Automobile electronic product remote diagnosis system and method based on wireless communication
By constructing a wireless communication remote diagnostic system that associates fault characteristics with records and divides path regions, the problem of identifying high-risk components during long-distance driving has been solved. This system enables efficient and accurate remote diagnostics and resource optimization, thereby improving the safety and stability of long-distance driving.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-15
- Publication Date
- 2026-04-14
AI Technical Summary
Existing remote diagnostic technologies are not adequately adapted to complex and changing environmental conditions during long-distance driving, making it difficult to quickly focus on high-risk vehicle electronic components, which may lead to safety hazards.
By building a remote diagnostic system for automotive electronic products based on wireless communication, historical fault information can be obtained, fault feature association records can be generated, path regions can be divided and fault feature labels can be assigned, differentiated diagnostic strategies can be executed, high-risk components can be accurately identified, and resource allocation can be optimized.
It enables accurate identification and targeted diagnosis of high-risk electronic components during long-distance driving routes, improving the response efficiency and safety of remote diagnosis, saving vehicle resources, and ensuring that no fault signals are missed.
Smart Images
Figure CN121857640A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automotive remote diagnostics technology, and more specifically, to a remote diagnostic system and method for automotive electronic products based on wireless communication. Background Technology
[0002] During long-distance travel, vehicles are constantly exposed to complex and changing environmental and weather conditions, which can significantly increase the risk of failure of vehicle electronic components.
[0003] Long-distance driving routes span a wide area, and the regional characteristics of different road sections vary significantly. The fault correlation and risk level of different electronic components vary in specific road sections. Current remote diagnostic technologies are mostly designed based on general scenarios and adopt a unified data transmission strategy, which is not fully adapted to the special characteristics of long-distance driving and makes it difficult to quickly focus on high-risk components. Once a core electronic component fails, it may not only cause the vehicle to break down and affect the journey, but may also cause safety hazards.
[0004] In view of this, the present invention proposes a remote diagnostic system and method for automotive electronic products based on wireless communication to solve the above problems. Summary of the Invention
[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a remote diagnostic method for automotive electronic products based on wireless communication, comprising: Obtain historical vehicle fault information, including the electronic components that failed and the fault characteristics at the time of the failure, and combine the electronic components that failed with the fault characteristics at the time of the failure to form a fault characteristic association unit; Based on the fault feature association unit, the key fault feature combinations and their fault feature orders that affect the faults of electronic components are determined, fault feature association records are generated, and a fault relationship database is constructed. Obtain the complete driving path of the vehicle, divide the path into segments based on the consistency of fault features, and assign a regional fault feature label to each path segment. The regional fault feature labels of each path segment are compared and analyzed with the key fault features of all electronic components in the fault relationship database to obtain the fault risk level of the electronic components in that path segment. Differentiated diagnostic strategies are implemented for the associated ECU data and sensor data of electronic components with different fault risk levels.
[0006] Furthermore, the fault characteristics include environmental fault characteristics and meteorological fault characteristics; The environmental fault characteristics include road type, road surface condition, and altitude. The meteorological fault characteristics include temperature, relative humidity, and weather conditions.
[0007] Furthermore, the method for constructing the fault relationship database is as follows: S101: Obtain all fault feature associated units as samples, and take all fault features corresponding to electronic components as candidate fault features; S102: Calculate the frequency of occurrence of candidate fault features in all samples, set a frequency threshold, remove candidate fault features whose frequency is greater than the frequency threshold, and take the remaining candidate fault features as valid candidate fault features. S103: Based on all valid candidate fault features, construct a fault feature combination tree, enumerate candidate fault feature combinations by increasing the order of a single fault feature in order of first order, second order, and third order, and count the number of times each candidate fault feature combination appears in the samples of the electronic component and the total number of samples of the electronic component, and calculate the fault frequency of the candidate fault feature combination. S104: Based on the failure frequency, candidate failure feature combinations are tested and screened to determine key failure feature combinations; S105: Associate each electronic component with a matching key fault feature combination, synchronously bind the combination order and fault frequency of the key fault feature combination, generate fault feature association records, and build a fault relationship library based on all fault feature association records.
[0008] Furthermore, the method for determining the combination of key failure characteristics is as follows: Set an order threshold for each fault feature order combination, and determine the candidate fault feature combination whose fault frequency is greater than the order threshold corresponding to its fault feature order as the key fault feature combination. If the selected critical fault feature combination contains another critical fault feature combination, then the included critical fault feature combination is discarded.
