Method and device for evaluating vehicle battery performance

Abstract

The present invention discloses a method and device for evaluating the performance of vehicle batteries, and relates to the technical field of battery performance evaluation. The technical solution of the present invention obtains target data of multiple vehicles on a big data platform. Since the target data is data on the performance of the vehicle's power battery as the vehicle performs historical driving tasks, survival analysis and hazard analysis can be performed on the target data of multiple vehicles to obtain a survival curve and a hazard curve for the power battery. The survival curve and the hazard curve are analyzed for indicators according to the performance evaluation period of the power battery to obtain a survival index and a hazard index for the power battery. Based on the survival index and the hazard index, the performance of the power battery during the performance evaluation period is determined. This technical solution realizes a scientific and comprehensive evaluation of the performance of the power battery and improves the accuracy of the evaluation of the performance of the power battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery performance evaluation, and in particular to a method and device for evaluating the performance of a vehicle battery. Background Art

[0002] With the rapid development of new energy vehicle technologies, the performance advantages and application prospects of power batteries have attracted considerable attention. As a core component of new energy vehicles, the accuracy of their performance evaluation is crucial for vehicle safety, reliability, and user experience. However, existing power battery evaluation methods often rely on limited experimental data and empirical models, making it difficult to fully reflect the performance changes of power batteries in actual use.

[0003] Therefore, how to improve the accuracy of power battery performance evaluation is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] In view of the above technical problems, the present invention provides a method and device for evaluating vehicle battery performance, which can accurately evaluate the performance of power batteries based on big data, thereby improving the accuracy of power battery performance evaluation.

[0005] The embodiment of the present invention provides the following solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for evaluating the performance of a vehicle battery, the method comprising:

[0007] Obtain target data of multiple vehicles on a big data platform, where the target data is data on changes in the performance of the vehicle's power battery during the vehicle's historical driving missions;

[0008] Perform survival analysis and hazard analysis on target data of multiple vehicles to obtain the survival curve and hazard curve of the power battery;

[0009] Perform indicator item analysis on the survival curve and the danger curve according to the performance evaluation period of the power battery to obtain the survival index and danger index of the power battery;

[0010] Determine the performance of the power battery during the performance evaluation period based on the survival index and danger index.

[0011] In an optional embodiment, obtaining target data of multiple vehicles on a big data platform includes:

[0012] Extract historical driving mission data on the big data platform according to the preset statistical table template to obtain the data statistical table for each vehicle;

[0013] Obtaining, based on the data represented by the data statistics table for each vehicle, first data indicating a change in the driving time of each vehicle as a function of the power battery performance, and second data indicating a change in the driving mileage of each vehicle as a function of the power battery performance;

[0014] Target data for each vehicle is obtained based on the first data and the second data.

[0015] In an optional embodiment, obtaining target data of each vehicle according to the first data and the second data includes:

[0016] Performing data cleaning on the first data and the second data of each vehicle to remove duplicate data and invalid data;

[0017] Filling missing values ​​on the first data and the second data after data cleaning to obtain the first data and the second data after data filling;

[0018] The first data and the second data after data filling are normalized to obtain target data corresponding to the vehicle.

[0019] In an optional embodiment, survival analysis and hazard analysis are performed on target data of multiple vehicles to obtain survival curves and hazard curves of power batteries, including:

[0020] Obtaining target faults of the multiple vehicles based on data features represented by target data of the multiple vehicles, wherein the target faults are faults related to the performance of the power batteries;

[0021] Determine the survival probability of power batteries under different usage cycles based on target failures of multiple vehicles and preset survival functions;

[0022] According to the corresponding relationship between the service life of the power battery and the survival probability, the survival curve of the power battery is obtained;

[0023] According to the survival curve and the preset hazard function, the hazard curve of the power battery is obtained.

[0024] In an optional embodiment, obtaining target faults of multiple vehicles according to data features represented by target data of multiple vehicles includes:

[0025] Determine the corresponding characteristic data in the target data based on the fault items related to the performance of the power battery in different component systems of each vehicle, where the component systems include the transmission system, braking system, electrical system, cooling system, and battery system;

[0026] The characteristic data of all fault items of each vehicle are statistically analyzed to obtain the target faults of multiple vehicles.

