Vehicle part maintenance method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle part maintenance method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining historical operation data of a target part of a target vehicle in n historical time periods, obtaining n groups of historical operation data, enabling each historical time period to correspond to one group of historical operation data, enabling n to be a positive integer, and enabling n to be a positive integer; determining a damage degree value corresponding to each historical time period of the target part in the n historical time periods based on the n groups of historical operation data to obtain n target damage degree values, and determining a residual life value of the target part based on the n target damage degree values to obtain a target residual life value; the damage degree value is inversely proportional to the residual life value, determining a maintenance strategy of the target part based on the target residual life value, and performing maintenance operation on the target part based on the maintenance strategy. By adopting the embodiment of the invention, the maintenance efficiency of the vehicle parts is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile maintenance, and in particular to a vehicle part maintenance method and device, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of automobile intelligence and electrification, the complexity of working conditions and failure risk of vehicle parts have significantly increased. The traditional maintenance mode based on fixed cycles has been difficult to accurately adapt to the actual degradation state of parts, often leading to excessive maintenance causing resource waste or maintenance lag causing failure risk. In the prior art, the evaluation of the remaining life of parts relies on experience models or single parameter analysis, lacks systematic mining of historical operation data and multi-dimensional damage degree quantification, and is difficult to accurately capture the degradation rules of parts under different working conditions, resulting in unreasonable maintenance of vehicle parts. Therefore, how to improve the maintenance efficiency of vehicle parts is a problem to be solved. SUMMARY

[0003] The present application provides a vehicle part maintenance method, device, electronic device and storage medium, which improves the maintenance efficiency of vehicle parts.

[0004] In a first aspect, the present application provides a vehicle part maintenance method, comprising:

[0005] Obtaining historical operation data of a target part of a target vehicle in n historical time periods to obtain n sets of historical operation data; each historical time period corresponds to a set of historical operation data, and n is a positive integer;

[0006] Determining a damage degree value corresponding to each historical time period of the target part in the n historical time periods based on the n sets of historical operation data to obtain n target damage degree values;

[0007] Determining a remaining life value of the target part based on the n target damage degree values to obtain a target remaining life value; the damage degree value is inversely proportional to the remaining life value;

[0008] Determining a maintenance strategy of the target part based on the target remaining life value;

[0009] Performing a maintenance operation on the target part based on the maintenance strategy.

[0010] In a second aspect, the present application provides a vehicle part maintenance device, comprising an acquisition unit and a processing unit.

[0011] The acquisition unit is configured to acquire historical operation data of a target component of a target vehicle in n historical time periods, to obtain n sets of historical operation data; each historical time period corresponds to a set of historical operation data, and n is a positive integer.

[0012] The processing unit is configured to determine a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n sets of historical operation data, to obtain n target damage degree values.

[0013] The processing unit is configured to determine a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n sets of historical operation data, to obtain n target damage degree values.

[0014] The processing unit is configured to determine a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n sets of historical operation data, to obtain n target damage degree values.

[0015] The processing unit is configured to determine a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n sets of historical operation data, to obtain n target damage degree values.

[0016] In a third aspect, an electronic device is provided, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor to enable the electronic device to perform the method of the first aspect.

[0017] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0018] In a fifth aspect, a computer program product is provided, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is executed by a computer to implement the method of the first aspect.

[0019] The embodiments of the present application have the following beneficial effects:

[0020] As can be seen, the vehicle component maintenance method described in the embodiments of the present application acquires historical operation data of a target component of a target vehicle in n historical time periods, to obtain n sets of historical operation data; each historical time period corresponds to a set of historical operation data, and n is a positive integer; determines a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n sets of historical operation data, to obtain n target damage degree values; determines a residual life value of the target component based on the n target damage degree values, to obtain a target residual life value; the damage degree value is inversely proportional to the residual life value; determines a maintenance strategy of the target component based on the target residual life value; and performs a maintenance operation on the target component based on the maintenance strategy, thereby improving the maintenance efficiency of the vehicle component. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the implementation methods or background technologies of the present application, the drawings required for use in the implementation methods or background technologies of the present application will be described below.

[0022] Figure 1 This is a flow chart of a vehicle parts maintenance method provided by an embodiment of the present application;

[0023] Figure 2 This is a flow chart for determining a damage degree value provided by an embodiment of the present application;

[0024] Figure 3 This is a flow chart of determining a target damage level value corresponding to first historical operating data provided by an embodiment of the present application;

[0025] Figure 4 This is a flow chart for determining the remaining life value provided by an embodiment of the present application;

[0026] Figure 5 This is a flowchart of determining a maintenance strategy provided by an embodiment of the present application;

[0027] Figure 6 This is a schematic diagram of a mapping table between a remaining life value and a maintenance strategy provided in an embodiment of the present application;

[0028] Figure 7 This is a schematic structural diagram of a vehicle parts maintenance device provided by an embodiment of the present application;

[0029] Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present invention, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] The terms "first", "second", and the like in the description and in the claims of the present application and above drawings are used for distinguishing between similar objects and not necessarily for describing a specific sequential or chronological order. The terms "comprises", "comprising", "includes", "including" and the like are to be construed open- ended, meaning that they include the listed steps or elements, but not excluding other not listed steps or elements. For example, a process, method, article, or apparatus that comprises a list of steps or elements is not necessarily limited to only those steps or elements, but can include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

[0032] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of other embodiments. It is expressly understood that the embodiments described herein are merely examples from a great number of possible embodiments.

[0033] See Figure 1 , Figure 1 is a flowchart of a vehicle part maintenance method provided by an embodiment of the application, including but not limited to the following steps:

[0034] S101: Obtain historical running data of a target part of a target vehicle in n historical time periods, to obtain n sets of historical running data.

[0035] In this embodiment, each historical time period corresponds to a set of historical running data, and n is a positive integer. The parts of the vehicle mainly include power system parts, chassis system parts, body system parts, electrical system parts, and auxiliary system parts.

[0036] The power system parts, as the "heart" of the vehicle, are responsible for providing power output. For example, the engine, which includes core components such as the cylinder block, piston, crankshaft, camshaft, etc., generates power through the combustion of fuel or gas; the transmission (such as a manual transmission or an automatic transmission) is used to adjust the speed and torque of power transmission to ensure smooth operation of the vehicle under different working conditions; sensors (such as a crankshaft position sensor and a throttle position sensor) and actuators (such as an oil injector and an ignition coil) in the engine control system are responsible for accurately controlling the working state of the engine.

[0037] The chassis system components support the overall structure of the vehicle and ensure driving performance. The transmission shaft and differential in the transmission system transmit power from the engine to the wheels; the frame and suspension (such as shock absorbers, springs, and lower swing arms) of the running system act as a buffer for road bumps and maintain the stability of the vehicle body; the steering engine, steering column, and steering tie rod of the steering system enable the steering operation of the vehicle; the brake disc, brake pad, master cylinder, and anti-lock braking system sensors of the braking system ensure effective braking of the vehicle and ensure driving safety.

[0038] The body system components constitute the external form and internal space of the vehicle. The body frame (such as doors, roofs, floors, and pillars) provides structural support and occupant protection; the door assembly includes door locks, window regulators, and weatherstrips; interior components such as instrument panels, seats, center consoles, and air outlet vents improve driving comfort and operational convenience; and body coverings (such as hoods, fenders, and trunk lids) serve both aesthetic and protective purposes.

[0039] The electrical system components are responsible for the power supply and electronic control of the vehicle. The battery and generator of the power supply system provide power for all electrical devices; the wiring harness, fuse, and relay of the circuit system form a power transmission network; the electronic control system includes the engine electronic control unit, anti-lock control unit, and body stability system module, which collect data through sensors and execute control instructions; the front headlight, tail light, turn signal, and fog light of the lighting system ensure visibility and signal transmission; and vehicle-mounted electronic devices such as multimedia hosts, car navigation systems, and rear-view cameras improve the intelligent experience.

