (backend layer and business layer) and deployment architecture are the core of the system, adopting a containerized deployment mode, and specifically include the following operating logic: Containerization encapsulation logic (Docker): The backend Spring Boot application and its runtime environment (JDK, configuration files, etc.) are packaged into a single, independent Docker image. Through the Docker container engine, the system can quickly launch multiple isolated running instances on a cloud server. This logic ensures a high degree of consistency between the development, testing, and production environments, avoiding the problem of "inconsistent environments causing system malfunctions."

[0066] Reverse proxy and load balancing logic (Nginx): The system deploys an Nginx service in front of the cloud server as a unified entry point. Nginx listens on port 80 and is responsible for receiving all HTTP requests from clients.

[0067] Static resource distribution: Nginx provides high-speed response for static resources such as images and JS scripts on the front end.

[0068] Dynamic request forwarding: For backend API requests, Nginx distributes them to specific Spring Boot instances within the backend Docker container cluster for processing, based on configured forwarding rules. If an instance experiences high load or crashes, Nginx can automatically remove it, ensuring high service availability.

[0069] Business logic (SpringBoot): A SpringBoot microservice running inside a Docker container. It is responsible for receiving requests forwarded by Nginx, executing the core data scheduling algorithm, and encapsulating the processing results into JSON format for return.

[0070] (3) Automated operation and maintenance (Jenkins) Functional logic: The system is configured with a Jenkins automated pipeline to enable automatic code building and deployment.

[0071] Operation flow: When developers submit new code to the code repository, Jenkins automatically triggers a build task. It executes Maven build and package commands, generates a new Docker image, and automatically replaces the old running container on the cloud server.

[0072] Function: Enables "one-click" rapid iteration and upgrade of the system, greatly shortening the launch cycle of new features.

[0073] (4) Data layer Deployment method: Deployed in the intranet environment of the cloud server, with access ports only open to the backend service layer to ensure data security.

[0074] Storage logic: Use a relational database (MySQL) to store vehicle data, vehicle trajectory data, etc.

[0075] Example 2: Overall System Operation Logic Based on Layered Architecture This embodiment describes in detail how the system achieves end-to-end connectivity from underlying data to upper-level display through layered collaboration. For example... Figure 2 As shown, the system's operational logic includes the following hierarchical interactions: The operational logic of the data perception and access layer: Function Description: Responsible for connecting to heterogeneous data sources.

[0076] Processing flow: By clicking to retrieve real-time vehicle information data, you can access the vehicle's large screen function and retrieve the latest vehicle data.

[0077] Function: Obtain vehicle data and display it on a large screen in real time, effectively ensuring data timeliness.

[0078] The core service layer's fusion processing logic: Functional Description: As the "brain" of the system, it is responsible for cleaning, verifying and integrating the incoming raw data.

[0079] Processing flow: When real-time vehicle status data is received, the core service layer extracts the unique identifier (VIN code) and looks up the corresponding static attribute mapping table in memory. If a match is found, dynamic information such as "vehicle location, battery level, and usage duration" is logically encapsulated with static information such as "responsible person, department, and usage nature" to generate a standardized "holographic vehicle object".

[0080] Function: It solves the problem of cumbersome querying caused by the separation of dynamic and static data in traditional technology, and realizes "one query, panoramic display".

[0081] Front-end presentation layer interactive rendering logic: Function Description: Responsible for converting processed data into a user-visual graphical interface.

[0082] Processing flow: The front-end adopts a component-based development model, breaking down the monitoring dashboard into "map component," "list component," and "statistics component." Each component automatically triggers a partial refresh by subscribing to changes in the data bus, rather than reloading the entire page.

[0083] Effect: Significantly reduces browser memory usage and improves page smoothness under large data volumes.

[0084] Example 3: Implementation Logic of Data Reception, Cleaning, Statistics and Historical Backtracking This embodiment describes in detail how the system starts by acquiring raw data, then cleans and performs statistical calculations, and supports retrospective analysis of historical data.

[0085] 1. The vehicle data acquisition system connects to an external platform through a heartbeat mechanism to acquire vehicle data in real time and saves the acquired raw data to the historical trajectory table of a relational database, providing a data foundation for subsequent data backtracking functions.

[0086] 2. Data Cleaning and Standardization Processing Logic: Due to the potential differences in data formats uploaded by terminals from different vehicle models or versions, the system performs rigorous cleaning and standardization before calculation. Standardized format: The system uniformly converts all speed units to "km / h" and timestamps to the format "yyyy-MM-ddHH:mm:ss".

[0087] Outlier filtering: Set reasonable physical thresholds (e.g., vehicle speed cannot exceed 300km / h, battery level cannot exceed 100%). If the parsed data exceeds the threshold, the system marks it as "dirty data" and discards it.

