Vehicle remote diagnosis method and device, electronic equipment and storage medium

Abstract

The application discloses a vehicle remote diagnosis method and device, electronic equipment and storage medium. The method comprises the following steps: firstly, obtaining the running data of each electronic control unit in the n electronic control units corresponding to the target vehicle within a preset time period to obtain n groups of running data; then, determining n fault repair difficulty values of the n electronic control units based on the n groups of running data; then, determining m fault repair difficulty values greater than a preset fault repair difficulty value in the n fault repair difficulty values; then, determining m electronic control units corresponding to the m fault repair difficulty values; then, obtaining m groups of running data corresponding to the m electronic control units; finally, performing remote fault diagnosis on the m electronic control units based on the m groups of running data to obtain a target diagnosis result, so that a target user performs a fault repair operation based on the target diagnosis result. The efficiency of vehicle remote diagnosis is improved by adopting the application.

Description

Technical Field

[0001] This invention relates to the field of vehicle remote diagnostics technology, and in particular to a vehicle remote diagnostics method, device, electronic device, and storage medium. Background Technology

[0002] With the increasing level of automotive electronics, the number and complexity of electronic control units (ECUs) in vehicles have significantly increased. A modern car typically carries dozens or even hundreds of ECUs responsible for critical tasks such as engine control, chassis adjustment, and body function management. The operational status of these ECUs directly affects the vehicle's safety, reliability, and fuel economy. ECU failures can be caused by various factors, including hardware aging, software anomalies, and environmental stress, and the difficulty of repairing different failures varies significantly. In traditional vehicle fault diagnosis, a uniform diagnostic strategy is usually used to test all ECUs, resulting in wasted diagnostic resources and low maintenance efficiency. Therefore, improving the efficiency of remote vehicle diagnostics is an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for remote vehicle diagnostics, which improves the efficiency of remote vehicle diagnostics.

[0004] In a first aspect, embodiments of this application provide a method for remote vehicle diagnostics, including:

[0005] Obtain the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, resulting in n sets of operating data; n is a positive integer.

[0006] Based on the n sets of operating data, the fault repair difficulty value corresponding to each of the n electronic control units is determined, and n fault repair difficulty values ​​are obtained.

[0007] Determine m fault repair difficulty values ​​from the n fault repair difficulty values ​​that are greater than a preset fault repair difficulty value; m is an integer less than n;

[0008] Determine the m electronic control units corresponding to the m fault repair difficulty values;

[0009] Obtain m sets of operating data corresponding to the m electronic control units;

[0010] Based on the m sets of operating data, remote fault diagnosis is performed on the m electronic control units to obtain target diagnosis results, enabling the target user to perform fault repair operations based on the target diagnosis results.

[0011] Secondly, embodiments of this application provide a vehicle remote diagnostic device, the device comprising: an acquisition unit and a processing unit;

[0012] The acquisition unit is used to acquire the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, and obtain n sets of operating data; n is a positive integer.

[0013] The processing unit is used to determine the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operating data, and obtain n fault repair difficulty values.

[0014] Determine m fault repair difficulty values ​​from the n fault repair difficulty values ​​that are greater than a preset fault repair difficulty value; m is an integer less than n;

[0015] Determine the m electronic control units corresponding to the m fault repair difficulty values;

[0016] Obtain m sets of operating data corresponding to the m electronic control units;

[0017] Based on the m sets of operating data, remote fault diagnosis is performed on the m electronic control units to obtain target diagnosis results, enabling the target user to perform fault repair operations based on the target diagnosis results.

[0018] Thirdly, embodiments of the present invention provide an electronic device, including: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor to cause the electronic device to perform the method as described in the first aspect.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that is executed by a processor to implement the method as described in the first aspect.

[0020] Fifthly, embodiments of the present invention provide a computer program product including a non-transitory computer-readable storage medium storing a computer program, such that a computer performs the method as described in the first aspect.

[0021] Implementing the embodiments of the present invention has the following beneficial effects:

[0022] As can be seen, the vehicle remote diagnostic method described in this embodiment of the invention first acquires the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, obtaining n sets of operating data, where n is a positive integer. Then, based on the n sets of operating data, the fault repair difficulty value corresponding to each of the n electronic control units is determined, resulting in n fault repair difficulty values. Next, m fault repair difficulty values ​​greater than the preset fault repair difficulty value are determined from the n fault repair difficulty values, where m is an integer less than n. Then, the m electronic control units corresponding to the m fault repair difficulty values ​​are determined. Then, the m sets of operating data corresponding to the m electronic control units are acquired. Finally, remote fault diagnosis is performed on the m electronic control units based on the m sets of operating data to obtain the target diagnostic result, enabling the target user to perform fault repair operations based on the target diagnostic result, thereby improving the efficiency of vehicle remote diagnostics. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0024] Figure 1 This is a flowchart of a vehicle remote diagnostic method provided in an embodiment of this application;

[0025] Figure 2 This is a flowchart illustrating how to determine the difficulty value of fault repair, as provided in an embodiment of this application.

[0026] Figure 3 This is a flowchart provided in an embodiment of the present application for determining a first fault severity value corresponding to a first electronic control unit;

[0027] Figure 4 This is a flowchart illustrating how to determine the fault repair difficulty value of a first electronic control unit, as provided in an embodiment of this application.

[0028] Figure 5 This is a flowchart of an embodiment of the present application for determining the aging degree value of a first electronic control unit;

[0029] Figure 6 This is a flowchart of a fault repair operation provided in an embodiment of this application;

[0030] Figure 7 This is a schematic diagram of the structure of a vehicle remote diagnostic device provided in an embodiment of this application;

[0031] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0033] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0034] In this document, the term "implementation" means that a specific feature, structure, or characteristic described in connection with an implementation may be included in at least one implementation of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same implementation, nor is it a separate or alternative implementation mutually exclusive with other implementations. It will be explicitly and implicitly understood by those skilled in the art that the implementations described herein can be combined with other implementations.

[0035] Please see Figure 1 , Figure 1 This is a flowchart of a vehicle remote diagnostic method provided in an embodiment of this application, including but not limited to the following steps:

[0036] S101: Obtain the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, and obtain n sets of operating data.

[0037] In this embodiment, n is a positive integer. Vehicle electronic control units are diverse in type and function. The powertrain control unit includes an engine control unit that controls fuel injection and ignition timing by collecting signals such as throttle opening and crankshaft speed; and a transmission control unit that optimizes shift logic based on parameters such as vehicle speed and accelerator pedal position to improve driving smoothness. The chassis and safety control unit includes a brake control unit that monitors wheel speed and vehicle posture in real time, adjusting braking force to prevent wheel lock-up on slippery surfaces or during emergency braking; and a suspension control unit that adjusts shock absorber damping according to road conditions and driving mode to optimize vehicle handling and comfort. The body electronic control unit includes a body control module that acts as a central hub managing functions such as headlight operation, window operation, and door lock control; and an air conditioning unit that automatically adjusts the airflow and temperature of the vents based on the vehicle's interior temperature and humidity. The vehicle is equipped with various electronic control units, including a control unit for adjusting seat position, heating, and ventilation; a control unit for driver assistance and intelligent systems, such as an adaptive cruise control unit that monitors the distance to the vehicle ahead using radar and cameras and automatically adjusts speed to maintain a safe distance; a lane keeping control unit that recognizes road markings and intervenes via steering wheel or provides warnings when the vehicle deviates from its lane; and an autonomous driving domain controller that integrates multi-sensor data to achieve advanced autonomous driving functions. New energy vehicle-specific control units include a battery management system that monitors the voltage, temperature, and state of charge of the power battery in real time, balances the charge of individual cells in the battery pack, and prevents overcharging and over-discharging; a motor controller that adjusts the motor's output power and torque based on throttle signals and optimizes energy recovery efficiency; and an onboard charger control unit that manages the charging process to ensure charging safety and efficiency. These electronic control units work together via a bus to form the vehicle's intelligent control network, enabling full-scenario functional control from power output to safety assistance.