[0040] The auxiliary system components assist in the normal operation of the vehicle. The compressor, condenser, evaporator, and blower of the air conditioning system regulate the temperature inside the vehicle; the fuel pump, fuel filter, and fuel injector of the fuel system provide clean fuel for the engine; the radiator, water pump, and coolant temperature sensor of the cooling system prevent the engine from overheating; the exhaust pipe and three-way catalyst of the exhaust system reduce exhaust pollutant emissions; and there are also body auxiliary components such as wipers and washers to ensure driving visibility in bad weather.

[0041] These components work together to form a complete functional system of the vehicle, and any component failure can affect the normal operation of the vehicle, so regular maintenance and repair are crucial.

[0042] To obtain the historical operation data of the target component of the target vehicle in n historical time periods, sensors built-in or deployed around the target component can be used to monitor the operating state in real time and generate data. The target component can be any component in the vehicle, and in this embodiment, the power battery is mainly used as an example.

[0043] The historical operation data can include the rotation speed, torque, fuel injection amount, oil temperature and fault code of the engine in the power system, the voltage, current, rotation speed, winding temperature of the motor and the state of charge value, charging and discharging times of the battery in the electric drive system, the brake pedal stroke, brake hydraulic pressure, brake pad wear amount of the brake system in the chassis system and the steering angle, assist motor current of the steering system, the power supply voltage fluctuation of the vehicle-mounted controller and the relay switching frequency in the electrical system, in addition to the general operation data such as vehicle speed, mileage, time stamp and external temperature. The data is grouped according to different time periods, and can comprehensively reflect the running state of the parts under historical working conditions.

[0044] S102: Determine the damage degree value corresponding to each historical time period of the target part based on the n sets of historical operation data, to obtain n target damage degree values.

[0045] In the embodiment, when determining the damage degree value corresponding to each historical time period based on the n sets of historical operation data, the damage evaluation threshold interval of each index can be set first according to the key performance indicators (such as the cylinder pressure of the engine, the winding temperature of the motor, the brake pad wear amount, etc.) of the target part, combined with the industry standard, the design threshold and the historical normal operation data range; then the feature extraction is performed on the operation data in each historical time period (such as the rotation speed and temperature data of the engine under continuous high load operation in a certain time period), the deviation degree of the actual operation parameters from the normal threshold is calculated, for example, the temperature over-temperature amplitude, the wear amount increment, etc. are converted into damage coefficients in the interval of 0-1 through normalization processing; for the parts coupled with multiple indexes (such as the battery involving voltage, temperature, charging and discharging depth, etc.), the weighted algorithm can be used to integrate the damage coefficients of each index, and the weight is determined through fault tree analysis or machine learning training according to the influence degree of the index on the service life of the part; then the working condition correction factor is introduced to adjust the comprehensive damage coefficient considering the complexity of the working condition in the time period (such as the damage of the transmission caused by frequent start-stop is higher than that caused by uniform driving); finally, the corrected damage coefficient of each historical time period is converted into the corresponding damage degree value, to form n target damage degree values. The process needs to ensure the accuracy of data preprocessing (denoising, missing value filling) and the fitting degree of the evaluation model to the failure mechanism of the part, so that the damage degree value can truly reflect the cumulative damage state of each period.

[0046] S103: Determine the residual life value of the target part based on the n target damage degree values, to obtain a target residual life value.

[0047] In the embodiment, the damage degree value is inversely proportional to the residual life value. When determining the target component residual life value based on n target damage degree values, a damage degree and residual life mapping model can be established first. The core is to convert discrete damage degree values into cumulative life loss representation. Specifically, linear interpolation or nonlinear fitting can be used, taking the brand new state of the component as the initial point (damage degree value is set to 0, residual life is set to the design life benchmark value), and taking the failure critical state as the terminal point (damage degree value is set to 100% or a specific failure threshold, residual life is 0). The n damage degree values are arranged in time sequence to form a life loss curve. In addition, a neural network or regression model can be trained using historical failure data through machine learning methods, and the n damage degree values and corresponding working condition parameters (such as temperature, load cycle number) are input to directly output the residual life prediction value. At the same time, the time series characteristics of the damage degree value need to be considered, and the recent damage degree change rate (such as whether the damage degree increase rate of the last three time periods is accelerating) is analyzed through a sliding window to dynamically correct the residual life prediction, so that the target residual life value can reflect both historical cumulative damage and real-time loss trend, providing accurate basis for maintenance strategy.

[0048] It can be seen that the core advantage of determining the residual life value based on n target damage degree values is to construct a precise mapping from historical loss to future life through quantitative analysis of phased damage data, providing scientific and quantitative support for predictive maintenance. Specifically, the damage trajectory formed by n damage degree values in time sequence can intuitively reflect the nonlinear characteristics of component loss (such as slow early running-in loss and accelerated aging in later period), avoiding one-sidedness of single indicator evaluation; by converting discrete damage degree into residual life value, the current damage position in the life cycle can be accurately located (such as a battery pack with a damage degree value of 40% and a residual life of 2 years), which is more data convincing than traditional experience judgment; combined with the change rate of damage degree in each time period (such as a 20% increase in damage degree in the last three months), the residual life prediction can be dynamically corrected to capture abnormal loss trend (such as shortened residual life due to abnormal wear of bearing); in addition, this method can uniformly quantify the cumulative damage under different working conditions (such as additional loss of air conditioner compressor in high temperature environment) through damage degree value, forming a standardized life evaluation framework, which is suitable for both single component life management and overall maintenance planning of multiple components in a fleet, ultimately realizing the upgrade from regular maintenance to condition-based maintenance, improving equipment reliability while optimizing maintenance resource input efficiency and reducing unplanned downtime losses.

[0049] S104: determining a maintenance strategy for the target component based on the target residual life value.

[0050] In this embodiment, when determining the maintenance strategy based on the target residual life value, a mapping rule between the residual life interval and the maintenance action needs to be established first, and a differentiated strategy is formulated in combination with the part importance level and the operation and maintenance cost. Specifically, the residual life can be divided into different decision intervals: when the residual life is greater than threshold A (such as 80% of the design life), it is determined to be in the normal use stage, and a regular inspection strategy (such as checking the brake pad wear every 5000 kilometers) is adopted, and the damage degree change trend is recorded simultaneously; if the residual life is between threshold A and threshold B (such as 30% of the design life), it indicates that the part enters the accelerated wear period, and needs to be upgraded to predictive maintenance, for example, according to the predicted value of the battery residual life of 2 years, a maintenance plan of equalizing charging every quarter is formulated, and the state monitoring frequency is increased (such as collecting motor temperature data every week); when the residual life is less than threshold B, it is considered to be in the risk period of imminent failure, and an emergency maintenance mechanism needs to be triggered, such as when the residual life of the transmission is less than 5000 kilometers, the filter element needs to be replaced immediately and the clutch plate needs to be checked, and in combination with the high-frequency failure mode (such as the sharp increase in damage caused by abnormal bearing temperature in a certain period) in the historical damage data, targeted detection items (such as vibration spectrum analysis) are added to the maintenance strategy. In addition, for a multi-part cooperative system (such as a powertrain), a comprehensive strategy needs to be formulated based on the residual life of each part, for example, when the engine residual life is 1 year and the transmission residual life is 0.5 year, the transmission overhaul is prioritized and the engine maintenance priority is evaluated simultaneously; at the same time, a cost-benefit model is introduced to quantify the input-output ratio of different maintenance strategies (such as the cost of replacing the filter element in advance is 500 yuan, which can avoid 2000 yuan of transmission repair cost caused by filter blockage), and finally a dynamic strategy including maintenance time window, operation content and resource allocation is formed to ensure that the maintenance action can prevent failure and avoid resource waste caused by over-maintenance.

[0051] S105: performing a maintenance operation on the target part based on the maintenance strategy.

