A joint debugging and testing system and method for a vehicle-mounted control platform

Abstract

The present invention relates to the field of vehicle control platform testing technology, and discloses a joint debugging test system and method for a vehicle control platform. The method first obtains user historical joint debugging test record data, determines the test cycle and constructs a data set, and then determines the periodic user characteristic data set according to personalized rules; then extracts scene features, constructs paths, analyzes and scores to determine the optimal data; groups the scene feature path nodes of the optimal data as a benchmark, analyzes the adapted scene features, and determines the recommended score range; then filters and pushes test data according to the score range, and realizes automatic periodic updates. The system includes test record acquisition, scene feature processing, test adaptation recommendation analysis, and feedback update modules. The method and system effectively utilize historical data to realize scientific analysis of test scenarios, personalized recommendations, and flexible periodic updates, thereby improving the efficiency and accuracy of joint debugging testing.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted control platform testing, and in particular to a joint debugging and testing system and method for a vehicle-mounted control platform. Background Art

[0002] With the rapid development of intelligent and connected vehicles, the functionality of vehicle control platforms is becoming increasingly complex. Their performance and stability directly impact vehicle safety, comfort, and reliability. Vehicle control platforms typically consist of multiple subsystems, and collaboration between these subsystems is crucial. Therefore, joint debugging and testing are crucial to ensuring the proper functioning of these platforms.

[0003] Traditional vehicle control platform joint debugging and testing suffers from numerous issues. The testing process lacks effective utilization of historical user test data. Previous testing methods often fail to systematically collect and analyze historical user test records, failing to extract valuable information from them. This results in a lack of targeted and scientific testing plan development. For example, different users may have different testing habits and needs, and traditional methods are unable to adapt test plans to these individual characteristics, resulting in low testing efficiency.

[0004] The construction and analysis of test scenarios are incomplete. Traditional testing methods struggle to comprehensively and accurately extract test scenario features and cannot construct a clear scenario feature path. This results in inaccurate scoring and analysis of test data, making it difficult to determine optimal test data. Furthermore, when determining the recommended scoring range for adapted joint debugging test data, a lack of scientific analysis methods and reasonable calculation models results in a mismatch between recommended test data and actual user needs.

[0005] Traditional joint debugging and testing systems lack effective feedback and update mechanisms. Once a test plan is finalized, it's difficult to adjust and optimize it based on user feedback and actual test results, making it unable to adapt to the continuous updates and development of in-vehicle control platforms. Furthermore, the test data screening and push process is not intelligent enough, failing to accurately push data based on user needs, impacting test efficiency and effectiveness.

[0006] With the continuous advancement of in-vehicle control platform technology, the requirements for joint debugging and testing are becoming increasingly stringent. Existing testing methods and systems are no longer able to meet these increasingly complex testing needs. There is an urgent need for a joint debugging and testing system and method that can fully utilize historical test data, accurately construct test scenarios, scientifically analyze test results, and dynamically update according to user needs. Summary of the Invention

[0007] The purpose of the present invention is to provide a joint debugging and testing system and method for a vehicle-mounted control platform to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a joint debugging and testing method for a vehicle-mounted control platform, the method comprising:

[0009] Obtain the user's historical vehicle control platform joint debugging test record data, determine the test cycle, build the corresponding periodic joint debugging test data set, and determine the periodic user characteristic joint debugging test data set based on user personalized screening rules;

[0010] Based on the periodic user characteristic joint debugging test data set, the corresponding test scenario features are extracted, the scenario feature path of the corresponding periodic user characteristic joint debugging test data is constructed, and the corresponding periodic user characteristic joint debugging test data score is analyzed to determine the periodic optimal user characteristic joint debugging test data; with the scenario feature path node of the periodic optimal user characteristic joint debugging test data as the benchmark node, the corresponding scenario feature path nodes of the periodic user characteristic joint debugging test data are grouped and processed respectively, the adaptation scenario features of the scenario feature path nodes of each optimal user characteristic joint debugging test data are analyzed, and the recommended score range of the adaptation joint debugging test data for the corresponding user is comprehensively adapted.

[0011] Preferably, the test period window is determined by calling the user's historical vehicle control platform joint debugging test record database, retrieving the user's historical joint debugging test record data within the corresponding period, and constructing a corresponding period joint debugging test data set; the period joint debugging test data set includes the test duration data and test type data of the corresponding test user and the corresponding test number data;

[0012] A personalized screening analysis is performed based on the average test duration of each test data in the periodic joint debugging test data set of the corresponding user, and a periodic user characteristic joint debugging test data set after the personalized screening analysis is determined.

[0013] Preferably, according to each periodic user characteristic joint debugging test data in the periodic user characteristic joint debugging test data set, scenario feature extraction of the corresponding periodic user characteristic joint debugging test data is performed respectively, corresponding scenario feature nodes are constructed, and node connection is performed to construct scenario feature paths of the corresponding periodic user characteristic joint debugging test data; according to the scenario feature paths corresponding to each periodic user characteristic joint debugging test data, scoring analysis is performed on each periodic user characteristic joint debugging test data; a corresponding scenario feature node feature matrix is ​​constructed for each scenario feature node of each periodic user characteristic joint debugging test data; the scenario feature node feature matrix is ​​composed of a weight of the test data proportion of the corresponding scenario feature and a type experience score combination; based on the scenario feature node feature matrix corresponding to each periodic user characteristic joint debugging test data, scenario feature node scoring analysis is performed;

[0014] According to the scenario feature node scoring data corresponding to the user characteristic joint debugging test data of each period, the node scores on the scenario feature path corresponding to the user characteristic joint debugging test data of each period are summed up and calculated to determine the scoring data corresponding to the user characteristic joint debugging test data of each period.

[0015] Preferably, based on the scoring data of the user characteristic joint debugging test data of each period, the maximum value of the output scoring data is the period optimal user characteristic joint debugging test data; then, each scenario feature node on the scenario feature path corresponding to the period optimal user characteristic joint debugging test data is used as a reference node, and the scenario feature nodes corresponding to the remaining period user characteristic joint debugging test data in the period user characteristic joint debugging test data set are used for node grouping processing; according to the node grouping processing result, the grouping adaptation state of the scenario feature node of the period optimal user characteristic joint debugging test data corresponding to each reference node and the scenario feature nodes corresponding to the remaining period user characteristic joint debugging test data are determined;

[0016] The regional range is constructed with each benchmark node as the center, and the density analysis of the scene feature nodes corresponding to the remaining periodic user characteristic joint debugging test data contained in the regional range is performed respectively; by gradually expanding the radius of the regional range, and calculating the ratio of the number of nodes to the area in the regional range corresponding to each radius, the node density value of the regional range corresponding to each radius is determined, and the regional range corresponding to the maximum node density value is taken as the best adaptation node regional range corresponding to the benchmark node; based on the best adaptation node regional range corresponding to each scene feature node of the periodic optimal user characteristic joint debugging test data, the type and score of each scene feature node in the best adaptation node division regional range are comprehensively analyzed. If there are two or more scene feature nodes of the same type, the scene feature nodes of the same type are classified and screened, and the maximum and minimum scores of the scene feature nodes of the same type are retained respectively; in the best adaptation node regional range where each scene feature node of the periodic optimal user characteristic joint debugging test data is located, the center is used as the starting point for the differential analysis. Perform a full traversal of the type of scene feature nodes. For scene feature nodes of the same type, perform branch traversals separately, and output the maximum value node traversal path and the minimum value node traversal path in the current best adaptation node area range; calculate the sum of the node scores of the maximum value node traversal path and the minimum value node traversal path respectively, and determine the upper and lower limits of the adaptation joint debugging test recommendation score of each scene feature node corresponding to the optimal user feature joint debugging test data of the current period, and construct the adaptation joint debugging test recommendation score range corresponding to the current scene feature node; integrate the adaptation joint debugging test recommendation score range of each node on the scene feature node path corresponding to the optimal user feature joint debugging test data of the current period, sum and average the upper and lower limits of each range, output the calculated upper limit sum average value as the upper limit of the period comprehensive adaptation joint debugging test recommendation score range, and output the calculated lower limit sum average value as the lower limit of the period comprehensive adaptation joint debugging test recommendation score range, and then construct the comprehensive adaptation joint debugging test recommendation score range corresponding to the current user period.

