Automobile electrical automation monitoring system and method

Abstract

The application relates to the technical field of electrical monitoring, and discloses an automobile electrical automation monitoring system and method, which are used for solving the problem that when automobile electrical monitoring is performed, vehicle-mounted function modules are replaced, added or deleted, and fixed priorities do not have representativeness, and the application comprises the following steps: acquiring an operating state parameter, evaluating an electrical fault index, determining whether an automobile currently has an explicit electrical fault according to the electrical fault index; if it is determined that the automobile currently does not have an explicit electrical fault, calculating an electrical performance stability index in a detection time period, determining whether the automobile currently has a deterioration fault risk, if it is determined that the automobile currently has a deterioration fault risk, acquiring function information of all function modules, evaluating a priority index, constructing a power guarantee priority matrix according to the priority index of each function module, and adaptively adjusting a strategy according to the power guarantee priority matrix, so that the stability and scene adaptability of vehicle function execution are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of electrical monitoring technology, and more specifically to an automotive electrical automation monitoring system and method. Background Technology

[0002] As a key support system in intelligent electric vehicles, automotive electrical automation monitoring systems have been widely applied in various fields such as passenger cars, autonomous driving platforms, emergency response vehicles, and special transportation equipment. These systems typically include multiple functional modules such as electrical status acquisition, power distribution, fault diagnosis, and operational strategy adjustment. By monitoring the real-time operating status of batteries, generators, and load modules, they enable centralized management of multi-source power and coordinated power supply to various functional components, providing electrical assurance for the stable operation of the entire vehicle and the execution of its tasks.

[0003] In existing technologies, to ensure power supply stability, systems generally adopt a power allocation strategy based on fixed priority rules. This means that when system power resources are scarce, power limiting or power interruption strategies are applied to lower-priority modules sequentially according to a pre-set module priority order, thereby prioritizing the operation of core modules such as power drive and steering control. These rules are typically set once at the system factory or during vehicle commissioning and are not adjusted during operation.

[0004] However, the above-mentioned technologies have at least the following technical problems:

[0005] In practical use, vehicle-mounted functional modules are often replaceable and can be added or removed. The combination and importance of their functions vary significantly across different vehicle models or mission scenarios, making a fixed module priority order difficult to adapt to changing operational needs. For example, in certain special scenarios, such as unmanned passenger shuttles, disaster relief vehicles, or medical transport vehicles, the vehicle may be equipped with mission-related functional modules such as remote communication modules, medical refrigeration boxes, and environmental control systems. The importance of these modules may be far higher than that of conventional configurations, but in a fixed-priority system, their power supply order cannot be dynamically adjusted. This can lead to critical mission modules being mistakenly limited or insufficiently powered when resources are scarce, affecting the stability of the vehicle's overall functionality and its adaptability to different scenarios. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides an automotive electrical automation monitoring system and method to solve the problems existing in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An automotive electrical automation monitoring system includes: an on-board electrical status acquisition module for real-time acquisition of operating status parameters of key electrical components of the vehicle, including battery voltage, battery current, battery temperature, remaining battery charge, generator output voltage, and electronic load operating power; filtering and time-series alignment of the operating status parameters to obtain preprocessed operating status parameters; an intelligent electrical fault diagnosis module for setting a detection time period, acquiring the preprocessed operating status parameters within the detection time period, evaluating an electrical fault index based on the preprocessed operating status parameters, and determining whether the vehicle currently has a manifest electrical fault based on the electrical fault index; if a manifest fault is determined, a preset diagnostic model library is invoked, and the fault type and location are output based on the electrical fault index and remaining battery charge, and the fault type and location are transmitted to an early warning module; and an electrical performance trend evaluation module, which determines whether the vehicle currently has no manifest electrical fault. The system calculates the electrical performance stability index within the detection period and determines whether the vehicle currently faces a risk of deterioration. The priority assessment module, if it determines that the vehicle faces a risk of deterioration, transmits the electrical performance stability index and the determination result to the early warning module. It then obtains the functional information of all functional modules installed in the vehicle, including actual output power, call count, operating status data, task scheduling records, and control operation data. Based on this functional information, it evaluates and obtains a priority index. The adaptive adjustment module for operating strategies constructs a power supply priority matrix based on the priority indices of each functional module and performs adaptive adjustment strategies based on this matrix. The early warning module receives transmission information from the electrical fault intelligent diagnosis module and the priority assessment module. If it receives transmission information from the electrical fault intelligent diagnosis module, it issues a fault early warning; if it receives transmission information from the priority assessment module, it issues a deterioration risk early warning.

[0009] Preferably, the step of evaluating the electrical fault index based on the preprocessed operating status parameters and determining whether the vehicle currently has a manifest electrical fault based on the electrical fault index is as follows: Obtain the preprocessed battery voltage and generator output voltage within the detection period, construct a battery voltage sequence and a generator output voltage sequence, calculate the mean and variance of the battery voltage sequence and the generator output voltage sequence to obtain the mean battery voltage, the variance battery voltage, the mean generator output voltage, and the variance generator output voltage; calculate the ratio of the battery voltage variance to the mean battery voltage to obtain the battery voltage stability deviation factor; calculate the ratio of the generator output voltage variance to the mean generator output voltage to obtain the generator output voltage stability deviation factor; obtain the preprocessed battery current within the detection period, construct a battery current sequence, calculate the current change rate between each sampling point to obtain a current change rate sequence, count the number of times the current change rate sequence exceeds a set change threshold, record this as the current change rate anomaly count, and calculate the ratio of the current change rate anomaly count to the total number of sampling points to obtain the current mutation frequency factor; obtain the preprocessed battery temperature within the detection period, construct... A battery temperature sequence is obtained, and the cumulative duration of temperatures exceeding the rated safe temperature upper limit is statistically analyzed. The ratio of this cumulative duration to the detection period duration is calculated to obtain the temperature anomaly accumulation factor. The pre-processed electronic load operating power within the detection period is acquired to obtain the electronic load power sequence. The mean of this electronic load power sequence is calculated to obtain the average electronic load power. The ratio of this average electronic load power to the system rated load power is calculated to obtain the power load deviation factor. The battery voltage stability deviation factor, generator output voltage stability deviation factor, current mutation frequency factor, temperature anomaly accumulation factor, and power load deviation factor are normalized. Based on these normalized factors, an electrical fault index is calculated. The electrical fault index is compared with a fault threshold. If the electrical fault index is greater than or equal to the fault threshold, a manifest electrical fault is determined to exist in the vehicle. If the electrical fault index is less than the fault threshold, no manifest electrical fault is determined to exist in the vehicle, and the system returns to the on-board electrical status acquisition module to continue collecting operating status parameters.