[0052] In this embodiment, when the target component is maintained based on the maintenance strategy, the maintenance actions, time windows and technical requirements preset in the strategy need to be converted into executable operation processes, combined with real-time state monitoring and resource allocation to achieve accurate maintenance. Specifically, if the maintenance strategy is "inspect every 5000 kilometers when the remaining life is greater than 80%", the operation needs to trigger the inspection task at the mileage node, for example, through the vehicle on-board diagnostic system to automatically push the inspection reminder, the maintenance personnel use the special diagnostic equipment to collect the current running data of the component (such as brake pad thickness, sensor signal), and compare and verify the wear trend with the historical damage degree value; when the strategy requires "perform predictive maintenance when the remaining life is between 30%-80%", such as quarterly equalization charging when the battery remaining life is 2 years, the operation needs to call the battery management system to perform deep charge and discharge cycle, and record parameters such as charging efficiency and voltage balance degree, if the damage degree growth rate is found to be abnormal in a certain period of time (such as voltage fluctuation exceeding the threshold during charging), an emergency plan (such as replacing the battery monomer) is immediately started; for the emergency maintenance strategy when the remaining life is less than 30%, such as filter replacement when the transmission remaining life is less than 5000 kilometers, the operation needs to strictly follow the maintenance manual process, first confirm the impurity content through oil analysis, then replace the filter according to the standard torque and supplement the transmission oil, and at the same time, non-destructive testing (such as ultrasonic flaw detection) is performed on related components such as clutch plate, the measured data (such as oil temperature when replacing oil, impurity composition of filter) during operation is synchronized to the maintenance file to form a closed-loop management. In addition, for the overall maintenance strategy of multiple components (such as cooperative maintenance of engine and transmission), maintenance resources need to be coordinated and scheduled, for example, cylinder pressure detection is performed on the engine while the transmission is being overhauled, to avoid repeated disassembly and repair; after the operation is completed, the maintenance effect needs to be verified, the damage degree value change before and after maintenance (such as a 15% decrease in engine vibration damage degree value after maintenance) is compared to evaluate the effectiveness of the strategy, if the wear after maintenance does not meet the expectation (such as the wear rate after brake pad replacement is still higher than the historical average), the historical data needs to be analyzed to adjust the maintenance strategy, to ensure that each maintenance operation not only accurately solves the current problem, but also provides data support for subsequent strategy optimization, to finally realize the reliability management and cost control of the component throughout its life cycle.

[0053] It can be seen that this method of determining the target spare part residual life and maintenance strategy based on historical operation data has many significant advantages. First, by collecting operation data of n historical time periods and calculating the corresponding damage degree values, the performance degradation state of the spare part at different periods can be quantitatively reflected, providing objective data support for residual life evaluation and avoiding errors caused by subjective judgment. Second, by using the inverse relationship between the damage degree value and the residual life value to establish the evaluation model, the residual service period of the spare part can be accurately predicted, making the maintenance plan more scientific and predictable, changing the blindness of traditional regular maintenance, and reducing the situation of excessive maintenance or maintenance not in time. Third, according to the target residual life value, the maintenance strategy can be formulated to realize the optimal allocation of maintenance resources, accurately arrange maintenance or replacement when the spare part approaches the end of life, guarantee the safety and reliability of equipment operation, reduce maintenance cost, and improve equipment efficiency. In addition, through the accumulation and analysis of historical data, this method can form a maintenance knowledge base for specific spare parts, providing a reference for the maintenance of subsequent similar equipment, continuously improving the accuracy and effectiveness of the maintenance strategy, and helping the development of equipment management towards intelligence and refinement.

[0054] Please refer to Figure 2 , Figure 2 is a flowchart provided by the embodiment of the present application for determining the damage degree value, including but not limited to the following steps:

[0055] S201: Determine the first temperature change data and the first insulation resistance attenuation data corresponding to the first historical operation data.

[0056] In the embodiment, the first historical operation data is any one of the n groups of historical operation data. When determining the first temperature change data and the first insulation resistance decay data corresponding to the first historical operation data, the raw data sequence related to temperature and insulation resistance needs to be extracted from the operation data of the target component in the corresponding historical time period. Specifically, first, according to the time range of the first historical operation data (such as data in a certain week or a certain 500-kilometer driving distance), the real-time temperature values (such as motor winding temperature, battery pack temperature, etc.) collected by all temperature sensors in the time period are filtered out to form a temperature data sequence arranged in time sequence. The first temperature change data reflecting the temperature fluctuation characteristics is obtained by calculating the temperature difference between adjacent time points or the temperature change trend over time. At the same time, the insulation resistance values (such as high-voltage system insulation resistance, cable insulation resistance, etc.) recorded by the insulation resistance detection device in the same time period are extracted from the operation data in the same time period to form an insulation resistance data sequence. The first insulation resistance decay data characterizing the degradation of insulation performance is obtained by analyzing the decay amplitude, decay rate or decay amount in a specific time interval of the resistance value in the sequence. For example, if the first historical operation data corresponds to a time period of 1000 kilometers of vehicle driving, the temperature values and insulation resistance values recorded every 10 kilometers in the mileage need to be sorted out from the sensor data in the mileage, and then the average change rate of the temperature in the mileage is calculated as the temperature change data, and the decay proportion (such as a decay of 15%) or decay rate (such as a decay of 1.5% per hundred kilometers) of the insulation resistance from the initial value to the current value is calculated as the insulation resistance decay data, to ensure that the two types of data can accurately reflect the temperature change characteristics and insulation performance degradation status of the target component in the historical time period.

[0057] S202: linear fitting is performed according to the first temperature change data and the first insulation resistance decay data respectively to obtain a temperature change straight line and an insulation resistance decay straight line.

[0058] In the embodiment, when linear fitting is performed according to the first temperature change data and the first insulation resistance decay data, first, the two types of data are sorted into two-dimensional coordinate sequences of time and corresponding parameter values, and then a linear regression model is established by using the least square method. For the temperature change data, the temperature values at each time point are taken as the dependent variable and the time is taken as the independent variable. The temperature change straight line equation is obtained by solving the coefficients that minimize the sum of the squared perpendicular distances of the data points from the straight line. Similarly, for the insulation resistance decay data, the time is taken as the independent variable and the insulation resistance value is taken as the dependent variable. The straight line equation that best represents the trend of the resistance value changing over time is fitted by the least square method, thereby obtaining the straight line models describing the temperature change trend and the insulation resistance decay trend, respectively. According to the above linear fitting method, linear fitting can be performed according to the first temperature change data and the first insulation resistance decay data respectively to obtain a temperature change straight line and an insulation resistance decay straight line.

[0059] S203: Determine the slope of the temperature change straight line and the insulation resistance decay straight line to obtain a temperature change slope and an insulation resistance decay slope.

[0060] In this embodiment, two points are selected on the temperature change straight line and the insulation resistance decay straight line, respectively, to calculate the slope of the temperature change straight line and the insulation resistance decay straight line, thereby obtaining the temperature change slope and the insulation resistance decay slope.

[0061] S204: Determine a first damage degree value corresponding to the temperature change slope and a second damage degree value corresponding to the insulation resistance decay slope.

[0062] In this embodiment, the first mapping relationship between the temperature change slope and the damage degree value can be preset, so that the first damage degree value corresponding to the temperature change slope can be determined based on the first mapping relationship.

[0063] The second mapping relationship between the insulation resistance decay slope and the damage degree value can be preset, so that the second damage degree value corresponding to the insulation resistance decay slope can be determined based on the second mapping relationship.

[0064] S205: Determine a target damage degree value corresponding to the first historical operation data based on the first damage degree value and the second damage degree value.

[0065] In this embodiment, when determining the target damage degree value based on the first damage degree value and the second damage degree value, the synergistic effect of temperature change and insulation resistance decay on the damage of the component needs to be considered comprehensively. Generally, a weighted fusion method can be used to linearly combine the two damage degree values according to the influence weight of temperature and insulation resistance on the aging of the component, or other reasonable aggregation methods can be used, such as taking the maximum value of the two to reflect the most severe damage state, or mapping the coupling relationship of the two through a nonlinear function, to finally form a single quantitative value that can fully represent the damage degree of the component under the set of historical operation data.