[0017] Preferably, based on the recommended score range of the comprehensive adaptation and joint debugging test within the user cycle, the pushed joint debugging test data is scored and filtered; if the score of the pushed joint debugging test data is within the recommended score range of the comprehensive adaptation and joint debugging test, it will be pushed to the user; otherwise, the test data will be filtered and excluded;

[0018] Implement automatic periodic update cycles, set the update time by the user, and update and re-analyze the recommended score range of the periodic comprehensive adaptation joint debugging test.

[0019] Preferably, the present invention also includes a joint debugging and testing system for a vehicle-mounted control platform, the system comprising a test record acquisition module, a scene feature processing module, a test adaptation recommendation analysis module, and a feedback update module;

[0020] The test record acquisition module obtains the user's historical vehicle control platform joint debugging test record data, determines the test cycle, constructs the corresponding period joint debugging test data set, and determines the period user characteristic joint debugging test data set based on the user personalized screening rules; the scene feature processing module extracts the corresponding test scene features based on the period user characteristic joint debugging test data set, constructs the scene feature path of the corresponding period user characteristic joint debugging test data, and analyzes the corresponding period user characteristic joint debugging test data score to determine the period optimal user characteristic joint debugging test data; the test adaptation recommendation analysis module uses the scene feature path node of the period optimal user characteristic joint debugging test data as the reference node, and performs grouping processing on the scene feature path nodes corresponding to the period user characteristic joint debugging test data, analyzes the adaptation scene features of the scene feature path nodes of each optimal user characteristic joint debugging test data, and comprehensively adapts the recommended score range of the adaptation joint debugging test data for the corresponding user; the feedback update module makes a push judgment on the push test based on the recommended score range of the adaptation joint debugging test data, and performs a cyclic update processing on the recommended score range of the period adaptive joint debugging test data.

[0021] Preferably, the test record acquisition module includes a test data acquisition unit and a test data personalization processing unit;

[0022] The test data acquisition unit determines the test cycle window by calling the user's historical vehicle control platform joint debugging test record database, calls the user's historical joint debugging test record data within the corresponding period, and constructs a corresponding period joint debugging test data set; the period joint debugging test data set includes the test duration data and test type data of the corresponding test user and the corresponding test number data;

[0023] The test data personalized processing unit performs personalized screening analysis based on the average test duration of each test data in the periodic joint debugging test data set of the corresponding user; and determines the periodic user characteristic joint debugging test data set after the personalized screening analysis.

[0024] Preferably, the scenario feature processing module includes a test scenario path construction unit and a test data scoring analysis unit;

[0025] The test scenario path construction unit extracts scenario features of the corresponding periodic user characteristic joint debugging test data according to each periodic user characteristic joint debugging test data in the periodic user characteristic joint debugging test data set, constructs corresponding scenario feature nodes, and connects the nodes to construct the scenario feature path of the corresponding periodic user characteristic joint debugging test data;

[0026] The test data scoring analysis unit performs scoring analysis on the user characteristic joint debugging test data of each period according to the scenario feature path of the user characteristic joint debugging test data corresponding to each period; it constructs a corresponding scenario feature node feature matrix for the scenario feature nodes of the user characteristic joint debugging test data of each period; the scenario feature node feature matrix is ​​composed of a weight of the test data proportion of the corresponding scenario feature and a type experience score combination; based on the scenario feature node feature matrix of the user characteristic joint debugging test data corresponding to each period, a scenario feature node scoring analysis is performed; according to the scenario feature node scoring data of the user characteristic joint debugging test data corresponding to each period, the node scores on the scenario feature path of the user characteristic joint debugging test data corresponding to each period are summed up and calculated to determine the scoring data of the user characteristic joint debugging test data corresponding to each period.

[0027] Preferably, the test adaptation recommendation analysis module includes a test feature node grouping processing unit and a test adaptation recommendation analysis unit;

[0028] The test feature node grouping processing unit outputs the maximum value of the scoring data as the periodic optimal user feature joint debugging test data based on the scoring data of each periodic user feature joint debugging test data; then, each scenario feature node on the scenario feature path corresponding to the periodic optimal user feature joint debugging test data is used as a reference node, and the scenario feature nodes corresponding to the remaining periodic user feature joint debugging test data in the periodic user feature joint debugging test data set are used for node grouping processing; according to the node grouping processing result, the grouping adaptation state of the scenario feature node corresponding to the periodic optimal user feature joint debugging test data of each reference node and the scenario feature nodes corresponding to the remaining periodic user feature joint debugging test data are determined;

[0029] The test adaptation recommendation analysis unit constructs a regional range with each benchmark node as the center, and performs density analysis on the scene feature nodes corresponding to the remaining periodic user characteristic joint debugging test data contained in the regional range; by gradually expanding the radius of the regional range, and calculating the ratio of the number of nodes to the area in the regional range corresponding to each radius, the node density value of the regional range corresponding to each radius is determined, and the regional range corresponding to the maximum node density value is taken as the best adaptation node regional range corresponding to the benchmark node; based on the best adaptation node regional range corresponding to each scene feature node of the periodic optimal user characteristic joint debugging test data, the type and score of each scene feature node in the best adaptation node division regional range are comprehensively analyzed. If there are two or more scene feature nodes of the same type, the scene feature nodes of the same type are classified and screened, and the maximum and minimum scores of the scene feature nodes of the same type are retained; in the best adaptation node regional range where each scene feature node of the periodic optimal user characteristic joint debugging test data is located, the center is used for the best adaptation node regional range. A full traversal of different types of scene feature nodes is performed as the starting point. For scene feature nodes of the same type, branch traversals are performed respectively, and the maximum value node traversal path and the minimum value node traversal path in the current best adaptation node area are output; the node scores of the maximum value node traversal path and the minimum value node traversal path are calculated and summed respectively, and the upper and lower limits of the adaptation joint debugging test recommendation scores of each scene feature node corresponding to the optimal user feature joint debugging test data of the current period are determined respectively, and the adaptation joint debugging test recommendation score range corresponding to the current scene feature node is constructed; the adaptation joint debugging test recommendation score range of each node on the scene feature node path corresponding to the optimal user feature joint debugging test data of the current period is integrated, and the upper and lower limits of each range are summed and averaged, and the upper and lower limits of the calculated upper limit sum average value is output as the upper limit of the period comprehensive adaptation joint debugging test recommendation score range, and the lower limit sum average value is output as the lower limit of the period comprehensive adaptation joint debugging test recommendation score range, and the comprehensive adaptation joint debugging test recommendation score range corresponding to the current user period is constructed.

[0030] Preferably, the feedback update module includes a test push judgment unit and a data automatic update unit;

[0031] The test push judgment unit calculates scores for the pushed joint debugging test data based on the recommended score range of the comprehensive adaptation joint debugging test within the user period, and performs screening; if the score of the pushed joint debugging test data is within the recommended score range of the comprehensive adaptation joint debugging test, the user is pushed; otherwise, the test data is screened and excluded;

[0032] The data automatic update unit realizes a periodic automatic update cycle, and updates and re-analyzes the recommended score range of the periodic comprehensive adaptation joint debugging test through the user setting the update time.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] By acquiring historical vehicle control platform joint debugging test records, determining test cycles and building corresponding joint debugging test datasets, and then determining periodic user-specific joint debugging test datasets based on user-specific screening rules, we fully utilize historical user test data. This approach can uncover user testing habits and demand characteristics from a large amount of historical data, providing strong data support for subsequent testing, making testing more targeted and improving testing efficiency.

