Vehicle abnormal positioning method and device, vehicle and medium

Abstract

This application provides a vehicle anomaly localization method, device, vehicle, and medium. The method, applied in the field of automotive power safety technology, includes: monitoring the current value of the battery in a vehicle's dormant state; if the detected current value exceeds an anomaly threshold, acquiring synchronously recorded current data and log data from the vehicle's dormant state, and extracting current characteristics and timestamps from the current data; querying wake-up records from the log data based on the timestamps of the current data; and locating the cause of the anomaly based on the current characteristics and wake-up records. This method can accurately analyze and quickly locate vehicle static current anomalies based on current waveform type and wake-up records, avoiding the problems of low localization efficiency and strong reliance on experience due to manual point-by-point inspection for vehicle static current fault diagnosis, and the inability to accurately locate the fault source due to reliance on battery sensor alarms.

Description

Technical Field

[0001] This application relates to the field of vehicle electrical control technology, and more specifically, to a vehicle anomaly location method, device, vehicle, and medium in the field of vehicle electrical control technology. Background Technology

[0002] Modern automotive electronic and electrical architectures are becoming increasingly complex. Even after the vehicle is turned off and locked, multiple associated controllers remain in a low-power sleep state to support basic functions such as keyless entry and anti-theft. In this sleep state, the battery continues to supply power to these controllers and standby circuits, thus creating the vehicle's quiescent current. However, if the quiescent current is too high, it can lead to over-discharge of the battery after the vehicle has been parked for an extended period, resulting in a failed start due to a dead battery.

[0003] In related technologies, static current anomalies usually rely on manual point-by-point inspection or fuse removal to locate the fault source, which is inefficient and highly dependent on experience. Although some vehicles are equipped with intelligent battery sensors to monitor the vehicle's static current, they can usually only trigger alarms when the current exceeds the limit, making it difficult to further locate the specific abnormal power consumption. This is especially true in scenarios where power consumption anomalies are caused by intermittent or short-term wake-ups, making fault location even more difficult. Summary of the Invention

[0004] This application provides a vehicle anomaly location method, device, vehicle, and medium. The method can accurately analyze and quickly locate vehicle static current anomalies based on current waveform type and wake-up records, avoiding problems such as low location efficiency and strong reliance on experience due to manual point-by-point inspection for vehicle static current fault diagnosis, and inability to accurately locate the fault source due to reliance on battery sensor alarms.

[0005] Firstly, a method for locating vehicle anomalies is provided. This method includes: monitoring the current value of the battery in the vehicle's dormant state; if the detected current value is greater than an abnormal threshold, acquiring the current data and log data synchronously recorded in the vehicle's dormant state, and extracting the current features and timestamps from the current data; querying the wake-up records of the log data based on the timestamps of the current data; and locating the cause of the anomaly based on the current features and the wake-up records.

[0006] Through the above technical solution, this embodiment of the application continuously monitors the battery current while the vehicle is in sleep mode, and simultaneously acquires current data and log data when the current value exceeds an abnormal threshold. This achieves complete data retention of the abnormal current occurrence process, providing basic data support for subsequent anomaly analysis. Simultaneously, by extracting current characteristics and timestamps, and combining them with wake-up records in the log data for correlation matching, a correspondence is established between abnormal current and specific wake-up events, thereby enabling precise location of the cause of the anomaly based on the correlation results.

[0007] In conjunction with the first aspect, in some possible implementations, the cause of the anomaly is located based on the current characteristics and the wake-up record, including: extracting shape features from the current characteristics; determining the waveform type of the current data based on the shape features; and locating the cause of the anomaly based on the waveform type and the wake-up record.

[0008] Through the above technical solution, by locating the cause of anomalies based on current characteristics and wake-up records, the physical manifestations of current changes can be correlated with triggering events inside the vehicle, thereby improving the judgment from "whether it is abnormal" to the accurate identification of "the source of the anomaly". At the same time, by matching current characteristics (such as amplitude, width and power) with wake-up records, normal wake-up behavior and abnormal power consumption behavior can be distinguished, avoiding misjudging legitimate function triggers as faults. Furthermore, when current characteristics and wake-up records are consistent, the corresponding wake-up source and associated controller can be quickly located, improving the location efficiency. When the two are inconsistent, potential network anomalies, power management anomalies or controller faults can be identified, thereby achieving effective differentiation of complex fault scenarios.

[0009] Combining the first aspect and the above implementation method, the cause of the anomaly is located based on the waveform type and the wake-up record, including: if the waveform type is a non-pulse waveform, the first associated controller is determined based on the wake-up record, and the cause of the anomaly is determined to be a power management state machine failure of the first associated controller; if the waveform type is a pulse waveform, the second associated controller is obtained by querying the database based on the pulse characteristics in the current characteristics, and the cause of the anomaly is located based on the second associated controller and the wake-up record.

[0010] Through the above technical solution, by locating the cause of anomalies based on waveform type and wake-up records, abnormal behavior can be classified first based on current waveforms, dividing abnormal scenarios into non-pulse anomalies and pulse anomalies, thereby reducing the complexity of subsequent analysis. For non-pulse waveforms, the first associated controller is determined by combining wake-up records, and further determined to be a power management state machine fault, which helps to quickly identify abnormal situations where the controller fails to enter sleep mode or is stuck in an intermediate state after wake-up, and achieves direct location of continuous abnormal power consumption. For pulse waveforms, the second associated controller is obtained by extracting pulse features and querying the database, and the actual current behavior can be matched with pre-established power consumption features, thereby identifying the corresponding wake-up source or controller. Furthermore, by performing consistency verification between the second associated controller and the wake-up records, different anomaly types such as normal wake-up, abnormal self-wake-up, and event-power consumption mismatch can be distinguished.

[0011] Combining the first aspect and the above implementation method, determining the first associated controller based on the wake-up record includes: identifying the wake-up event in the wake-up record; determining the wake-up source identifier in the vehicle that performs the wake-up action based on the wake-up event; and querying the database based on the wake-up source identifier to obtain the first associated controller.

[0012] By using the above technical solution, the first associated controller can be determined based on the wake-up record, and the original event record can be gradually transformed into a specific controller object, realizing the mapping from "event-level information" to "controller-level positioning". At the same time, by identifying the wake-up event and extracting the wake-up source identifier, the specific source that triggers the wake-up behavior can be clearly identified, avoiding vague judgments based solely on current changes. By querying the database to establish the correspondence between the wake-up source and the controller, it is helpful to quickly locate the target controller involved in the wake-up process, thereby providing a direct basis for subsequent abnormal cause determination.