[0038] The operating data of the electronic control unit mainly includes real-time parameters reflecting the working status of each system, such as throttle opening, crankshaft speed, fuel injection quantity, intake air temperature and pressure, ignition advance angle and emission-related data of the engine control unit; shift timing, clutch status, hydraulic system pressure and input / output speed of the transmission control unit; wheel speed, braking pressure, body roll rate and intervention signals of the brake control unit; headlight switch status, window position, door lock signal and temperature difference between the set temperature and the actual temperature of the air conditioning system of the body control module; radar detection distance, lane line position recognized by the camera, cruise speed setting and steering wheel angle of the driver assistance system; cell voltage, battery pack temperature, state of charge, charging and discharging current and health status of the battery management system of new energy vehicles; and output power, torque, motor speed and temperature of the motor controller. This data is transmitted through the bus and recorded in real time for system monitoring, fault diagnosis and performance optimization.

[0039] The preset time period is a pre-defined time range, which could be a past day, a month, or a specific mileage cycle of the vehicle. Various data generated by each electronic control unit (ECU) within this time period are collected using onboard sensors, communication buses, and diagnostic interfaces. This includes, but is not limited to, electrical parameters such as voltage, current, and temperature; functional data such as sensor input signals and actuator operating status; and system status information such as fault codes and communication error rates. Ultimately, a complete set of operational data records is generated for each ECU. The data from all ECUs is aggregated to obtain n sets of operational data, which will serve as the basis for subsequent analysis and diagnostics.

[0040] S102: Based on the n sets of operating data, determine the fault repair difficulty value corresponding to each of the n electronic control units, and obtain n fault repair difficulty values.

[0041] In this implementation, the difficulty of fault repair for each electronic control unit (ECU) needs to be determined based on n sets of operational data. A multi-dimensional analysis method can be used. First, the complexity of the data itself can be considered. By statistically analyzing the number and correlation of abnormal parameters in the operational data of each ECU, for example, if the engine control unit simultaneously exhibits multiple data anomalies such as abnormal fuel injection quantity, ignition advance angle deviation, and excessive emissions, its repair requires coordinating the investigation of multiple systems, making it relatively more difficult. Second, the scope of impact of the fault data should be considered. For example, a system fault in the brake control unit may be related to vehicle stability and braking safety, involving cross-verification of hardware (such as wheel speed sensors) and software (control logic). Repairing it requires consideration of functional safety standards, making it more difficult than a single-function vehicle control module fault.

[0042] A fault code classification mechanism can also be introduced, classifying the fault codes of each electronic control unit according to the fault code standard. For example, powertrain system faults usually require professional diagnostic equipment and mechanical debugging, and are more difficult to repair than Class B body electrical faults. At the same time, by combining maintenance history data, the average maintenance time, required professional tools, and qualified technicians of similar electronic control units under the same fault data pattern can be analyzed. For example, the repair difficulty of autonomous driving domain controllers, which involve algorithm debugging and multi-sensor calibration, can be determined by quantitative indicators such as the proportion of senior engineers involved in historical cases and the frequency of use of special programming equipment.

[0043] Alternatively, a data correlation model can be constructed, and machine learning algorithms can be used to analyze the parameter coupling relationships in the operational data. For example, if the state-of-charge estimation error of the battery management system is strongly correlated with temperature sensor data drift and charging / discharging current sampling deviation, hardware calibration and algorithm optimization need to be performed simultaneously during repair. The difficulty value can be assigned by the complexity of parameter decoupling. Finally, hardware integration and accessibility are combined. For example, a sensor fault integrated inside the gearbox may require disassembling the gearbox, while a faulty module installed independently can be directly replaced. The complexity of hardware repair operations can also be used as a weighting factor in the difficulty value calculation. By combining the above dimensions, a quantitative scoring system is formed, and finally, a corresponding fault repair difficulty value is generated for each electronic control unit.

[0044] S103: Determine m fault repair difficulty values ​​that are greater than the preset fault repair difficulty value among the n fault repair difficulty values.

[0045] In this implementation, m is an integer less than n. To determine the m difficulty values ​​among n fault repair difficulty values ​​that are greater than the preset fault repair difficulty value, it is necessary to first define the preset fault repair difficulty threshold, i.e., the preset fault repair difficulty value. This threshold can be set based on industry standards, maintenance experience, or internal company regulations. The advantage of doing so is that it can quickly focus on high-priority maintenance tasks: on the one hand, it can prioritize handling faults with high repair difficulty, avoiding delays in critical system repairs due to low-priority issues occupying resources. For example, it can prioritize resolving high-difficulty faults in the brake control unit, which is related to driving safety, rather than simple faults in the body control module; on the other hand, it helps to rationally allocate maintenance resources, deploying professional technicians, special tools, and spare parts in advance according to the characteristics of the m high-difficulty faults, thereby improving maintenance efficiency; in addition, it can provide a quantitative basis for maintenance decisions, optimizing maintenance processes by differentiating difficulty levels, developing detailed plans for high-difficulty faults, and using standardized processes for low-difficulty faults. It also facilitates management in assessing the complexity of maintenance tasks and in resource scheduling and cost accounting.

[0046] S104: Determine the m electronic control units corresponding to the m fault repair difficulty values.

[0047] In this implementation, among the n fault repair difficulty values ​​obtained for each of the electronic control units (ECUs), m fault repair difficulty values ​​that are greater than the preset fault repair difficulty value are first determined by comparing them with the preset fault repair difficulty value. Since each difficulty value uniquely corresponds to one ECU, it is necessary to trace back these m difficulty values ​​to their corresponding ECU entities. This process relies on data mapping relationships. For example, when collecting or calculating the difficulty values ​​in the early stage, each ECU has been labeled with a unique identifier (such as ECU type, installation location code, etc.). At this time, the m high difficulty values ​​can be matched one-to-one with the specific ECUs through this identifier, thus clarifying which ECUs each of the m difficulty values ​​corresponds to. For example, these may include ECUs with high repair difficulty such as brake control units, autonomous driving domain controllers, and battery management systems, thereby identifying the hardware objects that need to be focused on.

[0048] S105: Obtain m sets of operating data corresponding to the m electronic control units.

[0049] In this implementation, after identifying m key electronic control units (ECUs) requiring focused attention based on their fault repair difficulty (such as brake control units and battery management systems), it is necessary to further retrieve operational data from these units within a preset time period. This data is typically acquired using an on-board diagnostic system or remote communication interface, sending data request commands to the target ECU via a bus or dedicated protocol. The collected data includes, but is not limited to, real-time voltage and current, component temperature, raw sensor signals, fault code storage records, and communication message logs. Because these ECUs are more difficult to repair, the required data is often more in-depth and targeted than the initially collected n sets of data. For example, it may include high-frequency sampled waveform data (such as real-time power ripple curves), contextual data from historical fault occurrences (such as a full parameter snapshot 10 seconds before the fault), or response data related to functional tests (such as output feedback after simulating input signals). Ultimately, a complete and detailed set of operational data is generated for each high-difficulty ECU, forming m sets of specialized data, providing accurate analytical material for subsequent remote fault diagnosis.

[0050] S106: Based on the m sets of operating data, perform remote fault diagnosis on the m electronic control units to obtain target diagnosis results, so that the target user can perform fault repair operations based on the target diagnosis results.