[0035] In terms of test scenario feature extraction, scenario features are extracted based on a periodic user-feature joint debugging test dataset, scenario feature paths are constructed, and analysis and scoring are performed to determine the optimal periodic user-feature joint debugging test data. This scientific scenario feature processing method can comprehensively and deeply explore the characteristics of the test scenario and construct an accurate scenario feature path, thus fully covering various operating scenarios of the vehicle control platform, ensuring the completeness and accuracy of the test and effectively avoiding the omission of key test points.

[0036] This approach uses the scenario-specific path nodes of the periodically optimized user-specific joint debugging test data as the benchmark nodes for grouping, analyzing the adapted scenario characteristics, and comprehensively adapting the recommended scoring range for the adapted joint debugging test data, enabling personalized screening and recommendation of test data. Different users have different needs and usage habits. This method accurately filters and recommends appropriate test data based on their personalized characteristics, ensuring that the pushed test data is highly aligned with their actual needs, significantly reducing testing costs and time consumption, and improving the user's testing experience.

[0037] By allowing users to set their own update schedules, the recommended score range for periodic comprehensive adaptation and joint debugging tests is updated and reanalyzed, enabling an automatic update cycle. The development of an in-vehicle control platform is an iterative process, and testing requirements evolve accordingly. This flexible cycle update approach allows for timely adjustments to the test cycle based on actual user needs and development progress, tightly integrating testing with the development process and improving development efficiency.

[0038] In terms of test scoring, we construct a feature matrix for scenario feature nodes, comprehensively consider multiple factors such as test data weight and type experience score, conduct a scoring analysis on scenario feature nodes, and calculate the sum of these factors to determine the score for periodic user feature joint debugging test data. This comprehensive test scoring system comprehensively and objectively reflects the quality and value of test data, providing a reliable basis for test data screening and recommendation, and ensuring that the recommended test data is of high quality and reference value.

[0039] From a system architecture perspective, the system comprises a test record acquisition module, a scenario feature processing module, a test adaptation recommendation analysis module, and a feedback update module. Each module has distinct functions and works collaboratively. The test record acquisition module is responsible for data acquisition and personalized processing, the scenario feature processing module extracts and scores scenario features, the test adaptation recommendation analysis module implements adaptation recommendation analysis for test data, and the feedback update module performs test push judgment and automatic data updates. This clear module division and efficient collaborative working mechanism significantly improves the overall efficiency and quality of the test system, ensuring the smooth progress of joint debugging and testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a working principle diagram of the joint debugging and testing method of the vehicle-mounted control platform described in the present invention;

[0041] Figure 2 Obtain module design drawings for test records;

[0042] Figure 3 This is the design diagram of the scene feature processing module. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] See also Figure 1-Figure 3 The present invention relates to a joint debugging and testing system and method for a vehicle-mounted control platform, and the specific implementation steps are as follows:

[0045] Obtain the user's historical vehicle control platform joint debugging test record data, determine the test cycle, build the corresponding periodic joint debugging test data set, and determine the periodic user characteristic joint debugging test data set based on user personalized screening rules;

[0046] Based on the periodic user characteristic joint debugging test data set, the corresponding test scenario features are extracted, the scenario feature path of the corresponding periodic user characteristic joint debugging test data is constructed, and the corresponding periodic user characteristic joint debugging test data score is analyzed to determine the periodic optimal user characteristic joint debugging test data; with the scenario feature path node of the periodic optimal user characteristic joint debugging test data as the benchmark node, the corresponding scenario feature path nodes of the periodic user characteristic joint debugging test data are grouped and processed respectively, the adaptation scenario features of the scenario feature path nodes of each optimal user characteristic joint debugging test data are analyzed, and the recommended score range of the adaptation joint debugging test data for the corresponding user is comprehensively adapted.

[0047] Example 1: See Figure 2 When constructing a periodic joint debugging test dataset, the specific implementation is as follows: The user's historical vehicle control platform joint debugging test record database is accessed through the system interface. This database stores detailed records of the user's joint debugging tests performed over different time periods. When accessing the database, the test cycle window must be determined. This can be determined based on user settings or system default rules. For example, users can set the test cycle to one week, one month, one quarter, or any other time period based on their testing habits and needs. The system defaults to a test cycle of one month.

[0048] After determining the test cycle window, the system retrieves historical joint debugging test record data for users within the corresponding cycle from the database based on the cycle window. The retrieved data contains information from multiple dimensions, including test duration data, which accurately records the time spent on each joint debugging test. This time is stored and recorded in minutes, for example, a test duration of 30 minutes and another test duration of 90 minutes. Test type data categorizes the type of each test, including functional testing, performance testing, compatibility testing, security testing, and other different categories. Each type of test has its own specific test objectives and methods. Test number data is a unique identifier for each test record, used to distinguish different test records. This number can be automatically generated by the system and contains information such as timestamp, user ID, test type ID, etc. to ensure the uniqueness and traceability of the number.

[0049] After acquiring all historical joint debugging test records for the corresponding cycle, the system integrates and processes this data, correlating information from different dimensions to form a complete periodic joint debugging test data set. For example, information such as the test number, test duration, and test type of a test record is stored in a linked manner, ensuring that each piece of data clearly reflects the complete joint debugging test.

[0050] Perform personalized screening and analysis to determine the periodic user feature joint debugging test data set. First, the system calculates the average test duration of each test data point in the periodic joint debugging test data set. Specifically, the test duration of all test records within the period is added together and divided by the number of test records to obtain the average test duration. For example, if a user has 10 test records within a one-month period, and the test durations of each record are 30 minutes, 45 minutes, 60 minutes, 75 minutes, 90 minutes, 30 minutes, 45 minutes, 60 minutes, 75 minutes, and 90 minutes, respectively, the total test duration is 30 + 45 + 60 + 75 + 90 + 30 + 45 + 60 + 75 + 90 = 600 minutes, and the average test duration is 600 ÷ 10 = 60 minutes.

[0051] After obtaining the average test duration, the system will perform personalized screening and analysis based on the average test duration. The rules for personalized screening can be customized by the user, or the system default screening method can be used. For example, the user can set the screening threshold to ±20% of the average test duration, that is, to retain test data with a test duration between 80% and 120% of the average duration. According to the above example, the average test duration is 60 minutes, 80% is 48 minutes, and 120% is 72 minutes. Then the system will filter out test data with a test duration between 48 minutes and 72 minutes, and exclude test data outside this range.

[0052] During the screening process, the system evaluates the test duration of each test record. Test data that meets the screening criteria is retained, while data that doesn't is excluded. The retained test data constitutes the periodic user feature joint debugging test dataset. This dataset, filtered based on the user's test duration characteristics, more accurately reflects the user's testing habits and needs within a specific period.

[0053] In practice, personalized filtering rules can also be set based on other factors, such as test type and test number. For example, users can set requirements for not only test duration but also specific test types for retention. For example, suppose a user wishes to retain data for functional and performance tests with a duration between 48 and 72 minutes. The system then filters test types based on duration, retaining only records for functional and performance tests. This results in a periodic user feature joint debugging test data set that better meets the user's personalized needs.

[0054] Throughout the entire process, the system logs operations such as data retrieval, calculation, and screening to facilitate subsequent traceability and auditing. Log records include information such as operation time, operation type, and identification of the data involved, ensuring traceability and reliability of data processing.

[0055] The system also features data verification. When retrieving historical joint test records, it verifies the integrity and accuracy of the data to ensure that the data obtained is not missing or incorrect. If any data issues are found, the system will issue a warning and attempt to retrieve the data from the database or prompt the user to repair the data.

[0056] The periodic joint debugging test data set and the periodic user feature joint debugging test data set are stored in the system database for subsequent use and analysis. During storage, the data is backed up to prevent data loss. Backups can be performed regularly, for example, daily, to ensure data security and reliability.

[0057] The system offers flexible settings for determining the testing window. Users can manually enter the start and end times to define the testing window, or select pre-set window options such as the latest week, the latest month, or the latest quarter. The system automatically determines the testing window based on the user's selection and retrieves the corresponding data.