[0013] Combining the first aspect and the above implementation method, before querying the database based on the pulse characteristics in the current characteristics to obtain the second associated controller, the method further includes: collecting wake-up data under different wake-up events when the vehicle is in a dormant state; extracting the current data of the battery from the wake-up data and extracting the pulse characteristics of the current data; obtaining the controller list corresponding to the wake-up event and establishing a database based on the wake-up source identifier, controller list and pulse characteristics corresponding to the wake-up event.

[0014] Through the above technical solution, the embodiments of this application can collect wake-up data under different wake-up events and build a database before matching the controller, which can provide a standardized reference benchmark for subsequent anomaly localization. At the same time, by extracting pulse features from the current data and associating them with the corresponding wake-up events and controller list, a mapping relationship of "wake-up source - controller - power consumption feature" can be established. The database can cover a variety of typical wake-up scenarios. In the actual detection process, the corresponding controller can be quickly identified through feature matching, avoiding the complex calculations caused by real-time inference and improving localization efficiency.

[0015] Combining the first aspect and the above implementation method, the cause of the anomaly is located based on the second associated controller and the wake-up record, including: if no wake-up event is identified in the wake-up record, the cause of the anomaly is determined to be at least one of a software fault and a hardware fault in the second associated controller; if a wake-up event is identified in the wake-up record, when the first associated controller and the second associated controller are mismatched, the cause of the anomaly is determined to be at least one of a network fault and a power management fault in the vehicle.

[0016] Through the above technical solution, when no wake-up event is identified in the wake-up record, it indicates that the current change lacks external triggering basis. By directly attributing the anomaly to the spontaneous behavior of the second associated controller, it helps to quickly identify software anomalies or hardware faults of the controller and avoid ineffective investigation of external factors. When a wake-up event exists in the wake-up record but the first associated controller and the second associated controller are mismatched, by identifying the inconsistency between the "event source" and the "actual power consumption subject", complex faults caused by network signal error transmission, power path anomalies, or control logic anomalies can be effectively discovered. Furthermore, the embodiments of this application, by constructing a dual verification mechanism of "feature matching result + event record", realize the distinction between self-wake-up faults, false wake-up faults, and system-level anomalies, improving the accuracy and reliability of anomaly cause determination.

[0017] Combining the first aspect and the above implementation method, the second associated controller is obtained by querying the database based on the pulse features in the current features, including: calculating the similarity between the pulse features in the current features and the pulse features in the database; and obtaining the second associated controller from the database based on the similarity.

[0018] By using the above technical solution, the pulse characteristics in the current features can be compared with the pulse characteristics in the database to make a quantitative comparison between the actual current signal and the established standard power consumption characteristics, thereby avoiding subjective judgment based on human experience. At the same time, by matching based on similarity, the closest target feature can be selected from a variety of candidate features, so as to realize the automatic identification of the corresponding controller.

[0019] Secondly, a vehicle anomaly location device is provided, comprising: a monitoring module for monitoring the current value of the battery in the vehicle's dormant state; an extraction module for acquiring the current data and log data synchronously recorded in the vehicle's dormant state if the detected current value is greater than an abnormal threshold, and extracting the current characteristics and timestamps from the current data; a query module for querying the wake-up records of the log data based on the timestamps of the current data; and a location module for locating the cause of the anomaly based on the current characteristics and the wake-up records.

[0020] Thirdly, a vehicle is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described vehicle anomaly location method.

[0021] Fourthly, a computer-readable storage medium is provided, on which a computer program or instructions are stored, characterized in that, when the computer program or instructions are executed, the above-described vehicle anomaly location method is implemented. Attached Figure Description

[0022] Figure 1This is a schematic flowchart of the vehicle anomaly location method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the overall process of the vehicle anomaly location method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the vehicle anomaly location device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the vehicle structure provided in the embodiments of this application. Detailed Implementation

[0023] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0024] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0025] In vehicle electrical system design, the control of the vehicle's static current primarily relies on each associated controller entering a low-power sleep state after the vehicle is turned off and locked. The vehicle controller or body control module manages various wake-up signals to reduce standby power consumption. Under normal operating conditions, this mechanism ensures that each associated controller stably enters sleep mode, maintaining a low static current level and preventing excessive battery discharge during prolonged parking. However, when the vehicle experiences intermittent wake-up source anomalies or frequent short-term wake-up events, some associated controllers may repeatedly switch from sleep mode to operating mode without sufficient convergence, leading to periodic pulses or abnormal increases in static current. Furthermore, due to the complex wake-up paths between controllers and the lack of a unified causal correlation recording mechanism, the vehicle controller typically struggles to directly identify specific abnormal power consumption. Moreover, existing technologies for detecting abnormal static current often rely on threshold judgments using vehicle current sensors. While this can identify whether the current exceeds the limit, it cannot further distinguish the source of the anomaly or establish a correlation between wake-up events and current anomalies. In situations with multiple wake-up sources, especially in abnormal scenarios caused by intermittent triggering from door handles, keyless entry systems, or remote communication modules, fault location is difficult and diagnostic efficiency is low. Therefore, how to accurately identify abnormal static current and locate abnormal power consumption in situations with multiple wake-up sources and intermittent triggering has become a key technical issue in ensuring the safety of vehicle batteries and the electrical reliability of the entire vehicle.

[0026] The application scenarios or system architecture of the embodiments of this application will be described next.

[0027] The application scenarios of this application mainly address the problems of low location efficiency and strong reliance on experience due to manual point-by-point inspection in troubleshooting abnormal static current faults, and the inability to accurately locate the fault source due to reliance on battery sensor alarms. Specifically, this embodiment proposes a vehicle anomaly location method, which is particularly suitable for plug-in hybrid electric vehicles equipped with power batteries, motor control systems, battery management systems, and vehicle controllers. First, the current waveform and wake-up records of the vehicle in a static state are acquired, and the corresponding waveform type and event sequence information are extracted to provide a basic data source for static current anomaly analysis. Second, feature matching of the current signal is performed according to the waveform type to identify the possible corresponding power consumption fingerprint type, thereby realizing the identification of the source of abnormal current pulses. The initial classification improves the accuracy of anomaly source identification. Then, time-series analysis is performed based on wake-up records to determine the temporal correlation between abnormal pulses and each wake-up event. Time consistency verification enhances the ability to determine causal relationships and reduces the probability of false matching. Finally, the cause of the anomaly is determined based on feature matching results and time-series correlation results, and corresponding diagnostic result information and diagnostic fault codes are generated. This enables automated, accurate positioning and structured output of static current anomalies. Based on the current waveform type and wake-up records, the vehicle's static current anomalies can be accurately analyzed and quickly located. This avoids the problems of low positioning efficiency and strong reliance on experience due to manual point-by-point inspection, as well as the inability to accurately locate the fault source due to reliance on battery sensor alarms.