[0027] In an optional embodiment, the survival curve and the danger curve are analyzed for indicators respectively according to the performance evaluation period of the power battery to obtain the survival index and the danger index of the power battery, including:

[0028] Determine the median survival time, mean survival time, and hazard ratio of the power battery during the performance evaluation period based on the survival curve, wherein the preset comparison items include a mileage comparison item and / or a failure type comparison item;

[0029] Determine the cumulative risk value and / or curve slope of the power battery during the performance evaluation period based on the risk curve;

[0030] Characterize the survival indicators of power batteries based on median survival time, mean survival time and hazard ratio under pre-set comparison items;

[0031] Characterize the danger index of the power battery based on the cumulative risk value and / or curve slope.

[0032] In an optional embodiment, after determining the usage performance of the power battery during the performance evaluation period based on the survival index and the danger index, the method further includes:

[0033] When the performance evaluation period is the operation management period, output the management strategy of the operation vehicle in the subsequent management period;

[0034] Determine the maintenance and replacement time of power batteries on operating vehicles based on management strategies.

[0035] In a second aspect, an embodiment of the present invention further provides a vehicle battery performance evaluation device, the device comprising:

[0036] An acquisition module is used to acquire target data of multiple vehicles on a big data platform, wherein the target data is data on changes in the performance of the vehicle's power battery during the vehicle's historical driving missions;

[0037] The first acquisition module is used to perform survival analysis and hazard analysis on target data of multiple vehicles to obtain survival curves and hazard curves of power batteries;

[0038] A second obtaining module is used to perform index item analysis on the survival curve and the danger curve according to the performance evaluation period of the power battery to obtain the survival index and the danger index of the power battery;

[0039] The first determination module is used to determine the performance of the power battery during the performance evaluation period according to the survival index and the danger index.

[0040] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is coupled to the processor and stores instructions, which, when executed by the processor, enable the electronic device to perform the steps of any one of the methods in the first aspect.

[0041] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any one of the methods in the first aspect when executed by a processor.

[0042] Compared with the prior art, the vehicle battery performance evaluation method and device of the present invention have the following advantages:

[0043] The technical solution of the present invention obtains target data of multiple vehicles on a big data platform. Since the target data is data on the performance changes of the vehicles' power batteries during the execution of historical driving tasks, survival analysis and hazard analysis can be performed on the target data of multiple vehicles to obtain survival curves and hazard curves of the power batteries. Indicator items of the survival curves and hazard curves are analyzed according to the performance evaluation period of the power batteries to obtain survival indicators and hazard indicators of the power batteries. Based on the survival indicators and hazard indicators, the performance of the power batteries during the performance evaluation period is determined. This technical solution implements survival analysis and hazard analysis based on big data related to the performance of power batteries, accurately statistics the performance change characteristics of the power batteries during use, realizes a scientific and comprehensive evaluation of the performance of the power batteries, and improves the accuracy of the evaluation of the performance of the power batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A flowchart of a method for evaluating vehicle battery performance provided by an embodiment of the present invention;

[0046] Figure 2-1 A schematic diagram of a basic information table provided in an embodiment of the present invention;

[0047] Figure 2-2 Schematic diagram of a travel result table provided in an embodiment of the present invention Figure 1 ;

[0048] Figure 2-3 Schematic diagram 2 of a travel result table provided in an embodiment of the present invention;

[0049] Figure 2-4 A schematic diagram of a fault information statistics table provided in an embodiment of the present invention;

[0050] Figure 3 A flowchart of data preprocessing provided by an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of a survival curve provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of a curve of a hazard function provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of a danger curve provided by an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of the output result of the risk ratio provided by an embodiment of the present invention;

[0055] Figure 8 A schematic structural diagram of a vehicle battery performance evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field fall within the scope of protection of the embodiments of the present invention.

[0057] See also Figure 1 , Figure 1 This is a flowchart of a vehicle battery performance evaluation method provided by an embodiment of the present invention. This evaluation method can be applied to computer equipment or server equipment to implement power battery performance evaluation. The operating equipment can run this evaluation method, and there is no specific limitation here. The evaluation method includes:

[0058] S11. Obtain target data of multiple vehicles on a big data platform, wherein the target data is data on changes in the performance of the vehicle's power battery during the vehicle's execution of historical driving tasks.