[0058] For recording test duration data, the system requires that the start and end times of each joint debugging test be automatically recorded after the test is completed. The test duration is calculated by subtracting the start time from the end time, and this duration is automatically entered into the database. This avoids errors that may occur during manual recording and improves data accuracy.

[0059] When calculating the average test time, the system will consider all valid test records. Invalid test records, such as records of abnormal interruptions during the test process, will be marked and excluded from the calculation to ensure the accuracy of the average test time.

[0060] During the personalized screening and analysis process, the system also provides a visual interface, allowing users to intuitively see the screening conditions and results. Users can set the screening threshold and other conditions on the interface and view the filtered data in real time, so as to adjust the screening rules as needed.

[0061] Example 2: See Figure 3 When performing scene feature extraction and scoring analysis, the specific implementation method is as follows: the system starts the scene feature extraction program for each test data in the periodic user characteristic joint debugging test data set. Taking a certain functional test data as an example, the system will first parse the metadata of the test data, including the test type, test environment configuration, involved vehicle control modules, input and output parameters of the test case, etc. For example, when the test type is "vehicle multimedia system compatibility test", the system will extract scene features such as the vehicle system version, external device model, media file format, etc. targeted by the test. These features will be converted into independent scene feature nodes, each of which contains attribute information such as feature name, feature value, feature description, etc. The "feature name" of the scenario feature node refers to the category or attribute label of the core feature in the test scenario, which is used to identify the core dimension of the feature in the test scenario and is a general name for the scenario feature; the "feature value" is the specific value or specific type corresponding to the feature name, which is the specific manifestation of the feature in the test scenario and is used to quantify or clarify the actual content of the feature; the "feature description" is a supplementary explanation of the feature name and feature value, which is used to explain the specific meaning, function or test objective of the feature in the test scenario, and to clarify the relationship between the feature and the test logic.

[0062] After constructing scenario-specific nodes, the system connects these nodes into a scenario-specific path according to the logical order of test execution or data flow. For example, a complete compatibility test process might begin with device connection initialization, followed by media file loading, followed by playback control testing, and finally, exception handling. The system determines the order of each node based on the timestamps of the test steps or a pre-set test process template. When constructing a path by establishing directed edges between nodes, the system determines the order of each scenario-specific node based on the logical order of test execution or data flow. Taking the in-vehicle multimedia system compatibility test as an example, the "Device Connection Initialization" node in the test process serves as the starting node, followed by nodes such as "Media File Loading," "Playback Control Testing," and "Exception Handling." Directed edges between nodes clearly record the type of relationship, such as "precondition" (the previous node must complete before the next node can be started), "subsequent operation" (the next node is a continuation of the previous node), or "data transfer" (the test results of the previous node serve as input data for the next node). The system will refer to the timestamp of the test steps or the preset test process template to determine the order of the nodes, ensuring that the directed edge connections can truly reflect the actual execution process of the test. If a routine step is skipped due to an abnormal situation during the test, the system will adjust the directed edge connection relationship between the nodes according to the actual test log, so that the path accurately reflects the process changes. Each directed edge will record the type of association between the nodes, such as "preconditions", "subsequent operations" or "data transfer", etc., thereby forming a complete scenario feature path graph. The scenario feature path graph is a directed graph connected by directed edges according to the test logic or data flow after the feature nodes are extracted. It records the node association type, dynamically reflects the real process, and provides a structural basis for scoring analysis and path calculation.

[0063] During the scoring analysis phase, the system first constructs a feature matrix for each scenario feature node. This matrix is ​​constructed based on two pieces of data: the test data weight and the type experience score. The test data weight is calculated by dividing the number of test records containing the scenario feature in the user feature joint debugging test dataset during the statistical period by the total number of records in the dataset. For example, if there are 100 test records in the dataset, and 30 of them involve the scenario feature "4G network environment," the weight for this feature is 30%. The type experience score is generated by the system's pre-set domain knowledge model. For example, for critical features such as "security protocol verification," the system automatically assigns a higher experience score (e.g., 8 out of 10), while for "non-critical interface testing" features, a lower score (e.g., 4). These experience scores are regularly updated based on industry standards and importance analysis of historical test data.

[0064] After constructing the feature matrix, the system performs a scoring analysis on each scenario feature node. Specifically, a weighted calculation is performed using the weighted combination of the percentage and the empirical score. The weight assignment can be customized by the user or the system default (e.g., 40% for the percentage and 60% for the empirical score). For example, if a node has a percentage weight of 30% and an empirical score of 8, its node score is 0.3 × 10 (converting the percentage to a 10-point scale) × 40% + 8 × 60% = 1.2 + 4.8 = 6. The system generates a detailed scoring log for each node, documenting the weight calculation process and scoring basis. The weight calculation is based on the number of test records containing the scenario feature in the user feature joint debugging test dataset during the statistical period, divided by the total number of records in the dataset. The result is the test data percentage weight of the scenario feature node, and this calculation process is recorded in the scoring log.

[0065] After scoring all nodes, the system sums the node scores along the scenario feature path to obtain the overall score for the test data. For example, if a scenario feature path for a test data item contains three nodes with scores of 6, 7, and 5, the total score for the data item is 6 + 7 + 5 = 18. The system generates a scoring report for each test data item, which includes a detailed breakdown of the node scores, the total path score, and the corresponding grade (e.g., "high," "medium," or "low").

[0066] When determining the optimal user feature joint debugging test data for a given period, the system traverses the scoring reports for all test data and identifies the data item with the highest score. For example, if there are 20 test data items in a dataset and the highest score is 25, this data item is marked as the optimal user feature joint debugging test data for the given period. The system generates a special identifier for this data item and stores its complete scenario feature path and node scoring details for subsequent use as a benchmark node.

[0067] During feature extraction, the system supports parsing unstructured data. For example, from text descriptions in test logs, the system uses natural language processing to extract key scenario features, such as "high temperature environment test" and "battery voltage abnormality," and converts these unstructured features into standardized scenario feature nodes. The system's built-in NLP model is regularly trained using historical test data to improve feature extraction accuracy.

[0068] The construction of scenario feature paths also supports dynamic adjustment. If the execution process of a certain test data differs from the preset template (such as skipping a test step due to an abnormal situation), the system will adjust the node connection relationship according to the actual test log record. When the test execution process differs from the preset template, such as skipping a test step due to an abnormal situation, the system will adjust the node connection relationship according to the actual test log record, change the directed edge connection method between nodes, and ensure that the path can truly reflect the actual execution process of the test. After the adjustment, it will also be compared with the preset template to generate a difference report for reference to adjust the node connection relationship to ensure that the path can truly reflect the actual execution process of the test. The adjusted path will be compared with the preset template to generate a difference report for reference by the tester.

[0069] The system provides an administrator interface for updating type experience scores. Test administrators can manually adjust the experience score values ​​of various scenario features based on the latest industry standards or project requirements. For example, when a new safety protocol is added to the vehicle control platform, the administrator can increase the experience score of the "New Safety Protocol Verification" feature from 6 to 9 points and set the effective time to ensure that subsequent scoring and analysis uses the latest scoring standards.

[0070] During the scoring process, the system automatically handles missing data. If a scenario feature node in a piece of test data is missing some data (e.g., the test environment temperature is not recorded), the system will interpolate based on the historical data distribution of that feature or use a default score (e.g., the average empirical score for that feature type) to ensure the integrity of the scoring calculation. The system will also note how missing data was handled in the scoring report.

[0071] To improve computing efficiency, the system employs a distributed computing architecture. When processing large-scale, periodic user-feature joint debugging test datasets, scenario feature extraction and scoring analysis tasks are distributed to multiple computing nodes for parallel processing. Each node processes a portion of the test data, and the master node aggregates the results. This architecture significantly reduces the time required to process large-scale data, ensuring the system maintains efficient operation even with massive amounts of test data.