[0088] Missing value completion: If the "GPS signal" field is missing in a reported data packet, the system will automatically use the GPS coordinates of the previous valid data.

[0089] Standardized encapsulation: The cleaned data is encapsulated into standard objects that are common to the system.

[0090] 3. The real-time calculation logic of core business indicators is based on cleaned standard data. The system's statistical analysis module executes the following core logic: (1) Rules for calculating vehicle utilization rate Calculation formula: Utilization rate = (Total usage time / Working day duration) 100% Operation process: After clicking on vehicle information, the system retrieves the vehicle's usage time records from the database for the past week (or a specified time period) and calculates the percentage of its cumulative usage time to the total working days. It supports filtering by date to analyze usage rates for different time periods.

[0091] Function: To determine vehicle activity, track vehicle testing status, and assist in resource scheduling.

[0092] (2) Vehicle usage compliance rate Calculation formula: Compliance rate = (Number of compliant vehicles / Total number of vehicles) 100% Operation process: The system sets a threshold (e.g., 50% usage rate). When the vehicle usage rate is less than 50%, it is marked as "Not Qualified" and highlighted in red at the front end; if it exceeds 50%, it is marked as "Qualified" and highlighted in green at the front end.

[0093] Function: It enables automated determination of the test status of vehicles, eliminating the need for manual inspection of each vehicle.

[0094] (3) Online time statistics Operational process: The system maintains an online duration field for each vehicle. When a vehicle comes online (for the first time or recovering from offline), the start time is recorded; when a vehicle goes offline, the end time is recorded. The system calculates the single online duration by subtracting the start time from the end time, and then adds these values ​​to the total duration.

[0095] Function: It enables precise measurement of vehicle testing workload and records the time of each online and offline test.

[0096] 4. Historical Data Backtracking Logic: To meet the needs of analyzing past testing processes, the system provides a timeline-based historical data backtracking function: Retrospective Trigger: The user selects a historical time range (e.g., from 10:00:00 on 2023-10-01 to 12:00:00 on 2023-10-01) on the timeline component of the front-end interface.

[0097] Data retrieval: After receiving the backtracking request, the backend queries the vehicle trajectory table in the MySQL database for all historical trajectory points within that time period based on the VIN code and time range.

[0098] Track replay: The system sorts the retrieved historical data by timestamp and encapsulates it into a "backtracking data stream" to push to the front end.

[0099] Visualization: The front-end map component receives the retrospective data stream and can obtain the power-on and power-off times of the corresponding vehicles, as well as the distance the vehicles traveled during that time, enabling a complete review of historical test scenarios.

[0100] Compared with the prior art, the present invention has the following significant advantages: (1) Breaking down data silos and achieving real-time deep integration of "people-vehicle-status". This invention uses a data fusion engine built with the backend Spring Boot service and leverages the efficient query capabilities of MyBatis-Plus to successfully bind the physical operation data (location, mileage, power on / off status) of the Internet of Vehicles with the static attribute data (department, borrower information) of administrative management. This solves the problem of data fragmentation in traditional systems. Managers can obtain the real-time status of vehicles and the responsible person information in a single view without having to query across systems, greatly improving the completeness of data acquisition and the real-time nature of management response.

[0101] (2) Enhancing data value, upgrading from "single monitoring" to "assisted decision-making". This invention is not just about data display, but also automatically outputs higher-level derived indicators such as "test saturation", "vehicle compliance rate" and "resource utilization rate" through the built-in indicator calculation model in the back-end logic layer. It overcomes the limitation of existing technologies that can only display raw physical parameters, transforming dormant data into a basis for guiding decision-making, and helping managers to scientifically evaluate test progress and resource allocation.

[0102] (3) Optimize the interactive experience and significantly reduce the time cost of anomaly tracing. This invention is based on a responsive front-end and visual components built on the Vue.js framework. With the interactive design of "click to drill down", it realizes a seamless switch from the macro screen to the micro details. Effect: It changes the inefficient mode of relying on manual investigation of Excel spreadsheets in the traditional way. It can quickly locate the anomaly point through the graphical interface, improve the efficiency of problem investigation by more than 80%, and effectively reduce the workload of operation and maintenance personnel.

[0103] (4) Loosely coupled architecture ensures high availability and scalability of the system. This invention adopts a microservice architecture with front-end and back-end separation. The back-end provides standardized RESTful interfaces, and the front-end is reused through component-based development. This high cohesion and low coupling design means that when the system iterates its functions (such as adding a new car model or a new chart type), there is no need to refactor the entire code. Instead, hot-swappable updates can be performed on the corresponding modules, which significantly reduces the maintenance cost and upgrade difficulty of the system.