[0010] Preferably, the steps for obtaining the electrical performance stability index are as follows: obtaining the battery voltage sequence, battery current sequence, and electronic load power sequence; dividing the battery voltage sequence into several segments of equal length, calculating the voltage mean of each segment to obtain the sub-segment voltage sequence, and calculating the standard deviation of the mean difference between adjacent segments, denoted as the voltage fluctuation uniformity index; performing linear trend fitting on the battery current sequence to obtain a trend line, and calculating the root mean square error between the battery current sequence and the trend line based on all sampling points within the detection time period to obtain the current trend deviation root mean square error; performing histogram modeling on the electronic load power sequence distribution, calculating the KL divergence between the current distribution and the system calibration power distribution, denoted as the power distribution offset divergence coefficient; and adding the voltage fluctuation uniformity index, the current trend deviation root mean square error, and the power distribution offset divergence coefficient to a constant 1 and taking the reciprocal to calculate the electrical performance stability index.

[0011] Preferably, the step of determining whether the vehicle currently has a risk of deterioration based on the electrical performance stability index is as follows: comparing the electrical performance stability index with the performance stability threshold; if the electrical performance stability index is greater than or equal to the performance stability threshold, it is determined that the vehicle currently has no risk of deterioration; if the electrical performance stability index is less than the performance stability threshold, it is determined that the vehicle currently has a risk of deterioration.

[0012] Preferably, the priority index acquisition steps are as follows: Acquire the actual output power, call count, and operating status data of each functional module within the detection period; evaluate the operating value impact coefficient based on the actual output power, call count, and operating status data; acquire the task scheduling records of each functional module within the detection period; evaluate the task coupling activity coefficient based on the task scheduling records; acquire the control operation data of each functional module within the detection period; evaluate the historical control intervention impact coefficient based on the control operation data; normalize the operating value impact coefficient, task coupling activity coefficient, and historical control intervention impact coefficient; evaluate the priority index based on the normalized operating value impact coefficient, task coupling activity coefficient, and historical control intervention impact coefficient. The specific acquisition steps are as follows: In the formula, Represented as a priority index, This is expressed as the operational value impact coefficient after normalization. This is expressed as the normalized task coupling activity coefficient. This is expressed as the normalized historical impact coefficient of regulatory intervention. , , These are represented as the weighting coefficients of the normalized operational value impact coefficient, the normalized task coupling activity coefficient, and the normalized historical regulation and intervention impact coefficient.

[0013] Preferably, the steps for obtaining the operational value impact coefficient are as follows: For each functional module, obtain the actual output power of the functional module within the detection time period, construct an actual output power sequence, sum all data in the actual output power sequence to obtain the total output value of the module, obtain the total output power sequence of the vehicle within the detection time period, sum all data in the total output power sequence of the vehicle to obtain the total power output value of the system, and calculate the ratio of the total output value of the module to the total power output value of the system to obtain the module power contribution rate; For each functional module, obtain the number of times the functional module participates in scheduling or control system calls within the detection time period, recorded as the single module call count, obtain the total call count of all functional modules within the detection time period, and divide the single module... The ratio of the number of block calls to the total number of calls is used to obtain the function call frequency factor. For each functional module, the operating status data of the functional module within the detection period is obtained. According to the operating stability standard of the functional module, the stability of each sampling point is judged, and the number of sampling points that simultaneously meet the operating stability standard is counted and recorded as the number of stable sampling points. The ratio of the number of stable sampling points to the total number of sampling points within the detection period is used to obtain the stable operating time ratio parameter. The module power contribution rate, function call frequency factor, and stable operating time ratio parameter are normalized. The product of the normalized module power contribution rate, function call frequency factor, and stable operating time ratio parameter is calculated to obtain the operating value influence coefficient.

[0014] Preferably, the steps for obtaining the task coupling activity coefficient are as follows: Obtain the task scheduling records of all functional modules within the detection time period, construct a task execution record matrix, mark the corresponding position as 1 if a functional module is called in the task, otherwise mark it as 0, thus obtaining a binary task module participation matrix; based on the binary task module participation matrix, for any two functional modules, count the number of times they are simultaneously called in the same task to obtain the module collaboration frequency; for each functional module, summarize its collaboration frequency with all other functional modules to obtain the total module collaboration frequency; for each functional module, count the total number of tasks it actually participates in within the detection time period; calculate the ratio of the total module collaboration frequency to the total number of tasks plus 1 to obtain the task coupling activity coefficient.

[0015] Preferably, the steps for obtaining the historical control intervention impact coefficient are as follows: Obtain the control operation data recorded during the historical detection period; for each functional module, extract the number of control behaviors experienced by the functional module during the historical detection period, recorded as the historical control count; control behaviors include power limiting, intermittent power supply, or power reduction operations performed on the functional module; set a detection window; after each control behavior, count the number of times the functional module experiences operational abnormalities after control within the detection window, recorded as the abnormality count; calculate the ratio of the abnormality count to the historical control count plus 1 to obtain the historical control intervention impact coefficient.

[0016] Preferably, the step of constructing a power supply priority matrix based on the priority index of each functional module and implementing an adaptive adjustment strategy based on the power supply priority matrix is ​​as follows: All functional modules are sorted from high to low according to their priority index to construct the power supply priority matrix; the remaining battery power, generator output power, and current power demand of all functional modules are obtained at the current moment, and the total available power value and total load power value are calculated respectively; the difference between the available power value and the total load power value is calculated to obtain the power margin value; the power margin value is compared with the power safety threshold; if the power margin value is less than the power safety threshold, it is determined that the current situation is one of power resource shortage; if the power margin value is greater than the power safety threshold, it is determined that the current situation is one of power resource shortage. If the power margin is equal to the power safety threshold, the system is determined to be in a power safety state and maintains the existing power supply strategy. If the system is determined to be in a power shortage state, power limiting or intermittent power supply strategies are implemented sequentially, starting with the functional module with the lowest priority index in the power security priority matrix. If the system power margin is still less than the power safety threshold after adjusting a single functional module, the above process is repeated for the next higher priority functional module until the power margin value is greater than or equal to the power safety threshold. Furthermore, for functional modules with a priority index higher than the critical threshold in the power security priority matrix, the system does not implement power limiting or intermittent power supply strategies and continues to maintain its power supply stability.