[0028] Figure 1 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application.

[0029] For example, such as Figure 1 As shown, the vehicle anomaly localization method includes the following steps: In step S101, the current value of the battery is monitored when the vehicle is in sleep mode.

[0030] The battery is a power supply device used to provide electrical energy to the vehicle's electrical system; the current value in this application represents the total current output by the battery when the vehicle is in sleep mode, and is used to characterize the static power consumption level of the vehicle; the vehicle sleep mode is the operating state in which the vehicle is powered off and each electronic control unit enters a low-power operating mode.

[0031] Understandably, by monitoring the battery current value in the vehicle's dormant state, the total current change of the entire vehicle in a low-power state can be obtained in real time, thus providing a direct basis for judging whether there is abnormal power consumption. By continuously monitoring the current change, short-term and intermittent current fluctuations can be effectively captured, providing high-precision raw data support for subsequent extraction of current features and anomaly location analysis.

[0032] In this embodiment of the application, before monitoring the current value of the battery in the vehicle's dormant state, the method further includes: obtaining the current charge level of the battery and the ambient temperature; and determining an abnormal threshold based on at least one of the current charge level and the ambient temperature.

[0033] Among them, ambient temperature refers to the temperature parameter of the external environment in which the vehicle is located, and in this application, it represents the environmental factors that affect the performance of the battery and the power consumption level of electronic components; abnormal threshold is a preset current limit used to determine whether the current value is abnormal, and in this application, it represents the static current judgment standard that is dynamically adjusted based on the current battery charge and ambient temperature.

[0034] Understandably, by acquiring the current battery charge and ambient temperature before monitoring the current value, and determining the abnormal threshold based on the current charge and ambient temperature, the abnormal judgment standard is transformed from a fixed threshold to a dynamic threshold, thereby making the threshold more closely match the actual working state of the battery and environmental conditions. At the same time, since the discharge capacity and self-discharge characteristics of the battery vary under different charge and temperature conditions, the use of dynamically adjusted abnormal thresholds can effectively reduce false alarms or missed alarms caused by changes in the environment or charge status.

[0035] In this embodiment of the application, when determining the abnormal threshold based on the current battery charge and ambient temperature, a multi-factor joint adjustment method can be used to dynamically set the abnormal threshold.

[0036] Specifically, the strategy for dynamically setting the abnormal threshold is shown in Table 1.

[0037] Table 1

[0038] When the current state of charge of the battery is high, it indicates that the battery has sufficient energy reserves. This application can adopt a relatively lenient abnormal threshold to reduce the probability of misjudging short-term normal wake-up behavior. When the current battery charge is at a medium level, in order to ensure that the vehicle still has reliable starting ability while parked, the abnormal threshold is tightened accordingly to improve the sensitivity of identifying abnormal power consumption behavior. When the current battery charge is low, the abnormal threshold is further reduced to prioritize the remaining battery charge and can be combined with control strategies to restrict the operation of non-essential electrical equipment.

[0039] Meanwhile, ambient temperature also affects the setting of the anomaly threshold. When in a normal temperature environment, a baseline anomaly threshold can be used; when in a low temperature environment, due to the decrease in the effective capacity of the battery and the instantaneous increase in power consumption of some controllers during low-temperature startup, the anomaly threshold can be appropriately relaxed to avoid false judgments; when in a high temperature environment, due to the increased self-discharge of the battery and the potential increase in leakage current of devices, the anomaly threshold can be appropriately tightened to improve the anomaly detection capability.

[0040] For example, when a vehicle is parked in a low-temperature environment (such as -5°C) and the current battery charge is at a moderate level (such as 70%), the embodiments of this application, when determining the abnormal threshold, not only consider the current charge condition, but also combine the impact of low temperature on the battery starting performance, and comprehensively adjust the abnormal threshold so that it is between a lenient threshold and a strict threshold, thereby improving the detection accuracy of abnormal power consumption while ensuring the vehicle's starting ability.

[0041] In step S102, if the current value is detected to be greater than the abnormal threshold, the current data and log data synchronously recorded in the vehicle's sleep state are obtained, and the current features and timestamps in the current data are extracted.

[0042] Among them, current data refers to time-series data obtained by sampling the output current of the battery, and in this application, it represents continuous current measurement results used to reflect the current change process in the vehicle's dormant state; log data refers to a data set formed by recording various events during vehicle operation, and in this application, it represents event record data containing trigger information and time information of each wake-up source; current characteristics are parameters extracted from current data that characterize the current change law, and in this application, they represent characteristic parameters such as amplitude, width, charge, and slope of change used to describe the characteristics of the current waveform; timestamp is time information that identifies the time when the data occurs, and in this application, it represents a time stamp used to realize time alignment and correlation analysis between current data and log data.

[0043] Understandably, by acquiring synchronously recorded current and log data when the detected current value exceeds the abnormal threshold, and extracting current features and timestamps, the abnormal power consumption of the entire vehicle can be expanded from a single numerical judgment to a multi-dimensional data representation of "waveform features + event records," thus providing a sufficient information foundation for subsequent accurate analysis. At the same time, by extracting current features, the raw current data can be transformed into structured parameters with comparability and identifiability, facilitating pattern matching and anomaly identification. Furthermore, by using timestamps to achieve precise alignment between current data and log data, it is helpful to establish a temporal correlation between current changes and wake-up events, thereby improving the accuracy of anomaly localization.

[0044] In this embodiment of the application, after acquiring the current data synchronously recorded in the vehicle's sleep state, feature extraction processing is performed on the current data to transform the raw current data into identifiable current features.

[0045] Specifically, when a rise in current relative to the sleep baseline current is detected and exceeds a preset threshold, the corresponding current change is determined as a wake-up pulse event, and the corresponding pulse features are extracted based on the current waveform.

[0046] The pulse characteristics include: pulse amplitude, pulse width, pulse charge, and rising and falling edge slopes. Specifically, the pulse amplitude is the peak current of the current waveform, used to characterize the instantaneous power consumption level of the associated controller during the wake-up process; the pulse width is the duration of the current waveform under preset judgment conditions, used to characterize the duration of the wake-up state; the pulse charge is the result obtained by integrating the current waveform, used to characterize the energy consumption during a single wake-up process; and the rising and falling edge slopes characterize the rate of current change, thus reflecting the power-on and power-off dynamic characteristics of the associated controller.