[0066] This method of comprehensively assessing component damage using temperature change and insulation resistance decay data offers multiple technical advantages and application value. First, from a data acquisition perspective, selecting temperature and insulation resistance as core parameters has clear physical significance. The temperature change rate directly reflects the accumulated thermal stress during equipment operation, while the insulation resistance decay slope characterizes the aging rate of the insulation material. Both are key indicators of the health of electrical equipment, ensuring targeted and effective data collection. Extracting the slope parameter through linear fitting transforms the complex equipment degradation process into a quantitative linear feature, simplifying data processing while ensuring the scientific validity of trend analysis through fitting methods such as least squares. For example, a larger absolute value of the temperature change slope indicates more severe thermal aging of the equipment during that historical period; a more negative value of the insulation resistance decay slope (a faster resistance drop) indicates more severe insulation degradation. This quantitative representation provides the foundation for accurate damage assessment. By integrating the damage severity values ​​corresponding to temperature and insulation resistance, the one-sidedness of single-parameter assessments can be avoided. For example, under certain operating conditions, equipment may experience abnormal temperatures but not significant insulation degradation, or significant insulation degradation but temperatures within the normal range. Methods such as weighted fusion or extreme value aggregation can comprehensively reflect the true damage state under the influence of multiple coupled factors. This multi-dimensional indicator fusion mechanism not only considers the independent influence of different physical quantities but also reflects their differentiated contributions to equipment aging through weight assignment, ensuring that the target damage level more closely reflects the actual equipment degradation. Because the damage level is inversely proportional to the remaining lifespan, a target value based on a comprehensive assessment can more accurately establish a lifespan prediction model, thereby guiding the formulation of maintenance strategies. For example, if both the temperature and insulation damage levels corresponding to a set of historical data are high, an early warning can be provided that the equipment is nearing the end of its lifespan, preventing sudden failures. Conversely, if the damage levels are unbalanced, targeted adjustments to maintenance priorities (such as strengthening heat dissipation or insulation repair) can be made, optimizing the allocation of maintenance resources and reducing maintenance costs while improving equipment reliability.

[0067] See also Figure 3 , Figure 3 The flowchart of determining a target damage level value corresponding to first historical operation data provided by an embodiment of the present application includes but is not limited to the following steps:

[0068] S301: Determine a first reference weight corresponding to the second damage degree value.

[0069] In this embodiment, it may be a preset mapping relationship between damage degree values ​​and reference weights, and the first reference weight corresponding to the second damage degree value may be determined based on the mapping relationship.

[0070] S302: Obtain an average operating voltage of the target component in a historical time period corresponding to the first historical operation data.

[0071] In the embodiment, the insulation resistance is a measure of the ability of an insulating material to resist current, and its decay rate (i.e. the rate of decrease of resistance value per unit time) is closely related to the electric field strength that the insulating material is subjected to. The higher the average operating voltage, the greater the electric field strength inside the insulating material, and the high voltage can cause local discharge at a small defect (such as a bubble, impurity) inside the insulating material, continuously eroding the insulating structure, causing the resistance value to decrease rapidly, and the voltage increase will increase the conductor current (Ohm's law), further exacerbating the heating, and the electric field together accelerates the molecular chain rupture and carbonization of the insulating material (such as polymer), resulting in rapid resistance decay. In a high-voltage environment, the insulating material is prone to electrochemical reaction when in contact with moisture and impurities, forming a conductive channel, further reducing the insulation resistance.

[0072] The average operating voltage directly affects the rate of insulation resistance decay by regulating the electric field strength and thermal effect of the insulating material, and is positively correlated with the damage degree value. The higher the voltage, the faster the insulation resistance decays, and the greater the corresponding damage degree value, which needs to be included in the analysis as a key parameter in the equipment health state assessment to accurately predict the insulation life and develop maintenance strategies, so the average operating voltage of the target component in the historical time period corresponding to the first historical operation data needs to be obtained first.

[0073] S303: Determine a first optimization factor corresponding to the average operating voltage.

[0074] In the embodiment, the mapping relationship between the preset operating voltage and the optimization factor can be determined based on the mapping relationship to determine the first optimization factor corresponding to the average operating voltage.

[0075] S304: Optimize the first reference weight value based on the first optimization factor to obtain a first target weight value.

[0076] In the embodiment, the first target weight value is calculated according to the following formula:

[0077] First target weight value = first reference weight value x (1 + first optimization factor);

[0078] According to the above formula, the first reference weight value can be optimized based on the first optimization factor to obtain the first target weight value.

[0079] S305: Determine a second target weight value according to the first target weight value.

[0080] In the embodiment, the sum of the first target weight value and the second target weight value is 1. Since the sum of the first target weight value and the second target weight value is 1, after the first target weight value is determined, the second target weight value can be determined according to the first target weight value.

[0081] S306: Calculate a target damage degree value corresponding to the first historical operation data based on the first damage degree value, the second damage degree value, the first target weight value, and the second target weight value.

[0082] In this embodiment, the target damage degree value is calculated according to the following formula:

[0083] Target damage degree value = first damage degree value x second target weight value + second damage degree value x first target weight value.

[0084] According to the above formula, the target damage degree value corresponding to the first historical operation data can be calculated based on the first damage degree value, the second damage degree value, the first target weight value, and the second target weight value.

[0085] It can be seen that the average working voltage is used as the adjustment variable, and the corresponding first optimization factor is associated with the first reference weight value of the second damage degree value, so that the weight value can be dynamically optimized according to the voltage fluctuation. For example, when the average working voltage increases, the accelerating effect of voltage on insulation resistance decay is more significant (such as high voltage leading to an increase in the electric field strength of the insulation material and intensifying partial discharge), at this time the first optimization factor will increase accordingly, and then the first target weight value of the second damage degree value is increased (such as from the standard weight value of 40% to 60%), while the second target weight value is automatically adjusted to 40%, ensuring that the weight sum of the two is 1. This mechanism strengthens the weight of the influence of insulation resistance decay on damage degree under high voltage working condition through real-time feedback of voltage, avoids the deviation that may occur in traditional fixed weight value evaluation (such as ignoring the dominant effect of voltage on insulation aging), and makes the target damage degree value more consistent with the actual operation state of the equipment, providing a more accurate basis for subsequent residual life prediction and maintenance strategy formulation.

[0086] Additionally, the influence of the voltage stability of the target component in the historical time period corresponding to the first historical operation data on the damage degree value corresponding to the first historical operation data can also be considered. First, a reference damage degree value is determined based on the first damage degree value, the second damage degree value, the first reference weight value, and the second reference weight value, and the reference damage degree value is calculated according to the following formula:

[0087] Reference damage degree value = first damage degree value x second reference weight value + second damage degree value x first reference weight value.

[0088] According to the above formula, the reference damage degree value corresponding to the first historical operation data can be calculated based on the first damage degree value, the second damage degree value, the first reference weight value, and the second reference weight value.

[0089] For example, the average voltage sag amplitude of the target component in the historical time period corresponding to the first historical operation data is obtained. The greater the voltage sag amplitude (e.g., from the rated value to 50%) indicates that the voltage deviates from the normal value by a larger amplitude, and the influence on the equipment (e.g., precision instruments, frequency converters) that relies on stable voltage is more significant, which may cause the equipment to shut down, data loss or shortened service life. The voltage sag amplitude can be used to represent the stability of the voltage, which is one of the key indicators for evaluating the voltage stability. The voltage stability of the target component affects the normal operation of the target component and the determination of the component data, and further affects the determination of the damage degree value. Therefore, the average voltage sag amplitude of the target component in the historical time period corresponding to the first historical operation data is obtained.

[0090] For example, a second optimization factor corresponding to the average voltage sag amplitude is determined. Specifically, it can be a mapping relationship between a preset voltage sag amplitude and an optimization factor. Based on the mapping relationship, the second optimization factor corresponding to the average voltage sag amplitude can be determined.