[0051] In this implementation, firstly, a professional diagnostic platform performs multi-dimensional analysis on each set of operational data. Protocol parsing tools convert bus messages into readable parameters (such as engine speed and throttle opening). Explicit fault codes are identified using a fault code database, while latent anomalies (such as high-frequency noise in sensor signals) are analyzed using signal processing algorithms (such as Fourier transform). For example, for the operational data of the battery management system, the analysis should focus on cell voltage balance, over-limit charge / discharge rate records, and temperature sensor drift curves. Secondly, a fault feature extraction model is established. Machine learning algorithms (such as random forests) are used to train historical fault data to form anomaly pattern recognition rules. Taking the brake control unit as an example, if the solenoid valve drive current waveform of the brake control unit exhibits periodic distortion, accompanied by wheel speed sensor signal jumps, then the composite fault feature of "solenoid valve jamming + sensor magnetic ring contamination" is extracted. Simultaneously, the logical reasoning chain of the expert system is used to verify the correlation of multiple parameters. For example, if the fuel trim value of the engine control unit continuously exceeds the limit, it is necessary to correlate it with oxygen sensor voltage fluctuations, air flow meter data, and the carbon canister solenoid valve operating status to rule out the possibility of false alarms from a single parameter. Then, based on the mapping relationship between fault characteristics and the maintenance knowledge base, a diagnostic conclusion is generated. The diagnostic result must include fault location, impact analysis (e.g., "causing shift jerking and reduced transmission efficiency"), and repair suggestions (e.g., "replace the module and rewrite the transmission shift logic program"). For complex faults (e.g., multi-sensor fusion failure of the autonomous driving domain controller), a phased diagnostic solution needs to be output. First, the initial parameters of the sensors are reset using a remote calibration tool. If this is ineffective, the system should prompt on-site replacement of the LiDAR and calibration of the installation angle. Finally, the diagnostic results are structured into an actionable guide for the target user: for repair technicians, a technical document containing fault code details, hardware disassembly steps, and dedicated equipment debugging parameters is provided; for vehicle owners, it is transformed into a simplified description (e.g., "Power battery temperature sensor abnormal, it is recommended to go to a dealership to check the temperature control system"), with added risk level prompts (e.g., "repair immediately" or "next maintenance"). The entire process must ensure encrypted data transmission, and the diagnostic algorithm must have self-learning capabilities. By continuously accumulating new fault cases, the feature library is optimized to improve the accuracy of remote diagnosis and the operability of repair solutions.

[0052] It can be seen that, firstly, by collecting operational data from each electronic control unit (ECU) over a preset time period and calculating the difficulty of fault repair, key components with high repair difficulty can be quickly identified from among the numerous ECUs in the vehicle. This avoids indiscriminate diagnostic work on all units, thus prioritizing faults affecting driving safety or core system functions, such as addressing problems in the braking or powertrain control units, rather than secondary electrical faults. Secondly, by selectively acquiring more detailed operational data for the identified high-difficulty ECUs and conducting remote diagnostics, the amount of data processing during the diagnostic process can be significantly reduced. Resources can be concentrated on key components requiring in-depth analysis, such as retrieving core parameters like cell voltage and temperature for the battery management system, avoiding redundant information from interfering with judgment and improving diagnostic efficiency. Thirdly, this tiered processing method can provide repair personnel with… This process provides accurate diagnostic results, enabling technicians to identify the specific location, impact, and repair steps of complex faults in advance. This allows for the rational allocation of professional tools, spare parts, and technical resources, reducing on-site troubleshooting time and lowering repair costs. Furthermore, through data-driven difficulty assessment and targeted diagnosis, this process effectively avoids misdiagnosis caused by mixed data from the vehicle's electronic control units, improving diagnostic accuracy. For example, by combining sensor signals, fault codes, and historical operating data from the power control unit, it can accurately pinpoint hardware failures or software logic problems, rather than providing a generalized approach. Finally, the target diagnostic results can be directly translated into actionable repair plans. Whether it's a complex system-level fault or a hardware replacement requirement, it provides clear operational guidance for repair personnel, while also helping vehicle owners understand the severity of the fault and its priority, achieving efficient management of the entire process from fault detection to repair.

[0053] Please see Figure 2 , Figure 2 This application provides a flowchart for determining the difficulty value of fault repair, including but not limited to the following steps:

[0054] S201: Determine the first operating data corresponding to the first electronic control unit.

[0055] In this embodiment, the first operating data includes first voltage data and first temperature data, the first electronic control unit is any one of the n electronic control units, and the first operating data is the operating data corresponding to the first electronic control unit in the n sets of operating data.

[0056] The operational data includes voltage and temperature data. One of the n electronic control units (ECUs) in the target vehicle is selected as the first ECU (e.g., engine control unit or battery management system). All operational data of this ECU within a preset time period are retrieved from the previously collected n sets of operational data and designated as the first operational data. This data set primarily includes voltage fluctuations during the ECU's operation (i.e., first voltage data, such as power input voltage, chip power supply voltage, etc.) and temperature change records of key internal components (i.e., first temperature data, such as temperature values ​​of capacitors, chips, etc.). By clarifying the correspondence between the first ECU and the first operational data, a specific object and data foundation are provided for subsequent fault analysis based on voltage and temperature data.

[0057] S202: Based on the first voltage data, determine the duration of voltage anomalies and the range of extreme voltage anomalies of the first electronic control unit within the preset time period.

[0058] In this embodiment, the voltage abnormality duration refers to the cumulative time during which the voltage value of the electronic control unit exceeds the normal operating range within a preset time period. For example, if the normal voltage range of the electronic control unit is set to 10V to 14V, then when the voltage is lower than 10V or higher than 14V, this period will be counted as the abnormal duration. By accumulating all such time periods, the total voltage abnormality duration can be obtained, which reflects the duration of the voltage abnormality.

[0059] The voltage anomaly extreme range is the difference between the maximum and minimum voltage values ​​during the period when the voltage is in an abnormal state. For example, if the voltage drops to a minimum of 8V and rises to a maximum of 16V during an abnormal voltage period, then the voltage anomaly extreme range is 16V minus 8V, which equals 8V. This range reflects the severity of the voltage fluctuation during the anomaly. By determining these two indicators, the voltage anomaly status of the first electronic control unit can be comprehensively measured from both time and fluctuation amplitude dimensions, providing a quantitative basis for subsequent assessment of fault severity.

[0060] The duration of voltage anomalies directly reflects the cumulative time that the voltage deviates from the normal range. For example, sustained low voltage may cause chip malfunctions, while sustained high voltage may accelerate component aging. By statistically analyzing the duration of anomalies, the impact of voltage problems on the functional stability of electronic control units can be determined, providing a quantitative basis in the time dimension for assessing fault risk. The extreme value range of voltage anomalies reflects the severity of voltage fluctuations during anomalies. For example, a large extreme value range means that the voltage jumps sharply in a short period of time, which may cause problems such as electronic component breakdown and frequent triggering of circuit protection mechanisms. This indicator can help determine the risk of damage to hardware caused by voltage anomalies and reflects the severity of the problem more accurately than a single voltage over-limit value. Using these two indicators together can comprehensively describe the dual characteristics of voltage anomalies: duration and fluctuation amplitude. For example, if the duration of a voltage anomaly in an electronic control unit is short but the extreme value range is large, it may correspond to a transient pulse interference problem; if the duration of the anomaly is long and the extreme value range is small, it may be caused by the deterioration of the power module's voltage regulation performance, thus providing more detailed data support for subsequent analysis of fault causes and the formulation of repair strategies.

[0061] S203: Determine the first fault severity value corresponding to the first electronic control unit based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values.

[0062] In this embodiment, when determining the first fault severity value, the operating time of the first electronic control unit within a preset time period is first determined. Then, a reference fault severity value is calculated based on the ratio between the operating time and the voltage anomaly duration. Next, a corresponding first optimization factor is determined based on the voltage anomaly extreme value span. Finally, the reference fault severity value is adjusted using this optimization factor to obtain a first fault severity value that comprehensively reflects the voltage anomaly situation. Alternatively, the first fault severity value can be determined by comprehensively calculating the quantitative data of the voltage anomaly duration and the voltage anomaly extreme value span in the fault assessment system. For example, first, set a severity coefficient corresponding to a unit time for the duration of voltage anomalies (e.g., a certain score is recorded for each hour exceeding the normal range), and at the same time, set a severity coefficient corresponding to a unit amplitude for the extreme value span of voltage anomalies (e.g., a certain score is recorded for each 1V increase in the extreme value span). Then, convert the duration of voltage anomalies into corresponding scores, and also convert the extreme value span of voltage anomalies into corresponding scores. Then, according to a preset weight ratio (e.g., voltage anomaly duration accounts for 40%, and voltage anomaly extreme value span accounts for 60%), the scores of the two are weighted and summed to finally obtain a first fault severity value that can comprehensively reflect the degree of impact of voltage anomalies on the first electronic control unit. This calculation method can reflect both the impact of the duration of voltage anomalies and the harm of voltage fluctuation amplitude, making the severity value more comprehensive and accurate in reflecting the actual fault situation.

[0063] S204: Perform linear fitting based on the first temperature data to obtain a linear temperature change curve.

[0064] In this embodiment, when performing linear fitting based on the first temperature data to obtain a temperature change line, the temperature data can first be arranged into coordinate points in chronological order (time as the horizontal axis and temperature as the vertical axis). Then, a fitting algorithm such as the least squares method is used to calculate a line that minimizes the sum of the squares of the vertical distances from all data points to the line.