[0047] The above method can transform continuous current waveform data into structured feature parameters, and combined with the corresponding timestamps, provide basic data support for subsequent feature matching and anomaly localization.

[0048] In step S103, the wake-up record of the log data is queried based on the timestamp of the current data.

[0049] Understandably, by querying the wake-up records in the log data based on the timestamp of the current data, it is possible to establish a temporal correspondence between current changes and wake-up events, thereby enabling time-series correlation analysis of the causes of abnormal current. At the same time, by matching the occurrence time of current characteristics with specific wake-up events, it is helpful to filter out the triggering factors most relevant to abnormal power consumption from multi-source information, avoiding blind judgment based solely on current data.

[0050] In this embodiment of the application, a unified time synchronization mechanism is established before querying the wake-up record in the log data based on the timestamp of the current data, so as to ensure the consistency of the current data and the wake-up record in the time dimension.

[0051] Specifically, in one embodiment of this application, the current acquisition unit can continuously sample the battery output current to obtain current data reflecting the current change process, thereby capturing transient current changes during vehicle hibernation; the controller is used to monitor the trigger status of multiple wake-up sources and record wake-up events, wherein the wake-up sources include hard-wired wake-up sources and network wake-up sources. The acquisition unit used to collect current data and the controller used to record wake-up events in the vehicle use a unified time base and periodically send time synchronization messages through the communication bus to calibrate the local clock of each node, so that the timestamps of various types of data are consistent.

[0052] For example, in one embodiment of this application, a time synchronization message can be sent via a local area network bus or a controller area network bus to distribute the time to each control node, so that the time information recorded by the current acquisition unit and the vehicle body domain controller is under the same time reference. After the current feature corresponding to a certain timestamp in the current data is extracted, the wake-up record within the corresponding time period can be found in the log data based on the timestamp, thereby determining the wake-up event related to the current change.

[0053] By using the above method, precise time alignment between current data and wake-up records can be achieved, thereby improving the accuracy of correlation analysis between wake-up events and current changes and providing a reliable basis for subsequent anomaly localization.

[0054] In step S104, the cause of the anomaly is located based on the current characteristics and the wake-up record.

[0055] It is understandable that the cause of the anomaly is the root cause of the abnormal static current of the vehicle, and in this application, it represents the source of abnormal power consumption caused by abnormal operation of the associated controller, abnormal wake-up, or abnormal power management.

[0056] In this embodiment of the application, the method of locating the cause of the anomaly based on the current characteristics and the wake-up record includes: extracting the shape features from the current characteristics; determining the waveform type of the current data based on the shape features; and locating the cause of the anomaly based on the waveform type and the wake-up record.

[0057] Understandably, by locating the cause of anomalies based on current characteristics and wake-up records, the physical manifestations of current changes can be correlated with triggering events inside the vehicle, thereby improving the judgment from "whether it is abnormal" to the accurate identification of "the source of the anomaly." At the same time, by matching current characteristics (such as amplitude, width, and power) with wake-up records, normal wake-up behavior and abnormal power consumption behavior can be distinguished, avoiding misjudging legitimate function triggers as faults. Furthermore, when current characteristics and wake-up records are consistent, the corresponding wake-up source and associated controller can be quickly located, improving the location efficiency. When the two are inconsistent, potential network anomalies, power management anomalies, or controller faults can be identified, thereby achieving effective differentiation of complex fault scenarios.

[0058] In this embodiment of the application, the method of locating the cause of the abnormality based on the waveform type and the wake-up record includes: if the waveform type is a non-pulse waveform, then the first associated controller is determined based on the wake-up record, and the cause of the abnormality is determined to be a power management state machine failure of the first associated controller; if the waveform type is a pulse waveform, then the second associated controller is obtained by querying the database based on the pulse characteristics in the current characteristics, and the cause of the abnormality is located based on the second associated controller and the wake-up record.

[0059] Among them, waveform type is the classification result of current signal based on the change pattern of current data, and in this application, it represents the classification identifier used to distinguish whether the current change exhibits pulse characteristics; non-pulse waveform is a waveform in which the current changes continuously or has a stable plateau over time and does not have obvious pulse characteristics, and in this application, it represents the current change pattern corresponding to the controller not entering sleep mode normally or being in a continuous working state; pulse waveform is a transient change pattern in which the current rises significantly and falls back in a short period of time, and in this application, it represents the current change pattern corresponding to the instantaneous power consumption behavior triggered by a wake-up event; the first associated controller is the controller corresponding to the wake-up event determined according to the wake-up record, and in this application, it represents the target controller that performs the wake-up action or responds to the wake-up signal; the second associated controller is the controller matched from the database based on the pulse characteristics in the current characteristics, and in this application, it represents the controller that best matches the current waveform characteristics; the database is a data set storing wake-up events, controller information, and corresponding current characteristics, and in this application, it represents the power consumption characteristic library used for feature matching and correlation analysis; power management state machine fault is an abnormal situation in which the controller does not operate according to the preset state transition logic during power management.

[0060] Understandably, by locating the cause of anomalies based on waveform type and wake-up records, abnormal behavior can be classified based on current waveforms, dividing abnormal scenarios into non-pulse anomalies and pulse anomalies, thereby reducing the complexity of subsequent analysis. For non-pulse waveforms, the first associated controller can be identified by combining wake-up records, and further determined to be a power management state machine fault. This helps to quickly identify abnormal situations where the controller fails to enter sleep mode or is stuck in an intermediate state after wake-up, enabling direct location of continuous abnormal power consumption. For pulse waveforms, the second associated controller can be obtained by extracting pulse features and querying the database. The actual current behavior can be matched with pre-established power consumption features, thereby identifying the corresponding wake-up source or controller. Furthermore, by performing consistency verification between the second associated controller and the wake-up records, different anomaly types such as normal wake-up, abnormal self-wake-up, and event-power consumption mismatch can be distinguished.

[0061] In this embodiment of the application, when the waveform type of the current data is a non-pulse waveform, the corresponding first associated controller can be determined based on the wake-up record, and the cause of the abnormality can be further determined.

[0062] For example, when a vehicle is in sleep mode, a wake-up event is recorded, and the wake-up source identifier corresponding to the wake-up event indicates that a certain associated controller has been triggered. At the same time, the current data shows a corresponding current rise, reaching a peak value within a short period of time, for example, rising from the sleep baseline current (e.g., 10mA-20mA) to the peak current (e.g., 80mA-120mA). However, unlike the normal wake-up process, after reaching the peak value, the current does not drop back to the sleep baseline current within a preset time (e.g., 1s-5s), but instead remains at a relatively stable current plateau (e.g., 60mA-90mA) for a relatively long time (e.g., more than 30s, or even several minutes). By analyzing the current characteristics, it is identified that the current waveform contains a combination of "rising segment + stable plateau segment" characteristics and lacks a complete fallback process. Therefore, it can be determined that the current waveform does not conform to the pulse characteristics, thus identifying it as a non-pulse waveform.

