A fault statistical data visualization method, system and electronic device

Abstract

The application relates to the technical field of vehicle faults, and discloses a fault statistical data visualization method, a system and an electronic device, the method comprising the following steps: setting a fault statistical index and an index display type corresponding to the fault statistical index in advance; acquiring time series data generated by a target vehicle, wherein the time series data comprises diagnosis fault codes arranged according to fault times; classifying the diagnosis fault codes according to the fault statistical index to obtain a classification result; performing data statistics on the fault times and the diagnosis fault codes according to the classification result to obtain to-be-displayed data after statistics; performing visual rendering on the to-be-displayed data according to the index display type to obtain visual data; and generating a fault statistical report according to the visual data. The method can intuitively display fault statistical data when a vehicle is in fault, is convenient for users to understand, and can help the users to quickly locate and solve problems.

Description

Technical Field

[0001] This application relates to the field of vehicle fault technology, specifically to a method, system, and electronic device for visualizing fault statistics data. Background Technology

[0002] Diagnostic Trouble Codes (DTCs) are unique alphanumeric codes used to identify specific types of problems with a vehicle's onboard computer system. Technicians use these codes to diagnose and repair potential problems causing the malfunction. Each DTC corresponds to a specific component or system within the vehicle, such as the engine, transmission, or exhaust system. When a problem occurs, the onboard computer stores the relevant DTC in its memory, which can be accessed using scanning tools or through the onboard diagnostic system. DTCs are generated by the vehicle's diagnostic system when a fault is detected, and each DTC represents a specific fault or problem. These problems can involve various vehicle systems, such as the engine, transmission, and exhaust system.

[0003] Currently, the primary methods for viewing fault diagnostic codes (DTCs) are on-board diagnostic systems (OBD-II) and specialized automotive diagnostic tools (such as scanning tools). OBD-II systems are typically used to monitor the performance of different components and generate corresponding DTCs when problems occur. However, these methods suffer from drawbacks such as requiring specialized equipment, cumbersome operation procedures, untimely data updates, unintuitive information display, inconvenient data management, and low security. Clearly, there is an urgent need for a new method for visualizing fault statistics data to address at least one of these problems.

[0004] It should be noted that the above content only provides background information related to this application and does not necessarily constitute prior art. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, this application provides a method, system and electronic device for visualizing fault statistics data, so as to intuitively display the fault statistics data when a vehicle malfunctions, making it easier for users to understand and helping users to quickly locate and solve problems.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0007] According to one aspect of the embodiments of this application, a method for visualizing fault statistics data is provided, comprising: pre-setting fault statistics indicators and corresponding indicator display types, wherein the fault statistics indicators include the number of faults, severity, and frequency of occurrence; acquiring time-series data generated by a target vehicle, wherein the time-series data includes diagnostic fault codes arranged according to fault time; classifying the diagnostic fault codes according to the fault statistics indicators to obtain classification results; statistically analyzing the fault time and the diagnostic fault codes according to the classification results to obtain statistically analyzed data to be displayed; visualizing and rendering the data to be displayed according to the indicator display types to obtain visualized data; and generating a fault statistics report based on the visualized data.

[0008] In one embodiment of this application, after generating a fault statistics report based on the visualized data according to the foregoing scheme, the method further includes: pre-setting data permission tags corresponding to the fault statistics indicators; obtaining the target user and the user permissions corresponding to the target user; determining target data from the fault statistics report based on the comparison result between the user permissions and the data permission tags; and generating a target report based on the target data and the fault information corresponding to the diagnostic fault code.

[0009] In one embodiment of this application, based on the foregoing scheme, the method further includes: sending the target report to a user terminal corresponding to the target user, wherein the user terminal is equipped with a visualization page, the visualization page is used to display the target report, and / or executing page operation instructions input by the target user based on the target user's preset operation permissions.

[0010] In one embodiment of this application, based on the foregoing scheme, the visualization page executes the page operation instructions input by the target user based on the target user's preset operation permissions in at least one of the following ways: if the preset operation permissions include a first operation permission, then execute at least one of the data viewing instructions, data editing instructions, and data management instructions input by the target user; if the preset operation permissions include a second operation permission, then execute the data viewing instructions input by the target user.

[0011] In one embodiment of this application, based on the foregoing scheme, the method further includes at least one of the following: generating a periodic task, wherein the periodic task is used to update the target report in each preset update cycle and send the updated target report to the user terminal corresponding to the target user; monitoring the diagnostic fault codes of the target vehicle using a preset service monitoring system, and if a new diagnostic fault code is detected, updating the target report and sending the updated target report to the user terminal corresponding to the target user.

[0012] In one embodiment of this application, based on the foregoing scheme, the method further includes: acquiring historical fault codes, statistically analyzing the historical fault codes, and determining minimum support and minimum confidence based on the statistical results; extracting an initial itemset corresponding to the diagnostic fault code from the historical fault codes using a preset association rule mining algorithm, extracting a frequent itemset corresponding to the diagnostic fault code from the initial itemset based on the minimum support, and extracting association rules from the frequent itemset; filtering the association rules based on the minimum confidence to obtain association results; determining the associated fault codes of the diagnostic fault code based on the association results, and displaying the associated fault codes through a visualization page.

[0013] According to one aspect of the embodiments of this application, a fault statistics visualization system is provided, comprising: a target vehicle for generating time-series data, wherein the time-series data includes diagnostic fault codes arranged according to fault time; and a server for setting fault statistics indicators and corresponding indicator display types, wherein the fault statistics indicators include fault quantity, severity, and frequency of occurrence; acquiring the time-series data generated by the target vehicle; classifying the diagnostic fault codes according to the fault statistics indicators to obtain classification results; performing data statistics on the fault time and the diagnostic fault codes according to the classification results to obtain statistically analyzed data to be displayed; visualizing and rendering the data to be displayed according to the indicator display types to obtain visualized data; and generating a fault statistics report based on the visualized data.