[0065] S205: Determine the slope of the temperature change line to obtain the slope of the temperature change line.

[0066] In this embodiment, the slope of the temperature change line can be calculated by selecting any two points on the line, thus obtaining the slope of the temperature change line.

[0067] S206: Determine the fault severity value corresponding to the slope of the temperature change line to obtain the second fault severity value.

[0068] In this embodiment, it can be a preset mapping relationship between the slope of the temperature change line and the fault severity value. Based on this mapping relationship, the fault severity value corresponding to the slope of the temperature change line can be determined, and a second fault severity value can be obtained.

[0069] S207: Determine the fault repair difficulty value of the first electronic control unit based on the first fault severity value and the second fault severity value.

[0070] In this embodiment, when determining the fault repair difficulty value of the first electronic control unit based on the first fault severity value and the second fault severity value, it is necessary to first clarify the correlation and influence weight of the two in the fault assessment system. The two fault severity values ​​can be standardized first to eliminate dimensional differences (e.g., normalizing the values ​​to the 0-1 range). Then, weighting coefficients are set according to the characteristics of the fault type. The fault repair difficulty value of the first electronic control unit is then calculated based on these weighting coefficients, and a weighted summation is used to obtain the basic repair difficulty value. Furthermore, a mapping model can be established by combining historical repair data to map the combination of the two values ​​to preset difficulty levels (e.g., low, medium, high), ultimately obtaining a repair difficulty value that comprehensively reflects the complexity of the fault and the workload of the repair. This value must take into account the severity of the fault in both dimensions and their interactive influence.

[0071] As can be seen, firstly, by analyzing the two key indicators of voltage and temperature in the first set of operating data separately, the voltage data quantifies the severity of the fault by measuring the duration of anomalies and the range of extreme values. This simultaneously reflects the impact of the persistence and fluctuation amplitude of voltage anomalies on the electronic control unit, avoiding the one-sidedness of a single-dimensional assessment. At the temperature data level, the slope is obtained through linear fitting and converted into a fault severity value, dynamically capturing the potential fault signal of the temperature change rate. This allows for earlier detection of abnormal trends compared to simply monitoring the absolute temperature value. Secondly, combining the fault severity values ​​corresponding to voltage and temperature to determine the repair difficulty value achieves cross-validation of hardware (voltage anomaly) and operational status (temperature change) faults. This considers both the current fault severity and the potential risks that temperature anomalies may cause, making the repair difficulty assessment more consistent with the complexity of multiple factors intertwined in actual maintenance scenarios. Furthermore, this method reduces subjective judgment bias through data-driven quantitative analysis (such as fitting slopes and weighted calculations), and the logical coherence of each step forms a closed loop from data acquisition to final difficulty assessment. This facilitates rapid fault priority identification and repair strategy formulation in engineering practice, improving the efficiency and accuracy of fault diagnosis.

[0072] Please see Figure 3 , Figure 3 This application provides a flowchart for determining a first fault severity value corresponding to a first electronic control unit, including but not limited to the following steps:

[0073] S301: Determine the runtime of the first electronic control unit within the preset time period.

[0074] In this embodiment, the length of time that the first electronic control unit is actually in normal working condition within a preset time range is counted.

[0075] S302: Determine the reference fault severity value corresponding to the first electronic control unit based on the running time and the voltage abnormality duration.

[0076] In this embodiment, when determining the reference fault severity value corresponding to the first electronic control unit based on the runtime and voltage anomaly duration, it is first necessary to clarify that the runtime is the total duration of normal operation of the electronic control unit within a preset time period, and the voltage anomaly duration is the cumulative duration during which the voltage exceeds the normal range. The voltage anomaly duration and runtime are quantitatively compared, for example, by calculating the proportion of voltage anomaly duration to runtime. Then, this proportion is converted into a numerical range corresponding to the fault severity value using a preset mapping rule, thereby determining the reference fault severity value corresponding to the first electronic control unit.

[0077] S303: Determine the first optimization factor corresponding to the voltage anomaly extreme value span.

[0078] In this embodiment, it can be a preset mapping relationship between the voltage anomaly extreme value span and the optimization factor. Based on this mapping relationship, the first optimization factor corresponding to the voltage anomaly extreme value span can be determined.

[0079] S304: Optimize the reference fault severity value based on the first optimization factor to obtain the first fault severity value corresponding to the first electronic control unit.

[0080] In this embodiment, the first fault severity value corresponding to the first electronic control unit is calculated according to the following formula:

[0081] The first fault severity value corresponding to the first electronic control unit = reference fault severity value × (1 + first optimization factor);

[0082] Based on the above formula, the reference fault severity value can be optimized using the first optimization factor to obtain the first fault severity value corresponding to the first electronic control unit.

[0083] As can be seen, firstly, by using the runtime within a preset time period as a benchmark and calculating reference values ​​based on the duration of voltage anomalies, the proportion of voltage anomalies in actual working time can be intuitively reflected, demonstrating the persistence and frequency of anomalies. Secondly, introducing a first optimization factor corresponding to the extreme value span of voltage anomalies can further assess the severity of voltage fluctuations. The larger the extreme value span, the greater the deviation of the voltage from the normal range, and the higher the risk of damage to electronic components. By correcting the reference value through the optimization factor, the assessment results can be made more consistent with the actual fault hazards. This dual-layer assessment mechanism of time proportion and fluctuation amplitude considers both the time dimension of fault occurrence and the severity of anomalies, avoiding the one-sidedness of a single indicator. It provides a more scientific and comprehensive quantitative basis for subsequent fault diagnosis and maintenance priority determination, improving the accuracy and reliability of electronic control unit fault assessment.

[0084] Please see Figure 4 , Figure 4 This application provides a flowchart for determining the fault repair difficulty value of a first electronic control unit, including but not limited to the following steps:

[0085] S401: Determine the target fault severity value based on the first fault severity value and the second fault severity value.

[0086] In this embodiment, when determining the target fault severity value based on the first fault severity value and the second fault severity value, it is usually necessary to first clarify the weight relationship between the two. The weight can be set according to the degree of impact of the fault type on the system (for example, if the first fault has a more critical impact, it is given a higher weight). Then, the two values ​​are quantified and integrated by weighted summation. Alternatively, the maximum value can be taken according to the nature of the fault (suitable for single fault-dominated scenarios), or matching can be performed through a preset logical rule table (for example, if the first fault value exceeds the threshold, it is directly used as the target value; otherwise, it is combined with the second fault value for comprehensive judgment). The core is to transform the multi-dimensional fault severity index into a unified comprehensive evaluation value through a reasonable algorithm or rule to reflect the overall severity of the fault.

[0087] S402: Obtain the mapping relationship between the severity value of the fault and the difficulty value of the fault repair.

[0088] In this implementation, based on historical maintenance data, engineering practice experience, or industry standards, different levels of fault severity can be correlated and matched with corresponding repair difficulty to form a referenceable corresponding rule or data model. This determines the mapping relationship between fault severity values ​​and fault repair difficulty values. The mapping relationship can be a tabular mapping, with fault severity values ​​(e.g., 1-10 points) as rows and repair difficulty values ​​(e.g., low, medium, high, or specific values) as columns, clearly defining the repair difficulty corresponding to each severity range. It can also be a function mapping, establishing a linear or non-linear relationship between the two through mathematical formulas. Alternatively, it can be a rule mapping, setting logical rules based on expert experience, such as "when the severity value exceeds threshold A and is accompanied by type B fault, the repair difficulty is judged as high," or combining multiple dimensions such as fault type and impact range to comprehensively match the difficulty level.

[0089] S403: Determine the reference fault repair difficulty value corresponding to the target fault severity value based on the mapping relationship.

[0090] In this embodiment, when determining the reference fault repair difficulty value corresponding to the target fault severity value based on the mapping relationship, the target fault severity value must first be substituted into the established mapping rules. If the mapping relationship is in tabular form, the interval where the target value is located needs to be found, and the repair difficulty value corresponding to that interval needs to be directly matched; if it is a function mapping, the target value is substituted into the formula to calculate the specific value; if it is a rule mapping, the target value needs to be combined with additional conditions (such as fault type, scope of impact) to match the preset logic, thereby determining the corresponding reference difficulty level or value. The key to the whole process is to ensure that the matching logic between the target value and the mapping relationship is accurate, so that the resulting reference repair difficulty value can truly reflect the theoretical complexity of fault repair.