[0091] For example, the reference damage degree value is optimized based on the second optimization factor to obtain the target damage degree value. Specifically, the target damage degree value is calculated according to the following formula:

[0092] Target damage degree value = reference damage degree value × (1 + second optimization factor)

[0093] According to the above formula, the reference damage degree value can be optimized based on the second optimization factor to obtain the target damage degree value.

[0094] It can be seen that the average voltage sag amplitude (i.e. the amplitude of the voltage short-term sharp drop) has a unique impact on the insulation and operating state of the equipment. Although the average operating voltage may not exceed the rated value during the voltage sag, the voltage transient will cause current impact, electromagnetic stress fluctuation, and exacerbate the expansion of internal micro-defects of the insulation material (such as an increase in partial discharge frequency), which cannot be covered by the steady-state voltage evaluation. By associating the voltage sag amplitude with the reference damage degree value through the second optimization factor, the superimposed effect of the voltage sag on the damage can be quantified. As a common power quality problem, frequent voltage sags can accelerate insulation aging (such as motor turn-to-turn insulation being more prone to degradation under voltage sag impact). By incorporating the dynamic optimization factor into the calculation, the comprehensive damage of the operating environment to the equipment can be more comprehensively reflected. For example, under the same average operating voltage, the target damage degree value of a certain equipment under the condition of frequent voltage sags will be higher than that under the condition of stable voltage due to the amplification effect of the second optimization factor, thereby more accurately warning potential risks. This evaluation method fuses the steady-state voltage weight optimization and transient voltage sag correction, so that the target damage degree value can not only reflect the cumulative effect of long-term voltage action, but also reflect the sudden impact of transient disturbances such as voltage sags, providing a more practical quantitative basis for equipment remaining life prediction and maintenance strategy formulation, avoiding evaluation deviation caused by neglecting the voltage sag factor (such as underestimating the aging rate of the equipment), and improving the scientificity and timeliness of maintenance decisions.

[0095] Please refer to Figure 4 , Figure 4 is a flowchart provided by an embodiment of the present application for determining a remaining life value, including but not limited to the following steps:

[0096] S401: Determine the end time corresponding to each historical time period in the n historical time periods to obtain n end times.

[0097] In this embodiment, the end time points of each historical time period are extracted from the n groups of historical operation data to obtain n end times. These time points are used to establish a time dimension coordinate for subsequent analysis of the change of the damage degree over time, providing a time reference for analyzing the change of the damage degree over time.

[0098] S402: Linear fitting is performed based on the n target damage degree values and the n end times to obtain a damage degree change line.

[0099] In this embodiment, each end time and the corresponding target damage degree value are taken as coordinate points, and a straight line that best reflects the trend of the change of the damage degree over time is calculated through a linear fitting algorithm (such as the least squares method). This step converts discrete damage degree data into a continuous trend line, facilitating quantitative analysis of the time correlation of the damage degree, such as determining whether the damage is linearly increasing or accelerating.

[0100] S403: Determine the slope of the damage degree change straight line to obtain a target slope.

[0101] In the present embodiment, the slope of the damage degree change straight line can be determined by selecting any two points in the damage degree change straight line to obtain a target slope.

[0102] S404: Determine a life attenuation rate corresponding to the target slope.

[0103] In the present embodiment, the mapping relationship between the slope of the damage degree change straight line and the life attenuation rate can be pre-established, and the life attenuation rate corresponding to the target slope can be determined based on the mapping relationship.

[0104] S405: Obtain the quota life value and the used time length of the target component.

[0105] In the present embodiment, the inherent parameters of the equipment (the quota life, such as the design life of 10 years) and the actual running time (the used time length, such as the running time of 5 years) are extracted, and these two data are the basis for calculating the remaining life. The quota life provides a benchmark for life evaluation, and the used time length determines the position of the current equipment in the life cycle.

[0106] S406: Determine the target remaining life value based on the quota life value, the used time length, and the life attenuation rate.

[0107] In the present embodiment, the remaining life can be calculated by a life prediction model based on the quota life value, the used time length, and the life attenuation rate. For example, if the quota life is 10 years (3650 days), the used time length is 5 years (1825 days), and the life attenuation rate is 2% per year, the remaining life can be represented as: (quota life-used time length) x (1-accumulative attenuation ratio), or the time point when the damage degree reaches the critical value by slope extrapolation. This step converts the abstract damage degree into a specific remaining available time, providing a quantitative basis for equipment maintenance and replacement decisions, such as predicting that the equipment will reach the end of life in 2.5 years, and planning for maintenance or replacement in advance.

[0108] It can be seen that, first, by extracting the end time of n historical time periods and the target damage degree value, a dynamic trend model of equipment aging can be constructed with time as the horizontal axis and damage degree as the vertical axis. The slope of the straight line fitting (target slope) can quantify the rate of change of damage degree with time, for example, the greater the slope, the faster the equipment ages. This linearization simplifies the complex aging process into a calculable mathematical relationship, facilitating rapid positioning of the aging acceleration or deceleration stage. Second, the target slope is mapped to the life attenuation rate, realizing the physical meaning conversion from "damage degree change" to "life consumption". Combined with the rated life value and the time length used, the remaining life can be directly obtained through the life prediction model (such as linear extrapolation or attenuation ratio calculation). This calculation method converts the abstract aging state into an intuitive time indicator, providing a clear basis for maintenance decisions. In addition, the method has the advantages of data-driven objectivity and dynamic adaptability: the accumulation of n sets of historical data can cover the aging characteristics of the equipment under different working conditions, the linear fitting process can filter short-term fluctuations and capture long-term trends, avoiding the deviation of a single data point; and the calculation logic based on slope and attenuation rate can continuously update the model with the addition of new data, making the remaining life prediction more consistent with the actual operation state of the equipment, for example, when the equipment ages faster due to increased load in recent period, the new slope will reflect this change in real time, avoiding the defects of traditional fixed life assessment (such as "rated life - used time") that ignore the differences in actual working conditions. Finally, this evaluation method converts the equipment aging process into a calculable and predictable remaining life value through the quantitative chain of "time-damage degree-life attenuation", helping maintenance personnel to plan maintenance cycles and replacement strategies in advance, avoiding both resource waste caused by excessive maintenance and sudden failures caused by lagging life assessment, and improving the scientificity and economy of equipment management.

[0109] It needs to be explained that when calculating the target residual life value of the target component, the power load overload of the target component can also be considered. The power load overload means that the actual operating current or power of the component (such as a transformer, a cable, a motor, etc.) exceeds the rated design value. When overloaded, the current increases, the joule heat generated by the internal resistance of the component increases significantly, causing the temperature of the insulation material (such as the cable insulation layer, the motor winding insulation) to exceed the rated tolerance range. Overload may be accompanied by voltage fluctuation or harmonic increase, causing the electric field strength borne by the insulating medium to exceed the design threshold, triggering irreversible damage such as partial discharge and electrical tree growth. For example, sudden short circuit or instantaneous heavy load may directly cause thermal breakdown or mechanical deformation (such as transformer winding deformation under the impact of electric power) of the component, resulting in irreversible life loss or even direct failure. Even if the trip threshold is not reached, the continuous overload current will also cause the component to be in a chronic aging state. For example, when a transformer is operated for a long time under overload, the water content and acid value of the oil-immersed insulation paper will increase rapidly, and the insulation strength will decrease year by year. If this cumulative effect is not included in the life calculation, it will lead to an optimistic prediction of the residual life. The residual life assessment considering the overload condition can provide more accurate basis for operation and maintenance decision-making. When a certain component is frequently overloaded in the near future, the residual life calculation result will give an early warning of potential failure risk, prompting the operation and maintenance personnel to adjust the load distribution or arrange maintenance, so as to avoid sudden failure caused by overload accumulation. For equipment that has been running for a long time under light load, the actual aging rate is lower than that under rated conditions. Ignoring the overload factor may lead to over-maintenance. For overloaded equipment, if the accelerated aging is not considered, the best maintenance opportunity may be missed. Therefore, the average value of the power load overload of the target component in the used time length needs to be obtained.