[0014] In one embodiment of this application, based on the foregoing scheme, the target vehicle includes a vehicle gateway and at least one electronic controller; the electronic controller is used to generate the diagnostic fault code; the vehicle gateway connects to each of the electronic controllers, and the vehicle gateway is used to collect the diagnostic fault codes of each of the electronic controllers, obtain the fault time, generate the time series data based on the fault time and the diagnostic fault code, and send the time series data to the server.

[0015] In one embodiment of this application, based on the foregoing scheme, the server includes at least one of the following: a time-series database for storing time-series data sent by the vehicle gateway; a visualization module for rendering the data to be displayed according to the indicator display type to obtain visualized data, generating a fault statistics report based on the visualized data, and displaying the fault statistics report; and a service monitoring system for monitoring the diagnostic fault codes, and if the target vehicle generates a new diagnostic fault code, sending the fault statistics report to the user terminal corresponding to the target user.

[0016] According to one aspect of the embodiments of this application, an electronic device is provided, the electronic device comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the fault statistics visualization method as described in any of the above embodiments.

[0017] The beneficial effects of this application are as follows: This application obtains time-series data generated by the target vehicle by pre-setting fault statistical indicators and the corresponding indicator display types. The time-series data includes diagnostic fault codes arranged according to fault time. The diagnostic fault codes are classified according to the fault statistical indicators to obtain classification results. The fault time and diagnostic fault codes are statistically analyzed according to the classification results to obtain statistically analyzed data to be displayed. The data to be displayed is visualized according to the indicator display type to obtain visualized data. A fault statistical report is generated based on the visualized data. By using diagnostic fault codes to realize the statistical analysis of fault information, the fault information is displayed intuitively and concisely, making it convenient for users to quickly view and analyze fault data, reducing the time spent on manually interpreting diagnostic fault codes, and improving fault handling efficiency.

[0018] In addition, this application also allows access to a visualization page via a preset network address link to view fault statistics reports, without the need for professional diagnostic equipment, wiring harnesses, or professional diagnostic engineers, and is not limited by location; by setting different permissions for different personnel, data security is improved.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0021] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application;

[0022] Figure 2 This is a flowchart illustrating a method for visualizing fault statistics data, as shown in an exemplary embodiment of this application.

[0023] Figure 3 This is a schematic diagram of the system architecture of a fault statistics visualization method illustrated in an exemplary embodiment of this application;

[0024] Figure 4 This is a schematic diagram of the system architecture of a fault statistics visualization method illustrated in another exemplary embodiment of this application;

[0025] Figure 5 This is a schematic diagram illustrating data transmission of a fault statistics visualization method according to an exemplary embodiment of this application;

[0026] Figure 6 This is a block diagram illustrating a fault statistics visualization system as shown in an exemplary embodiment of this application;

[0027] Figure 7 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0028] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0029] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0030] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0031] First, it's important to clarify that an Electronic Control Unit (ECU) is a crucial electronic component widely used in electronics manufacturing and plays a vital role in daily life. An ECU is a miniaturized computer management center. It takes signal (data) acquisition, processing, analysis, judgment, and policy decisions as input, and outputs control commands to instruct actuators. It also provides stable power or reference voltage to sensors. All its functions are accomplished through a combination of hardware and software, with its core being a microcomputer system based on a microcontroller.

[0032] Grafana is an open-source monitoring dashboard and graph editor widely used for visualizing time-series data. A powerful data visualization tool, Grafana supports integration with various data sources (such as Elasticsearch, InfluxDB, Prometheus, etc.) and can transform complex data into easy-to-understand charts and dashboards. With Grafana, users can monitor system status, performance, and other metrics in real time, and perform in-depth data analysis and insights.

[0033] InfluxDB is an open-source database designed specifically for time-series data. Time-series data is a collection of data points that changes over time, such as monitoring data (CPU utilization, memory usage), sensor data from IoT devices, and stock market data. InfluxDB provides the ability to efficiently store, query, and analyze this data.

[0034] Prometheus is an open-source monitoring and alerting system based on a time-series database. It periodically captures the status of monitored components via HTTP, supports multiple language clients and rich visualization modes, such as Grafana integration, and can efficiently process large amounts of monitoring data, providing accurate alerting capabilities. Prometheus boasts independent and active community support and is suitable for monitoring virtualized environments, Docker containers, and Kubernetes clusters. With its multidimensional data model, PromQL query language, efficient storage, and accurate alerting, it excels in performance monitoring and alerting. Simply put, Prometheus is an open-source system monitoring and alerting tool that helps users monitor the health of applications and infrastructure in real time by collecting time-series data and providing query and visualization interfaces, enabling timely detection and response to potential problems.

[0035] Diagnostic Trouble Codes (DTCs) are unique alphanumeric codes used to identify specific types of problems in a vehicle's onboard computer system, facilitating the diagnosis and repair of potential issues leading to the malfunction. DTCs are generated by the vehicle's diagnostic system when a fault is detected, and each DTC represents a specific fault or problem. These problems can involve various vehicle systems, such as the engine, transmission, and exhaust system.