[0091] S404: Determine the aging level value of the first electronic control unit.

[0092] In this embodiment, determining the aging degree value of the first electronic control unit typically requires a comprehensive quantitative evaluation using multiple parameters. First, physical aging indicators such as the operating time (e.g., cumulative working hours), operating environment data (e.g., the number and duration of temperature and humidity exceeding limits), and electrical parameter degradation (e.g., the percentage decrease in capacitor capacity and the amount of chip threshold voltage drift) can be collected. A basic aging coefficient is then calculated using the aging model provided by the manufacturer. Simultaneously, performance degradation data (e.g., the increase in signal processing delay and the decrease in control accuracy) are monitored, and the performance degradation degree is determined by comparing it with the factory baseline value. Fault history records (e.g., the frequency of non-fatal faults) can also be introduced as an auxiliary dimension. The above multi-source data are integrated into a value ranging from 0 to 100 using a weighted algorithm; a higher value indicates a more severe aging degree. Finally, a quantitative aging degree value is formed to reflect its physical and performance degradation state.

[0093] S405: Determine the second optimization factor corresponding to the aging degree value.

[0094] In this embodiment, it can be a mapping relationship between a preset aging degree value and an optimization factor. Based on this mapping relationship, a second optimization factor corresponding to the aging degree value can be determined.

[0095] S406: Optimize the reference fault repair difficulty value according to the second optimization factor to obtain the fault repair difficulty value of the first electronic control unit.

[0096] In this embodiment, the fault repair difficulty value of the first electronic control unit is calculated according to the following formula:

[0097] The fault repair difficulty value of the first electronic control unit = the reference fault repair difficulty value × (1 + the second optimization factor);

[0098] Based on the above formula, the reference fault repair difficulty value can be optimized using the second optimization factor to obtain the fault repair difficulty value of the first electronic control unit.

[0099] As can be seen, integrating different types of fault severity values, such as voltage and temperature, into target values ​​avoids the one-sidedness of single indicators and comprehensively reflects the overall health status of the equipment. Secondly, by using mapping relationships, the abstract fault severity is transformed into an operable reference value for repair difficulty, providing a standardized basis for the allocation of maintenance resources. Finally, the degree of equipment aging is introduced as an optimization factor to dynamically adjust the repair difficulty assessment results, fully reflecting the additional complexity of aging equipment in maintenance (such as component fragility and compatibility issues). This hierarchical assessment mechanism not only improves the accuracy of fault diagnosis but also predicts maintenance time, costs, and technical requirements in advance, helping maintenance personnel to develop more scientific strategies (such as prioritizing the replacement of aging components rather than repair), ultimately shortening downtime, reducing maintenance risks, and achieving intelligent upgrades in industrial equipment maintenance.

[0100] Please see Figure 5 , Figure 5 This is a flowchart illustrating the determination of the aging degree value of a first electronic control unit according to an embodiment of this application, including but not limited to the following steps:

[0101] S501: Obtain the capacitance change data and ripple voltage change data of the first electronic control unit within a historical time period.

[0102] In this embodiment, the end time of the historical time period is earlier than the start time of the preset time period. Capacitor capacitance change data, as a key physical indicator of the capacitor, a core component of the electronic control unit, directly reflects the degree of capacitor aging. During long-term operation, capacitors experience capacitance decay due to factors such as electrolyte loss and temperature stress. If the capacitance of filter capacitors and energy storage capacitors in the electronic control unit decreases, it can lead to problems such as poor power supply stability and increased signal noise. Ripple voltage change data refers to the amplitude of AC voltage fluctuations superimposed on the power supply or signal lines of the electronic control unit, and its changes are strongly correlated with capacitor performance degradation. During normal operation, the capacitor suppresses ripple voltage through charging and discharging; however, when the capacitor capacitance decays or the equivalent series resistance increases, the filtering capability decreases, leading to an increase in ripple voltage.

[0103] Capacitor capacitance change data reveals the physical nature of aging (component material degradation), while ripple voltage change data reflects the electrical performance of aging (circuit performance degradation). By comparing the trends of both, a quantitative aging assessment model can be established, enabling full-chain monitoring from physical parameter early warning to functional anomaly prediction, providing a precise basis for formulating capacitor replacement or circuit optimization strategies in advance.

[0104] S502: Based on the capacitance change data and the ripple voltage change data, perform linear fitting to obtain the capacitance change line and the ripple voltage change line.

[0105] In this embodiment, linear fitting is performed based on capacitance change data and ripple voltage change data. First, the two types of data from historical time periods are organized into time series samples, and then a trend model is constructed using a linear regression algorithm. Specifically, first, with time as the horizontal axis and capacitance as the vertical axis, the capacitance measurement value at each sampling moment is marked as a coordinate point. A straight line equation is calculated using the least squares method, minimizing the sum of the squares of the vertical distances from all data points to this line. This line represents the capacitance change line, and its slope reflects the rate of capacitance decay over time. Similarly, with time as the horizontal axis and ripple voltage as the vertical axis, the ripple voltage values ​​at each sampling moment are used as coordinate points. A straight line equation is fitted using the same least squares method. This line represents the ripple voltage change line, and its slope reflects the rate of ripple voltage increase over time. This fitting method transforms discrete aging data into a continuous trend curve through mathematical modeling. It not only intuitively presents the linear law of capacitor performance degradation but also quantifies the aging rate through the slope, providing accurate numerical basis for subsequently determining the degree of aging based on the slope.

[0106] S503: Determine the slopes of the capacitance change line and the ripple voltage change line to obtain the slopes of the capacitance change line and the ripple voltage change line.

[0107] In this embodiment, the slope of the capacitance change line can be determined by taking any two points on the line. Similarly, the slope of the ripple voltage change line can be determined by taking any two points on the line.

[0108] S504: Determine the aging degree value corresponding to the slope of the linear change in capacitance to obtain the first aging degree value.

[0109] In this embodiment, it can be a preset mapping relationship between the slope of the capacitance change line and the aging degree value. Based on this mapping relationship, the aging degree value corresponding to the slope of the capacitance change line can be determined, and a first aging degree value can be obtained.

[0110] S505: Determine the aging degree value corresponding to the slope of the ripple voltage change line to obtain the second aging degree value.

[0111] In this embodiment, it can be a preset mapping relationship between the slope of the ripple voltage change line and the aging degree value. Based on this mapping relationship, the aging degree value corresponding to the slope of the ripple voltage change line can be determined, and a second aging degree value can be obtained.

[0112] S506: Determine the aging value of the first electronic control unit based on the first aging value and the second aging value.

[0113] In this embodiment, for example, a first reference weight corresponding to the first aging degree value is determined. Specifically, it can be a preset mapping relationship between aging degree values ​​and reference weights. Based on this mapping relationship, the first reference weight corresponding to the first aging degree value can be determined.

[0114] For example, the average operating temperature of the first electronic control unit during the historical time period is obtained. Specifically, temperature is a key environmental factor affecting the aging of electronic components. High temperatures can accelerate the evaporation of capacitor electrolytes and the degradation of semiconductor materials, so it is necessary to obtain the average operating temperature of the first electronic control unit during the historical time period.

[0115] For example, a fine-tuning parameter corresponding to the average operating temperature is determined. Specifically, it can be a preset mapping relationship between the operating temperature and the fine-tuning parameter. Based on this mapping relationship, the fine-tuning parameter corresponding to the average operating temperature can be determined.

[0116] For example, the first reference weight is adjusted based on the fine-tuning parameters to obtain the first target weight. Specifically, the first target weight is calculated according to the following formula:

[0117] First target weight = First reference weight × (1 + fine-tuning parameter);

[0118] The first reference weight can be adjusted based on the fine-tuning parameters according to the above formula to obtain the first target weight.

[0119] For example, a second target weight is determined based on the first target weight, and the sum of the first target weight and the second target weight is 1. Specifically, since the sum of the first target weight and the second target weight is 1, the second target weight corresponding to the second aging degree value can be determined after the first target weight is determined.