[0072] The system also features a scene feature library management function, which is used to store and maintain all extracted scene features. The library categorizes similar features, for example, grouping "4G network environment," "5G network environment," and "Wi-Fi network environment" into the "network connection" category, and establishes feature associations for each category. When extracting new scene features, the system first matches them within the feature library. If similar features exist, they are automatically associated, avoiding duplicate definitions and improving the standardization of feature management.

[0073] After the scoring analysis is complete, the system generates a visual scenario feature path diagram and a score distribution histogram. This visual interface allows testers to intuitively view the scenario feature distribution and score of each test data item, allowing them to quickly identify the characteristic patterns of high-scoring data and areas for improvement for low-scoring data. The visual interface supports interactive operations, such as clicking on a node to view its detailed score information or filtering for specific feature types for focused analysis.

[0074] The system also offers a scenario feature comparison function. Testers can select multiple test data points, and the system will automatically compare their scenario feature paths and node scores, generating a comparison report. The report highlights differences in feature composition and scoring, helping testers analyze the pros and cons of different test scenarios and providing data support for optimizing test strategies.

[0075] The entire process of scene feature extraction and scoring analysis strictly adheres to the principles of standardized data processing and traceability. The system records every operational step and intermediate results, from data input, feature extraction, matrix construction, scoring calculation, to output, forming a complete audit trail. When reviewing scoring results, testers can use the audit trail to trace back to the specific calculation process and data source, ensuring the accuracy and credibility of the scoring results.

[0076] Example 3: When performing node grouping and adaptation analysis using reference nodes, the specific implementation method is as follows: Based on the scoring results of the periodic user characteristic joint debugging test data, the system determines the periodic optimal user characteristic joint debugging test data. All scene feature nodes on the scene feature path corresponding to this data are set as reference nodes. For example, when the score of a certain test data is the maximum in the data set, nodes such as "In-Vehicle Network Initialization", "Sensor Data Acquisition", and "Control Instruction Sending" on its scene feature path are marked as reference nodes. Each reference node carries its corresponding feature attribute information, such as feature type, feature value, and score.

[0077] The system groups the scenario feature nodes corresponding to the remaining test data in the periodic user feature joint debugging test dataset. Grouping is based on the similarity of the node's characteristic attributes with those of the benchmark node, specifically by calculating the feature vector distance between the nodes. For example, for a scenario feature node "Vehicle Network Initialization - 4G Mode" in the remaining data, the system calculates the difference between it and the benchmark node "Vehicle Network Initialization" in dimensions such as feature type and network mode. The system first converts these attributes into quantifiable feature vectors. The feature type dimension assigns unique numerical identifiers to different types, and the network mode dimension maps 4G, 5G, and other types to specific numerical values. The benchmark node "Vehicle Network Initialization" has a corresponding vector value. The nodes to be compared follow the same rules to determine the vector values ​​for each dimension. The vector differences across these dimensions are then combined using a preset distance metric to determine an overall difference value. This difference value is then compared with a preset threshold. If the difference value is less than the preset threshold, the node is assigned to the same group as the benchmark node "Vehicle Network Initialization." During the grouping process, the system generates a group identifier for each group and records the node information within the group.

[0078] After completing the node grouping process, the system needs to determine the grouping adaptation status of each benchmark node and the remaining nodes. Adaptation status is determined based on the number of nodes within the group and the concentration of their features. For example, if the group corresponding to a benchmark node contains a large number of remaining nodes, and the feature attributes of these nodes are highly similar to those of the benchmark node, the group's adaptation status is judged to be "highly adapted." Conversely, if the number of nodes within the group is small or the feature differences are large, the group is judged to be "lowly adapted." The system generates an adaptation status report for each benchmark node, detailing the adaptation status of each group.

[0079] The system constructs a region around each benchmark node and begins analyzing the density of the nodes within it. Initially, the region radius is set to a small value, such as 0.1 unit distance (the unit distance is determined by the dimensionality of the feature vector space). The system calculates the ratio of the number of remaining nodes within the region to the region's area to obtain the initial node density. The system then gradually expands the region radius by 0.1 unit distance each time, recalculating the node density for the new region until the region radius reaches the preset maximum value (e.g., 1.0 unit distance).

[0080] During the density analysis process, the system will record the node density value corresponding to each radius. The node density refers to the ratio of the number of nodes contained in the area corresponding to a certain radius to the area of ​​the area, reflecting the density of the nodes in the area, and draw a curve showing the density value changing with the radius. When the maximum value of the node density value in the curve is found, the corresponding area range is the best-fit node area range for the reference node. For example, when the area radius is 0.5 unit distance, the node density value reaches the maximum. At this time, the circular area with a radius of 0.5 unit distance is the best-fit node area range. The system will mark the radius and center coordinates of the best-fit node area range for each reference node.

[0081] Within the best-fit node area, the system conducts a comprehensive analysis of the types and scores of each scene feature node. If there are two or more scene feature nodes of the same type, the system will classify and filter these nodes of the same type. The screening method is to retain the nodes with the highest and lowest scores and remove the nodes with intermediate scores. For example, in a certain area, there are 5 "Sensor Data Acquisition-Temperature Sensor" nodes of the same type, with scores of 6, 7, 8, 9, and 10 respectively. The system will retain the nodes with 6 and 10 points, and remove the nodes with 7, 8, and 9 points. The purpose of this is to retain the score boundary values ​​of nodes of this type and provide a reference for the subsequent construction of the score range.

[0082] When grouping nodes, the system supports user-defined grouping thresholds. Users can set the feature vector distance threshold through the system interface to adjust the strictness of node grouping. For example, if the user prefers stricter grouping, they can lower the threshold so that only nodes with very similar feature attributes are grouped together. If the user prefers more relaxed grouping, they can increase the threshold. The system will update the grouping results in real time based on the user-set threshold, and display the number of groups and the node distribution within each group.

[0083] For calculating feature vector distances, the system provides a variety of distance metrics, such as Euclidean distance, Manhattan distance, and cosine similarity. Users can select the appropriate distance metric based on the type of scene features. For example, Euclidean distance is more appropriate for numerical features, while cosine similarity is more suitable for textual features. The system uses Euclidean distance by default, and a switch option is provided on the interface.

[0084] When determining the optimal node range, the system automatically handles edge cases. If the node density consistently increases within the preset maximum radius, without reaching a maximum, the system will use the area corresponding to the maximum radius as the optimal node range and note this in the report. The system will also prompt the user to adjust the maximum radius or check the data distribution for abnormalities.

[0085] The system also features node density visualization, graphically displaying the optimal node area and node density distribution for each benchmark node. This visualization allows testers to intuitively see the node distribution around each benchmark node, as well as the locations of high-density areas. The visualization supports zooming and dragging, making it easier for testers to view detailed information.

[0086] When filtering nodes of the same type, the system records changes in the number of nodes and the score range before and after filtering, and generates a filtering report. The report lists all nodes and their scores before filtering for each group of nodes of the same type, as well as the remaining nodes and their scores after filtering. This report allows testers to understand the impact of filtering on the data and ensure that the filtering results meet expectations.

[0087] The system also provides a history record for node grouping and adaptation analysis. Testers can review the results of previous grouping and adaptation analyses, comparing results from different cycles or settings to identify patterns and optimize analysis parameters. The history record includes all operating parameters, intermediate results, and final results for easy tracing and reference.

[0088] During the entire node grouping and adaptation analysis process, the system backs up data in real time to prevent unexpected data loss. The frequency of backups can be configured by the user, for example, once after analysis of a benchmark node is complete, or every hour. Backup data is stored on independent storage media to ensure data security.

[0089] The system also features outlier node detection. During grouping and density analysis, it automatically identifies nodes that differ significantly from all baseline node attributes and cannot be grouped, or nodes that fall into extremely low-density areas in density analysis. These outlier nodes are flagged and recommended for manual review by testers to determine if they are valid data or require further processing.