[0017] Preferably, a method for monitoring automotive electrical automation includes the following steps: Step 1: Real-time acquisition of operating status parameters of key electrical components of the vehicle, including battery voltage, battery current, battery temperature, remaining battery charge, generator output voltage, and electronic load operating power. The operating status parameters are filtered and time-aligned to obtain pre-processed operating status parameters; Step 2: Setting a detection time period, acquiring the pre-processed operating status parameters within the detection time period, evaluating an electrical fault index based on the pre-processed operating status parameters, and determining whether the vehicle currently has a significant electrical fault based on the electrical fault index; if a significant fault is determined to exist, a preset diagnostic model library is called, combining the electrical fault index with the remaining battery charge. Step 3: If it is determined that there is no obvious electrical fault in the vehicle, the electrical performance stability index during the detection period is calculated, and the risk of deterioration fault is determined based on the electrical performance stability index; Step 4: If it is determined that there is a risk of deterioration fault in the vehicle, a deterioration fault risk warning is issued, and the functional information of all functional modules installed in the vehicle is obtained. The functional information includes actual output power, number of calls, operating status data, task scheduling records, and control operation data. The priority index is obtained based on the functional information; Step 5: A power supply priority matrix is ​​constructed based on the priority index of each functional module, and an adaptive adjustment strategy is performed based on the power supply priority matrix.

[0018] The technical effects and advantages of this invention are as follows:

[0019] The system acquires operating status parameters, evaluates the electrical fault index, and determines whether the vehicle currently has a visible electrical fault. If the vehicle does not currently have a visible electrical fault, the system calculates the electrical performance stability index within the detection period to determine whether the vehicle currently has a risk of deterioration. If the vehicle currently has a risk of deterioration, the system acquires the functional information of all functional modules, evaluates the priority index, constructs a power supply priority matrix based on the priority index of each functional module, and performs adaptive adjustment strategies based on the power supply priority matrix to effectively improve the stability of the vehicle's function execution and its adaptability to different scenarios. Attached Figure Description

[0020] Figure 1 This is a structural diagram of an automotive electrical automation monitoring system provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart of an automotive electrical automation monitoring method provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The automotive electrical automation monitoring system and method involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention provides an automotive electrical automation monitoring system, such as... Figure 1 As shown, the system includes:

[0024] The vehicle electrical status acquisition module is used to acquire the operating status parameters of key electrical components of the vehicle in real time. The operating status parameters include, but are not limited to, battery voltage, battery current, battery temperature, remaining battery power, generator output voltage, and electronic load operating power. The operating status parameters are filtered and time-series aligned to obtain pre-processed operating status parameters, which provide basic data support for subsequent diagnosis and trend assessment.

[0025] The intelligent electrical fault diagnosis module is used to set a detection time period, obtain preprocessed operating status parameters within the detection time period, evaluate the electrical fault index based on the preprocessed operating status parameters, and determine whether the vehicle currently has a visible electrical fault based on the electrical fault index. If the vehicle is determined to have a visible fault, the module calls the preset diagnostic model library, combines the electrical fault index with the remaining battery power to output the fault type and location, improves fault location and response efficiency, and transmits the fault type and location to the early warning module.

[0026] It should be noted that the detection time period can be adjusted according to the actual situation; for example, the detection time period can be 5 minutes or 3 minutes.

[0027] The pre-defined diagnostic model library refers to a collection of multiple diagnostic models constructed based on historical performance data of different types of electrical faults and known fault samples. The model library includes various sub-models such as voltage anomaly model, current surge model, temperature overheating model, and residual power decay model. Each model is used to determine the characteristic patterns and response conditions of a specific fault type.

[0028] The diagnostic model can be constructed using threshold rules, distribution matching, boundary recognition, or machine learning models trained based on historical operating conditions. In actual operation, by calling the matching model and combining the currently detected electrical fault index with the remaining battery power, it outputs the most likely fault type and fault location, thereby achieving refined fault location and graded response decisions.

[0029] It should be noted that calling the preset diagnostic model library and combining the electrical fault index with the remaining battery power output fault type and location is existing technology, and this embodiment will not describe its specific steps in detail.

[0030] In this embodiment, it should be specifically explained that the step of obtaining the electrical fault index based on the preprocessed operating status parameters and determining whether the vehicle currently has a manifest electrical fault based on the electrical fault index is as follows:

[0031] The preprocessed battery voltage and generator output voltage within the detection period are obtained, and the battery voltage sequence and generator output voltage sequence are constructed. The mean and variance of the battery voltage sequence and generator output voltage sequence are calculated to obtain the mean battery voltage, the variance battery voltage, the mean generator output voltage, and the variance generator output voltage.

[0032] The battery voltage variance is calculated by dividing it by the battery voltage mean to obtain the battery voltage stability deviation factor, which reflects the fluctuation range of the battery voltage. The larger the voltage stability deviation factor, the worse the voltage stability and the higher the risk of failure. The generator output voltage variance is calculated by dividing it by the generator output voltage mean to obtain the generator output voltage stability deviation factor.

[0033] The preprocessed battery current within the detection period is obtained, and a battery current sequence is constructed. The rate of change of current between each sampling point is calculated to obtain a rate of change of current sequence. The number of times the rate of change of current exceeds the set threshold is counted and recorded as the number of abnormal rate of change of current. The ratio of the number of abnormal rate of change of current to the total number of sampling points is calculated to obtain the current mutation frequency factor, which is used to characterize the abnormal fluctuation characteristics of current.

[0034] The pre-processed battery temperature is obtained during the detection period, and a battery temperature sequence is constructed. The cumulative duration of the temperature exceeding the upper limit of the rated safe temperature is counted, and the ratio of the cumulative duration to the duration of the detection period is calculated to obtain the temperature anomaly accumulation factor, which is used to characterize the risk accumulation effect of excessive temperature.

[0035] The preprocessed electronic load operating power during the detection period is obtained, and an electronic load power sequence is constructed. The mean of the electronic load power sequence is calculated to obtain the average power of the electronic load. The ratio of the average power of the electronic load to the rated load power of the system is calculated to obtain the power load offset factor. If the power load offset factor is significantly higher than 1, it indicates that the system is in an overload state for a long time.