[0036] Currently, the main methods for viewing DTCs are through on-board diagnostic systems (OBD-II) and specialized automotive diagnostic tools (such as scanning tools). The disadvantages include: 1. Faults cannot be detected promptly during testing. Traditional diagnostic tools often lack real-time data updates, meaning technicians may not see the latest vehicle status. 2. When a fault occurs during testing, relevant key data is not captured, such as CPU utilization, network load, CAN bus load, CAN frame loss rate, CAN forwarding latency, interface success rate, probability of different abnormal return values ​​from the interface, and abnormal input parameters at the time of the fault. 3. Specialized equipment is required, and the operation is cumbersome. Viewing DTCs using OBD-II and scanning tools relies on specialized automotive diagnostic equipment. These devices are typically expensive and inconvenient to carry, making them impractical for ordinary car owners or small repair shops. Compared to visual interfaces, obtaining DTCs via OBD-II and scanning tools is more complex. Technicians need to connect the device, select the correct operating mode, and interpret the output, requiring a certain level of professional knowledge and experience. Furthermore, to become proficient in using OBD-II and scanning tools, technicians need professional training to learn how to operate the device and interpret DTC codes, increasing learning costs and time. 4. Information display is not intuitive and lacks a graphical interface. Unlike intuitive graphical and chart displays, DTCs read by professional tools are usually displayed in numerical codes and text. This display method is not intuitive, especially for non-professionals, making it difficult to understand. 5. Difficulty in quickly identifying problems. In emergencies, quickly locating and resolving problems is crucial. Because OBD-II and scanning tools provide raw data, technicians need to spend more time analyzing and judging the cause of the fault. In contrast, visual displays can quickly highlight key issues, helping users take immediate action. 6. Inconvenient data management due to difficulties in data archiving and management. DTC data obtained through professional tools typically requires manual recording and management, which not only increases workload but is also prone to errors. If DTC data needs to be compiled into reports, traditional tools require manual writing and formatting, which is inefficient and error-prone. 7. Low security. Traditional diagnostic tools lack effective encryption and protection measures during data transmission and storage, potentially leading to data security risks such as DTC data leakage or malicious tampering. For large repair shops or fleet management, different levels of technicians have different access requirements for DTC data. Traditional tools struggle to achieve granular access control, resulting in insufficient access control. 8. Poor mobility, inconvenient for field use. Traditional diagnostic tools are usually bulky, making them inconvenient for use when on the go or in the field. 9. Inability to provide timely feedback. In environments far from repair stations or without network access, traditional tools struggle to transmit data in real time to the back office or expert teams for emergency diagnosis and support.

[0037] Figure 1This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application.

[0038] Reference Figure 1 As shown, the system architecture may include a data acquisition device 101 and a computer device 102. The computer device 102 may be at least one of a desktop graphics processing unit (GPU) computer, a GPU computing cluster, or a neural network computer. The data acquisition device 101 is used to collect time-series data generated by the target vehicle. The time-series data includes diagnostic fault codes arranged according to fault time. In this embodiment, after acquiring the above data, the data acquisition device 101 provides it to the computer device 102 for processing. Those skilled in the art can use the computer device 102 to pre-set fault statistical indicators and corresponding indicator display types. The fault statistical indicators include the number of faults, severity, and frequency of occurrence. The diagnostic fault codes are classified according to the fault statistical indicators to obtain classification results. Fault time and diagnostic fault codes are statistically analyzed according to the classification results to obtain statistically analyzed data to be displayed. The data to be displayed is then visualized according to the indicator display type to obtain visualized data, and a fault statistical report is generated based on the visualized data. It should be noted that the data acquisition device 101 and computer device 102 provided in this embodiment are merely examples and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0039] It should be noted that the fault statistics visualization method provided in this application embodiment is generally executed by computer device 102, and correspondingly, the fault statistics visualization system is generally set in computer device 102.

[0040] Figure 2 This is a schematic flowchart illustrating a fault statistics visualization method according to an exemplary embodiment of this application. This fault statistics visualization method can be executed by a computing processing device, which may be... Figure 1 The computer device 102 shown is illustrated. (Refer to...) Figure 2 As shown, this method for visualizing fault statistics includes at least steps S210 to S240, which are detailed below:

[0041] In step S210, fault statistics indicators and the corresponding indicator display types are preset.

[0042] In one embodiment of this application, fault statistics indicators and corresponding indicator display types are set according to requirements. For example, fault statistics duration refers to the length of time faults are statistically analyzed (e.g., one day, one week, one month), and the statistical indicators (number of faults, severity, frequency of occurrence, etc.) and statistical content are also included. The indicator display type corresponding to the fault statistics indicators can be set based on relevant information (e.g., data type), or the corresponding fault statistics indicators can be determined based on the characteristics of the indicator display type and the applicable data. The fault statistics indicators include the number of faults, severity, and frequency of occurrence.

[0043] In this embodiment, various types of charts can be displayed through Grafana's data visualization interface. The indicator display types include, but are not limited to, one or more of the following: indicator cards, dashboards, pie charts, donut charts, stacked bar charts, percentage stacked bar charts, bar charts, line charts, area charts, histograms, and scatter plots, to meet different data display needs. Specifically: Indicator cards are used to highlight individual important data points, such as system status or key performance indicators; dashboards display data in the form of arc-shaped pointers, suitable for showing the proportion of a metric relative to its maximum value; pie charts use the size of different sectors to show the proportion of each part to the total, suitable for displaying the distribution of categorical data; donut charts are similar to pie charts but have an empty center, saving space and being aesthetically pleasing, also used to display percentage data; stacked bar charts have multiple bars overlapping, each bar representing a category, and the length representing the numerical value, used to compare data across multiple dimensions; percentage stacked bar charts... Bar charts, similar to stacked bar charts, but where the length of each section represents a percentage of the total bar length; bar charts use bar length to represent data size, suitable for comparing different categories of data; line charts connect data points with lines to show data trends over time, suitable for time series data; area charts, where the area below a line chart is filled with color, can more intuitively show the cumulative effect of data; histograms use height to represent frequency, suitable for showing the distribution of data; scatter plots use points to represent data, with the horizontal and vertical axes representing two numerical variables, suitable for viewing the relationship between two variables. In summary, through these chart types and rich configuration options, Grafana can flexibly display data from various data sources, thereby helping engineers better monitor and analyze the status and performance of environments such as ECU systems, networks, and databases.

[0044] In step S220, the time series data generated by the target vehicle is obtained.

[0045] The time-series data includes diagnostic fault codes arranged according to the time of the fault.

[0046] In one embodiment of this application, time-series data sent by the target vehicle is received, wherein the diagnostic fault code is generated from the fault time and the diagnostic fault code. Time-series data refers to a set of data arranged at certain time intervals to describe the measurement value of a measured subject at each point in time within a time range. This data can reflect the change state or degree of a thing or phenomenon over time, such as snapshot information such as the number of times a DTC occurs, its frequency, and the vehicle speed at the time of occurrence.