[0063] Based on this, after identifying the first associated controller by combining the wake-up records, the operating status of the associated controller after the corresponding wake-up event is further analyzed. If the associated controller fails to complete the state switch and enter sleep mode within the preset time limit (such as 10s-60s) after executing the corresponding function, or if its internal sub-modules remain active, resulting in continuous power consumption, the cause of the abnormality can be determined to be a power management state machine failure of the first associated controller.

[0064] Furthermore, embodiments of this application can further refine the judgment of anomalies by combining the duration of the current plateau, the steady-state current amplitude, and the type of the corresponding wake-up event. For example, when the duration of the current plateau exceeds a preset threshold (e.g., 60s) or the steady-state current amplitude is higher than a benchmark value (e.g., higher than 120% of the normal wake-up steady-state current), it can be determined as a serious non-sleep anomaly; when the duration is short but occurs frequently, it can be determined as an intermittent non-sleep anomaly.

[0065] In another embodiment of this application, when the waveform type of the current data is a pulse waveform, the second associated controller can be obtained by querying the database based on the pulse characteristics in the current characteristics, and the cause of the abnormality can be located by combining the second associated controller and the wake-up record.

[0066] For example, when the vehicle is in a dormant state, periodic current pulses are detected in the current data, with peak currents ranging from 100mA to 200mA and pulse widths ranging from 100ms to 500ms. The time intervals between adjacent pulses are also relatively consistent (e.g., occurring once every 300s). By extracting the pulse characteristics and matching them with a database, the corresponding second associated controller can be determined.

[0067] Based on this, analysis is performed using wake-up records: if no wake-up event corresponding to the second associated controller is detected, it can be determined that the controller has abnormal self-wake-up; if a wake-up event is detected but the corresponding first associated controller and second associated controller are inconsistent, it can be determined that there is a network fault or power management abnormality.

[0068] In this embodiment of the application, determining the first associated controller based on the wake-up record includes: identifying a wake-up event in the wake-up record; determining the wake-up source identifier in the vehicle that performs the wake-up action based on the wake-up event; and querying the database based on the wake-up source identifier to obtain the first associated controller.

[0069] Among them, the wake-up event is the trigger information recorded in the log data, which in this application represents the trigger behavior that enables the vehicle to enter the working state from the sleep state; the wake-up source identifier is the identification information used to characterize the source of the wake-up event, which in this application represents a unique marker corresponding to a specific trigger source (such as a door handle sensor, network message or control signal).

[0070] Understandably, by identifying the first associated controller based on the wake-up record, the original event record can be gradually transformed into a specific controller object, realizing the mapping from "event-level information" to "controller-level location". At the same time, by identifying the wake-up event and extracting the wake-up source identifier, the specific source that triggered the wake-up behavior can be clearly identified, avoiding vague judgments based solely on current changes. By querying the database to establish the correspondence between the wake-up source and the controller, it is helpful to quickly locate the target controller involved in the wake-up process, thereby providing a direct basis for subsequent anomaly cause determination.

[0071] For example, when the vehicle is in sleep mode, the wake-up records detect wake-up events including "remote air conditioning start request" and "vehicle positioning periodic reporting trigger". Based on the wake-up events, the corresponding wake-up source identifiers can be identified as the remote control terminal and the vehicle communication module, respectively. Further, by querying the database based on the wake-up source identifier, the first associated controller corresponding to the remote control terminal can be identified as the remote communication control module, and the first associated controller corresponding to the vehicle communication module can be identified as the remote information processing control module.

[0072] In this embodiment of the application, before querying the database based on the pulse characteristics in the current characteristics to obtain the second associated controller, the method further includes: collecting wake-up data under different wake-up events when the vehicle is in a dormant state; extracting the current data of the battery from the wake-up data and extracting the pulse characteristics of the current data; obtaining the controller list corresponding to the wake-up event and establishing a database based on the wake-up source identifier, controller list and pulse characteristics corresponding to the wake-up event.

[0073] Among them, wake-up data is a set of data collected when the vehicle is in sleep mode and is triggered by different wake-up events. In this application, it represents the raw data containing current change information and corresponding wake-up event information; current data is the time-series data obtained by sampling the battery output current. In this application, it represents the continuous measurement data used to reflect the current change during the wake-up process; pulse characteristics are characteristic parameters extracted from the current data to characterize transient current changes. In this application, it represents a set of parameters describing the pulse amplitude, width, charge, and change slope; controller list is a set of multiple controllers associated with the wake-up event. In this application, it represents a set of electronic control units that participate in the operation under the trigger of the corresponding wake-up event.

[0074] It is understood that the embodiments of this application can provide a standardized reference benchmark for subsequent anomaly localization by collecting wake-up data under different wake-up events and building a database before performing controller matching. At the same time, by extracting pulse features from the current data and associating them with the corresponding wake-up events and controller list, a mapping relationship of "wake-up source - controller - power consumption features" can be established. The database can cover a variety of typical wake-up scenarios. In the actual detection process, the corresponding controller can be quickly identified through feature matching, avoiding the complex calculations brought about by real-time inference and improving localization efficiency.

[0075] During the database construction process, wake-up data under different wake-up events is collected when the vehicle is in a dormant state. The wake-up data includes total static current waveform data and wake-up source trigger timing data.

[0076] The total static current waveform data is collected by the intelligent battery sensor, which captures instantaneous changes and subtle pulse changes in current using a high-frequency sampling method to form raw current data. The wake-up source trigger timing data is recorded by the vehicle domain controller, which records the trigger timestamps of each potential wake-up source.

[0077] The current data is aligned with the wake-up source trigger timing data through a time synchronization mechanism to ensure the correspondence between current changes and wake-up events in time, thereby providing a basis for subsequent correlation analysis.

[0078] Based on this, the current data is processed to extract pulse features, which include pulse amplitude, pulse width, and pulse charge. The pulse charge is obtained by integrating the current-time curve.

[0079] Furthermore, the wake-up source identifier and its controller list corresponding to each wake-up event are obtained, and the wake-up source identifier, pulse characteristics and controller list are associated and stored to form a database. Finally, the database is stored in a structured form, as shown in Table 2. Table 2 includes wake-up source events, typical amplitude A, typical width W, typical power Q and associated controller list.