[0036] The battery voltage stability deviation factor, generator output voltage stability deviation factor, current surge frequency factor, temperature anomaly accumulation factor, and power load offset factor are normalized. Based on these normalized factors, the electrical fault index is calculated. The specific steps for obtaining the index are as follows:

[0037] ;

[0038] In the formula, This is expressed as an electrical fault index. This represents the normalized battery voltage stability deviation factor. This is expressed as the generator output voltage stability deviation factor after normalization. This is expressed as the normalized current mutation frequency factor. This is expressed as the normalized cumulative factor for temperature anomalies. It is represented as the power load offset factor after normalization. It converts multiple characteristic parameters related to the stability of the electrical system into standardized sub-indices, and then expresses the comprehensive amplification effect of system anomalies by multiplication and superposition. The growth trend is controlled by square root operation, so that the index rises rapidly when multiple anomalies exist at the same time, thereby improving the response capability to complex faults.

[0039] The electrical fault index is compared with a fault threshold. If the electrical fault index is greater than or equal to the fault threshold, a manifest electrical fault is determined to exist in the vehicle. If the electrical fault index is less than the fault threshold, no manifest electrical fault is determined to exist in the vehicle, and the system returns to the onboard electrical status acquisition module to continue collecting operating status parameters. The fault threshold is obtained through an adaptive threshold method, a dynamic threshold generation method that automatically determines the judgment threshold based on current data characteristics. Its core principle is that it does not use a fixed value as the judgment standard, but rather autonomously calculates the threshold most suitable for the current operating state based on the statistical characteristics of the measured parameters over a specific time period, such as mean, standard deviation, and fluctuation amplitude. This allows for flexible adjustment of the judgment boundary according to real-time changes in data distribution, improving the accuracy and environmental adaptability of threshold settings.

[0040] The electrical performance trend assessment module, if it is determined that there is no obvious electrical fault in the car at present, calculates the electrical performance stability index during the detection period, and determines whether there is a risk of deterioration fault in the car at present based on the electrical performance stability index;

[0041] In this embodiment, it should be specifically explained that the steps for obtaining the electrical performance stability index are as follows:

[0042] Obtain the battery voltage sequence, battery current sequence, and electronic load power sequence;

[0043] The battery voltage sequence is divided into several segments of equal length. The average voltage of each segment is calculated to obtain the voltage sequence of the sub-segment. The standard deviation of the difference between the means of adjacent segments is calculated and denoted as the voltage fluctuation uniformity index. This index is used to assess whether the voltage change is continuous and whether there is a long-period non-equilibrium phenomenon. The smaller the voltage fluctuation uniformity index value, the more uniform the voltage fluctuation and the more stable the system.

[0044] Linear trend fitting is performed on the battery current sequence to obtain a trend line. Based on all sampling points within the detection period, the mean square error between the battery current sequence and the trend line is calculated to obtain the mean square error of the current trend deviation, which is used to determine whether the current has nonlinear fluctuations. The larger the mean square error of the current trend deviation, the more unstable the current behavior.

[0045] The power sequence distribution of the electronic load is modeled using a histogram, such as 10 intervals. The KL divergence between the current distribution and the system calibration power distribution is calculated and denoted as the power distribution offset divergence coefficient. The specific steps for obtaining this coefficient are as follows:

[0046] ;

[0047] In the formula, This is expressed as the power distribution offset divergence coefficient, used to reflect whether load behavior exhibits a mode deviation. This represents the i-th load power data in the electronic load power sequence. This represents the i-th calibration power data in the system calibration power distribution. To ensure that the denominator is a very small number, preventing situations where the denominator is zero and the formula is meaningless;

[0048] KL divergence is a commonly used information theory metric that measures the degree of difference between two probability distributions. It indicates the extent to which an actual probability distribution deviates from another reference probability distribution.

[0049] The system calibration power distribution refers to the standard reference distribution formed by statistically modeling the power output sequence based on historical operating data under normal operating conditions of the electronic load. This distribution, by normalizing the frequency of occurrence in different power ranges, yields a probability density function representing the power behavior characteristics of the electronic load, which reflects the proportion of each typical power level in a steady state.

[0050] The electrical performance stability index is calculated by adding the voltage fluctuation uniformity index, the current trend deviation standard deviation, and the power distribution deviation divergence coefficient to a constant 1 and taking the reciprocal. This index is used to evaluate the overall fluctuation of electrical performance under non-fault conditions. The larger the electrical performance stability index value, the more stable the system operation.

[0051] In this embodiment, it should be specifically explained that the step of determining whether the vehicle currently has a risk of deterioration or failure based on the electrical performance stability index is as follows:

[0052] The electrical performance stability index is compared with the performance stability threshold. If the electrical performance stability index is greater than or equal to the performance stability threshold, it is determined that the vehicle currently has no risk of deterioration failure; if the electrical performance stability index is less than the performance stability threshold, it is determined that the vehicle currently has a risk of deterioration failure. The performance stability threshold is obtained through an adaptive threshold method.

[0053] The electrical performance trend assessment module can identify potential deterioration risks or operational anomalies by analyzing the dynamic changes of key parameters such as voltage, current, and power, even when no obvious faults are currently present in the vehicle. This enables early detection and warning of fault precursors, helping to improve the system's forward-looking safety assurance capabilities and avoid the impact of sudden electrical faults on the overall vehicle performance.

[0054] If the priority assessment module determines that the car is at risk of deterioration or failure, it will transmit the electrical performance stability index and the determination result to the early warning module. It will obtain the functional information of all functional modules installed in the car, including actual output power, number of calls, operating status data, task scheduling records and control operation data. The priority index will be obtained based on the functional information.

[0055] In this embodiment, it should be specifically explained that the steps for obtaining the priority index are as follows:

[0056] The actual output power, number of calls, and operating status data of each functional module are obtained during the detection period. The operating value impact coefficient is evaluated based on the actual output power, number of calls, and operating status data.

[0057] Obtain the task scheduling records of each functional module within the detection period, and evaluate the task coupling activity coefficient based on the task scheduling records.