[0047] In step S230, the diagnostic fault codes are classified according to the fault statistics indicators to obtain the classification results. The fault time and diagnostic fault codes are statistically analyzed according to the classification results to obtain the statistical data to be displayed.

[0048] In one embodiment of this application, diagnostic fault codes are classified according to a preset classification standard corresponding to fault statistical indicators to obtain classification results. Diagnostic fault codes are then filtered according to a preset duration to obtain filtered diagnostic fault codes. The filtered diagnostic fault codes are then statistically analyzed according to the classification results to obtain statistical data to be displayed. For example, if the preset duration is one month, and six faults with diagnostic fault codes AABACB occurred within this month, with fault dates of January 1st, January 6th, January 10th, January 18th, January 23rd, and January 27th respectively, diagnostic fault code A corresponds to a general fault, diagnostic fault code B corresponds to a minor fault, and diagnostic fault code C corresponds to a serious fault. Taking the number of faults as a fault statistics indicator as an example, the diagnostic fault codes are classified according to the first preset classification standard for the number of faults, resulting in classification results (e.g., each diagnostic fault code). The fault time and diagnostic fault codes are statistically analyzed according to the classification results to obtain the number of faults after each classification result (A: 3, B: 2, C: 1, unit: number). If the total number of faults needs to be counted, the number of faults after each classification result is summed to obtain the total number of faults (6 faults). Taking the severity of faults as a fault statistics indicator as an example, the diagnostic fault codes are classified according to the second preset classification standard for severity, resulting in classification results (e.g., general faults, minor faults, severe faults). The fault time and diagnostic fault codes are statistically analyzed according to the classification results to obtain the number of faults after each classification result (severe faults: 1, general faults: 2, minor faults: 3, unit: number). Taking the frequency of occurrence as an example of a fault statistics indicator, the diagnostic fault codes are classified according to the third preset classification standard of the frequency of occurrence, and the classification results are obtained (such as time granularity, such as month, week, day, etc.). The fault time and the diagnostic fault codes are statistically analyzed according to the classification results to obtain the number of faults after the classification results are statistically analyzed (taking a classification result of 1 week as an example, the frequency of occurrence is 1.5 times per week). It should be noted that this embodiment is only for illustrative purposes and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0049] In this embodiment, the data to be displayed includes, but is not limited to, data corresponding to the number of faults, data corresponding to the severity, and data corresponding to the frequency of occurrence. For example, the fault time and diagnostic fault codes can be statistically analyzed based on the fault statistics duration corresponding to the fault statistics indicators to obtain the data to be displayed. For example, by statistically analyzing fault information within one month, the data to be displayed would be: 6 faults, 1 serious fault, 2 general faults, and 3 minor faults, with an occurrence frequency of 1.5 times per week. It should be noted that this embodiment is only for illustrative purposes and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0050] In step S240, the data to be displayed is visualized according to the indicator display type to obtain visualized data, and a fault statistics report is generated based on the visualized data.

[0051] In one embodiment of this application, taking the severity of a fault as an example, assuming the index display type corresponding to the severity is a histogram, the severity in the data to be displayed is visualized and rendered according to the histogram to obtain visualized severity data. A fault statistics report is generated based on the visualized data. Still taking severity as an example, the severity in the fault statistics report is: severe fault: 1, general fault: 2, minor fault: 3, unit number.

[0052] In one embodiment of this application, after generating a fault statistics report based on visualized data, data permission tags corresponding to fault statistics indicators are pre-set; target users and their corresponding user permissions are obtained; target data is determined from the fault statistics report based on the comparison results between user permissions and data permission tags; and a target report is generated based on the target data and the fault information corresponding to the diagnostic fault codes.

[0053] In this embodiment, taking the severity of the fault as an example, assuming that the target user's user permissions include viewing the severity, the target data determined from the fault statistics report includes severe fault: 1, general fault: 2, and minor fault: 3. Assuming that the corresponding diagnostic fault codes are P0300, P0107, P0115, P0111, P0112, and P3009, the fault information corresponding to the diagnostic fault codes are as follows: P0300: Throttle position sensor / switch circuit low, P0107: Intake pressure sensor input voltage too low, P0115: Engine coolant temperature sensor wiring fault, P0111: Intake air temperature sensor wiring fault, P0112: Intake air temperature sensor circuit voltage too low, and P3009: Brake pedal position sensor A circuit range / performance. The illustrative target reports are as follows: Critical fault: 1, P0300: Throttle position sensor / switch circuit low; General fault: 2, P0107: Intake pressure sensor input voltage too low, P0115: Engine coolant temperature sensor wiring fault; Minor fault: 3, P0111: Intake air temperature sensor wiring fault, P0112: Intake air temperature sensor line voltage too low, P3009: Brake pedal position sensor A circuit range / performance. It should be noted that the target data and diagnostic fault codes in this embodiment are merely illustrative examples and should not impose any limitations on the functionality or scope of use of this application's embodiments.

[0054] In one embodiment of this application, the target report is sent to the user terminal corresponding to the target user. The user terminal is equipped with a visualization page, which is used to display the target report and / or execute page operation instructions input by the target user based on the target user's preset operation permissions.

[0055] In this embodiment, the function of the visualization page is determined based on user permissions. If the user permissions include first role permissions, the visualization page of the user terminal corresponding to the target user is used to display the target report and execute the page operation instructions input by the target user based on the target user's preset operation permissions. If the user permissions include second role permissions, the visualization page of the user terminal corresponding to the target user is used to display the target report. It should be noted that the user permissions are set according to actual needs, and this application does not impose any restrictions on them.

[0056] In one embodiment of this application, a periodic task is generated, wherein the periodic task is used to update the target report in each preset update cycle and send the updated target report to the user terminal corresponding to the target user; the target vehicle's diagnostic fault codes are monitored by using a preset service monitoring system, and if a new diagnostic fault code is detected, the target report is updated and the updated target report is sent to the user terminal corresponding to the target user.