[0009] Furthermore, the method for obtaining regional fault feature labels is as follows: S201: Upon receiving a confirmed navigation command, obtain the sequence of latitude and longitude coordinates of the complete driving route planned and generated by the vehicle navigation system; S202: Using a single coordinate point as the smallest matching unit, the spatial overlay analysis algorithm binds the coordinate point with spatial fault features and meteorological fault features. S203: Perform a consistency check on the fault characteristics of adjacent coordinate points; S204: If all fault characteristics of adjacent coordinate points meet the consistency requirement, then the two coordinate points will be merged into a unified path area segment. If all fault characteristics of adjacent coordinate points do not meet the consistency requirements, then instant segmentation is triggered, setting the current coordinate point as the end point of the previous path segment and the next coordinate point as the start point of the new path segment. S205: Perform length checks on each path segment and merge them based on the check results; S206: Extract the fault features of any coordinate point within the path segment as the regional fault feature label of that path segment.
[0010] Furthermore, the method for verifying the consistency of fault characteristics is as follows: According to the spatial order of the latitude and longitude coordinate points, the consistency of all environmental and meteorological fault characteristics of two adjacent coordinate points is checked one by one. If any of the road type, road surface condition, or weather condition changes, or if the altitude, temperature, or relative humidity crosses a range, the fault characteristics of adjacent coordinate points are determined to not meet the consistency requirements; otherwise, the fault characteristics of adjacent coordinate points are determined to meet the consistency requirements.
[0011] Furthermore, the method for merging based on the verification results is as follows: After segmentation, the length of all path segments is checked. The actual length of each path segment is obtained based on the vehicle navigation system. A segment length threshold is set. If the actual length of a path segment is less than the segment length threshold, it is determined to be an excessively short segment. It is then merged into the adjacent preceding path segment, and the fault characteristics of the coordinate points are unified with the merged path segment. If it is the first path segment, it is merged into the following path segment.
[0012] Furthermore, the methods for obtaining the fault risk level include: S301: Obtain the matching degree of each combination of key fault features of electronic components in the path area segment; S302: Calculate the risk value for each combination of critical fault characteristics; S303: The risk value of an electronic component is the sum of the risk values of all combinations of its critical failure characteristics. S303: Set a risk threshold. When the risk value of an electronic component in a path area is greater than or equal to the risk threshold, the electronic component is determined to be at level two risk. If the risk value is less than the risk threshold, the electronic component is determined to be at level three risk. S304: For each path segment, sort them from highest to lowest risk value, and determine the electronic component with the highest risk value as Level 1 risk.
[0013] Furthermore, the method for obtaining the matching degree of key fault feature combinations is as follows: Set the initial value of the matching degree S to 1; Verify each fault feature label of the path area segment with the fault features in the combination of the key fault features; If a fault feature is found in the combination of critical fault features that is not present in the regional fault feature label, i.e. a mismatch, then 1 / k is deducted from S, where k is the order of the fault feature of the combination of fault features, and the S after deduction is the matching degree of the combination of fault features. Record all fault features that successfully match the regional fault feature labels, i.e., fault features that do not mismatch, as used fault features in subsequent comparisons. Continue comparing the next combination of key failure characteristics for this electronic component; When performing subsequent comparisons, if the subsequent combination of key fault features contains a fault feature that was recorded as a used fault feature in the previous comparison, then that fault feature will be directly regarded as mismatched with the regional fault feature label in this comparison.
[0014] A wireless communication-based remote diagnostic system for automotive electronic products, used to implement the wireless communication-based remote diagnostic method for automotive electronic products, includes: Information acquisition module: Acquires historical vehicle fault information, including the electronic components that failed and the fault characteristics at the time of the failure, and combines the electronic components that failed with the fault characteristics at the time of the failure to form a fault characteristic association unit; Information processing module: Based on the fault feature association unit, determine the key fault feature combinations and their fault feature orders that affect the faults of electronic components, generate fault feature association records, and build a fault relationship database; Path segmentation module: Obtains the complete driving path planned by the vehicle, divides the path into segments based on the consistency of fault features, and assigns a regional fault feature label to each segment. Feature comparison module: Compares and analyzes the regional fault feature labels of each path segment with the key fault features of all electronic components in the fault relationship database to obtain the fault risk level of the electronic components in that path segment. Differential Diagnostic Module: Executes differentiated diagnostic strategies for associated ECU data and sensor data of electronic components with different fault risk levels.