[0090] To improve processing efficiency, the system employs indexing technology. When grouping a large number of scene feature nodes, an index of their attributes is first created, accelerating the calculation of similarities between nodes and reference nodes and grouping them. For large datasets, indexing can significantly reduce processing time and improve system responsiveness.

[0091] The system integrates the results of node grouping and adaptation analysis into a comprehensive report. This report includes information such as the grouping of each benchmark node, the optimal node region, and the results of similar node screening. The report is presented in a combination of tables and graphs, making it easier for testers to understand and use. Testers can use this report to construct recommended scoring ranges for subsequent adaptation and joint debugging tests.

[0092] Example 4: When traversing and constructing a scoring range within the optimal adaptation node region, taking a vehicle control platform joint debugging test scenario as an example, it is assumed that the scenario feature path corresponding to the period-optimal user characteristic joint debugging test data includes three reference nodes: "Power Module Initialization," "Communication Protocol Verification," and "Sensor Data Analysis." The system first determines the optimal adaptation node region with the "Power Module Initialization" reference node as the center. This region contains multiple scenario feature nodes from the remaining test data, such as "Power Module Initialization - 12V Input," "Power Module Initialization - Overvoltage Protection Test," and "Power Module Initialization - Low Power Mode."

[0093] Starting from the reference node "Power Module Initialization," the system fully traverses all the different scenario-specific nodes within the area. During the traversal process, if nodes of the same type are encountered, such as "Power Module Initialization - 12V Input" and "Power Module Initialization - 24V Input," a branching traversal mechanism is initiated. For example, the main traversal path starts from the reference node and first visits nodes related to "Communication Protocol Verification." When a power input node of the same type is encountered, a branch is branched off to traverse all nodes of that type, ensuring that every node is visited. The traversal order follows the depth-first principle, meaning that all nodes in a branch are visited first, and then the main path is traced back to continue visiting other branches.

[0094] After the traversal is complete, the system outputs the maximum and minimum node traversal paths within the current optimally adapted node region. Assume the maximum path is: Power Module Initialization → Power Module Initialization - 24V Input → Communication Protocol Verification - Latest Version → Sensor Data Analysis - Temperature Sensor. The node scores on this path are 8, 9, 7, and 6, respectively, for a total of 30 points. The minimum path is: Power Module Initialization → Power Module Initialization - Overvoltage Protection Test → Communication Protocol Verification - Old Version → Sensor Data Analysis - Fault Simulation. The node scores are 8, 5, 4, and 3, respectively, for a total of 20 points. This determines the recommended minimum score for the adaptation and joint debugging test for the "Power Module Initialization" benchmark node to be 20 points and 30 points.

[0095] Next, the "Communication Protocol Verification" benchmark node is processed. Its optimal node region includes nodes such as "Communication Protocol Verification - TCP / IP," "Communication Protocol Verification - UDP," and "Communication Protocol Verification - Encryption Algorithm A." The system performs a full traversal centered on this benchmark node, branching off for similar communication protocol nodes (such as TCP / IP and UDP). Assuming the node scores for the maximum traversal path are 7, 9, and 8, summing to 24, and the node scores for the minimum path are 7, 5, and 6, summing to 18, the benchmark node score range is 18 to 24.

[0096] The optimal fit for the "Sensor Data Parsing" benchmark node includes nodes such as "Temperature Sensor Parsing," "Pressure Sensor Parsing," and "Sensor Data Parsing - Outlier Handling." After traversing the nodes, the maximum path score sum was 22, and the minimum path score sum was 16, resulting in a score range of 16 to 22.

[0097] The system integrates the recommended score ranges for the adaptation and joint debugging test of these three benchmark nodes, and averages the upper and lower limits of each range. The upper sum is 30 + 24 + 22 = 76 points, with an average of 76 ÷ 3 = 25.33 points. The lower sum is 20 + 18 + 16 = 54 points, with an average of 54 ÷ 3 = 18 points. This ultimately creates a recommended score range for the periodic comprehensive adaptation and joint debugging test of 18 to 25.33 points.

[0098] In practice, the traversal algorithm prioritizes paths with higher-scoring nodes to improve efficiency. For example, if the system detects that a branch has generally high node scores, it will prioritize traversing that branch to avoid wasting computing resources on lower-scoring branches. The system also records the order in which each node was visited and the path details, creating a traversal log for easy review.

[0099] For regions containing multiple nodes of the same type, such as four nodes related to "Communication Protocol Verification," the system will first sort the nodes by score, retaining the highest and lowest scoring nodes, and then traverse the nodes after removing the intermediate nodes. For example, if four nodes have scores of 5, 6, 8, and 9, the nodes with scores of 5 and 9 will be retained after screening, and the remaining nodes will not be included in the traversal to reduce the computational workload.

[0100] During the traversal process, if a node's attribute is missing, the system will supplement it based on the historical rating data of that type of node. For example, if a "Sensor Data Parsing" node does not have a recorded rating, the system will query the average rating of similar nodes as a temporary value to ensure the traversal process is uninterrupted. The final report will indicate that the node's rating is an estimate.

[0101] The system also supports manual adjustment of traversal paths and scoring ranges. Testers can view the automatically generated traversal paths through the interface. If they find that a critical path has not been accessed, they can manually add that path node, and the system will recalculate the scoring range. For example, if a tester finds that the "Power Module Initialization - Short Circuit Protection" node is not included in the traversal path, they can manually add it to the maximum value path, and the system will recalculate and adjust the scoring upper limit.

[0102] Throughout the traversal and scoring range construction process, the system displays a real-time progress bar and intermediate results, allowing testers to view information such as the currently processed benchmark node, the number of nodes traversed, and the generated scoring range. For large datasets, the system activates parallel computing mode, distributing the traversal tasks of different benchmark nodes across multiple processor cores to accelerate processing.

[0103] Once the scoring ranges for all benchmark nodes are integrated, the system generates a visual scoring range distribution diagram, color-coding the scoring intervals and overall scoring ranges for each benchmark node. This diagram allows testers to intuitively understand the scoring boundaries for each test phase, providing a clear reference for subsequent test data delivery. Furthermore, the system exports detailed data from the entire process as an Excel file, including each node's score, traversal path, and scoring range calculation process, for easy archiving and further analysis.

[0104] Example 5: When pushing and updating the recommended score range based on the comprehensive adaptation joint debugging test, the specific implementation method is as follows: the system performs a score calculation on the pushed joint debugging test data, and this calculation process is consistent with the scoring process of periodic user characteristic joint debugging test data. Taking a new test data as an example, the system first extracts its scene feature nodes, such as "In-vehicle Central Control System Startup Test", "Bluetooth Connection Stability Test", and "Navigation Path Planning Test", and then constructs a feature matrix for each node. The node score is calculated by combining the test data proportion weight and type experience score, and then the node scores on the path are summed to obtain the total score of the test data.

[0105] Assuming the recommended score range for periodic comprehensive adaptation and joint debugging tests is 18 to 25.33 points, if a piece of test data has a total score of 22, which falls within this range, the system will mark it as pushable and push it to the user through the user interface or message notification. If another piece of test data has a score of 15, which is below the lower limit of the range, the system will automatically exclude it from push. When pushing, the system will also include the scenario feature path and score details of the test data, so that users can easily understand the basis for the recommendation.

[0106] Regarding periodic updates, the system allows users to set their own update schedule. Users can select the update cycle time unit (e.g., day, week, month) and specific time point through the system settings interface. For example, if a user sets the update time to "Every Monday 00:00," the system will automatically trigger the update process every Monday morning. During the update, the system first retrieves the user's historical vehicle control platform joint debugging test record data for the current cycle, determines the new test cycle window, constructs the periodic joint debugging test dataset, and generates a new periodic user feature joint debugging test dataset based on the user's personalized filtering rules.