[0120] For example, the aging degree value of the first electronic control unit is determined based on the first aging degree value, the second aging degree value, the first target weight, and the second target weight. Specifically, the aging degree value of the first electronic control unit is calculated according to the following formula:

[0121] The aging degree value of the first electronic control unit = first aging degree value × first target weight + second aging degree value × second target weight;

[0122] According to the above formula, the aging value of the first electronic control unit can be determined based on the first aging value, the second aging value, the first target weight, and the second target weight.

[0123] It can be seen that by coupling dynamic weight adjustment with temperature factors, the aging degree of electronic control units can be accurately quantified: setting basic weights based on the first aging degree value can reflect the initial differences in importance of different aging indicators, while introducing fine-tuning parameters corresponding to historical average operating temperatures can dynamically correct the weights according to the physical law that "the higher the temperature, the faster the aging rate" (such as Arrhenius's law). The weights of parameters that accelerate aging at high temperatures (such as capacitor capacity decay) are increased, while the weights of parameters with less impact at low temperatures (such as ripple voltage changes) are adjusted accordingly, ensuring that the weight allocation fits the actual working conditions; through the constraint that the weight sum is 1, the dual-parameter evaluation system always maintains logical consistency. Finally, the comprehensive result calculated by combining dynamic weights and dual aging degree values ​​can not only reflect the differences in the basic impact of different indicators, but also correct the accelerated effect of the environment on aging through the temperature adaptation mechanism, avoiding the evaluation bias caused by fixed weights, and providing a more practical quantitative basis for the life prediction and fault warning of electronic control units.

[0124] In summary, by combining quantitative analysis of historical data with multi-dimensional indicators, a systematic assessment of the aging degree of electronic control units (ECUs) is achieved. Acquiring data on capacitance and ripple voltage changes over historical periods allows for the capture of component degradation trends based on long-term operational data, avoiding the randomness errors of single-point sampling. Linear fitting transforms discrete data into trend curves, with the slope serving as a quantitative indicator. The capacitance slope reflects the rate of physical degradation, while the ripple voltage slope reflects the degree of electrical performance deterioration. These two slopes correspond to the first and second aging degree values, respectively, forming a two-dimensional assessment system of "physical degradation + electrical performance." Finally, the combined indicators determine the overall aging degree. This approach captures the core attenuation characteristics of component lifespan through capacitance changes and reflects performance fluctuations under actual operating conditions through ripple voltage changes, avoiding the one-sidedness of single-parameter assessments. This provides a trend-based and comprehensive quantitative basis for ECU aging early warning and maintenance strategy formulation.

[0125] Please see Figure 6 , Figure 6 This is a flowchart of a fault repair operation provided in an embodiment of this application, including but not limited to the following steps:

[0126] S601: Determine mn fault repair difficulty values ​​that are less than or equal to the preset fault repair difficulty value among the n fault repair difficulty values.

[0127] In this embodiment, among the n calculated electronic control unit fault repair difficulty values, the less difficult ones are selected, that is, the mn fault repair difficulty values ​​that are less than or equal to the preset fault repair difficulty value among the n fault repair difficulty values.

[0128] S602: Determine the mn electronic control units corresponding to the mn fault repair difficulty values.

[0129] In this embodiment, by selecting mn fault repair difficulty values ​​that are less than or equal to a preset fault repair difficulty value, and establishing a clear association between these difficulty values ​​and the corresponding electronic control units, the specific electronic control units that need to be prioritized for fault repair operations are accurately located. This provides a target object for subsequent targeted acquisition of their operating data and implementation of repairs, ensuring that maintenance resources can be efficiently focused on equipment with lower repair difficulty, thereby optimizing maintenance strategies and rationally allocating resources.

[0130] S603: Obtain mn sets of operating data corresponding to the mn electronic control units.

[0131] In this embodiment, acquiring the mn sets of operating data corresponding to these mn electronic control units is to collect various data generated by these electronic control units during actual operation (such as real-time or historical data of voltage, current, temperature, operating frequency, communication signals, etc.) to provide specific analytical basis for subsequent fault repair operations. This enables maintenance personnel to accurately locate the root cause of the fault based on these data that reflect the actual operating status of the equipment, and then formulate more targeted repair strategies, avoiding blind repairs and improving the efficiency and accuracy of fault repair.

[0132] S604: Perform fault repair operations on the mn electronic control units based on the mn group of operating data.

[0133] In this embodiment, when troubleshooting mn electronic control units based on these mn sets of operating data, the first step is to comprehensively analyze each set of data. For example, check for abnormal fluctuations in voltage data, determine if there are heat dissipation problems in the equipment through temperature data, identify any fluctuations in operating frequency data, and check for any signal transmission failures in the communication data. Then, compare these analyzed data characteristics with common fault conditions of electronic control units. For instance, if the capacitance change exceeds the normal range and the ripple voltage is also constantly increasing, it can be determined that the fault is caused by capacitor aging; if the temperature continues to rise while the equipment's operating speed is slowing down, the heat dissipation component may be faulty. Next, different repair plans are formulated based on the comparison results: if it is a software problem, such as incorrect parameter settings, the parameters are reset or the software program is updated; if it is a hardware component failure, such as a burnt-out resistor, the corresponding component is replaced; if multiple components fail simultaneously, such as a sensor failing to collect data, causing malfunction of the actuator, the sensor, signal transmission line, and control module are checked sequentially. During the repair process, it is necessary to continuously collect equipment operating data to verify the repair effect. For example, after replacing parts, check whether the relevant voltage and temperature data have returned to normal. After adjusting parameters, observe whether the equipment's working status is stable until all data meet the normal standards. Finally, record the entire process of troubleshooting, repairing, and verifying the fault to provide a reference for handling similar faults in the future.

[0134] As can be seen, firstly, based on a preset difficulty threshold, mn items with lower repair difficulty are selected from n faults. This prioritizes handling problems that are short-lived and easy to solve, quickly restoring some equipment functions and avoiding excessive consumption of maintenance resources on high-difficulty faults. Secondly, the selected difficulty values ​​are matched one-to-one with electronic control units, allowing for precise location of the repair target and preventing inefficiency caused by ambiguous repair objectives. Next, the operating data of these units is acquired, enabling the analysis of the essence of the fault through multi-dimensional information such as voltage, temperature, and communication, avoiding misjudgments caused by relying solely on experience. Finally, repair plans are formulated based on the data, such as targeted measures like software calibration and hardware replacement. Combined with real-time data verification of the repair effect, this not only improves the accuracy of fault location but also accumulates maintenance experience through a closed-loop process. Ultimately, it maximizes system recovery efficiency with limited resources, reducing maintenance costs and equipment downtime.

[0135] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0136] As can be seen, the vehicle remote diagnostic method described in this embodiment of the invention first acquires the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, obtaining n sets of operating data, where n is a positive integer. Then, based on the n sets of operating data, the fault repair difficulty value corresponding to each of the n electronic control units is determined, resulting in n fault repair difficulty values. Next, m fault repair difficulty values ​​greater than the preset fault repair difficulty value are determined from the n fault repair difficulty values, where m is an integer less than n. Then, the m electronic control units corresponding to the m fault repair difficulty values ​​are determined. Then, the m sets of operating data corresponding to the m electronic control units are acquired. Finally, remote fault diagnosis is performed on the m electronic control units based on the m sets of operating data to obtain the target diagnostic result, enabling the target user to perform fault repair operations based on the target diagnostic result, thereby improving the efficiency of vehicle remote diagnostics.

[0137] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a vehicle remote diagnostic device provided in an embodiment of this application. The vehicle remote diagnostic device 700 includes: an acquisition unit 701 and a processing unit 702.

[0138] The acquisition unit 701 is used to acquire the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, to obtain n sets of operating data; n is a positive integer.

[0139] The processing unit 702 is used to determine the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operating data, and obtain n fault repair difficulty values.

[0140] Determine m fault repair difficulty values ​​from the n fault repair difficulty values ​​that are greater than a preset fault repair difficulty value; m is an integer less than n;

[0141] Determine the m electronic control units corresponding to the m fault repair difficulty values;

[0142] Obtain m sets of operating data corresponding to the m electronic control units;

[0143] Based on the m sets of operating data, remote fault diagnosis is performed on the m electronic control units to obtain target diagnosis results, enabling the target user to perform fault repair operations based on the target diagnosis results.