[0058] Acquire the control operation data of each functional module within the detection period, and evaluate the historical control intervention impact coefficient based on the control operation data;

[0059] The operational value impact coefficient, task coupling activity coefficient, and historical regulation and intervention impact coefficient are normalized. Specifically, in this embodiment, vector normalization can be used. This involves constructing a three-dimensional vector from the operational value impact coefficient, task coupling activity coefficient, and historical regulation and intervention impact coefficient. The norm of the vector is obtained by calculating the sum of squares of its components and taking the square root. Each component is then divided by this norm, completing the normalization process. The purpose of this normalization method is to ensure that the three types of impact coefficients have the same order of magnitude and measurement standard when evaluating the priority index, avoiding evaluation bias caused by differences in the original scale. Since vector normalization is existing technology and the algorithm is publicly transparent, this embodiment does not elaborate on its specific algorithm steps. The priority index is obtained by evaluating the normalized operational value impact coefficient, task coupling activity coefficient, and historical regulation and intervention impact coefficient. The specific steps are as follows:

[0060] ;

[0061] In the formula, Represented as a priority index, This is represented as the normalized operational value impact coefficient. The operational value impact coefficient measures the degree to which a specific functional module supports the operational goals of the vehicle's electrical system, reflecting its functional importance and energy utilization efficiency under current operating conditions. Since high operational value modules typically undertake critical tasks, such as safety control, core communication, or power management, their operational stability directly impacts the overall system performance. Therefore, the operational value impact coefficient is directly proportional to the priority index; that is, the higher the operational value, the higher the corresponding priority. In situations of power resource scarcity or strategy adjustments, priority should be given to ensuring the normal operation of this module. This is represented as the normalized task coupling activity coefficient. The task coupling activity coefficient measures the frequency of interaction and dependencies between a functional module and other modules during multi-task execution, reflecting its degree of collaboration and core participation in the system task chain. A higher coupling activity coefficient indicates that the module plays a pivotal role in system operation, and its operational status has a synergistic impact on multiple task modules. Therefore, the task coupling activity coefficient is directly proportional to the priority index; that is, the higher the coupling degree, the higher the priority. Its continuous operation should be prioritized during resource allocation to maintain the overall stability and continuity of system tasks. This represents the normalized historical control intervention impact coefficient. The historical control intervention impact coefficient measures whether a module experiences operational anomalies or performance degradation after being subjected to control actions such as power rationing, delayed power supply, or power reduction in the past, reflecting its operational sensitivity and controllability. If the module maintains stable performance throughout multiple control actions, it indicates that its operation has low dependence on power resources and strong controllability. Therefore, the historical control intervention impact coefficient is inversely proportional to the priority index. , , The weighting coefficients are represented as follows: the weighting coefficient of the normalized operational value impact coefficient, the weighting coefficient of the normalized task coupling activity coefficient, and the weighting coefficient of the normalized historical regulation and intervention impact coefficient. , , , Obtained through the analytic hierarchy process, for example , , The values ​​can be 0.3, 0.3, or 0.4. The Analytic Hierarchy Process (AHP) is a decision analysis method based on hierarchical structure and pairwise comparisons, often used to determine weight coefficients in multi-index comprehensive evaluation. First, the influencing factors are constructed into a structural model with primary and secondary hierarchical relationships. Then, the relative importance of each factor is judged pairwise to form a judgment matrix. Finally, through steps such as eigenvalue calculation and matrix consistency testing, the objective weight of each factor in the comprehensive index is obtained.

[0062] In this embodiment, it should be specifically explained that the steps for obtaining the operational value impact coefficient are as follows:

[0063] For each functional module, the actual output power of the functional module during the detection period is obtained, and an actual output power sequence is constructed. All data in the actual output power sequence are summed to obtain the total output value of the module. The total output power sequence of the whole vehicle during the detection period is obtained, and all data in the total output power sequence of the whole vehicle are summed to obtain the total power output value of the system. The ratio of the total output value of the module to the total power output value of the system is calculated to obtain the module power contribution rate, which is used to measure the proportion of the module in the overall power output. The larger the value, the higher its support for the operation of the whole vehicle electrical system and the greater its operational value. It should be noted that the actual output power can be obtained by collecting the voltage and current data of the functional module during the detection period and calculating it point by point according to the power calculation formula. That is, the product of voltage and current at each moment is used as the actual output power value at that moment.

[0064] For each functional module, the number of times the functional module participates in scheduling or control system calls within the detection period is obtained and recorded as the single module call count. The total number of calls of all functional modules within the detection period is obtained. The ratio of the single module call count to the total number of calls is calculated to obtain the functional call frequency factor, which is used to reflect the functional activity level of the module. The larger the value, the more frequently the module is called.

[0065] For each functional module, the operating status data of the functional module within the detection period is obtained. According to the operating stability standard of the functional module, the stability of each sampling point is judged. The number of sampling points that simultaneously meet the operating stability standard is counted and recorded as the number of stable sampling points. The ratio of the number of stable sampling points to the total number of sampling points within the detection period is calculated to obtain the stable operating time ratio parameter, which is used to characterize the operating stability of the module during the detection period. The larger the value, the more reliable its operation.

[0066] Operational stability criteria refer to a set of threshold rules used to determine whether a functional module is in a normal operating state at a certain sampling point. They are usually set based on a reasonable range of key operating parameters (such as current, voltage, temperature, etc.). For example, if the current of a functional module should be maintained between 0.8A and 1.2A during stable operation, then when determining the stability of each sampling point, it is only counted as a stable sampling point if the current value of that sampling point falls within the above range.

[0067] The module power contribution rate, function call frequency factor, and stable operation time ratio are normalized. The operation value impact coefficient is obtained by multiplying the normalized module power contribution rate, function call frequency factor, and stable operation time ratio.

[0068] In this embodiment, it should be specifically explained that the steps for obtaining the task coupling activity coefficient are as follows:

[0069] Obtain the task scheduling records of all functional modules within the detection time period, construct a task execution record matrix, where each row of the matrix corresponds to a specific task and each column corresponds to a functional module. If a functional module is called in the task, the corresponding position is marked as 1, otherwise it is marked as 0, thus obtaining a binary task module participation matrix.

[0070] Based on the binary task module participation matrix, for any two functional modules, count the number of times they are called simultaneously in the same task to obtain the module collaboration frequency. For each functional module, summarize its collaboration frequency with all other functional modules to obtain the total module collaboration frequency.