[0059] Specifically, the big data platform can be a demonstration vehicle operation platform or a monitoring platform for power battery data management. It can collect data on changes in power battery performance as the number of driving missions varies across a large number of vehicles. Target data can include the number of times a power battery is charged, or it can include mileage and travel time as measured by historical driving missions. For example, as the number of historical driving missions increases, the charging and discharging times of a power battery change accordingly. Data representing these charging and discharging times is defined as target data. Since charging and discharging times change with the age of a power battery, this data can be used to determine the performance of the power battery. The target data can include multiple types of data related to changes in power battery performance. Each type of data is labeled and stored in association with data related to changes in power battery performance. It is understood that power batteries can be solid-state batteries or other types of batteries, without specific limitation. Big data analysis plays a crucial role, not only revealing patterns in driving behavior and power battery usage habits, but also providing powerful support for fault diagnosis and early warning.

[0060] Exemplarily, step S11 includes sub-steps S11-1 to S11-3, which are described in detail as follows:

[0061] S11-1. Extract historical driving mission data from the big data platform based on the preset statistical table template to obtain a statistical table for each vehicle. The big data platform uses onboard sensors, wireless communication equipment, and Internet of Things technology to collect vehicle operation and power battery usage data, and conducts a series of calculations and analyses to ultimately obtain vehicle operation and battery usage analysis results, providing users with driving behavior analysis, battery usage habit analysis, battery life analysis, fault diagnosis and early warning functions, etc. The template for collecting data on the big data platform can be found in Figure 2-1 to Figure 2-4 , specifically including the basic information table vin_base_info, the trip results table trip_result_info, the trip results table trip_speed_statistics, and the fault information statistics table alarm_base. These tables comprehensively collect data from historical driving missions. Statistical table templates can be set based on target data extraction requirements, such as including various vehicle fault types, fault time, mileage, and driving time. Using statistical table templates, you can accurately extract the data required for subsequent analysis.

[0062] S11-2. Based on the data represented in the statistical table for each vehicle, obtain first data representing the change in driving time of each vehicle as a function of power battery performance, and second data representing the change in mileage of each vehicle as a function of power battery performance. The first data may represent the change in driving time as a function of various vehicle faults, such as battery failure and short circuit faults that occur with increasing driving time. Similarly, the second data represents the change in power battery performance as mileage increases, and corresponding electrical system faults may be collected as driving time increases.

[0063] S11-3. Obtain target data for each vehicle based on the first data and the second data. The target data can be represented as a data set. The first data and the second data reflect the performance variation characteristics of the power battery from different dimensions. Using them as target data can make subsequent analysis more comprehensive and accurate.

[0064] In practical applications, since the first and second data are obtained through various sensors or estimation algorithms, if the sensor or vehicle system fails, the data may be inaccurate. Based on this, sub-step S11-3 is implemented based on the following steps. Specifically, it includes:

[0065] The first step is to clean the first and second data of each vehicle to remove duplicate and invalid data. Figure 3 , Figure 3 This is a flowchart of data preprocessing provided by an embodiment of the present invention. The collected raw data needs to be cleaned to remove duplicate and invalid data records, including outliers, to ensure data quality and integrity. The embodiment of the present invention uses the IQR (Interquartile Range) method for outlier processing.

[0066] #Example: Detect and remove outliers using the IQR method

[0067] Q1 = data.quantile(0.25)

[0068] Q3 = data.quantile(0.75)

[0069] IQR=Q3-Q1

[0070] data=data[~((data<(Q1-1.5*IQR))|(data>(Q3+1.5*IQR))).any(axis=1)]

[0071] The second step is to fill missing values ​​in the first and second data after data cleaning to obtain the first and second data after data filling. For missing values ​​in the data, data filling is performed in an appropriate manner, such as using interpolation methods or filling based on statistical information of historical data to reduce the impact of missing data on the analysis results; check whether there are missing values ​​in the data set and perform processing. In this embodiment of the present invention, the mean filling method is used for processing. The core code is as follows:

[0072] data = df[col]

[0073] print(data.isnull().sum()) / / Check for missing values ​​in the data. Assume that the original data is in DataFrame format, the variable name is df, and the field to be analyzed is col

[0074] data.fillna(data.mean(), inplace=True) / / fill missing values ​​with mean

[0075] The third step is to normalize the first and second data after data filling to obtain the target data of the corresponding vehicle. Data standardization / normalization is a key step in data preprocessing. It aims to convert the raw data into a unified scale, thereby eliminating the dimensional differences between different data features and improving the stability and accuracy of the model. The core code is as follows:

[0076] from sklearn.preprocessing import StandardScaler

[0077] scaler = StandardScaler()

[0078] data_scaled=scaler.fit_transform(data)

[0079] After this series of data preprocessing steps, high-quality, standardized target data can be obtained, laying a solid foundation for subsequent analysis. After obtaining the target data of multiple vehicles, step S12 is entered.