[0080] Table 2

[0081] In addition, the database includes a baseline power consumption fingerprint library learned from historical data before the vehicle leaves the factory or under normal operating conditions. The baseline power consumption fingerprint library serves as the initial database to characterize the power consumption characteristics corresponding to different wake-up events under standard operating conditions.

[0082] During actual vehicle operation, the baseline power consumption fingerprint database is updated based on continuously collected wake-up data to form the latest power consumption fingerprint database. The update process can be achieved through cloud data backhaul or in-vehicle self-learning, so that the database can be dynamically optimized and calibrated according to the actual vehicle usage.

[0083] In this embodiment of the application, the method of locating the cause of the anomaly based on the second associated controller and the wake-up record includes: if no wake-up event is identified in the wake-up record, the cause of the anomaly is determined to be at least one of a software fault and a hardware fault in the second associated controller; if a wake-up event is identified in the wake-up record, the cause of the anomaly is determined to be at least one of a network fault and a power management fault in the vehicle when the first associated controller and the second associated controller are mismatched.

[0084] Among them, software failure refers to an abnormal situation that occurs during the execution of the controller program, which in this application represents a program error that causes the controller to wake up abnormally or work abnormally; hardware failure refers to a physical abnormality in the controller or its related circuits, which in this application represents damage to the device or abnormality in the circuit that causes abnormal power consumption; network failure refers to an abnormal situation in the signal transmission in the vehicle communication network, which in this application represents a communication abnormality that causes the wake-up signal to be transmitted incorrectly or triggered falsely; power management failure refers to an abnormality that occurs during the switching of the controller's power state, which in this application represents an abnormal power consumption problem caused by the controller not performing power-on or sleep control according to the preset strategy.

[0085] Understandably, when no wake-up event is identified in the wake-up record, it indicates that the current change lacks external triggering basis. By directly attributing the anomaly to the spontaneous behavior of the second associated controller, it helps to quickly identify software anomalies or hardware failures of the controller and avoid ineffective troubleshooting of external factors. When a wake-up event exists in the wake-up record but the first associated controller and the second associated controller do not match, by identifying the inconsistency between the "event source" and the "actual power consumption subject", complex faults caused by network signal error transmission, power path anomalies, or control logic anomalies can be effectively discovered. Furthermore, the embodiments of this application, by constructing a dual verification mechanism of "feature matching result + event record", realize the differentiation between self-wake-up faults, false wake-up faults, and system-level anomalies, improving the accuracy and reliability of anomaly cause determination.

[0086] In this embodiment of the application, after obtaining the second associated controller by querying the database based on the pulse characteristics in the current characteristics, the cause of the abnormality can be determined by combining the wake-up record.

[0087] For example, when the vehicle is in a dormant state, periodic current pulses are detected in the current data. These pulses have basically the same characteristics, and the time interval between adjacent pulses is basically fixed (e.g., once every 300 seconds). By extracting characteristic parameters such as the amplitude, width, and charge of the pulses and matching them with pulse characteristics in the database, the corresponding second associated controller can be determined.

[0088] Based on this, querying the wake-up record may result in the following two scenarios: If no wake-up event is detected near the timestamp corresponding to the pulse, it indicates that the current pulse is not triggered by an external wake-up source, but is caused by an abnormality in the second associated controller itself. Therefore, it can be determined that the cause of the abnormality is at least one of the software and hardware faults in the second associated controller, such as abnormal triggering of the controller's internal timer or abnormality of related circuits leading to periodic self-wake-up. If a wake-up event is detected near the timestamp corresponding to the pulse, the first associated controller can be determined based on the wake-up event, and the first associated controller and the second associated controller can be matched and analyzed. When the two are inconsistent, it indicates that the wake-up source is inconsistent with the actual power consumption subject, so the cause of the abnormality can be determined to be at least one of the vehicle's network fault and power management fault, such as abnormal triggering of communication messages, network wake-up path error or power distribution abnormality leading to false wake-up.

[0089] Furthermore, to illustrate the specific manifestations of the mismatch between the first and second associated controllers, the following examples are provided: This application records a wake-up event sequence consisting of trunk switch triggering and door handle triggering. However, the current pulse characteristics detected in the current data highly match the power consumption fingerprint of another controller, and its amplitude and power are significantly higher than the baseline power consumption characteristics corresponding to the wake-up event, indicating an inconsistency between the wake-up event and the actual power consumption behavior. Based on this, a consistency comparison between the wake-up event and the current characteristics confirms a mismatch, indicating that the controller indicated by the wake-up record is inconsistent with the controller actually generating power consumption. The cause typically corresponds to a vehicle network fault or power management anomaly, such as abnormal communication messages or network path errors leading to the false wake-up of a non-target controller, or abnormal coupling or short circuits in the power path causing an unexpected triggering of a high-power controller. Through this method, the cause of the anomaly can be located based on the inconsistency between the wake-up event and the power consumption fingerprint, and the anomaly can be attributed to the network system or power management system.

[0090] In this embodiment of the application, obtaining the second associated controller by querying the database based on the pulse features in the current features includes: calculating the similarity between the pulse features in the current features and the pulse features in the database; and obtaining the second associated controller from the database based on the similarity.

[0091] Here, similarity is an index used to measure the degree of similarity between two pulse features. In this application, it represents the value obtained by matching the current feature with the pulse feature in the database.

[0092] Understandably, by calculating the similarity between the pulse features in the current characteristics and the pulse features in the database, the actual current signal can be quantitatively compared with the established standard power consumption features, thereby avoiding subjective judgment based on human experience. At the same time, by matching based on similarity, the closest target feature can be selected from a variety of candidate features, thereby achieving automatic identification of the corresponding controller.

[0093] Specifically, the pulse features extracted from the abnormal current curve, including pulse amplitude A, pulse width W, and pulse charge Q, are compared with the power consumption fingerprint records pre-stored in the database to calculate the similarity between the features and determine the feature matching relationship.

[0094] Similarity calculation can be performed using methods such as Euclidean distance or cosine similarity to measure the degree of difference between the current pulse features and the power consumption fingerprints corresponding to each wake-up source in the database. The higher the similarity, the higher the degree of matching.

[0095] Based on the calculated similarity results, the power consumption fingerprint record with the highest matching degree is selected from the database, and the wake-up source or controller corresponding to the power consumption fingerprint record is determined as the second associated controller.

[0096] By using the above method, the source of abnormal current can be located based on the similarity of pulse characteristics, thereby identifying the vehicle controller most relevant to the abnormal current characteristic.

[0097] In this embodiment of the application, after locating the cause of the abnormality based on the waveform type and wake-up record, corresponding diagnostic result information and diagnostic fault codes can also be generated.