[0107] The system extracts scenario features from the new periodic user feature joint debugging test dataset, constructs a scenario feature path, analyzes the scores of each test data, and determines the optimal new periodic user feature joint debugging test data. Using the scenario feature path nodes of this data as reference nodes, the system then performs node grouping and adaptation analysis, determining the optimal node range for each reference node. This system then constructs a new recommended scoring range for the adaptation joint debugging test, ultimately integrating these scores to create a new recommended scoring range for the comprehensive periodic adaptation joint debugging test.

[0108] Before pushing test data, the system verifies the data source to ensure it comes from a reliable test repository or user-specified data source. For externally imported test data, the system first performs format and integrity checks, such as checking whether the data contains necessary scenario feature fields and whether the test ID is unique. If any data anomalies are found, the import is rejected and the user is notified.

[0109] The push mechanism supports multi-terminal adaptation, adjusting the presentation format of pushed content based on the user's device type (e.g., PC vs. mobile). For example, when pushing to a mobile device, the system simplifies the scenario-specific path of the test data into a list format, highlighting key nodes and scores; on a PC, the complete path diagram and detailed score report are displayed. The system also records the user's push history to avoid repeatedly pushing the same test data.

[0110] When users provide feedback on pushed test data (e.g., marking it as "useful" or "unuseful"), the system incorporates this feedback into the analysis for the next update cycle. For example, if a user repeatedly marks a certain type of test data as "unuseful," the system will reduce the weight of that type of data in subsequent personalized screening, or adjust the screening threshold to improve the accuracy of the pushed data.

[0111] During the update cycle, the system retains the historical recommended score ranges for comprehensive adaptation and joint debugging tests, allowing users to easily view the range change trends across different cycles. For example, users can use historical records to view changes in the score range over the past three months, analyze the changing trends of their testing needs, and adjust personalized filtering rules or update schedule settings.

[0112] The system also features an update exception handling mechanism. If an exception such as data acquisition failure or calculation error occurs during the update process, the system automatically rolls back to the last successful update and notifies the user of the cause of the exception and the progress of the handling via email or message. For example, if a database connection failure prevents access to historical test data, the system will pause the update process, log the exception, and prompt the user to check the database connection.

[0113] To improve update efficiency, the system performs incremental updates on historical data. When the user sets a shorter update cycle (e.g., daily updates), the system does not reprocess all historical data. Instead, it processes only newly added test records and merges them with historical data for analysis, thus reducing computing resource consumption and update time.

[0114] After the scoring range is updated, the system generates an update report comparing the old and new scoring ranges and noting the score changes for key nodes. For example, the report will show that the upper score limit for the "Power Module Initialization" benchmark node has been adjusted from 30 to 28 points, and the lower score limit has been adjusted from 20 to 19 points. The report will also explain the reason for the change (e.g., a general decrease in the score for this node in the newly added test data).

[0115] The system supports manually triggered updates. Users can manually initiate the periodic update process at any time through the interface, without having to wait for the preset update time. For example, after completing a new batch of tests and wishing to immediately update the recommended score range, the user can click the "Update Now" button. The system will then reanalyze the data and generate a new score range according to the process.

[0116] Throughout the push and update process, the system strictly adheres to data security regulations, encrypting the storage and transmission of user test data to prevent data leakage. The push interface uses identity authentication and permission control mechanisms to ensure that only authorized users can receive test data pushes, and unauthorized users cannot access related information.

[0117] The system performs statistical analysis on push notifications, such as calculating metrics like user view rate and effectiveness, but does not include specific experimental results. These statistics are used to optimize push notification strategies and update mechanisms. For example, if push notifications have a low view rate during a certain time period, the system may recommend adjusting the timing of push notifications or the presentation of the content.

[0118] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0119] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A joint debugging and testing method for a vehicle-mounted control platform, characterized by: The method comprises the following steps: Obtain the user's historical vehicle control platform joint debugging test record data, determine the test cycle, build the corresponding periodic joint debugging test data set, and determine the periodic user characteristic joint debugging test data set based on user personalized screening rules; Based on the periodic user characteristic joint debugging test data set, corresponding test scenario features are extracted, the scenario feature path of the corresponding periodic user characteristic joint debugging test data is constructed, and the corresponding periodic user characteristic joint debugging test data scores are analyzed to determine the periodic optimal user characteristic joint debugging test data; using the scenario feature path node of the periodic optimal user characteristic joint debugging test data as the reference node, the corresponding scenario feature path nodes of the periodic user characteristic joint debugging test data are grouped and processed respectively, the adaptation scenario features of the scenario feature path node of each optimal user characteristic joint debugging test data are analyzed, and the recommended score range of the corresponding user adaptation joint debugging test data is comprehensively determined; By calling the user's historical vehicle control platform joint debugging test record database, determining the test cycle window, calling the user's historical joint debugging test record data within the corresponding period, and constructing the corresponding period joint debugging test data set; the period joint debugging test data set includes the test duration data and test type data of the corresponding test user and the corresponding test number data; Perform personalized screening and analysis based on the average test duration of each test data in the periodic joint debugging test data set of the corresponding user, and determine the periodic user characteristic joint debugging test data set after personalized screening and analysis; According to the user characteristic joint debugging test data of each period in the periodic user characteristic joint debugging test data, scenario features of the corresponding periodic user characteristic joint debugging test data are extracted respectively, corresponding scenario feature nodes are constructed, and the nodes are connected to construct scenario feature paths of the corresponding periodic user characteristic joint debugging test data; according to the scenario feature paths of the corresponding periodic user characteristic joint debugging test data, scoring analysis is performed on the user characteristic joint debugging test data of each period; a corresponding scenario feature node feature matrix is ​​constructed for the scenario feature nodes of the user characteristic joint debugging test data of each period; the scenario feature node feature matrix is ​​composed of a weight of the test data proportion of the corresponding scenario feature and a type experience score; based on the scenario feature node feature matrix of the corresponding periodic user characteristic joint debugging test data, scenario feature node scoring analysis is performed; According to the scenario feature node scoring data corresponding to the user characteristic joint debugging test data of each period, the node scores on the scenario feature path corresponding to the user characteristic joint debugging test data of each period are summed up and calculated to determine the scoring data corresponding to the user characteristic joint debugging test data of each period.

2. The joint debugging and testing method of a vehicle-mounted control platform according to claim 1, characterized in that: Based on the scoring data of the user characteristic joint debugging test data of each period, the maximum value of the output scoring data is the optimal user characteristic joint debugging test data of the period; then, each scenario feature node on the scenario feature path corresponding to the optimal user characteristic joint debugging test data of the period is used as the reference node, and the scenario feature nodes corresponding to the remaining user characteristic joint debugging test data of each period in the periodic user characteristic joint debugging test data set are used for node grouping processing; According to the node grouping processing result, determine the group adaptation status of the scenario feature node of the optimal user characteristic joint debugging test data of each benchmark node corresponding to the period and the scenario feature nodes corresponding to the user characteristic joint debugging test data of the remaining periods; A regional range is constructed with each benchmark node as the center, and density analysis is performed on the scene feature nodes corresponding to the remaining periodic user feature joint debugging test data contained in the regional range; by gradually expanding the radius of the regional range and calculating the ratio of the number of nodes to the area in the regional range corresponding to each radius, the node density value of the regional range corresponding to each radius is determined, and the regional range corresponding to the maximum node density value is taken as the best adaptation node regional range corresponding to the benchmark node; based on the best adaptation node regional range corresponding to each scene feature node of the periodic optimal user feature joint debugging test data, the type and score of each scene feature node in the best adaptation node division regional range are comprehensively analyzed. If there are two or more scene feature nodes of the same type, the scene feature nodes of the same type are classified and screened, and the maximum and minimum scores of the scene feature nodes of the same type are respectively retained; in the best adaptation node regional range where each scene feature node of the periodic optimal user feature joint debugging test data is located, a full traversal of different types of scene feature nodes is performed with the center as the starting point. For the scene feature nodes of the same type, branch traversal is performed respectively, and the maximum value node traversal path and the minimum value node traversal path in the current best adaptation node regional range are output; Calculate the sum of the node scores of the maximum value node traversal path and the minimum value node traversal path respectively, determine the upper and lower limits of the adaptation joint debugging test recommendation score of each scenario feature node corresponding to the optimal user characteristic joint debugging test data of the current period, and construct the adaptation joint debugging test recommendation score range corresponding to the current scenario feature node; integrate the adaptation joint debugging test recommendation score range of each node on the scenario feature node path corresponding to the optimal user characteristic joint debugging test data of the current period, sum and average the upper and lower limits of each range, output the calculated upper limit sum average value as the upper limit of the period comprehensive adaptation joint debugging test recommendation score range, and output the calculated lower limit sum average value as the lower limit of the period comprehensive adaptation joint debugging test recommendation score range, and then construct the comprehensive adaptation joint debugging test recommendation score range corresponding to the current user period.