[0071] For each functional module, count the total number of tasks it actually participated in during the detection period;

[0072] The task coupling activity coefficient of a module is calculated by comparing the total frequency of module collaboration with the total number of tasks plus 1. The operation of adding 1 to the denominator is used to avoid the division by zero error when the number of tasks is zero, without affecting the relative calculation result of the normal value. The task coupling activity coefficient is used to measure the degree of linkage between the target module and other modules during multi-task execution. The larger the value, the higher the frequency of collaboration in the tasks it participates in, indicating that the module has stronger coupling and activity in the system task chain.

[0073] By evaluating the frequency and degree of interaction between functional modules and other modules during multi-task execution, the collaborative criticality of each module in the task chain can be quantified. This helps identify key modules that occupy a core position in system operation and influence multiple modules, thereby giving them higher priority in priority assessment and ensuring the overall consistency of task execution and the stability of system scheduling.

[0074] In this embodiment, it should be specifically explained that the steps for obtaining the historical regulatory intervention impact coefficient are as follows:

[0075] Obtain the control operation data recorded during the historical detection period. For each functional module, extract the number of control behaviors experienced by the functional module during the historical detection period and record it as the historical control number. Control behaviors include power limiting, intermittent power supply, or power reduction operations performed on the functional module.

[0076] Set a detection window. After each adjustment action, count the number of times the functional module in the detection window experiences an operational anomaly after the adjustment. This number is recorded as the anomaly count. Anomalies include, but are not limited to, operational status feedback anomalies, output power fluctuation exceeding limits, or functional response anomalies.

[0077] The historical control intervention impact coefficient is calculated by comparing the number of abnormal events with the number of historical control events plus 1. The historical control intervention impact coefficient is used to characterize the operational sensitivity of the target module after the control action. The larger the value, the more sensitive the module is to power resource control and the worse its controllability, and the lower its priority should be.

[0078] By analyzing whether power modules exhibit operational anomalies after experiencing power curtailment, delayed power supply, or power reduction, the sensitivity of these modules to changes in power resources is quantified, thereby assessing their controllability. The introduction of this indicator helps to rationally adjust priority allocation during periods of power scarcity, avoiding excessive restrictions on modules susceptible to regulation, thus improving overall system stability and task continuity.

[0079] The adaptive adjustment module for operation strategy is used to construct a power security priority matrix based on the priority index of each functional module, and to perform adaptive adjustment strategy based on the power security priority matrix.

[0080] In this embodiment, it is necessary to specifically explain the steps of constructing a power supply priority matrix based on the priority index of each functional module, and then performing an adaptive adjustment strategy based on the power supply priority matrix:

[0081] All functional modules are sorted from high to low according to their priority index to construct a power supply priority matrix. The power supply priority matrix is ​​a one-dimensional ordered structure. Each position corresponds to a functional module number. The sorting position represents its power supply priority. The higher the priority, the earlier the position.

[0082] The system obtains the current remaining battery power, generator output power, and current power requirements of all functional modules. It then calculates the total available power and total load power. It should be noted that the total available power is calculated by estimating the power of the remaining battery power based on a preset discharge capacity per unit time and summing it with the generator output power. This total available power is used to characterize the vehicle's power supply capacity at that moment. The total load power is the sum of the current power requirements of all functional modules.

[0083] The preset discharge capacity per unit time is a reference value obtained from the battery's factory calibration test or long-term operation data analysis. It is usually the maximum power or current that can be continuously output per unit time under standard temperature and load conditions through a constant current discharge experiment. This value can be obtained from the specifications provided by the battery manufacturer or typical discharge performance data collected by the vehicle system during the calibration phase.

[0084] The difference between the available power value and the total load power value is calculated to obtain the power margin value. The power margin value is compared with the power safety threshold. If the power margin value is less than the power safety threshold, it is determined that the current power resource shortage is in effect. If the power margin value is greater than or equal to the power safety threshold, it is determined that the current power safety is in effect, and the existing power supply strategy is maintained. The power safety threshold is obtained through an adaptive threshold method.

[0085] If it is determined that the current power resources are in a state of shortage, then according to the sorting order of each functional module in the power security priority matrix, starting from the functional module with the lowest current priority index, power limiting or intermittent power supply strategies will be implemented in sequence.

[0086] If the system power margin is still less than the power safety threshold after adjusting a single functional module, the above process is repeated for the next higher priority functional module until the power margin value is greater than or equal to the power safety threshold.

[0087] Furthermore, for functional modules in the power supply priority matrix whose priority index is higher than the critical threshold, the system does not implement power limiting or intermittent power supply strategies, continuously maintaining their power supply stability to ensure the reliable operation of critical functions under power shortage conditions. The critical threshold is obtained through an adaptive threshold method and is used to distinguish between critical functional modules that require priority protection and controllable ordinary modules. Under power shortage conditions, modules with a priority index higher than this threshold will be identified as critical modules, and the system will not implement power limiting or intermittent power supply operations on them, thereby ensuring the stable operation of core functions such as drive control and braking systems, and improving the safety and rationality of the overall strategy.

[0088] The early warning module is used to receive transmission information from the electrical fault intelligent diagnosis module and the priority evaluation module. If it receives transmission information from the electrical fault intelligent diagnosis module, it will issue a fault early warning prompt; if it receives transmission information from the priority evaluation module, it will issue a deterioration fault risk early warning prompt.

[0089] In this embodiment, it is necessary to specifically describe a method for monitoring automotive electrical automation, such as... Figure 2 As shown, it includes the following steps:

[0090] Step 1: Real-time acquisition of operating status parameters of key electrical components of the vehicle. Operating status parameters include battery voltage, battery current, battery temperature, remaining battery charge, generator output voltage, and electronic load operating power. The operating status parameters are filtered and time-aligned to obtain pre-processed operating status parameters.

[0091] Step 2: Set the detection time period, obtain the preprocessed operating status parameters within the detection time period, evaluate the electrical fault index based on the preprocessed operating status parameters, determine whether the vehicle currently has a visible electrical fault based on the electrical fault index; if the vehicle currently has a visible fault, call the preset diagnostic model library, combine the electrical fault index and the remaining battery power to output the fault type and location, and provide a fault warning prompt.

[0092] Step 3: If it is determined that there is no obvious electrical fault in the car, calculate the electrical performance stability index during the detection period, and determine whether there is a risk of deterioration fault in the car based on the electrical performance stability index.