[0057] In this embodiment, preferably, taking Prometheus as the service monitoring system, if Prometheus detects a new diagnostic fault code, it updates the target report and sends the updated target report to the user terminal corresponding to the target user; if Prometheus does not detect a new diagnostic fault code, it updates the target report in each preset update cycle and sends the updated target report to the user terminal corresponding to the target user.

[0058] In one embodiment of this application, the visualization page executes page operation instructions input by the target user based on the target user's preset operation permissions in at least one of the following ways: if the preset operation permissions include a first operation permission, then execute at least one of the data viewing instruction, data editing instruction, and data management instruction input by the target user; if the preset operation permissions include a second operation permission, then execute the data viewing instruction input by the target user.

[0059] In one embodiment of this application, a visualization page is accessed via a preset network address link, allowing the target user to view the target report through the visualization page.

[0060] In this embodiment, users are required to log in before accessing the visualization page via a preset network address link to verify their identity and confirm that they are the target user, thus avoiding security risks caused by data leakage. By managing account permissions for the visualization page's URL (network address link), different permissions can be distributed to different users, improving the security of DTC data.

[0061] In one embodiment of this application, historical fault codes are obtained, and statistics are performed on the historical fault codes. Based on the statistical results, minimum support and minimum confidence are determined. An initial itemset corresponding to the diagnostic fault code is extracted from the historical fault codes using a preset association rule mining algorithm. Based on the minimum support, frequent itemsets corresponding to the diagnostic fault codes are extracted from the initial itemsets, and association rules are extracted from the frequent itemsets. The association rules are then filtered based on the minimum confidence to obtain association results. Based on the association results, the associated fault codes of the diagnostic fault codes are determined, and the associated fault codes are displayed through a visualization page.

[0062] In this embodiment, historical fault diagnosis codes (i.e., historical fault codes) from multiple vehicles, different time periods, and different fault types are collected to ensure comprehensive data coverage and a sufficient sample size to support subsequent association rule mining. After acquiring the historical fault codes, they are preprocessed. Preprocessing includes, but is not limited to, deduplication (removing duplicate data records), handling missing values ​​(which can be done by deleting, replacing, or using interpolation methods), handling outliers (checking for outliers in the dataset and deleting, replacing, or using interpolation methods if found), data standardization (standardizing the fault diagnosis codes to ensure that fault diagnosis codes from different sources and formats can be used for association rule mining on the same platform), and data transformation (converting the fault diagnosis codes into a transaction dataset format, where each transaction contains a series of concurrently occurring fault diagnosis codes). Preprocessing the historical fault codes improves their data quality, thereby facilitating subsequent data analysis efficiency and increasing the speed of rule analysis. Statistical analysis of the historical fault codes is performed to determine the minimum support and minimum confidence based on the statistical results, facilitating the subsequent selection of frequent itemsets and association rules. Using a pre-defined association rule mining algorithm (such as the Apriori algorithm or FP-Growth algorithm), a set of all possible items (i.e., fault codes) is extracted from historical fault codes to form an initial itemset. Based on minimum support, frequent itemsets are selected from the initial itemset; these frequent itemsets represent fault code combinations that frequently appear together in historical data. Further association rules are extracted from the frequent itemsets; these rules describe the correlation between fault codes. The extracted association rules are then filtered based on minimum confidence to retain those with high confidence and reliability. Based on the filtered association results, associated fault codes are identified for each diagnostic fault code. These associated fault codes have a high statistical co-occurrence rate and may be significant for fault diagnosis and prediction. Finally, the associated fault codes are displayed on a visual page, helping users intuitively understand the complex relationships between fault codes. This allows for the development of targeted maintenance strategies based on the associated fault codes, improving maintenance efficiency and accuracy, and enabling fault early warning through associated fault codes, thus identifying and resolving potential problems in advance.

[0063] In one embodiment of this application, corresponding troubleshooting suggestions are provided to the user based on the fault information corresponding to the diagnostic fault code. For example, P0115: Engine coolant temperature sensor circuit, the corresponding troubleshooting suggestions are: 1. Replace the sensor: If the sensor itself is faulty, it is recommended to replace it with a new engine coolant temperature sensor. 2. Check the circuit: Check whether there are problems such as open circuit, short circuit or poor contact in the sensor circuit. At the same time, check whether the engine wiring is damaged or short-circuited. 3. Consider the control unit: Replace the engine control unit (ECU).

[0064] In one embodiment of this application, a fault statistics visualization system is proposed to achieve real-time visualization of DTCs reported by each ECU, monitoring of key data indicators, alarms, and automatic generation of fault reports during vehicle diagnostics. In a vehicle, most ECU data communication is conducted via the CAN protocol, and the vehicle gateway controller (GW) acts as the bridge for this communication. These ECUs are connected to the GW via the CAN bus, forming a downstream device of the GW, and their DTCs can be sent to the GW. Therefore, to upload all DTC data from the vehicle, the DTC data can first be sent to the gateway via the CAN bus, and then the GW, equipped with an Ethernet interface, uploads the data via Ethernet. This solves the problems of uploading DTC data from all ECUs in the vehicle to the server, visualizing the DTC data, monitoring and alarming, and automatically generating fault reports.