[0110] For example, the target fine-tuning parameter corresponding to the average power load is determined. Specifically, it can be a preset mapping relationship between the average power load and the fine-tuning parameter. Based on the mapping relationship, the target fine-tuning parameter corresponding to the average power load can be determined.

[0111] For example, the reference residual life value is adjusted based on the target fine-tuning parameter to obtain the target residual life value. Specifically, the target residual life value is calculated according to the following formula:

[0112] Target residual life value = reference residual life value x (1 + target fine-tuning parameter)

[0113] According to the above formula, the reference residual life value can be adjusted based on the target fine-tuning parameter to obtain the target residual life value.

[0114] It can be seen that by fusing the basic model of rated life with the power load overload data in actual operation, the dynamic and accurate evaluation of the target component residual life is realized: the reference residual life is calculated by the rated life value, the used time length and the life decay rate, and the standardized life evaluation benchmark is constructed, but this benchmark does not consider the acceleration effect of load overload on equipment aging in actual operation, and the average value of power load overload in the used time length is obtained and matched with the corresponding target fine-tuning parameter, which can quantify the additional life loss caused by overload, for example, when the equipment is running at 120% rated load for a long time, the fine-tuning parameter corresponding to the average value of overload will correct the reference residual life downward, which intuitively reflects the actual situation that overload causes the life loss of the equipment. This method converts the ideal rated life evaluation into dynamic prediction that fits the real running track of the equipment through the logic of "basic model + working condition correction", which can not only cover the differentiated effects of different working conditions such as short-term high overload and long-term light overload on life, but also solve the contradiction between "rated working condition assumption" and "actual load fluctuation" in traditional evaluation, avoid the maintenance lag or misjudgment risk caused by overestimating the residual life due to ignoring the overload factor, and finally make the target residual life value more accurately reflect the equipment health status, provide a scientific basis for operation and maintenance decision-making, and help to reasonably plan the maintenance cycle, optimize resource allocation and improve equipment operation safety.

[0115] Please refer to Figure 5 , Figure 5 is a flowchart for determining a maintenance strategy provided by the embodiment of the present application, including but not limited to the following steps:

[0116] S501: When the target residual life value is greater than or equal to the first residual life value, determining that the maintenance strategy is to perform a preventive maintenance operation on the target component.

[0117] In the embodiment, when the target residual life value of the target component obtained by evaluation is greater than or equal to the first residual life value set in advance, it means that the current residual available life of the component is relatively sufficient, although it does not reach the degree of immediate replacement or emergency repair, but in order to prevent potential failure and prolong the service life, the maintenance strategy determined at this time is to perform a "preventive maintenance operation" on it. For example, performing insulation oil detection, dust cleaning or lubricating parts on the transformer, etc. Such operations aim to reduce normal loss by early intervention and eliminate hidden troubles in the embryonic stage to avoid small problems from evolving into big failures.

[0118] It should be noted that preventive maintenance operations on target components are based on a maintenance strategy whereby they have sufficient remaining life but require early intervention. Specific operations will vary depending on the component type, application scenario, and aging mechanism. Remove impurities such as dust, oil, and metal debris from the surface of components (such as blowing or wiping motor housings and transformer heat sinks) to prevent impurity accumulation that could lead to poor heat dissipation or reduced insulation performance. Apply anti-rust paint or insulating coatings to metal components (such as insulating spray coatings on switchgear busbars), or replace aged sealing strips (such as waterproof seals on cable connectors) to prevent environmental factors (humidity and corrosion) from accelerating aging. Replace or replenish lubricating grease on rotating parts such as bearings and gearboxes (such as adding lithium-based grease to motor bearings) to reduce friction loss, lower operating temperature, and reduce noise. Tighten loose bolts and nuts (such as transformer terminals and internal connectors of switchgear) to avoid poor contact or reduced structural stability due to vibration. Detect electrical parameters (such as cable insulation resistance and circuit breaker contact resistance), mechanical parameters (such as motor shaft vibration amplitude), and temperature parameters (such as transformer winding temperature) of components, compare historical data to determine whether there are abnormal trends, and use infrared thermal imaging, ultrasonic flaw detection and other means to detect potential defects inside components (such as local overheating of transformer cores and cracks in pressure vessels). Perform action tests on components such as relays and sensors (such as circuit breaker opening and closing tests) to ensure that they can respond normally in the event of a fault. Calibrate the set values ​​of instruments and protection devices (such as current transformer ratio verification) to ensure measurement accuracy and protection logic accuracy.

[0119] S502: When the target remaining life value is less than the first remaining life value and greater than the second remaining life value, determining that the maintenance strategy is to perform fault diagnosis and fault repair operations on the target component.

[0120] In this embodiment, if the target remaining life value is less than the first remaining life value, but still greater than another lower threshold "second remaining life value", it indicates that the remaining life of the component is in the "warning range". Although it has not completely failed, there may be signs of potential failure or performance degradation. At this time, the maintenance strategy turns to "fault diagnosis and fault repair operations": first, it is necessary to locate the specific fault point or degradation cause through professional detection means (such as infrared temperature measurement, vibration analysis, electrical characteristics testing, etc.), and then repair the problem, such as replacing aging capacitors, repairing local insulation damage, etc. This strategy emphasizes "precise positioning + targeted repair", which can not only avoid the waste of resources caused by premature replacement, but also prevent the expansion of faults due to delayed maintenance.

[0121] S503: When the target remaining life value is less than the second remaining life value, determining that the maintenance strategy is to replace the target component.

[0122] In the embodiment, when the target residual life value is less than the second residual life value, it indicates that the residual life of the component is close to exhaustion, and continued use can cause functional failure, safety accidents or systematic failure (such as the risk of motor winding aging to short circuit). At this time, the maintenance strategy is directly set as "replace the target component", that is, the failure risk is completely eliminated by replacing it with a new component. This decision is based on the bottom line threshold of life assessment, ensuring the safety and reliability of equipment operation, avoiding production interruptions or safety accidents caused by over-service of components, and also optimizing the overall performance of the equipment within a reasonable period.

[0123] Please refer to Figure 6 , Figure 6 is a schematic diagram of a mapping table of residual life values and maintenance strategies provided by the embodiment of the present application. In the Figure 6 , the mapping table 600 of residual life values and maintenance strategies includes a plurality of maintenance strategies and a residual life value range corresponding to each maintenance strategy in the plurality of maintenance strategies, wherein the maintenance strategies include preventive maintenance operation on the target component, fault diagnosis and fault repair operation on the target component, and replacement of the target component. When the target residual life value is greater than or equal to the first residual life value, the maintenance strategy is determined to be the preventive maintenance operation on the target component, when the target residual life value is less than the first residual life value and greater than the second residual life value, the maintenance strategy is determined to be the fault diagnosis and fault repair operation on the target component, and when the target residual life value is less than the second residual life value, the maintenance strategy is determined to be the replacement of the target component.

[0124] It can be seen that by setting the first residual life value and the second residual life value as thresholds, the residual life of the target component is divided into different intervals and matched with differentiated maintenance strategies, which can ensure the safe operation of the equipment while optimizing the allocation of resources. When the target residual life value is not less than the first residual life value, preventive maintenance can eliminate potential hazards such as dust accumulation and insufficient lubrication in advance, extending the service life of the component at a lower cost; if the residual life value is between the two thresholds, targeted fault diagnosis and repair can accurately locate early problems such as insulation aging and poor contact, avoiding the expansion of faults while reducing unnecessary replacement costs; and when the residual life value is less than the second residual life value, directly replacing the component can completely eliminate the failure risk and prevent safety accidents or systematic failure caused by over-service of the component. This hierarchical strategy not only avoids the waste of resources caused by premature maintenance, but also realizes precise control of the maintenance timing through threshold management, taking into account safety, economy and reliability in equipment operation and maintenance.