[0098] For example, if the pulse feature matching result points to the power consumption fingerprint corresponding to the "left front door handle", and the timing verification result shows that there is a left front door handle trigger record within a preset time range before the abnormal pulse occurs, it can be determined that there is a correlation between the wake-up event and the abnormal current pulse, thereby locating the cause of the abnormality.

[0099] Based on the above location results, a diagnostic fault code is generated. The diagnostic fault code includes the fault module identifier and the abnormality type information. The abnormality type includes failure to sleep after wake-up, exceeding the self-wake-up frequency limit, and exceeding the single power consumption limit. The diagnostic result information includes quantitative data related to current characteristics to characterize the degree of abnormality, such as the deviation relationship between the actual pulse power and the reference power consumption fingerprint, as well as trigger context information to indicate the source of the wake-up event when the abnormality occurs.

[0100] Ultimately, the diagnostic results and fault codes can be uploaded to the remote service platform via the vehicle-mounted remote communication terminal, or provided to diagnostic equipment for reading, and will prompt the user with the cause of the abnormal static current, providing a basis for fault diagnosis and repair.

[0101] For example, in one specific embodiment of this application, an abnormal current pulse occurs when the vehicle is stationary, with a peak current of approximately 2.3A and a duration of approximately 120ms. The system performs timing analysis based on the event sequence and discovers a wake-up record of the left front door handle within 200ms prior to the anomaly. This wake-up signal occurs between t0 and 180ms and lasts approximately 60ms. Based on the timing matching results, it is determined that this wake-up event is associated with the abnormal pulse.

[0102] Based on this, a diagnostic fault code is generated, which includes the fault module identifier (left front door handle control module), the abnormality type (no sleep after wake-up, single power consumption exceeds the standard) and quantitative information (single wake-up power consumption is 15mC, which exceeds the baseline value of 10mC by about 50%). At the same time, the diagnostic result information and diagnostic fault code are recorded, such as the trigger context (left front door handle is continuously triggered to wake up).

[0103] Ultimately, the diagnostic results and fault codes can be uploaded to the remote service platform via the vehicle-mounted remote communication terminal, or provided to diagnostic equipment for reading, and will prompt the user with the cause of the abnormal static current, providing a basis for fault diagnosis and repair.

[0104] In summary, the specific process of the embodiments of this application is as follows: Figure 2 As shown: In step S201, after the vehicle enters a sleep state, the current value of the battery in the sleep state is monitored, the current data is obtained and recorded. In step S202, the current value is compared with a preset abnormal threshold to determine whether the current value is greater than the abnormal threshold. If the current value is not greater than the abnormal threshold, proceed to step S203; if the current value is greater than the abnormal threshold, proceed to step S204. In step S203, if the current value is not greater than the abnormal threshold, the historical information corresponding to the current data in the database is updated to indicate that the vehicle is in a normal sleep state. The database update process is as follows: the current data and wake-up records are time-aligned through a time synchronization mechanism, and pulse features are extracted based on the aligned current data. The pulse features include pulse amplitude, pulse width, and pulse charge. The pulse features are associated with the corresponding wake-up source identifier and associated controller list and stored to update the power consumption fingerprint database. In step S204, if the current value is greater than the abnormal threshold, the current data synchronously recorded by the vehicle in the sleep state is acquired, and the current data is used to extract features to obtain current features. In step S205, log data corresponding to the current data is obtained. The log data includes event record data containing trigger information and time information of each wake-up source. In step S206, the wake-up record of the vehicle during the sleep state is obtained. The wake-up record includes the wake-up time, wake-up source and corresponding controller information. In step S207, correlation analysis is performed based on current characteristics, log data, and wake-up records to determine the associated controller corresponding to the current anomaly, and the cause of the fault is located based on the matching relationship between the current characteristics and the wake-up records. The specific process is as follows: the corresponding pulse type is determined based on the current characteristics, and the pulse characteristics are matched with the wake-up source identifier in the wake-up records; the candidate controllers corresponding to the wake-up sources are screened by combining the operating status and communication status of each controller in the log data; when the pulse characteristics match the wake-up records, the controller corresponding to the matched wake-up source is determined as the associated controller; when the pulse characteristics do not match the wake-up records or there is an abnormal state in the log data, the cause of the current anomaly is determined. In step S208, a corresponding diagnostic fault code is generated based on the cause of the fault; In step S209, the diagnostic fault codes and diagnostic results related to the current abnormality are recorded and uploaded to the cloud server; In step S210, an abnormality prompt message is generated and output to the user through the vehicle display terminal or mobile terminal.

[0105] According to the vehicle anomaly localization method proposed in this application, firstly, the current waveform and wake-up records of the vehicle in a static state are acquired, and the corresponding waveform type and event sequence information are extracted to provide basic data for static current anomaly analysis. Secondly, feature matching of the current signal is performed according to the waveform type to identify the possible corresponding power consumption fingerprint type, thereby achieving preliminary classification of the source of abnormal current pulses and improving the accuracy of anomaly source identification. Then, time-series analysis is performed in conjunction with the wake-up records to determine the temporal correlation between abnormal pulses and each wake-up event. The ability to determine causal correlation is enhanced through time consistency verification, reducing the probability of false matching. Finally, the cause of the anomaly is determined based on the feature matching results and time-series correlation results, and corresponding diagnostic result information and diagnostic fault codes are generated. This achieves automated, accurate localization and structured output of static current anomalies, thereby accurately analyzing and quickly locating vehicle static current anomalies based on current waveform type and wake-up records. This avoids the problems of low localization efficiency and strong reliance on experience due to manual point-by-point investigation, as well as the inability to accurately locate fault sources due to reliance on battery sensor alarms.

[0106] Figure 3 This is a schematic diagram of the structure of a vehicle abnormality location device provided in an embodiment of this application.

[0107] For example, such as Figure 3 As shown, the vehicle anomaly location device 10 may include: a monitoring module 100, an extraction module 200, a query module 300, and a location module 400.

[0108] The monitoring module 100 is used to monitor the current value of the battery in the vehicle's sleep state; the extraction module 200 is used to obtain the current data and log data synchronously recorded in the vehicle's sleep state if the detected current value is greater than the abnormal threshold, and extract the current characteristics and timestamps from the current data; the query module 300 is used to query the wake-up records of the log data based on the timestamps of the current data; and the location module 400 is used to locate the cause of the abnormality based on the current characteristics and wake-up records.

[0109] In this embodiment, the positioning module 400 is further used to extract shape features from the current features; determine the waveform type of the current data based on the shape features; and locate the cause of the anomaly based on the waveform type and the wake-up record.