[0144] In some possible implementations, in determining the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operating data, and obtaining n fault repair difficulty values, the processing unit 702 is specifically used for:

[0145] Determine the first operating data corresponding to the first electronic control unit; the first operating data includes first voltage data and first temperature data, the first electronic control unit is any one of the n electronic control units, and the first operating data is the operating data corresponding to the first electronic control unit in the n sets of operating data;

[0146] Based on the first voltage data, determine the duration of voltage anomalies and the range of extreme voltage anomalies of the first electronic control unit within the preset time period.

[0147] The first fault severity value corresponding to the first electronic control unit is determined based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values.

[0148] A straight line is obtained by fitting the first temperature data;

[0149] Determine the slope of the temperature change line to obtain the slope of the temperature change line;

[0150] Determine the fault severity value corresponding to the slope of the temperature change line to obtain the second fault severity value;

[0151] The fault repair difficulty value of the first electronic control unit is determined based on the first fault severity value and the second fault severity value.

[0152] In some possible implementations, in determining the first fault severity value corresponding to the first electronic control unit based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values, the processing unit 702 is specifically used for:

[0153] Determine the runtime of the first electronic control unit within the preset time period;

[0154] The reference fault severity value corresponding to the first electronic control unit is determined based on the runtime and the voltage anomaly duration.

[0155] Determine the first optimization factor corresponding to the voltage anomaly extreme value span;

[0156] The reference fault severity value is optimized based on the first optimization factor to obtain the first fault severity value corresponding to the first electronic control unit.

[0157] In some possible implementations, the processing unit 702 is specifically configured to: determine the fault repair difficulty value of the first electronic control unit based on the first fault severity value and the second fault severity value;

[0158] The target fault severity value is determined based on the first fault severity value and the second fault severity value;

[0159] Obtain the mapping relationship between fault severity values ​​and fault repair difficulty values;

[0160] Based on the mapping relationship, a reference fault repair difficulty value corresponding to the target fault severity value is determined;

[0161] Determine the aging level value of the first electronic control unit;

[0162] Determine a second optimization factor corresponding to the aging degree value;

[0163] The fault repair difficulty value of the first electronic control unit is obtained by optimizing the reference fault repair difficulty value according to the second optimization factor.

[0164] In some possible implementations, the processing unit 702 is specifically configured to: determine the aging level value of the first electronic control unit.

[0165] Acquire the capacitance change data and ripple voltage change data of the first electronic control unit within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period;

[0166] Based on the capacitance change data and the ripple voltage change data, a straight line is fitted to obtain the capacitance change line and the ripple voltage change line.

[0167] Determine the slopes of the capacitance change line and the ripple voltage change line to obtain the slopes of the capacitance change line and the ripple voltage change line.

[0168] Determine the aging degree value corresponding to the slope of the linear change in capacitance to obtain the first aging degree value;

[0169] Determine the aging degree value corresponding to the slope of the ripple voltage change line to obtain the second aging degree value;

[0170] The aging degree value of the first electronic control unit is determined based on the first aging degree value and the second aging degree value.

[0171] In some possible implementations, in determining the aging degree value of the first electronic control unit based on the first aging degree value and the second aging degree value, the processing unit 702 is specifically configured to:

[0172] Determine the first reference weight corresponding to the first aging degree value;

[0173] The average operating temperature of the first electronic control unit during the historical time period is obtained.

[0174] Determine the fine-tuning parameters corresponding to the average operating temperature;

[0175] The first reference weight is adjusted based on the fine-tuning parameters to obtain the first target weight;

[0176] A second target weight is determined based on the first target weight; the sum of the first target weight and the second target weight is 1.

[0177] The aging value of the first electronic control unit is determined based on the first aging value, the second aging value, the first target weight, and the second target weight.

[0178] In some possible implementations, the processing unit 702 is further specifically used for:

[0179] Determine mn fault repair difficulty values ​​that are less than or equal to the preset fault repair difficulty value from among the n fault repair difficulty values;

[0180] Determine the mn electronic control units corresponding to the mn fault repair difficulty values;

[0181] Obtain mn sets of operating data corresponding to the mn electronic control units;

[0182] Based on the mn sets of operating data, fault repair operations are performed on the mn electronic control units.

[0183] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 8 As shown, the electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. These are connected via a bus 804. The memory 803 stores computer programs and data, and the transceiver 801 can transmit data stored in the memory 803 to the processor 802. The program includes instructions for performing the following steps:

[0184] Obtain the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, resulting in n sets of operating data; n is a positive integer.

[0185] Based on the n sets of operating data, the fault repair difficulty value corresponding to each of the n electronic control units is determined, and n fault repair difficulty values ​​are obtained.

[0186] Determine m fault repair difficulty values ​​from the n fault repair difficulty values ​​that are greater than a preset fault repair difficulty value; m is an integer less than n;

[0187] Determine the m electronic control units corresponding to the m fault repair difficulty values;

[0188] Obtain m sets of operating data corresponding to the m electronic control units;

[0189] Based on the m sets of operational data, remote fault diagnosis is performed on the m electronic control units to obtain the target diagnosis result, thus enabling the target...

[0190] The target user performs fault repair operations based on the target diagnostic results.

[0191] In some possible implementations, in determining the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operational data, and obtaining n fault repair difficulty values, the above procedure includes instructions for performing the following steps:

[0192] Determine the first operating data corresponding to the first electronic control unit; the first operating data includes first voltage data and first temperature data, the first electronic control unit is any one of the n electronic control units, and the first operating data is the operating data corresponding to the first electronic control unit in the n sets of operating data;

[0193] Based on the first voltage data, determine the duration of voltage anomalies and the range of extreme voltage anomalies of the first electronic control unit within the preset time period.

[0194] The first fault severity value corresponding to the first electronic control unit is determined based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values.

[0195] A straight line is obtained by fitting the first temperature data;

[0196] Determine the slope of the temperature change line to obtain the slope of the temperature change line;

[0197] Determine the fault severity value corresponding to the slope of the temperature change line to obtain the second fault severity value;

[0198] The fault repair difficulty value of the first electronic control unit is determined based on the first fault severity value and the second fault severity value.

[0199] In some possible implementations, the above procedure includes instructions for performing the following steps in determining the first fault severity value corresponding to the first electronic control unit based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values:

[0200] Determine the runtime of the first electronic control unit within the preset time period;

[0201] The reference fault severity value corresponding to the first electronic control unit is determined based on the runtime and the voltage anomaly duration.

[0202] Determine the first optimization factor corresponding to the voltage anomaly extreme value span;

[0203] The reference fault severity value is optimized based on the first optimization factor to obtain the first fault severity value corresponding to the first electronic control unit.

[0204] In some possible implementations, the procedure includes instructions for performing the following steps in determining the fault repair difficulty value of the first electronic control unit based on the first fault severity value and the second fault severity value:

[0205] The target fault severity value is determined based on the first fault severity value and the second fault severity value;

[0206] Obtain the mapping relationship between fault severity values ​​and fault repair difficulty values;

[0207] Based on the mapping relationship, a reference fault repair difficulty value corresponding to the target fault severity value is determined;

[0208] Determine the aging level value of the first electronic control unit;

[0209] Determine a second optimization factor corresponding to the aging degree value;

[0210] The fault repair difficulty value of the first electronic control unit is obtained by optimizing the reference fault repair difficulty value according to the second optimization factor.

[0211] In some possible implementations, the above procedure includes instructions for performing the following steps in determining the degree of aging of the first electronic control unit:

[0212] Acquire the capacitance change data and ripple voltage change data of the first electronic control unit within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period;

[0213] Based on the capacitance change data and the ripple voltage change data, a straight line is fitted to obtain the capacitance change line and the ripple voltage change line.

[0214] Determine the slopes of the capacitance change line and the ripple voltage change line to obtain the slopes of the capacitance change line and the ripple voltage change line.

[0215] Determine the aging degree value corresponding to the slope of the linear change in capacitance to obtain the first aging degree value;

[0216] Determine the aging degree value corresponding to the slope of the ripple voltage change line to obtain the second aging degree value;

[0217] The aging degree value of the first electronic control unit is determined based on the first aging degree value and the second aging degree value.