[0080] S12. Perform survival analysis and hazard analysis on target data of multiple vehicles to obtain survival curves and hazard curves of power batteries.

[0081] Specifically, survival analysis and hazard ratio analysis are both statistical techniques. Survival analysis is used to analyze life expectancy or the time until an event occurs. A survival curve shows the probability of survival at different time points. Figure 4 , Figure 4This is a diagram of a survival curve. A survival curve typically starts at 1 and gradually decreases over time, reflecting the decrease in survival probability. A survival curve can be used to characterize the survival probability of a power battery at different usage times to assess the risk of subsequent use.

[0082] Hazard analysis, closely related to survival analysis, focuses on the risk, or rate, of an event occurring. In survival analysis, the hazard function describes the conditional probability of an event occurring at a given point in time or time period, given that the individual has survived to that point in time. A hazard curve is a graphical representation of hazard analysis that displays the hazard rate over time. Unlike a survival curve, a hazard curve is not necessarily monotonically decreasing over time; it may increase, decrease, or remain constant over time, depending on the data characteristics of interest. Hazard curves can help identify risk factors and how they affect the likelihood of an event occurring over time.

[0083] Survival analysis is a statistical method that uses statistical inference on one or more non-negative random variables to study survival phenomena and response time data and their statistical patterns. It considers both outcomes and survival time, making full use of the incomplete information provided by the data to describe the distribution characteristics of survival time and analyze the main factors affecting survival time. The main difference between survival analysis and other multivariate analyses is that it considers the length of time it takes for each observation to reach a certain outcome.

[0084] In survival analysis and hazard analysis, analytical strategies are closely related to the survival function S(t), the hazard function h(t), and the cumulative hazard function H(t). The survival function S(t) describes the probability of an event occurring later than time t; the hazard function h(t) gives the instantaneous potential of the event per unit time, assuming that the individual has survived until time t. This function can be a constant risk or change over time, such as increasing, decreasing, or first decreasing and then increasing; while the cumulative hazard function H(t) represents the cumulative number of events experienced before time t. Together, these concepts form the basic framework of survival analysis and hazard analysis, used to study the relationship between influencing factors and survival time and outcomes, and to predict individual survival at different factor levels.

[0085] Exemplarily, step S12 includes sub-steps S12-1 to S12-4, which are described in detail as follows:

[0086] S12-1. Target faults for multiple vehicles are obtained based on the data characteristics represented by target data from multiple vehicles. Target faults are faults related to the performance of the power battery. Target data is monitoring data associated with driving time and / or mileage. Abnormal data indicates a possible vehicle fault. Based on the above analysis, various vehicle faults can be identified, with faults related to battery performance being the target faults. Examples include battery heat dissipation failure and charging failure.

[0087] Specifically, the target faults of multiple vehicles may be obtained based on the following method, including the following steps:

[0088] The first step is to identify the corresponding feature data in the target data based on the failure items related to the performance of the power battery in each vehicle's component systems. Component systems include the transmission, braking, electrical, cooling, and battery systems. In big data survival analysis of driving time and mileage, the failures involved refer to failures in the vehicle's major component systems, which can affect the vehicle's normal operation, safety, or performance. These component systems include the engine, transmission, braking, electrical, cooling, and suspension systems. Engine failures include engine stall, ignition system failure, and fuel system issues; transmission system failures include transmission failure, clutch failure, and drive shaft problems; braking system failures include brake failure, brake fluid leaks, and brake disc / pad wear; electrical system failures include battery failure, generator failure, and line shorts or breaks; cooling system failures include radiator failure, water pump failure, and coolant leaks; and battery pack failures include abnormal battery temperature, voltage, and pressure differentials.

[0089] It should be noted that scrapes, minor scratches, or damage to the vehicle's paint are not considered "faults" that affect the performance of the power battery, but rather fall under the category of appearance or body damage. In survival analysis, this type of damage is generally not taken into account unless it is directly related to the failure of a major system or component of the vehicle. Target faults can be set for different vehicles. For example, for a hybrid vehicle, the engine drives the generator to charge the power battery. If the engine fails at this time, it will affect the performance of the power battery, and the engine failure will be included in the category of target faults. If the vehicle is a pure electric vehicle and the power source is only the power battery, there is no relevant data on engine failure.