3. The joint debugging and testing method of a vehicle-mounted control platform according to claim 2, characterized in that: Based on the recommended score range for comprehensive adaptation and joint debugging tests within the user cycle, score and filter the pushed joint debugging test data. If the score of the pushed joint debugging test data falls within the recommended score range for comprehensive adaptation and joint debugging tests, it will be pushed to the user; otherwise, the test data will be filtered out. Implement automatic periodic update cycles, set the update time by the user, and update and re-analyze the recommended score range of the periodic comprehensive adaptation joint debugging test.

4. A joint debugging and testing system for a vehicle-mounted control platform, characterized by: The system includes a test record acquisition module, a scene feature processing module, a test adaptation recommendation analysis module and a feedback update module; The test record acquisition module acquires the user's historical vehicle control platform joint debugging test record data, determines the test cycle, constructs a corresponding periodic joint debugging test data set, and determines the periodic user characteristic joint debugging test data set based on the user personalized screening rules; the scenario feature processing module extracts the corresponding test scenario features based on the periodic user characteristic joint debugging test data set, constructs the scenario feature path of the corresponding periodic user characteristic joint debugging test data, and analyzes the corresponding periodic user characteristic joint debugging test data score to determine the periodic optimal user characteristic joint debugging test data; the test adaptation recommendation analysis module uses the scenario feature path node of the periodic optimal user characteristic joint debugging test data as the reference node, and performs grouping processing on the scenario feature path nodes corresponding to the periodic user characteristic joint debugging test data, analyzes the adaptation scenario features of the scenario feature path nodes of each optimal user characteristic joint debugging test data, and comprehensively determines the recommended score range of the corresponding user's adaptation joint debugging test data; the feedback update module makes a push judgment on the push test based on the recommended score range of the adaptation joint debugging test data, and performs a cyclic update processing on the recommended score range of the periodic adaptation joint debugging test data; The test record acquisition module includes a test data acquisition unit and a test data personalization processing unit; The test data acquisition unit determines the test cycle window by calling the user's historical vehicle control platform joint debugging test record database, calls the user's historical joint debugging test record data within the corresponding period, and constructs a corresponding period joint debugging test data set; the period joint debugging test data set includes the test duration data and test type data of the corresponding test user and the corresponding test number data; The test data personalized processing unit performs personalized screening analysis based on the average test duration of each test data in the periodic joint debugging test data set of the corresponding user; Determine the periodic user feature joint debugging test data set after personalized screening and analysis; The scenario feature processing module includes a test scenario path construction unit and a test data scoring analysis unit; The test scenario path construction unit extracts scenario features of the corresponding periodic user characteristic joint debugging test data according to each periodic user characteristic joint debugging test data in the periodic user characteristic joint debugging test data set, constructs corresponding scenario feature nodes, and connects the nodes to construct the scenario feature path of the corresponding periodic user characteristic joint debugging test data; The test data scoring analysis unit performs scoring analysis on the user characteristic joint debugging test data of each period according to the scenario feature path corresponding to the user characteristic joint debugging test data of each period; it constructs the corresponding scenario feature node feature matrix for the scenario feature nodes of the user characteristic joint debugging test data of each period ; The scenario feature node feature matrix is ​​composed of the weight of the test data proportion of the corresponding scenario feature and the type experience score combination; based on the scenario feature node feature matrix of the user feature joint debugging test data corresponding to each period, the scenario feature node score analysis is performed; according to the scenario feature node score data of the user feature joint debugging test data corresponding to each period, the node score on the scenario feature path of the user feature joint debugging test data corresponding to each period is summed up and calculated to determine the score data of the user feature joint debugging test data corresponding to each period.

5. The vehicle-mounted control platform joint debugging and testing system according to claim 4, characterized in that: The test adaptation recommendation analysis module includes a test feature node grouping processing unit and a test adaptation recommendation analysis unit; The test feature node grouping processing unit outputs the maximum value of the score data as the periodic optimal user feature joint debugging test data based on the score data of each periodic user feature joint debugging test data; then, each scenario feature node on the scenario feature path corresponding to the periodic optimal user feature joint debugging test data is used as the reference node, and the scenario feature nodes corresponding to the remaining periodic user feature joint debugging test data in the periodic user feature joint debugging test data set are used for node grouping processing; According to the node grouping processing result, determine the group adaptation status of the scenario feature node of the optimal user characteristic joint debugging test data of each benchmark node corresponding to the period and the scenario feature nodes corresponding to the user characteristic joint debugging test data of the remaining periods; The test adaptation recommendation analysis unit constructs a regional range with each benchmark node as the center, and performs density analysis on the scene feature nodes corresponding to the remaining periodic user characteristic joint debugging test data contained in the regional range; by gradually expanding the radius of the regional range, and calculating the ratio of the number of nodes to the area in the regional range corresponding to each radius, the node density value of the regional range corresponding to each radius is determined, and the regional range corresponding to the maximum node density value is taken as the best adaptation node regional range corresponding to the benchmark node; based on the best adaptation node regional range corresponding to each scene feature node of the periodic optimal user characteristic joint debugging test data, the best adaptation node regional range is comprehensively analyzed. The type and score of each scene feature node in the best-fit node division area. If there are two or more scene feature nodes of the same type, the scene feature nodes of the same type are classified and screened, and the maximum and minimum scores of the scene feature nodes of the same type are retained respectively; in the best-fit node area where each scene feature node of the periodic optimal user feature joint debugging test data is located, a full traversal of different types of scene feature nodes is performed with the center as the starting point. For the scene feature nodes of the same type, branch traversal is performed respectively, and the traversal path of the maximum value node and the minimum value node in the current best-fit node area are output; Calculate the sum of the node scores of the maximum value node traversal path and the minimum value node traversal path respectively, determine the upper and lower limits of the adaptation joint debugging test recommendation score of each scenario feature node corresponding to the optimal user characteristic joint debugging test data of the current period, and construct the adaptation joint debugging test recommendation score range corresponding to the current scenario feature node; integrate the adaptation joint debugging test recommendation score range of each node on the scenario feature node path corresponding to the optimal user characteristic joint debugging test data of the current period, sum and average the upper and lower limits of each range, output the calculated upper limit sum average value as the upper limit of the period comprehensive adaptation joint debugging test recommendation score range, and output the calculated lower limit sum average value as the lower limit of the period comprehensive adaptation joint debugging test recommendation score range, and then construct the comprehensive adaptation joint debugging test recommendation score range corresponding to the current user period.

6. The vehicle-mounted control platform joint debugging and testing system according to claim 5, characterized in that: The feedback update module includes a test push judgment unit and a data automatic update unit; The test push judgment unit calculates scores for the pushed joint debugging test data based on the recommended score range of the comprehensive adaptation joint debugging test within the user period, and performs screening; if the score of the pushed joint debugging test data is within the recommended score range of the comprehensive adaptation joint debugging test, the user is pushed; otherwise, the test data is screened and excluded; The data automatic update unit realizes a periodic automatic update cycle, and updates and re-analyzes the recommended score range of the periodic comprehensive adaptation joint debugging test through the user setting the update time.

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