[0218] In some possible implementations, in determining the aging degree value of the first electronic control unit based on the first aging degree value and the second aging degree value, the above procedure includes instructions for performing the following steps:

[0219] Determine the first reference weight corresponding to the first aging degree value;

[0220] The average operating temperature of the first electronic control unit during the historical time period is obtained.

[0221] Determine the fine-tuning parameters corresponding to the average operating temperature;

[0222] The first reference weight is adjusted based on the fine-tuning parameters to obtain the first target weight;

[0223] A second target weight is determined based on the first target weight; the sum of the first target weight and the second target weight is 1.

[0224] The aging value of the first electronic control unit is determined based on the first aging value, the second aging value, the first target weight, and the second target weight.

[0225] In some possible implementations, the above procedure includes instructions for performing the following steps:

[0226] Determine mn fault repair difficulty values ​​that are less than or equal to the preset fault repair difficulty value from among the n fault repair difficulty values;

[0227] Determine the mn electronic control units corresponding to the mn fault repair difficulty values;

[0228] Obtain mn sets of operating data corresponding to the mn electronic control units;

[0229] Based on the mn sets of operating data, fault repair operations are performed on the mn electronic control units.

[0230] It should be understood that the electronic devices mentioned in this application may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablets, PDAs, laptops, mobile internet devices (MIDs) or wearable devices, servers, edge computing nodes, etc. The above-mentioned electronic devices are merely examples and not exhaustive, and include, but are not limited to, the electronic devices described above.

[0231] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any of the methods described in the above method embodiments.

[0232] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0233] It should be noted that, for the sake of simplicity, the aforementioned methods are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are optional, and the actions and modules involved are not necessarily essential to this application.

[0234] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0235] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0236] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0237] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0238] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0239] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0240] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for remote vehicle diagnostics, characterized in that, include: Obtain the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, resulting in n sets of operating data; n is a positive integer. Based on the n sets of operational data, the fault repair difficulty value corresponding to each of the n electronic control units is determined, resulting in n fault repair difficulty values; the fault repair difficulty value is used to reflect the fault complexity and repair workload of the electronic control unit. Determine the m fault repair difficulty values ​​that are greater than the preset fault repair difficulty value from among the n fault repair difficulty values; m is an integer less than n; Determine the m electronic control units corresponding to the m fault repair difficulty values; Obtain m sets of operating data corresponding to the m electronic control units; Based on the m sets of operational data, remote fault diagnosis is performed on the m electronic control units to obtain target diagnosis results, enabling target users to perform fault repair operations based on the target diagnosis results. Wherein, when the operating data includes voltage data and temperature data, the step of determining the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operating data yields n fault repair difficulty values, including: Determine the first operating data corresponding to the first electronic control unit; the first operating data includes first voltage data and first temperature data, the first electronic control unit is any one of the n electronic control units, and the first operating data is the operating data corresponding to the first electronic control unit in the n sets of operating data; Based on the first voltage data, determine the duration of voltage anomalies and the range of extreme voltage anomalies of the first electronic control unit within the preset time period. The first fault severity value corresponding to the first electronic control unit is determined based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values. A straight line is obtained by fitting the first temperature data; Determine the slope of the temperature change line to obtain the slope of the temperature change line; Determine the fault severity value corresponding to the slope of the temperature change line to obtain the second fault severity value; The fault repair difficulty value of the first electronic control unit is determined based on the first fault severity value and the second fault severity value.

2. The method as described in claim 1, characterized in that, The determination of the first fault severity value corresponding to the first electronic control unit based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values ​​includes: Determine the runtime of the first electronic control unit within the preset time period; The reference fault severity value corresponding to the first electronic control unit is determined based on the runtime and the voltage anomaly duration. Determine the first optimization factor corresponding to the voltage anomaly extreme value span; The reference fault severity value is optimized based on the first optimization factor to obtain the first fault severity value corresponding to the first electronic control unit.

3. The method as described in claim 2, characterized in that, The step of determining the fault repair difficulty value of the first electronic control unit based on the first fault severity value and the second fault severity value includes: The target fault severity value is determined based on the first fault severity value and the second fault severity value; Obtain the mapping relationship between fault severity values ​​and fault repair difficulty values; Based on the mapping relationship, a reference fault repair difficulty value corresponding to the target fault severity value is determined; Determine the aging level value of the first electronic control unit; Determine a second optimization factor corresponding to the aging degree value; The fault repair difficulty value of the first electronic control unit is obtained by optimizing the reference fault repair difficulty value according to the second optimization factor.

4. The method as described in claim 3, characterized in that, Determining the aging level value of the first electronic control unit includes: Acquire the capacitance change data and ripple voltage change data of the first electronic control unit within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period; Based on the capacitance change data and the ripple voltage change data, a straight line is fitted to obtain the capacitance change line and the ripple voltage change line. Determine the slopes of the capacitance change line and the ripple voltage change line to obtain the slopes of the capacitance change line and the ripple voltage change line. Determine the aging degree value corresponding to the slope of the linear change in capacitance to obtain the first aging degree value; Determine the aging degree value corresponding to the slope of the ripple voltage change line to obtain the second aging degree value; The aging degree value of the first electronic control unit is determined based on the first aging degree value and the second aging degree value.

5. The method as described in claim 4, characterized in that, Determining the aging degree value of the first electronic control unit based on the first aging degree value and the second aging degree value includes: Determine the first reference weight corresponding to the first aging degree value; The average operating temperature of the first electronic control unit during the historical time period is obtained. Determine the fine-tuning parameters corresponding to the average operating temperature; The first reference weight is adjusted based on the fine-tuning parameters to obtain the first target weight; A second target weight is determined based on the first target weight; the sum of the first target weight and the second target weight is 1. The aging value of the first electronic control unit is determined based on the first aging value, the second aging value, the first target weight, and the second target weight.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Determine mn fault repair difficulty values ​​that are less than or equal to the preset fault repair difficulty value from among the n fault repair difficulty values; Determine the mn electronic control units corresponding to the mn fault repair difficulty values; Obtain mn sets of operating data corresponding to the mn electronic control units; Based on the mn sets of operating data, fault repair operations are performed on the mn electronic control units.

7. A vehicle remote diagnostic device, characterized in that, The device includes: an acquisition unit and a processing unit; The acquisition unit is used to acquire the operating data of each of the n electronic control units corresponding to the target vehicle within a preset time period, and obtain n sets of operating data; n is a positive integer. The processing unit is used to determine the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operating data, thereby obtaining n fault repair difficulty values; the fault repair difficulty value is used to reflect the fault complexity and repair workload of the electronic control unit. Determine m fault repair difficulty values ​​from the n fault repair difficulty values ​​that are greater than a preset fault repair difficulty value; m is an integer less than n; Determine the m electronic control units corresponding to the m fault repair difficulty values; Obtain m sets of operating data corresponding to the m electronic control units; Based on the m sets of operational data, remote fault diagnosis is performed on the m electronic control units to obtain target diagnosis results, enabling target users to perform fault repair operations based on the target diagnosis results. Wherein, when the operating data includes voltage data and temperature data, the step of determining the fault repair difficulty value corresponding to each of the n electronic control units based on the n sets of operating data yields n fault repair difficulty values, including: Determine the first operating data corresponding to the first electronic control unit; the first operating data includes first voltage data and first temperature data, the first electronic control unit is any one of the n electronic control units, and the first operating data is the operating data corresponding to the first electronic control unit in the n sets of operating data; Based on the first voltage data, determine the duration of voltage anomalies and the range of extreme voltage anomalies of the first electronic control unit within the preset time period. The first fault severity value corresponding to the first electronic control unit is determined based on the duration of the voltage anomaly and the range of the voltage anomaly extreme values. A straight line is obtained by fitting the first temperature data; Determine the slope of the temperature change line to obtain the slope of the temperature change line; Determine the fault severity value corresponding to the slope of the temperature change line to obtain the second fault severity value; The fault repair difficulty value of the first electronic control unit is determined based on the first fault severity value and the second fault severity value.

8. An electronic device, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for performing the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method as described in any one of claims 1-6.

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