[0125] In summary, implementing the embodiment of the present application has the following beneficial effects:

[0126] It can be seen that the vehicle part maintenance method described in the embodiment of the application obtains historical operation data of a target part of a target vehicle in n historical time periods, obtains n sets of historical operation data, each historical time period corresponds to a set of historical operation data, n is a positive integer, determines a damage degree value corresponding to each historical time period of the target part in the n historical time periods based on the n sets of historical operation data, obtains n target damage degree values, determines a residual life value of the target part based on the n target damage degree values, obtains a target residual life value; the damage degree value is inversely proportional to the residual life value, determines a maintenance strategy of the target part based on the target residual life value, and performs a maintenance operation on the target part based on the maintenance strategy, thereby improving the maintenance efficiency of the vehicle part.

[0127] Please refer to Figure 7 , Figure 7 is a structural schematic diagram of a vehicle part maintenance device provided by the embodiment of the application, and the vehicle part maintenance device 700 comprises an acquisition unit 701 and a processing unit 702.

[0128] The acquisition unit 701 is configured to acquire historical operation data of a target part of a target vehicle in n historical time periods, and obtain n sets of historical operation data; each historical time period corresponds to a set of historical operation data, and n is a positive integer.

[0129] The processing unit 702 is configured to determine a damage degree value corresponding to each historical time period of the target part in the n historical time periods based on the n sets of historical operation data, and obtain n target damage degree values.

[0130] Determine a residual life value of the target part based on the n target damage degree values, and obtain a target residual life value; the damage degree value is inversely proportional to the residual life value.

[0131] Determine a maintenance strategy of the target part based on the target residual life value.

[0132] Perform a maintenance operation on the target part based on the maintenance strategy.

[0133] In some possible embodiments, in terms of determining a damage degree value corresponding to each historical time period of the target part in the n historical time periods based on the n sets of historical operation data and obtaining n target damage degree values, the processing unit 702 is specifically configured to:

[0134] Determine first temperature change data and first insulation resistance attenuation data corresponding to first historical operation data; the first historical operation data is any one of the n sets of historical operation data.

[0135] linear fitting is performed on the first temperature change data and the first insulation resistance decay data respectively to obtain a temperature change line and an insulation resistance decay line;

[0136] determining the slopes of the temperature change line and the insulation resistance decay line to obtain a temperature change slope and an insulation resistance decay slope;

[0137] determining a first damage degree value corresponding to the temperature change slope and a second damage degree value corresponding to the insulation resistance decay slope;

[0138] determining a target damage degree value corresponding to the first historical operation data based on the first damage degree value and the second damage degree value.

[0139] In some possible implementation manners, in the determination of the target damage degree value corresponding to the first historical operation data based on the first damage degree value and the second damage degree value, the processing unit 702 is specifically configured to:

[0140] determining a first reference weight value corresponding to the second damage degree value;

[0141] obtaining an average working voltage of the target component in a historical time period corresponding to the first historical operation data;

[0142] determining a first optimization factor corresponding to the average working voltage;

[0143] optimizing the first reference weight value based on the first optimization factor to obtain a first target weight value;

[0144] determining a second target weight value according to the first target weight value; the sum of the first target weight value and the second target weight value is 1;

[0145] performing calculation based on the first damage degree value, the second damage degree value, the first target weight value and the second target weight value to obtain the target damage degree value corresponding to the first historical operation data.

[0146] In some possible implementation manners, in the calculation of the target damage degree value corresponding to the first historical operation data based on the first damage degree value, the second damage degree value, the first target weight value and the second target weight value, the processing unit 702 is specifically configured to:

[0147] determining a reference damage degree value based on the first damage degree value, the second damage degree value, the first target weight value and the second target weight value;

[0148] obtaining an average voltage sag amplitude of the target component in a historical time period corresponding to the first historical operation data;

[0149] determining a second optimization factor corresponding to the average voltage sag amplitude;

[0150] optimizing the reference damage degree value based on the second optimization factor to obtain the target damage degree value.

[0151] In some possible implementation manners, in determining the target residual life value of the target component based on the n target damage degree values, the processing unit 702 is specifically configured to:

[0152] determining an ending time corresponding to each of the n historical time periods to obtain n ending times;

[0153] performing linear fitting based on the n target damage degree values and the n ending times to obtain a damage degree change line;

[0154] determining a slope of the damage degree change line to obtain a target slope;

[0155] determining a life attenuation rate corresponding to the target slope;

[0156] obtaining a rated life value and a used time length of the target component;

[0157] determining the target residual life value based on the rated life value, the used time length and the life attenuation rate.

[0158] In some possible implementation manners, in determining the target residual life value based on the rated life value, the used time length and the life attenuation rate, the processing unit 702 is specifically configured to:

[0159] determining a reference residual life value of the target component based on the rated life value, the used time length and the life attenuation rate;

[0160] obtaining an average value of power load overload of the target component in the used time length;

[0161] determining a target fine-tuning parameter corresponding to the average value of power load;

[0162] adjusting the reference residual life value based on the target fine-tuning parameter to obtain the target residual life value.

[0163] In some possible implementation manners, in determining a maintenance strategy of the target component based on the target residual life value, the processing unit 702 is specifically configured to:

[0164] determining the maintenance strategy as a fault diagnosis and fault repair operation on the target component when the target residual life value is less than the first residual life value and greater than a second residual life value;

[0165] determining the maintenance strategy as a fault diagnosis and fault repair operation on the target component when the target residual life value is less than the first residual life value and greater than a second residual life value;

[0166] determining the maintenance strategy as a fault diagnosis and fault repair operation on the target component when the target residual life value is less than the first residual life value and greater than a second residual life value;

[0167] Referring to Figure 8 , Figure 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 8 , the electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. They are connected through a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above-mentioned programs include instructions for performing the following steps:

[0168] obtaining historical running data of a target component of a target vehicle in n historical time periods, to obtain n groups of historical running data; each historical time period corresponds to a group of historical running data, and n is a positive integer;

[0169] determining a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n groups of historical running data, to obtain n target damage degree values;

[0170] determining a residual life value of the target component based on the n target damage degree values, to obtain a target residual life value; the damage degree value is inversely proportional to the residual life value;

[0171] determining a maintenance strategy of the target component based on the target residual life value;

[0172] performing a maintenance operation on the target component based on the maintenance strategy.

[0173] In some possible embodiments, in terms of determining a damage degree value corresponding to each historical time period of the target component in the n historical time periods based on the n groups of historical running data, to obtain n target damage degree values, the above-mentioned programs include instructions for performing the following steps:

[0174] determining first temperature change data and first insulation resistance attenuation data corresponding to first historical running data; the first historical running data is any one of the n groups of historical running data;

[0175] linear fitting is performed on the first temperature change data and the first insulation resistance decay data respectively to obtain a temperature change line and an insulation resistance decay line;

[0176] slopes of the temperature change line and the insulation resistance decay line are determined to obtain a temperature change slope and an insulation resistance decay slope;

[0177] a first damage degree value corresponding to the temperature change slope and a second damage degree value corresponding to the insulation resistance decay slope are determined;

[0178] a target damage degree value corresponding to the first historical operation data is determined based on the first damage degree value and the second damage degree value.

[0179] In some possible implementation manners, in the aspect of determining the target damage degree value corresponding to the first historical operation data based on the first damage degree value and the second damage degree value, the above program includes instructions for performing the following steps:

[0180] a first reference weight value corresponding to the second damage degree value is determined;

[0181] an average working voltage of the target component in a historical time period corresponding to the first historical operation data is obtained;

[0182] a first optimization factor corresponding to the average working voltage is determined;

[0183] the first reference weight value is optimized based on the first optimization factor to obtain a first target weight value;

[0184] a second target weight value is determined according to the first target weight value; a sum of the first target weight value and the second target weight value is 1;

[0185] the first historical operation data corresponding to the target damage degree value is obtained based on the first damage degree value, the second damage degree value, the first target weight value and the second target weight value.