[0015] The technical effects and advantages of the wireless communication-based remote diagnostic system and method for automotive electronic products proposed in this invention are as follows: First, this invention utilizes a feature filtering mechanism driven by historical fault big data to accurately pinpoint key fault feature combinations and their orders for electronic components. A sample library is constructed based on fault feature association units, irrelevant features are eliminated, and candidate fault feature combinations are generated through enumeration. A dual filtering process using fault frequency and order thresholds is employed to determine key fault feature combinations, while redundant combinations are eliminated. This ensures that each combination is a set of core features strongly correlated with the fault, providing accurate and efficient core evidence for subsequent risk assessment and laying the foundation for risk identification.
[0016] Secondly, this invention constructs a precise matching system covering the entire process of path segmentation, feature matching, and risk classification. Long-distance routes are divided into path segments with uniform characteristics, each assigned a unique fault feature label, and the matching degree is calculated. Then, through risk value calculation and three-level risk classification, high-risk components in different road segments are identified. This system effectively solves the problem of missed risk identification caused by the large span and variable characteristics of long-distance travel routes, achieving targeted identification of high-risk components.
[0017] Then, the present invention formulates a differentiated diagnostic strategy based on a three-level risk level to achieve optimal allocation of vehicle resources. It accurately adapts to the constraints of limited vehicle communication bandwidth and precious driving range during long-distance driving, which avoids the waste of resources caused by high-frequency transmission of full data and ensures that fault signals of high-risk components are not missed, thus balancing transmission efficiency and diagnostic accuracy.
[0018] In summary, this invention organically integrates precise identification of key fault feature combinations, accurate matching throughout the entire process, and differentiated transmission of risk levels. It boasts significant advantages such as high feature correlation, accurate risk identification, efficient resource allocation, and strong scenario adaptability. It fully adapts to the special characteristics of long-distance driving routes with large spans and varied environments, accurately identifies high-risk electronic components in different road sections, and saves onboard resources through differentiated strategies. It also improves the response efficiency and professionalism of remote diagnostics, providing drivers with reliable fault warning protection and effectively enhancing the safety and stability of long-distance driving. Attached Figure Description
[0019] Figure 1 This is a flowchart of the remote diagnostic method for automotive electronic products based on wireless communication in Embodiment 1 of the present invention; Figure 2 This is an example diagram of matching degree calculation in Embodiment 1 of the present invention; Figure 3 This is a block diagram of a remote diagnostic system for automotive electronic products based on wireless communication, as shown in Embodiment 2 of the present invention. Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Example 1 See Figure 1 As shown, this embodiment provides a remote diagnostic method for automotive electronic products based on wireless communication, including: Historical vehicle fault information is obtained, including the electronic components that failed and the fault characteristics at the time of the failure. The electronic components that failed and the fault characteristics at the time of the failure are combined into a fault characteristic association unit. The fault characteristics include environmental fault characteristics and meteorological fault characteristics.
[0022] The fault feature association unit includes the following structure: a1: Identifier for electronic components, corresponding to a unique name in the vehicle's electronic system component list. Faulty electronic component identifiers are obtained by reading fault codes through the on-board diagnostic system or electronic control unit. The fault codes are associated with a preset "electronic component-identifier" mapping library, such as ISO standards or automaker-defined codes, which can directly match the unique electronic component name.
[0023] In this embodiment, the electronic components specifically refer to those electronic components in the core control, driver assistance, and body control categories of automobiles whose operating status or failure risk is related to environmental conditions and weather changes. These include, but are not limited to, driver assistance vision sensing electronic components, battery management control electronic components, in-vehicle navigation and positioning electronic components, vehicle stability control modules, and braking electronic control electronic components. Purely functional electronic products that are not related to environmental / meteorological factors, such as in-vehicle USB charging modules, interior ambient lighting, and in-vehicle audio head units, are excluded as they only rely on basic power supply.
[0024] a2: Environmental fault characteristics, including road type, road surface condition, and altitude.
[0025] The road types include urban roads, expressways, mountain roads, and rural roads, and the type information in the road attributes is matched based on the vehicle's latitude and longitude.