[0093] Step 4: If it is determined that the car is currently at risk of deterioration, a deterioration fault risk warning will be issued. The functional information of all functional modules currently installed in the car will be obtained. The functional information includes actual output power, number of calls, operating status data, task scheduling records and control operation data. The priority index will be obtained based on the functional information.

[0094] Step 5: Construct a power supply priority matrix based on the priority index of each functional module, and implement an adaptive adjustment strategy based on the power supply priority matrix.

[0095] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle electrical automation monitoring system, characterized in that, The system includes; The vehicle electrical status acquisition module is used to acquire the operating status parameters of key electrical components of the vehicle in real time. The operating status parameters include battery voltage, battery current, battery temperature, remaining battery power, generator output voltage and electronic load operating power. The operating status parameters are filtered and time-aligned to obtain pre-processed operating status parameters. The intelligent electrical fault diagnosis module is used to set a detection time period, obtain preprocessed operating status parameters within the detection time period, evaluate the electrical fault index based on the preprocessed operating status parameters, determine whether the vehicle currently has a visible electrical fault based on the electrical fault index; if the vehicle currently has a visible fault, it calls the preset diagnostic model library, combines the electrical fault index and the remaining battery power to output the fault type and location, and transmits the fault type and location to the early warning module. The electrical performance trend assessment module, if it is determined that there is no obvious electrical fault in the car at present, calculates the electrical performance stability index during the detection period, and determines whether there is a risk of deterioration fault in the car at present based on the electrical performance stability index; If the priority assessment module determines that the car is at risk of deterioration, it will transmit the electrical performance stability index and the determination result to the early warning module to obtain the functional information of all functional modules installed in the car. The functional information includes actual output power, number of calls, operating status data, task scheduling records and control operation data. The priority index is obtained based on the functional information. The adaptive adjustment module for operation strategy is used to construct a power security priority matrix based on the priority index of each functional module, and to perform adaptive adjustment strategy based on the power security priority matrix. The early warning module is used to receive the transmission information from the electrical fault intelligent diagnosis module and the priority evaluation module. If the transmission information from the electrical fault intelligent diagnosis module is received, a fault early warning prompt will be issued. If the priority assessment module receives the transmission information, a deterioration fault risk warning will be issued.

2. The automotive electrical automation monitoring system according to claim 1, characterized in that: The step of obtaining the electrical fault index based on the preprocessed operating status parameters and determining whether the vehicle currently has a manifest electrical fault based on the electrical fault index is as follows: The preprocessed battery voltage and generator output voltage within the detection period are obtained, and the battery voltage sequence and generator output voltage sequence are constructed. The mean and variance of the battery voltage sequence and generator output voltage sequence are calculated to obtain the mean battery voltage, the variance battery voltage, the mean generator output voltage, and the variance generator output voltage. The battery voltage stability deviation factor is obtained by calculating the ratio of the battery voltage variance to the battery voltage mean. The generator output voltage stability deviation factor is obtained by calculating the ratio of the generator output voltage variance to the generator output voltage mean. The preprocessed battery current within the detection period is obtained, a battery current sequence is constructed, the current change rate between each sampling point is calculated, a current change rate sequence is obtained, the number of times the current change rate sequence exceeds the set change threshold is counted and recorded as the current change rate abnormality number, and the ratio of the current change rate abnormality number to the total number of sampling points is calculated to obtain the current change frequency factor. The pre-processed battery temperature is obtained within the detection period, a battery temperature sequence is constructed, the cumulative duration of the temperature exceeding the upper limit of the rated safe temperature is counted, and the ratio of the cumulative duration to the detection period duration is calculated to obtain the temperature anomaly accumulation factor. The preprocessed electronic load operating power during the detection period is obtained to obtain the electronic load power sequence. The mean of the electronic load power sequence is calculated to obtain the average power of the electronic load. The ratio of the average power of the electronic load to the rated load power of the system is calculated to obtain the power load offset factor. The battery voltage stability deviation factor, generator output voltage stability deviation factor, current mutation frequency factor, temperature anomaly accumulation factor, and power load offset factor are normalized. The electrical fault index is calculated based on the normalized battery voltage stability deviation factor, generator output voltage stability deviation factor, current mutation frequency factor, temperature anomaly accumulation factor, and power load offset factor. The electrical fault index is compared with the fault threshold. If the electrical fault index is greater than or equal to the fault threshold, it is determined that the vehicle currently has a visible electrical fault. If the electrical fault index is less than the fault threshold, it is determined that the vehicle currently does not have a visible electrical fault, and the system returns to the on-board electrical status acquisition module to continue collecting operating status parameters.

3. The automotive electrical automation monitoring system according to claim 2, characterized in that, The steps for obtaining the electrical performance stability index are as follows: Obtain the battery voltage sequence, battery current sequence, and electronic load power sequence; The battery voltage sequence is divided into several segments of equal length. The voltage mean of each segment is calculated to obtain the voltage sequence of the sub-segment. The standard deviation of the difference between the means of adjacent segments is calculated and denoted as the voltage fluctuation uniformity index. Linear trend fitting is performed on the battery current sequence to obtain a trend line. Based on all sampling points within the detection period, the root mean square error between the battery current sequence and the trend line is calculated to obtain the root mean square error of the current trend deviation. Histogram modeling is performed on the power sequence distribution of the electronic load, and the KL divergence between the current distribution and the system calibration power distribution is calculated and denoted as the power distribution offset divergence coefficient. The electrical performance stability index is calculated by adding the voltage fluctuation uniformity index, the current trend deviation standard deviation, and the power distribution deviation divergence coefficient to a constant 1 and taking the reciprocal.

4. The automotive electrical automation monitoring system according to claim 1, characterized in that: The steps for determining whether a vehicle currently has a risk of deterioration or failure based on the electrical performance stability index are as follows: The electrical performance stability index is compared with the performance stability threshold. If the electrical performance stability index is greater than or equal to the performance stability threshold, it is determined that the vehicle currently has no risk of deterioration or failure; if the electrical performance stability index is less than the performance stability threshold, it is determined that the vehicle currently has a risk of deterioration or failure.