[0065] The fault statistics visualization system in this embodiment has the following functions: 1. It establishes a time-series database based on InfluxDB to store DTC information in the form of time-series data. Through the point-tracking interface deployed on the GW, it can store the DTCs of all ECUs in the vehicle. 2. It configures a data visualization interface based on Grafana. After engineers configure the data they are interested in, it can be displayed graphically to anyone accessing the page, and different permissions can be granted to different personnel. 3. It configures a monitoring and alarm system (service monitoring system) based on the Alertmanager function in Prometheus. It can be configured to promptly notify engineers and emergency response personnel through office software group chat (such as DingTalk, Lark), email, SMS, and telephone when the vehicle experiences DCT faults of different degrees. In other words, the prompting method is determined based on the severity of the fault. Specifically, the target prompting method is determined from the preset prompting methods based on the matching result between the severity of the diagnosed fault code and the preset severity. The target prompting method is then used to prompt the target user. The preset prompting methods include, but are not limited to, one or more of text prompts and voice prompts. Text prompts include, but are not limited to, prompts via group chats on office software, emails, and SMS messages. Voice prompts include, but are not limited to, prompts via alarm sounds, dialing the target user's phone, and playing prompt voice messages. 4. Design an automatic fault report generation tool. Based on preset content and statistics of faults occurring in the vehicle over a certain period of time (preset time period), an online fault report, i.e., a fault statistics report, is automatically generated. This report includes one or more of the following: number of faults, severity, frequency of occurrence, etc. The fault occurrence is displayed concisely on the web page, and fault reports can be sent periodically to the target user's corresponding user terminal, such as the user's mobile phone.

[0066] Figure 3This is a schematic diagram of the system architecture of a fault statistics visualization method illustrated in an exemplary embodiment of this application. (Refer to...) Figure 3 As shown, in an exemplary embodiment, the diagnostic fault code visualization system aims to provide a real-time, intuitive data display platform for vehicle fault status. An InfluxDB time-series database is established on the server, and Grafana is used to connect to Prometheus as a visualization tool. The Prometheus-Alertmanager function is configured with monitoring and alarm capabilities. This system can effectively collect, store, and display DTC data reported during vehicle use, helping developers, testers, users, maintenance personnel, and emergency responders quickly understand fault information and respond promptly. The fault statistics visualization system includes a data collection layer, a data storage layer, a data visualization layer, a network communication layer, and a security and access control layer. (Refer to...) Figure 4 , Figure 4 This is a schematic diagram of the system architecture of a fault statistics visualization method illustrated in another exemplary embodiment of this application. The following is a brief description of each layer:

[0067] 1. The data collection layer is responsible for collecting DTC data from the CAN bus and sending this data to the InfluxDB database on the server via the Gateway (GW). Its key components include: a data acquisition agent, which is agent software installed on the GW, responsible for monitoring CAN bus messages and capturing DTC data when changes occur; a logging API, an API provided in the GW for the data acquisition agent to use, used to send the captured data to the server in a specific format; and ECUs, each ECU acquiring DTC data and saving the DTC data to be uploaded by each ECU (ECU1 to ECUn) connected to the GW in a preset format via the CAN bus. When these ECUs send a message to the GW indicating a change in DTC data, the GW parses the DTC data of the message according to the communication protocol and calls the logging API of the InfluxDB database to complete the logging and saving of the DTC data for that ECU. The logging API, also known as the tracing API or logging API, is a technical interface used in software systems to collect and record user behavior, system status, performance data, or other important information. These APIs are typically integrated into applications to automatically record key events or data points during application runtime, and then send this data to backend services for storage, analysis, or display.

[0068] 2. Data storage layer, responsible for receiving DCT data from the ECU and storing it in the InfluxDB time-series database. Its key components include: InfluxDB database: a high-performance time-series database used to store data points from the GW; and data receiving service: a service running on the server responsible for receiving data points sent by the ECU and writing them to InfluxDB.

[0069] 3. Network communication layer, ensuring secure and reliable data transmission between the GW and the server. Key components include: Ethernet communication hardware and software: An ECU (Electronic Control Unit) supporting Ethernet communication should have an Ethernet MAC (Media Access Control) and PHY (Physical Layer) chip, MII / RMII / GMII / RGMII interfaces, necessary software protocol stacks, and related network testing and security mechanisms; Network environment setup: Ensure the FreeRTOS operating system on the ECU is configured with a network protocol stack, such as lwIP, which supports TCP / IP and HTTP protocols; Ethernet connection: The GW connects to the server via Ethernet to ensure stable network communication. Connection methods include: Ordinary Ethernet gateway: connecting via T-BOX (Telematics BOX); Central gateway: with its own 4G / 5G communication capabilities. Data transmission protocol: Define the communication protocol between the GW and the server, using the HTTP protocol. Refer to [link / reference] for specific connection methods. Figure 5 , Figure 5 This is a schematic diagram illustrating data transmission of a fault statistics visualization method, as shown in an exemplary embodiment of this application. Figure 5 It can be seen that GW is connected to the InfluxDB server via Ethernet.

[0070] 4. The data visualization layer includes visualization panels, providing a user interface that allows users to configure and view data visualizations. Key components include: Prometheus: an open-source monitoring and alerting tool used to query and display data from InfluxDB; and Grafana visualization panels: connecting to Prometheus using Grafana. This involves configuring Grafana dashboards to display key metrics such as API success rates. It also includes an automatic fault report generation tool, which automatically generates online fault reports based on set parameters and statistics of vehicle faults occurring over a period of time. These reports include the number, severity, and frequency of faults, providing a concise web-based display of fault occurrences and periodically sending reports to the target user's mobile device. Key steps include: fault report layout design: designing an HTML template suitable for displaying vehicle DTC fault data; and backend development: using Python web frameworks such as Flask to build the backend service, resulting in the Flask framework. This framework implements routing, time-series database connections, and report sending APIs to achieve report generation and delivery. Report Generation Code: Write Python code to classify DTC data by severity, count their quantity and occurrence comments, and dynamically populate the designed report template to generate a report. Report Push: Write code to send fault reports to users periodically (e.g., monthly), and summarize fault occurrence information within the monthly statistical period. Tool Deployment: Deploy the automatic fault report generation tool to the server and connect to a time-series database to begin collecting and summarizing DTC data.

[0071] 5. Security and Access Management Layer: This layer protects system security and prevents unauthorized access. Key aspects include: Authentication Mechanism: Ensuring only authenticated users can access the system's visualization interface URL. Authorization Policy: Defining different user permissions, such as viewing, editing, or managing the DTC data visualization panel. For example, development engineers have permission to view, edit, or manage the DTC data visualization panel; test engineers have permission to view the DTC data visualization panel; maintenance personnel have permission to view the DTC data visualization panel, and automakers can provide DTC data visualization panel accounts to authorized 4S stores and repair shops; emergency response personnel have permission to view the DTC data visualization panel, and automakers can provide DTC data visualization panel accounts to professionals, as well as configure automatic direct alarms and reports to automaker customer service when specific fault DTCs (such as braking or power abnormalities) occur. Users have permission to view the DTC data visualization panel and have editing permissions for some data information.