[0026] The road surface conditions include normal, water accumulation, snow accumulation, and icing. Road surface conditions are obtained based on real-time traffic warnings issued by the transportation department. If no traffic warning is issued, the road surface condition is considered normal.
[0027] The altitude is divided into intervals. For example, A≤1000m is the first altitude interval, 1000m<A≤2000m is the second altitude interval, 2000m<A≤3000m is the third altitude interval, 3000m<A≤4000m is the fourth altitude interval, and 4000m<A is the fifth altitude interval, which is extracted based on the vehicle's latitude and longitude.
[0028] a3: Meteorological fault characteristics, based on real-time weather forecasts issued by meteorological departments, including temperature, relative humidity, and weather conditions.
[0029] The temperature T is divided into intervals. For example, T≤20℃ is the first temperature interval, -20℃<T≤0℃ is the second temperature interval, 0℃<T≤25℃ is the third temperature interval, 25℃<T≤35℃ is the fourth temperature interval, 35℃<T≤45℃ is the fifth temperature interval, and 45℃<T is the sixth temperature interval.
[0030] The relative humidity H is divided into intervals. For example, H≤30%RH is the first humidity interval, 30%RH<H≤50%RH is the second humidity interval, 50%RH<H≤70%RH is the third humidity interval, 70%RH<H≤85%RH is the fourth humidity interval, and 85%RH<H is the fifth humidity interval.
[0031] The weather conditions include sunny, cloudy, light rain, moderate rain, heavy rain, torrential rain, light snow, moderate snow, heavy snow, and blizzard.
[0032] It should be noted that the above embodiments are the main implementation forms, used to achieve a balance between performance and complexity. However, depending on the actual application scenario, vehicle configuration, and diagnostic accuracy requirements, the dimensions of fault characteristics can be expanded. For example, "road congestion" can be added to the environmental fault characteristics, classified into three levels: "smooth traffic, slow traffic, and congested traffic," to accurately assess the fault risk of the vehicle's driver assistance following sensor electronic components and braking control electronic components. In congested road sections, frequent vehicle starts and stops and close following distances can easily lead to a surge in the signal recognition frequency of the driver assistance following sensor electronic components, and the pressure regulation function of the braking control electronic components will be in a high-frequency working state continuously, increasing the probability of failure. "Road material" can also be added, classified into "asphalt, cement, gravel, and frozen soil," to supplement the judgment of the fault risk of the vehicle's vehicle stability control electronic components and tire pressure monitoring electronic components. Through this extended design, the remote diagnostic needs of vehicle electronic products under different road conditions and usage scenarios can be adapted. It retains the simplicity of the core solution while flexibly improving the accuracy of fault correlation according to actual diagnostic accuracy requirements, making the solution more versatile and adaptable.
[0033] Based on the fault feature association unit, the key fault feature combinations and their fault feature orders that affect the faults of electronic components are determined, fault feature association records are generated, and a fault relationship database is constructed.
[0034] Methods for constructing a fault relation database include: S101: Obtain all fault feature associated units as samples, and take all fault features corresponding to electronic components as candidate fault features.
[0035] S102: Calculate the frequency of occurrence of candidate fault features in all samples, set a frequency threshold, remove candidate fault features whose frequency is greater than the frequency threshold, and take the remaining candidate fault features as valid candidate fault features.
[0036] The frequency threshold is set by those skilled in the art based on their own experience.
[0037] S103: Based on all valid candidate fault features, construct a fault feature combination tree. Enumerate candidate fault feature combinations by increasing the order of a single fault feature in ascending order of first-order, second-order, and third-order. Count the number of occurrences of each candidate fault feature combination in the samples of the electronic component and the total number of samples of that electronic component. Calculate the fault frequency of the candidate fault feature combination. ; in, This indicates the failure frequency of the candidate fault feature combination in the electronic component. This represents the number of times the candidate fault feature combination appears in the electronic component sample. This represents the total number of samples for this electronic component.
[0038] S104: Based on the failure frequency, candidate failure feature combinations are tested and screened to determine key failure feature combinations.
[0039] The specific method is as follows: Set an order threshold for each fault feature order combination, and identify candidate fault feature combinations whose fault frequency is greater than the order threshold corresponding to their fault feature order as key fault feature combinations.
[0040] If the selected critical fault feature combination contains another critical fault feature combination, then the included critical fault feature combination is discarded.
[0041] The order threshold is set by those skilled in the art based on their own experience.