[0110] In this embodiment of the application, the positioning module 400 is further configured to determine the first associated controller based on the wake-up record if the waveform type is a non-pulse waveform, and determine the cause of the abnormality as a power management state machine failure of the first associated controller; if the waveform type is a pulse waveform, query the database based on the pulse characteristics in the current characteristics to obtain the second associated controller, and locate the cause of the abnormality based on the second associated controller and the wake-up record.

[0111] In this embodiment, the positioning module 400 is further used to identify wake-up events in the wake-up record; determine the wake-up source identifier that performs the wake-up action in the vehicle based on the wake-up event; and query the database based on the wake-up source identifier to obtain the first associated controller.

[0112] In this embodiment, the system further includes: before querying the database based on the pulse characteristics in the current characteristics to obtain the second associated controller, a module is established to collect wake-up data under different wake-up events when the vehicle is in a dormant state; extract the current data of the battery from the wake-up data, and extract the pulse characteristics of the current data; obtain the controller list corresponding to the wake-up event, and establish a database based on the wake-up source identifier, controller list and pulse characteristics corresponding to the wake-up event.

[0113] In this embodiment of the application, the positioning module 400 is further configured to determine the cause of the abnormality as at least one of a software fault and a hardware fault in the second associated controller if no wake-up event is identified in the wake-up record; and to determine the cause of the abnormality as at least one of a network fault and a power management fault in the vehicle when the first associated controller and the second associated controller do not match, if a wake-up event is identified in the wake-up record.

[0114] In this embodiment, the positioning module 400 is further used to calculate the similarity between the pulse features in the current features and the pulse features in the database; and to obtain the second associated controller from the database based on the similarity.

[0115] In this embodiment of the application, the method further includes: before monitoring the current value of the battery in the vehicle's dormant state, determining the current charge level of the battery and the ambient temperature; and determining an abnormal threshold based on at least one of the current charge level and the ambient temperature.

[0116] According to the vehicle anomaly localization device proposed in this application, firstly, the current waveform and wake-up record of the vehicle in a static state are acquired, and the corresponding waveform type and event sequence information are extracted to provide basic data for static current anomaly analysis. Secondly, feature matching of the current signal is performed according to the waveform type to identify the possible corresponding power consumption fingerprint type, thereby achieving preliminary classification of the source of abnormal current pulse and improving the accuracy of anomaly source identification. Then, time sequence analysis is performed in conjunction with the wake-up record to determine the temporal correlation between the abnormal pulse and each wake-up event. The ability to judge causal correlation is enhanced through time consistency verification, reducing the probability of false matching. Finally, the cause of the anomaly is determined based on the feature matching result and the time sequence correlation result, and the corresponding diagnostic result information and diagnostic fault code are generated. This achieves automated, accurate localization and structured output of static current anomalies, thereby accurately analyzing and quickly locating vehicle static current anomalies based on the current waveform type and wake-up record. This avoids the problems of low localization efficiency and strong reliance on experience due to manual point-by-point investigation, and the inability to accurately locate the fault source due to reliance on battery sensor alarms.

[0117] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0118] When the processor 402 executes the program, it implements the drift method provided in the above embodiments.

[0119] Furthermore, the vehicle also includes: Communication interface 403 is used for communication between memory 401 and processor 402.

[0120] The memory 401 is used to store computer programs that can run on the processor 402.

[0121] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0122] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0123] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0124] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0125] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0126] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0127] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A vehicle abnormal positioning method characterized by comprising: The method includes: Monitor the battery current value when the vehicle is in sleep mode; If the detected current value is greater than the abnormal threshold, the current data and log data synchronously recorded in the vehicle's sleep state are obtained, and the current features and timestamps in the current data are extracted. Query the wake-up record of the log data based on the timestamp of the current data; The cause of the anomaly is determined based on the current characteristics and the wake-up record.

2. The vehicle abnormal positioning method according to claim 1, characterized by, The step of locating the cause of the anomaly based on the current characteristics and the wake-up record includes: Extract the shape features from the current features; The waveform type of the current data is determined based on the shape characteristics; The cause of the anomaly can be determined based on the waveform type and the wake-up record.

3. The vehicle abnormal positioning method according to claim 2, characterized by, The step of locating the cause of the anomaly based on the waveform type and the wake-up record includes: If the waveform type is a non-pulse waveform, then the first associated controller is determined according to the wake-up record, and the cause of the anomaly is determined to be a power management state machine failure of the first associated controller. If the waveform type is a pulse waveform, then the second associated controller is obtained by querying the database based on the pulse characteristics in the current characteristics, and the cause of the abnormality is located based on the second associated controller and the wake-up record.

4. The vehicle anomaly localization method according to claim 3, characterized in that, The step of determining the first associated controller based on the wake-up record includes: Identify the wake-up events in the wake-up record; The wake-up source identifier that performed the wake-up action in the vehicle is determined based on the wake-up event; The first associated controller is obtained by querying the database based on the wake-up source identifier.

5. The vehicle anomaly localization method according to claim 3, characterized in that, Before querying the database based on the pulse characteristics in the current characteristics to obtain the second associated controller, the process further includes: Collect wake-up data under different wake-up events when the vehicle is in sleep mode; Extract the battery current data from the wake-up data, and extract the pulse characteristics of the current data; Obtain the list of controllers corresponding to the wake-up event, and establish the database based on the wake-up source identifier corresponding to the wake-up event, the list of controllers, and the pulse characteristics.

6. The vehicle anomaly localization method according to claim 3, characterized in that, The step of locating the cause of the anomaly based on the second associated controller and the wake-up record includes: If no wake-up event is identified in the wake-up record, the cause of the anomaly is determined to be at least one of a software fault and a hardware fault in the second associated controller. If a wake-up event is detected in the wake-up record, then if the first associated controller and the second associated controller do not match, the cause of the anomaly is determined to be at least one of the vehicle's network fault and power management fault.

7. The vehicle anomaly localization method according to claim 3, characterized in that, The step of querying the database based on the pulse characteristics in the current characteristics to obtain the second associated controller includes: Calculate the similarity between the pulse features in the current characteristics and the pulse features in the database; A second association controller is obtained from the database based on the similarity.

8. The vehicle anomaly location method according to claim 1, characterized in that, Before monitoring the battery current value during vehicle hibernation, the following steps are also included: Obtain the current battery charge and ambient temperature; The abnormal threshold is determined based on at least one of the current battery level and the ambient temperature.

9. A vehicle, characterized in that, The vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle anomaly localization method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the vehicle anomaly location method according to any one of claims 1-8.

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