[0072] In one embodiment of this application, the steps for building a fault statistics visualization system are described as follows: Install and configure the InfluxDB database on a pre-set server; install Prometheus and configure a connection to InfluxDB; develop a data logging API for the InfluxDB database on the server; configure the network environment to ensure the gateway (GW) can communicate with the server via Ethernet; develop or integrate a data acquisition agent on the GW, and integrate and compile the InfluxDB data logging API code; ensure that key vehicle ECUs can send DTC data to the GW via the CAN bus; after receiving DTC information carrying the downstream ECU, the gateway parses the data logging data of the message according to the communication protocol and calls the InfluxDB database data logging API to complete the data logging and saving of the downstream ECU's DTC data; connect to Prometheus on Grafana, create a visualization panel, and configure the data indicators of interest, i.e., fault statistics indicators; implement security measures, including user authentication and authorization. Users are divided into administrators and ordinary users, i.e., first-role permissions and second-role permissions. Administrators can view and configure the data indicators of interest at any time, while users can view these indicators at any time. Make the Grafana data visualization panel URL public, allowing all developers, test engineers, users, maintenance personnel, emergency responders, and other users to observe real-time vehicle DTC data; configure a real-time alarm system, using Alertmanager in Prometheus to provide timely alerts; configure data metric hazard levels, and when monitored DTC data shows preset specific anomalies (such as braking, power, vehicle collision status), notify engineers, automotive customer service personnel, and other professionals via different methods based on these hazard levels, such as group chats on office software (e.g., DingTalk, Lark), email, SMS, and telephone; deploy an automatic fault report generation tool to the server; and conduct system testing to ensure data accuracy and correct visualization.

[0073] In one embodiment of this application, the fault statistics visualization also requires maintenance and upgrades. This includes, but is not limited to, the following: regularly checking system performance and optimizing database and query efficiency; updating data collection points and visualization panels (i.e., visualization pages) according to testing needs; and opening up Grafana data visualization panel URLs for account applications, making it convenient for all industry professionals (such as 4S stores and repair shops) to obtain accounts to view their company's vehicle DTC visualization panels through legitimate channels.

[0074] This application realizes a complete process for collecting, storing, and visualizing DTC data from most of the vehicle's ECUs to the server. This not only improves the accessibility and understandability of the vehicle's DTC data but also significantly enhances the efficiency and quality of vehicle fault detection, location analysis, and response through real-time monitoring and analysis. With continuous operation and maintenance, the system will become a powerful tool to help relevant personnel better locate and handle vehicle faults.

[0075] This application offers the following advantages: A visual interface allows for intuitive display of DTC data, eliminating the need to re-interpret the information stored in the DTC according to protocols. It also enables rapid viewing and analysis of DTC data, reducing the time spent on manual DTC information interpretation and improving fault handling efficiency. All DTC information can be viewed simply by accessing the URL of the visual interface, making the viewing process easy and accessible anywhere, without the need for specialized diagnostic equipment, wiring harnesses, or professional diagnostic engineers. Real-time monitoring of the operating status of each ECU and visual analysis of large amounts of DTC data reveals patterns and trends in fault data, helping to identify potential problems promptly and prevent faults from occurring. Account permission management of the visual interface URL allows for the distribution of different permissions to different personnel, enhancing the security of DTC data. When a vehicle experiences a serious fault, the monitoring system can promptly notify the vehicle manufacturer's customer service personnel, professional emergency response personnel, and other relevant personnel, improving the response speed of fault diagnosis. This is especially true when a vehicle experiences a preset serious fault, such as braking failure or impact that affects the safety of passengers, enabling immediate detection and response. Furthermore, the collected DTC information can be statistically analyzed through code, such as calculating the number of vehicle faults, frequency, and severity. It generates fault statistics reports based on the statistical data to provide an intuitive and concise display of fault information, and can send the periodically compiled fault information to the target user's corresponding user terminal, such as the user's mobile phone.

[0076] Figure 6 This is a block diagram illustrating a fault statistics visualization system according to an exemplary embodiment of this application. The device can be applied to... Figure 1 The implementation environment shown is specifically configured in computer device 102. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0077] like Figure 6 As shown, this exemplary fault statistics visualization system includes a target vehicle 610 and a server 620.

[0078] The system includes a target vehicle 610 for generating time-series data, which includes diagnostic fault codes arranged according to fault time. A server 620 is used to set fault statistical indicators and their corresponding display types. These indicators include the number of faults, severity, and frequency of occurrence. The system acquires the time-series data generated by the target vehicle, classifies the diagnostic fault codes according to the fault statistical indicators, obtains the classification results, statistically analyzes the fault time and diagnostic fault codes according to the classification results, obtains the statistically analyzed data to be displayed, visualizes the data according to the indicator display type, and generates a fault statistical report based on the visualized data.

[0079] In one embodiment of this application, the target vehicle includes a vehicle gateway and at least one electronic controller; the electronic controller is used to generate diagnostic fault codes; the vehicle gateway connects to each electronic controller, and the vehicle gateway is used to collect the diagnostic fault codes of each electronic controller, obtain the fault time, generate time series data based on the fault time and diagnostic fault codes, and send the time series data to the server.

[0080] In one embodiment of this application, the server includes at least one of the following: a time-series database for storing time-series data sent by the vehicle gateway; a visualization module for rendering the data to be displayed according to the indicator display type to obtain visualized data, generating a fault statistics report based on the visualized data, and displaying the fault statistics report; and a service monitoring system for monitoring and diagnosing fault codes, and if a new diagnostic fault code is generated by the target vehicle, sending a fault statistics report to the user terminal corresponding to the target user.