[0090] In the second step, the characteristic data of all fault items of each vehicle are counted to obtain the target faults of multiple vehicles. Each vehicle has a chassis number (vin), total mileage (total_mile), total running time (minutes) (total_run_time), total charging time (minutes) (total_charge_time), vehicle use (vehicleuse), mileage of the trip (kilometers) (run_mile), driving time of the trip (minutes) (run_time), maximum speed (km / h) (max_speed), maximum speed (r / min) (max_rpm), average torque / torque (N*m) (average_rpm), average battery temperature (average_battery_temperature), average motor temperature (average_driver_motor_temperature), fault level (alarm_level), fault name (alarm_name), fault occurrence province or municipality (alarm_pro), fault occurrence city (alarm_city), fault occurrence time (alarm_time), fault occurrence date (date_time), fault occurrence hour (alarm_hour), SOC (State of Charge), etc.

[0091] The key derived features involved in survival analysis include: the number of vehicles (n_vehicles), the time from failure to failure (days) (failure_times), the observation cutoff time (days) (censor_times), the actual observed time (observed_times), whether the event occurred (1 for failure, 0 for no failure but the observation ended) (event_observed), and the mileage at the time of failure (mileage_at_event).

[0092] The number of vehicles (n_vehicles) can be obtained from the basic information table (vin_base_info). The total number of vehicles used in the survival analysis is obtained from the vehicle number (vin) records with a total mileage (total_mile) greater than 50 km. The time (days) until failure (failure_times), the vehicle's final mileage, and the total mileage before the first failure can be obtained from the cumulative mileage at the time of failure (mileage_at_event) in the fault information statistics table (alarm_base).

[0093] The observation cutoff time (censor_times) can be obtained from the data date (data_time) field in the vehicle basic information table (vin_base_info) based on the vehicle chassis number (vin). The actual observed time (observed_times) is calculated based on three features: the chassis number, the time from failure (failure_times), and the observation cutoff time (censor_times).

[0094] Whether an event occurred (event_observed) is based on whether a vehicle fault event has occurred: 1 for a fault; 0 for no fault but observation has ended. The fault classification (alarm_class) can be obtained from the fault information statistics table (alarm_base) based on the vehicle's vehicle identification number (vin) and the earliest fault occurrence date (date_time). Specifically, it includes 1: engine fault; 2: transmission system fault; 3: brake system fault; 4: electrical system fault; 5: cooling system fault; 6: battery pack fault; 7: other fault. The cumulative mileage at the time of the fault (mileage_at_event) is the total mileage accumulated by the vehicle when the first fault occurred. This can be obtained from the fault information statistics table (alarm_base) based on the vehicle's vehicle identification number (vin) and the earliest fault occurrence date (date_time).

[0095] So far, the characteristic data of all fault items of each vehicle have been counted, and the target faults of multiple vehicles have been obtained.

[0096] S12-2. Determine the survival probability of the power battery under different usage cycles based on the target failures of multiple vehicles and a preset survival function. The survival probability represents the probability that a patient or event will survive longer than time t, and is represented by S(t): S(t) = P(T ≥ t). For example, the 5-year survival rate is S(5) = P(T>5). The curve drawn with time t as the horizontal axis and S(t) as the vertical axis is called the survival curve. It is a descending curve. The steeper the slope, the lower the survival rate or the shorter the survival time. Its slope represents the mortality rate.

[0097] Specifically, to perform survival analysis using the Survival Analysis algorithm, you can import the relevant data for the target failure into the lifelines library, a third-party Python package. Within the lifelines library, the fit() method of the KaplanMeierFitter class is used to calculate the Kaplan-Meier survival function estimate. The Kaplan-Meier method is a nonparametric statistical method used to estimate the probability of survival (or non-event occurrence) from the time of an event until a specific point in time. In survival analysis, the event here typically refers to the occurrence of the outcome of interest, such as a vehicle failure.

[0098] Initialize the Kaplan-Meier estimator. KaplanMeierFitter is a class in the lifelines library that is used to perform Kaplan-Meier survival analysis. This class can help estimate survival curves and compare survival differences between different groups. The Kaplan-Meier survival function is a non-parametric method used to estimate the proportion of individuals who survive at different time points. Specifically, it includes:

[0099] Step 1: Define and initialize variables, including time and event_observed. The time variable is an array or list of the survival times of each observation. The event_observed variable is a Boolean array or list indicating whether the event was observed (usually True) or not (usually False, possibly due to right censoring, etc.) at each time point.