[0042] S105: Associate each electronic component with a matching key fault feature combination, synchronously bind the combination order and fault frequency of the key fault feature combination, generate fault feature association records, and build a fault relationship library based on all fault feature association records.
[0043] In this embodiment, a sample library is constructed based on the fault feature association unit. First, irrelevant features that occur too frequently are removed. Then, candidate fault feature combinations are generated by enumerating the order. The fault feature combinations are determined by combining the fault frequency and the order threshold for dual screening. At the same time, redundant combinations are removed to ensure that each combination is a set of core features that are strongly correlated with the fault. This solves the problems of fault feature generalization and weak correlation, and provides accurate and efficient core basis for subsequent risk assessment, laying the foundation for risk identification.
[0044] Obtain the complete driving path of the vehicle, divide the path into segments based on the consistency of fault characteristics, and assign a regional fault characteristic label to each path segment.
[0045] Specific methods include: S201: Upon receiving a confirmed navigation command, obtain the sequence of latitude and longitude coordinates of the complete driving route planned and generated by the vehicle navigation system.
[0046] The latitude and longitude coordinate point sequence is a continuous sequence of coordinate points formed by uniformly sampling the complete driving path at preset distance intervals.
[0047] S202: Using a single coordinate point as the smallest matching unit, the coordinate point is bound to spatial fault features and meteorological fault features through a spatial overlay analysis algorithm.
[0048] Road type is determined by calling automotive-grade high-precision maps and extracting the type attribute of the road from the spatial association of coordinate points; road surface condition is determined based on the public real-time road condition warnings issued by the provincial traffic operation monitoring center, and is marked as normal by default when there is no warning information; altitude is based on free elevation data opened by the National Geospatial Data Cloud, and the real-time altitude is obtained by interpolation of coordinate points and then matched with the reference interval; temperature is synchronized with the grid meteorological data published by the National Meteorological Information Center, and the real-time temperature value is extracted according to the region to which the coordinate point belongs and matched with the reference interval; relative humidity is obtained from the public data of the China Meteorological Administration's ground meteorological observation stations and matched with the reference interval; weather status integrates the free and public forecast information of the China Meteorological Administration and local meteorological bureaus.
[0049] S203: Perform a consistency check on the fault characteristics of adjacent coordinate points.
[0050] The method for consistency verification is as follows: According to the spatial order of the latitude and longitude coordinate points, the consistency of all environmental and meteorological fault characteristics of two adjacent coordinate points is checked one by one.
[0051] If any of the road type, road surface condition, or weather condition changes, or if the altitude, temperature, or relative humidity crosses a range, the fault characteristics of adjacent coordinate points are determined to not meet the consistency requirements; otherwise, the fault characteristics of adjacent coordinate points are determined to meet the consistency requirements.
[0052] S204: If all fault characteristics of adjacent coordinate points meet the consistency requirement, then the two coordinate points will be merged into a unified path area segment. If all fault characteristics of adjacent coordinate points do not meet the consistency requirements, instant segmentation is triggered, setting the current coordinate point as the end point of the previous path segment and the next coordinate point as the start point of the new path segment.
[0053] S205: Perform length checks on each path segment and merge them based on the check results.
[0054] The specific method is as follows: After segmentation, the length of all path segments is checked. The actual length of each path segment is obtained based on the vehicle navigation system. A segment length threshold is set. If the actual length of a path segment is less than the segment length threshold, it is determined to be an excessively short segment. It is then merged into the adjacent preceding path segment, and the fault characteristics of the coordinate points are unified with the merged path segment. If it is the first path segment, it is merged into the following path segment.
[0055] S206: Extract the fault features of any coordinate point within the path segment as the regional fault feature label of that path segment.
[0056] The regional fault feature labels of each path segment are compared and analyzed with the key fault features of all electronic components in the fault relationship database to obtain the fault risk level of the electronic components in that path segment.
[0057] The fault risk levels include Level 1, Level 2, and Level 3.
[0058] The methods for obtaining the fault risk level include: S301: Obtain the matching degree of each critical fault feature combination of electronic components in the path area segment.