[0081] It should be noted that the fault statistics visualization system and the fault statistics visualization method provided in the above embodiments belong to the same concept. The specific ways in which each module and unit performs operations have been described in detail in the method embodiments and will not be repeated here. In practical applications, the fault statistics visualization system provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0082] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the fault statistics visualization method provided in the above embodiments.

[0083] Figure 7A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 7 The computer system 700 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0084] like Figure 7 As shown, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes, such as executing the methods provided in the various embodiments described above, based on a program stored in Read-Only Memory (ROM) 702 or a program loaded from Storage Unit 708 into Random Access Memory (RAM) 703. The RAM 703 also stores various programs and data required for system operation. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An Input / Output (I / O) interface 705 is also connected to the bus 704.

[0085] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0086] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs various functions defined in the system of this application.

[0087] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0089] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0090] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a computer's processor, causes the computer to perform the fault statistics visualization method provided in the various embodiments described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0091] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0092] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the fault statistics visualization method provided in the various embodiments described above.

[0093] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0094] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0095] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for visualizing fault statistics data, characterized in that, include: Pre-set fault statistics indicators and corresponding indicator display types, wherein the fault statistics indicators include the number of faults, severity, and frequency of occurrence; Acquire time-series data generated by the target vehicle, wherein the time-series data includes diagnostic fault codes arranged according to the fault time; The diagnostic fault codes are classified according to the fault statistics indicators to obtain classification results. The fault time and the diagnostic fault codes are statistically analyzed according to the classification results to obtain the statistical data to be displayed. The data to be displayed is visualized and rendered according to the indicator display type to obtain visualized data, and a fault statistics report is generated based on the visualized data. Obtain historical fault codes, perform statistical analysis on the historical fault codes, and determine the minimum support and minimum confidence based on the statistical results; An initial itemset corresponding to the diagnostic fault code is extracted from the historical fault codes using a preset association rule mining algorithm. Based on the minimum support, a frequent itemset corresponding to the diagnostic fault code is extracted from the initial itemset, and association rules are extracted from the frequent itemset. The association results are obtained by filtering the association rules based on the minimum confidence level. Based on the association results, the associated fault codes of the diagnostic fault codes are determined, and the associated fault codes are displayed on a visualization page.

2. The method for visualizing fault statistics data according to claim 1, characterized in that, After generating a fault statistics report based on the visualized data, the method further includes: Pre-set the data permission tags corresponding to the fault statistics indicators; Obtain the target user and the user permissions corresponding to the target user; The target data is determined from the fault statistics report based on the comparison result between the user permissions and the data permission tags. A target report is generated based on the target data and the fault information corresponding to the diagnostic fault codes.

3. The method for visualizing fault statistics data according to claim 2, characterized in that, The method further includes: The target report is sent to the user terminal corresponding to the target user, wherein the user terminal is equipped with a visualization page, the visualization page is used to display the target report, and / or execute the page operation instructions input by the target user based on the target user's preset operation permissions.

4. The method for visualizing fault statistics data according to claim 3, characterized in that, The visualization page executes the page operation instructions input by the target user based on the target user's preset operation permissions through at least one of the following methods: If the preset operation permissions include the first operation permission, then at least one of the data viewing instructions, data editing instructions, and data management instructions input by the target user shall be executed; If the preset operation permissions include the second operation permission, then the data viewing instruction input by the target user is executed.

5. The method for visualizing fault statistics data according to claim 2, characterized in that, The method further includes at least one of the following: Generate a periodic task, wherein the periodic task is used to update the target report in each preset update cycle and send the updated target report to the user terminal corresponding to the target user; The system monitors the diagnostic fault codes of the target vehicle using a pre-set service monitoring system. If a new diagnostic fault code is detected, the target report is updated and sent to the user terminal corresponding to the target user.

6. A fault statistics visualization system, characterized in that, include: The target vehicle is used to generate time-series data, wherein the time-series data includes diagnostic fault codes arranged according to the time of failure; On the server side, the system is used to set fault statistics indicators and corresponding indicator display types. These indicators include the number of faults, severity, and frequency of occurrence. The system acquires time-series data generated by the target vehicle, classifies the diagnosed fault codes according to the fault statistics indicators, obtains classification results, statistically analyzes the fault time and the diagnosed fault codes according to the classification results, obtains statistically analyzed data to be displayed, and visualizes the data according to the indicator display types to obtain visualized data. A fault statistics report is then generated based on the visualized data. The system also acquires historical fault codes, statistically analyzes them, determines minimum support and minimum confidence based on the statistical results, extracts the initial itemset corresponding to the diagnosed fault code from the historical fault codes using a preset association rule mining algorithm, extracts frequent itemsets corresponding to the diagnosed fault code from the initial itemset based on the minimum support, extracts association rules from the frequent itemsets, filters the association rules based on the minimum confidence, obtains association results, determines the associated fault codes of the diagnosed fault code based on the association results, and displays the associated fault codes through a visualization page.

7. The fault statistics visualization system according to claim 6, characterized in that, The target vehicle includes a vehicle gateway and at least one electronic controller; The electronic controller is used to generate the diagnostic fault codes; The vehicle gateway connects to each of the electronic controllers. The vehicle gateway is used to collect the diagnostic fault codes of each of the electronic controllers, obtain the fault time, generate the time series data based on the fault time and the diagnostic fault codes, and send the time series data to the server.

8. The fault statistics visualization system according to claim 7, characterized in that, The server-side includes at least one of the following: A time-series database is used to store time-series data sent by the vehicle gateway; The visualization module is used to render the data to be displayed according to the indicator display type to obtain visualized data, generate a fault statistics report based on the visualized data, and display the fault statistics report. The service monitoring system is used to monitor the diagnostic fault codes. If the target vehicle generates a new diagnostic fault code, the system sends the fault statistics report to the user terminal corresponding to the target user.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the fault statistics visualization method as described in any one of claims 1 to 5.

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