[0104] The vehicle testing status monitoring method proposed in this application uses the vehicle identification code as the primary key to associate and fuse the operating status data and management attribute data of the same vehicle, generating a holographic vehicle object containing both dynamic and static information. Based on the holographic vehicle object, the method calculates the vehicle's comprehensive testing indicators. In response to user access requests, real-time data containing the comprehensive testing indicators is pushed to the front-end interactive interface, which then performs status monitoring and anomaly tracing analysis of the comprehensive testing indicators. This solves the problems of single-dimensional status monitoring and difficulty in anomaly tracing caused by the separation of physical operating data and business management data in related technologies, achieving intuitive and visual monitoring and accurate anomaly location based on comprehensive testing indicators.

[0105] Next, the vehicle test status monitoring device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0106] Figure 3 This is a block diagram of a vehicle test status monitoring device according to an embodiment of this application.

[0107] like Figure 3 As shown, the vehicle test status monitoring device 10 includes: an acquisition module 100, a processing module 200, and a monitoring module 300.

[0108] The system includes: an acquisition module 100 for acquiring vehicle test operation status data and management attribute data; a processing module 200 for associating and fusing the same vehicle's operation status data and management attribute data using the vehicle identification code as the primary key, generating a holographic vehicle object containing both dynamic and static information, and calculating the vehicle's comprehensive test indicators based on the holographic vehicle object; and a monitoring module 300 for responding to user access requests by pushing real-time data containing comprehensive test indicators to the front-end interactive interface, and performing status monitoring and anomaly tracing analysis of the comprehensive test indicators through the front-end interactive interface.

[0109] Furthermore, in some embodiments, the processing module 200 is used to: load management attribute data into a memory cache, use the vehicle identification code in the running status data as a key to search for the corresponding management attribute data in the memory cache; logically bind the searched management attribute data with the running status data, and perform time-series alignment processing to generate a holographic vehicle object.

[0110] Furthermore, in some embodiments, the processing module 200 is configured to: calculate the actual usage time of the vehicle within a preset time period based on the mileage information and power-on / off status of the holographic vehicle object; calculate the vehicle utilization rate based on the ratio of the actual usage time to the preset working time; generate a vehicle utilization compliance rate based on the comparison result between the vehicle utilization rate and the preset compliance threshold; calculate the vehicle online time based on the cumulative difference in timestamps of the power-on / off status; and obtain a comprehensive test index based on the vehicle utilization rate, the vehicle utilization compliance rate, and the online time.

[0111] Furthermore, in some embodiments, the comprehensive test index also includes an abnormal state weight value. The vehicle test state monitoring method is further used to: obtain the vehicle's offline duration, mileage zero growth duration, and data reporting interruption duration within a preset monitoring window; configure a first weight coefficient, a second weight coefficient, and a third weight coefficient for the offline duration, mileage zero growth duration, and data reporting interruption duration, respectively; calculate the first product between the offline duration and the first weight coefficient, the second product between the mileage zero growth duration and the second weight coefficient, and the third product between the data reporting interruption duration and the third weight coefficient, and use the sum of the first product, the second product, and the third product as the abnormal state weight value; when the abnormal state weight value exceeds a preset alarm threshold, generate an abnormal marker for the vehicle, and the abnormal marker is used for differentiated highlighting in the front-end interactive interface.

[0112] Furthermore, in some embodiments, the monitoring module 300 is used to: graphically display the comprehensive test indicators through the chart component of the front-end interactive interface to generate a monitoring view; in response to the user's click operation on the monitoring screen view, obtain the vehicle identification code corresponding to the clicked target object; obtain the vehicle's full life cycle status change log based on the vehicle identification code, and perform status monitoring and anomaly tracing analysis based on the full life cycle status change log.

[0113] It should be noted that the foregoing explanation of the vehicle test status monitoring method embodiment also applies to the vehicle test status monitoring device of this embodiment, and will not be repeated here.

[0114] The vehicle testing status monitoring device proposed in this application uses the vehicle identification code as the primary key to associate and fuse the operating status data and management attribute data of the same vehicle, generating a holographic vehicle object containing both dynamic and static information. Based on the holographic vehicle object, the device calculates the vehicle's comprehensive testing indicators. In response to user access requests, the device pushes real-time data containing the comprehensive testing indicators to the front-end interactive interface, and performs status monitoring and anomaly tracing analysis on the comprehensive testing indicators through the front-end interactive interface. This solves the problems of single-dimensional status monitoring and difficulty in anomaly tracing caused by the separation of physical operating data and business management data in related technologies, achieving intuitive and visual monitoring and accurate anomaly location based on comprehensive testing indicators.

[0115] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0116] When processor 402 executes the program, it implements the vehicle test status monitoring method provided in the above embodiments.