[0059] See Figure 2 As shown, the method includes: Set the initial value of the matching degree S to 1; Verify each fault feature label of the path area segment with the fault features in the combination of the key fault features; If a fault feature is found in the combination of critical fault features that is not present in the regional fault feature label, i.e. a mismatch, then 1 / k is deducted from S, where k is the order of the fault feature combination, and the S after deduction is the matching degree of the fault feature combination. Record all fault features that successfully match the regional fault feature labels, i.e., fault features that do not mismatch, as used fault features in subsequent comparisons. Continue comparing the next combination of key failure characteristics for this electronic component; When performing subsequent comparisons, if the subsequent combination of key fault features contains a fault feature that was recorded as a used fault feature in the previous comparison, then that fault feature will be directly regarded as mismatched with the regional fault feature label in this comparison.
[0060] For example, a critical fault feature combination A1 for an electronic component is of order four (k=4), containing fault features a, b, c, and d. If only fault feature b is missing from the current region's fault feature label, then the matching degree of A1 is 1.0 - 1 / 4 = 0.75. In this case, fault features a, c, and d are recorded as used fault features. If another critical fault feature combination A2 for the same electronic component is of order two (k=2), containing fault features a and e, since fault feature a has already been recorded as a used fault feature, fault feature a is directly considered a mismatch during the comparison of A2. If fault feature e actually exists in the region label, then A2 is only deducted due to the mismatch of fault feature a, and its matching degree is 1.0 - 1 / 2 = 0.5.
[0061] In this embodiment, by using order-differentiated deduction and matching degree calculation rules for subsequent mismatch of used features, the risk value of the same area feature is not artificially high due to repeated calls by multiple key fault features. This makes the risk assessment of electronic components more consistent with the real fault association scenarios of long-distance road sections and improves the accuracy of risk judgment.
[0062] S302: Calculate the risk value for each combination of critical failure characteristics. The formula is: ; Where P represents the failure frequency of the key fault feature combination in the electronic component, S represents the matching degree of the key fault feature combination, k represents the order of the key fault feature combination, and α represents the order correction coefficient.
[0063] S303: The risk value of an electronic component is the sum of the risk values of all combinations of its critical failure characteristics.
[0064] S303: Set a risk threshold. When the risk value of an electronic component in a path area is greater than or equal to the risk threshold, the electronic component is determined to be at level two risk. If the risk value is less than the risk threshold, the electronic component is determined to be at level three risk.
[0065] S304: For each path segment, sort them from highest to lowest risk value, and determine the electronic component with the highest risk value as Level 1 risk.
[0066] The risk threshold is set by those skilled in the art based on their own experience.
[0067] In this embodiment, a precise matching system covering the entire process of path segmentation, feature matching, and risk grading is constructed. Through fault feature consistency checks and length verification, long-distance routes are divided into regions with uniform features and assigned unique fault feature labels, ensuring the targeted matching of road segment features with component fault features. The matching degree calculation employs a differential deduction rule based on order, combined with a design for subsequent comparisons of used features to avoid misjudgments caused by repeated matching. Furthermore, by accumulating the risk values of all key fault feature combinations and applying a three-level risk grading system, high-risk components in different road segments are accurately identified. This effectively solves the problem of missed risk assessments caused by the large span of long-distance travel routes and the variability of road segment features, achieving targeted identification of high-risk electronic components.
[0068] Differentiated diagnostic strategies are implemented for the associated ECU data and sensor data of electronic components with different fault risk levels.
[0069] The ECU data and sensor data are key data for electronic components to achieve core functions. ECU data refers to the processed data generated by the electronic control unit (ECU) corresponding to the electronic component during operation. It is secondary data output by the ECU after processing the original signal. Sensor data refers to the original sensing signals collected by various sensors associated with the electronic component. The data has not undergone complex processing and is the original signal that objectively reflects the real-time status.
[0070] The differentiated diagnostic strategy includes: For electronic components with a risk level of 3, the associated ECU data and sensor data are maintained at the set basic acquisition and upload frequency to only meet the needs of routine condition monitoring. For electronic components at risk level 2, the frequency of acquiring and uploading associated ECU data and sensor data will be increased to twice the base frequency to strengthen status tracking. For electronic components at the first-level risk level, the frequency of acquiring and uploading associated ECU data and sensor data will be increased to three times the base frequency. At the same time, the warning threshold will be lowered. The warning threshold refers to the criteria for triggering remote diagnostic warnings, such as the critical value of the fluctuation range of core parameters of electronic components and the number of times abnormal data occurs. Lowering the threshold can reduce the warning triggering threshold, identify potential failure risks of key electronic components earlier, and improve the sensitivity and timeliness of diagnostic warnings.