5. The automotive electrical automation monitoring system according to claim 1, characterized in that: The steps for obtaining the priority index are as follows: The actual output power, number of calls, and operating status data of each functional module are obtained during the detection period. The operating value impact coefficient is evaluated based on the actual output power, number of calls, and operating status data. Obtain the task scheduling records of each functional module within the detection period, and evaluate the task coupling activity coefficient based on the task scheduling records. Acquire the control operation data of each functional module within the detection period, and evaluate the historical control intervention impact coefficient based on the control operation data; The operational value impact coefficient, task coupling activity coefficient, and historical control and intervention impact coefficient are normalized. Based on these normalized coefficients, a priority index is obtained. The specific steps for obtaining this index are as follows: ； In the formula, Represented as a priority index, This is expressed as the operational value impact coefficient after normalization. This is expressed as the normalized task coupling activity coefficient. This is expressed as the normalized historical impact coefficient of regulatory intervention. , , These are represented as the weighting coefficients of the normalized operational value impact coefficient, the normalized task coupling activity coefficient, and the normalized historical regulation and intervention impact coefficient.

6. The automotive electrical automation monitoring system according to claim 5, characterized in that: The steps for obtaining the operational value impact coefficient are as follows: For each functional module, the actual output power of the functional module during the detection period is obtained, and an actual output power sequence is constructed. All data in the actual output power sequence are summed to obtain the total output value of the module. The total output power sequence of the whole vehicle during the detection period is obtained, and all data in the total output power sequence of the whole vehicle are summed to obtain the total power output value of the system. The ratio of the total output value of the module to the total power output value of the system is calculated to obtain the module power contribution rate. For each functional module, the number of times the functional module participates in scheduling or control system calls within the detection period is obtained and recorded as the single module call count. The total number of calls of all functional modules within the detection period is obtained, and the ratio of the single module call count to the total number of calls is calculated to obtain the functional call frequency factor. For each functional module, the operating status data of the functional module within the detection time period is obtained. According to the operating stability standard of the functional module, the stability of each sampling point is judged. The number of sampling points that simultaneously meet the operating stability standard is counted and recorded as the number of stable sampling points. The ratio of the number of stable sampling points to the total number of sampling points within the detection time period is calculated to obtain the stable operating time percentage parameter. The module power contribution rate, function call frequency factor, and stable operation time ratio are normalized. The operational value impact coefficient is obtained by multiplying the normalized module power contribution rate, function call frequency factor, and stable operation time ratio.

7. The automotive electrical automation monitoring system according to claim 5, characterized in that: The steps for obtaining the task coupling activity coefficient are as follows: Obtain the task scheduling records of all functional modules within the detection time period, construct a task execution record matrix. If a functional module is called in the task, the corresponding position is marked as 1, otherwise it is marked as 0, thus obtaining a binary task module participation matrix. Based on the binary task module participation matrix, for any two functional modules, count the number of times they are called simultaneously in the same task to obtain the module collaboration frequency. For each functional module, summarize its collaboration frequency with all other functional modules to obtain the total module collaboration frequency. For each functional module, count the total number of tasks it actually participated in during the detection period; The task coupling activity coefficient is obtained by calculating the ratio of the total frequency of module collaboration to the total number of tasks plus 1.

8. The automotive electrical automation monitoring system according to claim 5, characterized in that: The steps for obtaining the historical regulatory intervention impact coefficient are as follows: Obtain the control operation data recorded during the historical detection period. For each functional module, extract the number of control behaviors experienced by the functional module during the historical detection period and record it as the historical control number. Control behaviors include power limiting, intermittent power supply, or power reduction operations performed on the functional module. Set up a detection window. After each adjustment action, count the number of times the functional module in the detection window experiences operational abnormalities after the adjustment, and record it as the number of abnormalities. The historical intervention impact coefficient is obtained by calculating the ratio of the number of abnormal events to the number of historical intervention events plus 1.

9. The automotive electrical automation monitoring system according to claim 1, characterized in that: The steps of constructing a power supply priority matrix based on the priority index of each functional module, and then adaptively adjusting the strategy based on the power supply priority matrix are as follows: All functional modules are sorted from high to low priority index to construct a power supply priority matrix. Obtain the current remaining battery power, generator output power, and current power requirements of all functional modules, and calculate the total available power and total load power respectively. The difference between the available power value and the total load power value is calculated to obtain the power margin value. The power margin value is compared with the power safety threshold. If the power margin value is less than the power safety threshold, it is determined that the current power resource shortage is in effect. If the power margin value is greater than or equal to the power safety threshold, it is determined that the current power safety state is maintained and the existing power supply strategy is maintained. If it is determined that the current power resources are in a state of shortage, then according to the sorting order of each functional module in the power security priority matrix, starting from the functional module with the lowest current priority index, power limiting or intermittent power supply strategies will be implemented in sequence. If the system power margin is still less than the power safety threshold after adjusting a single functional module, the above process is repeated for the next higher priority functional module until the power margin value is greater than or equal to the power safety threshold. Furthermore, for functional modules whose priority index in the power supply priority matrix is ​​higher than the critical threshold, the system does not implement power limiting or intermittent power supply strategies, and continuously maintains their power supply stability.

10. A method for monitoring automotive electrical automation, used to implement the automotive electrical automation monitoring system according to any one of claims 1-9, characterized in that: Includes the following steps: Step 1: Real-time acquisition of operating status parameters of key electrical components of the vehicle. Operating status parameters include battery voltage, battery current, battery temperature, remaining battery charge, generator output voltage, and electronic load operating power. The operating status parameters are filtered and time-aligned to obtain pre-processed operating status parameters. Step 2: Set the detection time period, obtain the preprocessed operating status parameters within the detection time period, evaluate the electrical fault index based on the preprocessed operating status parameters, determine whether the vehicle currently has a visible electrical fault based on the electrical fault index; if the vehicle currently has a visible fault, call the preset diagnostic model library, combine the electrical fault index and the remaining battery power to output the fault type and location, and provide a fault warning prompt. Step 3: If it is determined that there is no obvious electrical fault in the car, calculate the electrical performance stability index during the detection period, and determine whether there is a risk of deterioration fault in the car based on the electrical performance stability index. Step 4: If it is determined that the car is currently at risk of deterioration, a deterioration fault risk warning will be issued. The functional information of all functional modules currently installed in the car will be obtained. The functional information includes actual output power, number of calls, operating status data, task scheduling records and control operation data. The priority index will be obtained based on the functional information. Step 5: Construct a power supply priority matrix based on the priority index of each functional module, and implement an adaptive adjustment strategy based on the power supply priority matrix.

Images