[0117] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0118] The memory 401 is used to store computer programs that can run on the processor 402.

[0119] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0120] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0121] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0122] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0123] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the vehicle test status monitoring method described above.

[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0125] 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0126] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0128] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0129] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0130] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring vehicle test status, characterized in that, Includes the following steps: Acquire operational status data and management attribute data for vehicle testing; Using the vehicle identification number as the primary key, the operating status data of the same vehicle is associated and fused with the management attribute data to generate a holographic vehicle object containing both dynamic and static information. Based on the holographic vehicle object, the comprehensive test indicators of the vehicle are calculated. In response to a user's access request, real-time data containing the comprehensive test metrics is pushed to the front-end interactive interface, and the status of the comprehensive test metrics and anomaly tracing analysis are performed through the front-end interactive interface.

2. The method according to claim 1, characterized in that, The method of associating and fusing the operational status data of the same vehicle with the management attribute data using the vehicle identification number as the primary key includes: The management attribute data is loaded into the memory cache, and the corresponding management attribute data is searched in the memory cache using the vehicle identification code in the running status data as the key. The found management attribute data is logically bound to the running status data and time-series aligned to generate the holographic vehicle object.

3. The method according to claim 1, characterized in that, The calculation of the vehicle's comprehensive test indicators based on the holographic vehicle object includes: Based on the mileage information and power-on / off status of the holographic vehicle object, calculate the actual usage time of the vehicle within a preset time period; The vehicle utilization rate is calculated based on the ratio of the actual usage time to the preset working time. The vehicle usage compliance rate is generated based on the comparison between the vehicle usage rate and the preset compliance threshold. The vehicle's online duration is calculated by accumulating the timestamp differences between the power-on and power-off states; The comprehensive test index is obtained based on the vehicle usage rate, the vehicle usage compliance rate, and the online duration.

4. The method according to claim 1, characterized in that, The comprehensive test index also includes an abnormal state weight value, and the vehicle test state monitoring method further includes: Acquire the vehicle's offline time, mileage zero-growth time, and data reporting interruption time within the preset monitoring window; Configure a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient for the offline duration, the mileage zero-growth duration, and the data reporting interruption duration, respectively; Calculate the first product between the offline duration and the first weight coefficient, the second product between the zero mileage growth duration and the second weight coefficient, and the third product between the data reporting interruption duration and the third weight coefficient. The sum of the first product, the second product, and the third product is used as the abnormal state weight value. When the abnormal state weight value exceeds a preset alarm threshold, an abnormal marker is generated for the vehicle. The abnormal marker is used for differentiated highlighting in the front-end interactive interface.

5. The method according to claim 1, characterized in that, The step of monitoring the status of the comprehensive test indicators and performing anomaly tracing analysis through the front-end interactive interface includes: The comprehensive test indicators are graphically displayed using the chart component of the front-end interactive interface, generating a monitoring view; In response to a user's click operation on the monitoring screen, the vehicle identification code corresponding to the clicked target object is obtained; Based on the vehicle identification code, obtain the vehicle's full lifecycle status change log, and perform status monitoring and anomaly tracing analysis based on the full lifecycle status change log.

6. A vehicle testing status monitoring device, characterized in that, include: The acquisition module is used to acquire operational status data and management attribute data for vehicle testing. The processing module is used to associate and fuse the operating status data of the same vehicle with the management attribute data using the vehicle identification code as the association primary key, generate a holographic vehicle object containing dynamic and static information of the vehicle, and calculate the comprehensive test index of the vehicle based on the holographic vehicle object. The monitoring module is used to respond to user access requests by pushing real-time data containing the comprehensive test indicators to the front-end interactive interface, and to perform status monitoring and anomaly tracing analysis on the comprehensive test indicators through the front-end interactive interface.

7. The apparatus according to claim 6, characterized in that, The processing module is used for: The management attribute data is loaded into the memory cache, and the corresponding management attribute data is searched in the memory cache using the vehicle identification code in the running status data as the key. The found management attribute data is logically bound to the running status data and time-series aligned to generate the holographic vehicle object.

8. The apparatus according to claim 6, characterized in that, The processing module is used for: Based on the mileage information and power-on / off status of the holographic vehicle object, calculate the actual usage time of the vehicle within a preset time period; The vehicle utilization rate is calculated based on the ratio of the actual usage time to the preset working time. The vehicle usage compliance rate is generated based on the comparison between the vehicle usage rate and the preset compliance threshold. The vehicle's online duration is calculated by accumulating the timestamp differences between the power-on and power-off states; The comprehensive test index is obtained based on the vehicle usage rate, the vehicle usage compliance rate, and the online duration.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the computer program to implement the vehicle test status monitoring method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the vehicle test status monitoring method as described in any one of claims 1-5.

Images