[0071] After completing the driving of a path segment, the remaining path segment division and fault risk level acquisition of electronic components in the path segment are re-executed.
[0072] In this embodiment, a differentiated diagnostic strategy is formulated based on a three-level risk level to achieve early detection and early warning of potential faults. This strategy is precisely adapted to the constraints of limited onboard communication bandwidth and precious battery life during long-distance driving. It avoids the waste of resources caused by high-frequency transmission of all data while ensuring that fault signals of high-risk components are not missed, thus balancing transmission efficiency and diagnostic accuracy.
[0073] Example 2 See Figure 3 As shown, this embodiment provides a remote diagnostic system for automotive electronic products based on wireless communication, used to implement the remote diagnostic method for automotive electronic products based on wireless communication, including: Information acquisition module: Acquires historical vehicle fault information, including the electronic components that failed and the fault characteristics at the time of the failure, and combines the electronic components that failed with the fault characteristics at the time of the failure to form a fault characteristic association unit; Information processing module: Based on the fault feature association unit, determine the key fault feature combinations and their fault feature orders that affect the faults of electronic components, generate fault feature association records, and build a fault relationship database; Path segmentation module: Obtains the complete driving path planned by the vehicle, divides the path into segments based on the consistency of fault features, and assigns a regional fault feature label to each segment. Feature comparison module: Compares and analyzes the regional fault feature labels of each path segment with the key fault features of all electronic components in the fault relationship database to obtain the fault risk level of the electronic components in that path segment. Differential Diagnostic Module: Executes differentiated diagnostic strategies for associated ECU data and sensor data of electronic components with different fault risk levels.
[0074] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0075] In conclusion, the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A remote diagnostic method for automotive electronic products based on wireless communication, characterized in that, include: Obtain historical vehicle fault information, including the electronic components that failed and the fault characteristics at the time of the failure, and combine the electronic components that failed with the fault characteristics at the time of the failure to form a fault characteristic association unit; Based on the fault feature association unit, the key fault feature combinations and their fault feature orders that affect the faults of electronic components are determined, fault feature association records are generated, and a fault relationship database is constructed. Obtain the complete driving path of the vehicle, divide the path into segments based on the consistency of fault features, and assign a regional fault feature label to each path segment. The regional fault feature labels of each path segment are compared and analyzed with the key fault features of all electronic components in the fault relationship database to obtain the fault risk level of the electronic components in that path segment. Differentiated diagnostic strategies are implemented for the associated ECU data and sensor data of electronic components with different fault risk levels.
2. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 1, characterized in that, The fault characteristics include environmental fault characteristics and meteorological fault characteristics; The environmental fault characteristics include road type, road surface condition, and altitude. The meteorological fault characteristics include temperature, relative humidity, and weather conditions.
3. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 1, characterized in that, The method for constructing the fault relationship database is as follows: S101: Obtain all fault feature associated units as samples, and take all fault features corresponding to electronic components as candidate fault features; S102: Calculate the frequency of occurrence of candidate fault features in all samples, set a frequency threshold, remove candidate fault features whose frequency is greater than the frequency threshold, and take the remaining candidate fault features as valid candidate fault features. S103: Based on all valid candidate fault features, construct a fault feature combination tree, enumerate candidate fault feature combinations by increasing the order of a single fault feature in order of first order, second order, and third order, and count the number of times each candidate fault feature combination appears in the samples of the electronic component and the total number of samples of the electronic component, and calculate the fault frequency of the candidate fault feature combination. S104: Based on the failure frequency, candidate failure feature combinations are tested and screened to determine key failure feature combinations; S105: Associate each electronic component with a matching key fault feature combination, synchronously bind the combination order and fault frequency of the key fault feature combination, generate fault feature association records, and build a fault relationship library based on all fault feature association records.
4. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 3, characterized in that, The method for determining the combination of key fault features is as follows: Set an order threshold for each fault feature order combination, and determine the candidate fault feature combination whose fault frequency is greater than the order threshold corresponding to its fault feature order as the key fault feature combination. If the selected critical fault feature combination contains another critical fault feature combination, then the included critical fault feature combination is discarded.
5. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 1, characterized in that, The method for obtaining the regional fault feature tags is as follows: S201: Upon receiving a confirmed navigation command, obtain the sequence of latitude and longitude coordinates of the complete driving route planned and generated by the vehicle navigation system; S202: Using a single coordinate point as the smallest matching unit, the spatial overlay analysis algorithm binds the coordinate point with spatial fault features and meteorological fault features. S203: Perform a consistency check on the fault characteristics of adjacent coordinate points; S204: If all fault characteristics of adjacent coordinate points meet the consistency requirement, then the two coordinate points will be merged into a unified path area segment. If all fault characteristics of adjacent coordinate points do not meet the consistency requirements, then instant segmentation is triggered, setting the current coordinate point as the end point of the previous path segment and the next coordinate point as the start point of the new path segment. S205: Perform length checks on each path segment and merge them based on the check results; S206: Extract the fault features of any coordinate point within the path segment as the regional fault feature label of that path segment.
6. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 5, characterized in that, The method for verifying the consistency of fault characteristics is as follows: According to the spatial order of the latitude and longitude coordinate points, the consistency of all environmental and meteorological fault characteristics of two adjacent coordinate points is checked one by one. If any of the road type, road surface condition, or weather condition changes, or if the altitude, temperature, or relative humidity crosses a range, the fault characteristics of adjacent coordinate points are determined to not meet the consistency requirements; otherwise, the fault characteristics of adjacent coordinate points are determined to meet the consistency requirements.
7. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 5, characterized in that, The method for merging based on the verification results is as follows: After segmentation, the length of all path segments is checked. The actual length of each path segment is obtained based on the vehicle navigation system. A segment length threshold is set. If the actual length of a path segment is less than the segment length threshold, it is determined to be an excessively short segment. It is then merged into the adjacent preceding path segment, and the fault characteristics of the coordinate points are unified with the merged path segment. If it is the first path segment, it is merged into the following path segment.
8. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 1, characterized in that, The fault risk levels include Level 1 risk level, Level 2 risk level, and Level 3 risk level; The methods for obtaining the fault risk level include: S301: Obtain the matching degree of each combination of key fault features of electronic components in the path area segment; S302: Calculate the risk value for each combination of critical fault characteristics; S303: The risk value of an electronic component is the sum of the risk values of all combinations of its critical failure characteristics. S303: Set a risk threshold. When the risk value of an electronic component in a path area is greater than or equal to the risk threshold, the electronic component is determined to be at level two risk. If the risk value is less than the risk threshold, the electronic component is determined to be at level three risk. S304: For each path segment, sort them from highest to lowest risk value, and determine the electronic component with the highest risk value as Level 1 risk.
9. The remote diagnostic method for automotive electronic products based on wireless communication according to claim 8, characterized in that, The method for obtaining the matching degree of the key fault feature combination is as follows: Set the initial value of the matching degree S to 1; Verify each fault feature label of the path area segment with the fault features in the combination of the key fault features; If a fault feature is found in the combination of critical fault features that is not present in the regional fault feature label, i.e. a mismatch, then 1 / k is deducted from S, where k is the order of the fault feature of the combination of fault features, and the S after deduction is the matching degree of the combination of fault features. Record all fault features that successfully match the regional fault feature labels, i.e., fault features that do not mismatch, as used fault features in subsequent comparisons. Continue comparing the next combination of key failure characteristics for this electronic component; When performing subsequent comparisons, if the subsequent combination of key fault features contains a fault feature that was recorded as a used fault feature in the previous comparison, then that fault feature will be directly regarded as mismatched with the regional fault feature label in this comparison.
10. A remote diagnostic system for automotive electronic products based on wireless communication, used to implement the remote diagnostic method for automotive electronic products based on wireless communication as described in any one of claims 1-9, characterized in that, include: Information acquisition module: Acquires historical vehicle fault information, including the electronic components that failed and the fault characteristics at the time of the failure, and combines the electronic components that failed with the fault characteristics at the time of the failure to form a fault characteristic association unit; Information processing module: Based on the fault feature association unit, determine the key fault feature combinations and their fault feature orders that affect the faults of electronic components, generate fault feature association records, and build a fault relationship database; Path segmentation module: Obtains the complete driving path planned by the vehicle, divides the path into segments based on the consistency of fault features, and assigns a regional fault feature label to each segment. Feature comparison module: Compares and analyzes the regional fault feature labels of each path segment with the key fault features of all electronic components in the fault relationship database to obtain the fault risk level of the electronic components in that path segment. Differential Diagnostic Module: Executes differentiated diagnostic strategies for associated ECU data and sensor data of electronic components with different fault risk levels.