[0186] In some possible implementation manners, in the aspect of obtaining the first historical operation data corresponding to the target damage degree value based on the first damage degree value, the second damage degree value, the first target weight value and the second target weight value, the above program includes instructions for performing the following steps:

[0187] a reference damage degree value is determined based on the first damage degree value, the second damage degree value, the first target weight value and the second target weight value;

[0188] obtaining an average voltage sag amplitude of the target component in a historical time period corresponding to the first historical operation data;

[0189] determining a second optimization factor corresponding to the average voltage sag amplitude;

[0190] optimizing the reference damage degree value based on the second optimization factor to obtain the target damage degree value.

[0191] In some possible implementation manners, in the step of determining a residual life value of the target component based on the n target damage degree values to obtain a target residual life value, the foregoing program includes instructions for performing the following steps:

[0192] determining an ending time corresponding to each of the n historical time periods to obtain n ending times;

[0193] performing linear fitting based on the n target damage degree values and the n ending times to obtain a damage degree change line;

[0194] determining a slope of the damage degree change line to obtain a target slope;

[0195] determining a life attenuation rate corresponding to the target slope;

[0196] obtaining a rated life value and a used time length of the target component;

[0197] determining the target residual life value based on the rated life value, the used time length and the life attenuation rate.

[0198] In some possible implementation manners, in the step of determining the target residual life value based on the rated life value, the used time length and the life attenuation rate, the foregoing program includes instructions for performing the following steps:

[0199] determining a reference residual life value of the target component based on the rated life value, the used time length and the life attenuation rate;

[0200] obtaining an average power load overload of the target component in the used time length;

[0201] determining a target fine-tuning parameter corresponding to the average power load;

[0202] adjusting the reference residual life value based on the target fine-tuning parameter to obtain the target residual life value.

[0203] In some possible implementation manners, in the step of determining a maintenance strategy of the target component based on the target residual life value, the foregoing program includes instructions for performing the following steps:

[0204] when the target remaining life value is greater than or equal to the first remaining life value, determining the maintenance strategy as a preventive maintenance operation on the target component;

[0205] when the target remaining life value is less than the first remaining life value and greater than a second remaining life value, determining the maintenance strategy as a fault diagnosis and fault repair operation on the target component;

[0206] when the target remaining life value is less than the second remaining life value, determining the maintenance strategy as replacement of the target component.

[0207] It should be understood that the electronic device in the present application can include a smart phone (such as an Android phone, an iOS phone, a Windows Phone, etc.), a tablet computer, a palm computer, a notebook computer, a mobile Internet device (MID, Mobile Internet Devices for short), or a wearable device or a server, an edge computing node, etc. The above electronic devices are only examples and are not exhaustive, and include but are not limited to the above electronic devices.

[0208] The embodiments of the present application also provide a computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement part or all steps of any one of the methods described in the above method embodiments.

[0209] The embodiments of the present application also provide a computer program product including a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform part or all steps of any one of the methods described in the above method embodiments.

[0210] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the action order described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0211] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0212] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments described above is merely illustrative, and the division of units can be changed according to actual needs. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0213] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0214] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software program module.

[0215] If the integrated unit is realized in the form of a software program module and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for making a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the methods in the embodiments. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, etc. Various media that can store program codes.

[0216] Those of ordinary skill in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc.

[0217] The above describes the embodiments of the present application in detail, and the principles and embodiments of the present application are described by using specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific embodiments and application range will be changed, and the above description of the embodiments should not be understood as a limitation of the present application.

Claims

1. A vehicle parts maintenance method, characterized in that: include: Obtain historical operating data of target components of a target vehicle within n historical time periods to obtain n sets of historical operating data; each historical time period corresponds to a set of historical operating data, and n is a positive integer; Determining the damage degree value corresponding to each of the n historical time periods for the target component based on the n sets of historical operation data to obtain n target damage degree values; Determining the remaining life value of the target component based on the n target damage degree values ​​to obtain a target remaining life value; The damage degree value is inversely proportional to the remaining life value; Determining a maintenance strategy for the target component based on the target remaining life value; Perform maintenance operations on the target component based on the maintenance strategy.

2. The method according to claim 1, wherein When the historical operation data includes temperature change data and insulation resistance attenuation data, determining the damage degree value corresponding to each of the n historical time periods for the target component based on the n sets of historical operation data to obtain n target damage degree values ​​includes: Determining first temperature change data and first insulation resistance attenuation data corresponding to first historical operation data; the first historical operation data is any one set of historical operation data among the n sets of historical operation data; Performing straight line fitting based on the first temperature change data and the first insulation resistance attenuation data to obtain a temperature change straight line and an insulation resistance attenuation straight line; Determining the slopes of the temperature change straight line and the insulation resistance attenuation straight line to obtain a temperature change slope and an insulation resistance attenuation slope; determining a first damage degree value corresponding to the temperature change slope and a second damage degree value corresponding to the insulation resistance attenuation slope; A target damage degree value corresponding to the first historical operation data is determined based on the first damage degree value and the second damage degree value.

3. The method according to claim 2, wherein The determining, based on the first damage degree value and the second damage degree value, a target damage degree value corresponding to the first historical operation data includes: determining a first reference weight corresponding to the second damage degree value; Obtaining an average operating voltage of the target component within a historical time period corresponding to the first historical operating data; determining a first optimization factor corresponding to the average operating voltage; Optimizing the first reference weight based on the first optimization factor to obtain a first target weight; Determine a second target weight according to the first target weight; the sum of the first target weight and the second target weight is 1; A target damage degree value corresponding to the first historical operation data is obtained by performing calculation based on the first damage degree value, the second damage degree value, the first target weight, and the second target weight.

4. The method according to claim 3, wherein The calculating based on the first damage degree value, the second damage degree value, the first target weight, and the second target weight to obtain a target damage degree value corresponding to the first historical operation data includes: determining a reference damage degree value based on the first damage degree value, the second damage degree value, the first target weight, and the second target weight; Obtaining an average voltage sag amplitude of the target component during a historical time period corresponding to the first historical operating data; determining a second optimization factor corresponding to the average voltage sag amplitude; The reference damage degree value is optimized based on the second optimization factor to obtain the target damage degree value.

5. The method according to claim 4, wherein The determining the remaining life value of the target component based on the n target damage degree values ​​to obtain the target remaining life value includes: Determine the end time corresponding to each of the n historical time periods to obtain n end times; Performing linear fitting based on the n target damage degree values ​​and the n end times to obtain a damage degree change line; Determining the slope of the damage degree change line to obtain a target slope; determining a life decay rate corresponding to the target slope; Obtaining the lifespan and usage time of the target component; The target remaining life value is determined based on the credit life value, the used time and the life decay rate.

6. The method according to claim 5, wherein The determining the target remaining life value based on the credit life value, the used time, and the life decay rate includes: Determining a reference remaining life value of the target component based on the rated life value, the used time, and the life decay rate; Obtaining an average value of power load overload of the target component during the usage time; determining a target fine-tuning parameter corresponding to the average value of the power load; The reference remaining life value is adjusted based on the target fine-tuning parameter to obtain the target remaining life value.

7. The method according to any one of claims 1 to 6, wherein: Determining the maintenance strategy of the target component based on the target remaining life value includes: When the target remaining life value is greater than or equal to a first remaining life value, determining that the maintenance strategy is to perform a preventive maintenance operation on the target component; When the target remaining life value is less than the first remaining life value and greater than the second remaining life value, determining that the maintenance strategy is to perform fault diagnosis and fault repair operations on the target component; When the target remaining life value is less than the second remaining life value, the maintenance strategy is determined to be replacing the target component.

8. A vehicle parts maintenance device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire historical operating data of a target component of a target vehicle within n historical time periods to obtain n groups of historical operating data; each historical time period corresponds to a group of historical operating data, and n is a positive integer; The processing unit is configured to determine, based on the n sets of historical operating data, a damage degree value corresponding to each of the n historical time periods for the target component, to obtain n target damage degree values; Determining the remaining life value of the target component based on the n target damage degree values ​​to obtain a target remaining life value; the damage degree value is inversely proportional to the remaining life value; Determining a maintenance strategy for the target component based on the target remaining life value; Perform maintenance operations on the target component based on the maintenance strategy.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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