[0100] Step 2: Sorting and grouping. The fit() method sorts the observations according to the time array. The observations are divided into different risk sets (also called risk groups) based on the sorted order. Each risk set contains individuals who are still under observation at the current time point.

[0101] Step 3: Calculate the survival probability. For each time point t (starting from the smallest time point), calculate the proportion of individuals who did not experience the event at all time points before that time point, that is, the survival probability.

[0102] If an event is observed at time point t (i.e., event_observed[i] == True), the number of individuals in the current risk set is used to calculate the decrease in the survival probability. Specifically, the calculation formula for the survival probability S(t) is:

[0103] S(t)=S(t-Δt)×(1-dt / nt)

[0104] Where dt is the number of individuals who have experienced the event at time point t, nt is the number of individuals who have not experienced the event before (including) time point t (i.e., the size of the current risk set), and S(t-Δt) is the survival probability at the previous time point (for the first time point, it is usually assumed to be 1).

[0105] Step 4: Handle right censoring. If no event is observed at time point t (i.e., event_observed[i] == False), the survival probability remains unchanged because there is no information indicating that the survival probability will decrease after point t. This situation usually occurs when the individual is still alive at the end of data collection or when the individual is lost to follow-up.

[0106] Step 5. Output. After the above steps, the fit() method will calculate the survival probability at a series of time points, which will be used to draw the subsequent survival curve.

[0107] S12-3. Obtain a survival curve for the power battery based on the corresponding relationship between the battery's lifespan and the survival probability. The KaplanMeierFitter method, fit(), combines time data with event observations and uses the Kaplan-Meier method to calculate the survival probability at each time point, thereby obtaining a nonparametric estimate of the survival function. These calculations are crucial for understanding the temporal and event patterns in survival data.

[0108] Survival curve: A graph that connects the survival rates corresponding to each time point, with observation (follow-up) time on the horizontal axis and survival rate on the vertical axis. The survival curve is a descending curve, and analysis should pay attention to the curve's height and slope. A flat survival curve indicates a high survival rate or a long survival period, while a steep survival curve indicates a low survival rate or a short survival period.

[0109] Survival function S(t): P(T≥t), which represents the probability that an individual’s survival time is greater than t.

[0110]

[0111] Please continue reading Figure 4 The curve in the figure shows how the survival probability of the power battery changes over the vehicle's driving time. In the early stages of vehicle operation (the early part of the timeline), the survival probability is high, close to 1.0, indicating that the vehicle is performing well during this period and is unlikely to fail. However, as time passes (the horizontal axis moves to the right), from 0 to 6000 days, the survival probability gradually decreases, indicating an increasing likelihood of power battery failure or malfunction. Furthermore, the slope of the curve reflects the rate of vehicle failure; the larger the slope, the faster the failure rate.

[0112] The upper boundary (the highest point on the vertical axis, i.e., the position where the survival probability is 1.0) represents the ideal state where the vehicle has not experienced a failure or any situation that renders the vehicle inoperable at any given time point. In short, if the survival probability is 1.0, it means the power battery is fully functional or not yet phased out. The lower boundary (the lowest point on the vertical axis, i.e., the position where the survival probability is 0.0) represents the state where the vehicle has experienced a failure or is no longer available before the given time point. A survival probability of 0.0 means the power battery has failed or been phased out. <00002​​​​​​​​​​​​​​​​​​​​​​

[0120] The survival function S(t) represents the probability that an individual survives to time t or longer: S(t) = P(T ≥ t). The calculation method has been described previously and will not be further elaborated here. The cumulative hazard function (CHF) is the integral of the hazard function from time 0 to t, representing the cumulative risk of an event occurring before t: H(t) = ∫0th(s)ds. Its relationship to the survival function S(t) is: S(t) = eH(t).

[0121] Therefore, the hazard function can be derived from the survival function by taking the natural logarithm of both sides of S(t) = eH(t) to obtain lnS(t) = -H(t), taking the derivative of both sides with respect to t, and using the chain rule and the definition of H(t) (i.e., H(t) is the integral of h(t)) to obtain dlnS(t) / dt = S′(t) / S(t) = -h(t).

[0122] Since S′(t) is the derivative of S(t), and S(t) is a decreasing function with respect to t (i.e., the probability of survival decreases as time increases), S′(t) < 0. Therefore, h(t) can be solved as: h(t) = -S′(t) / S(t), which means that in order to calculate h(t), we need to know the specific form of S(t) and find its derivative S′(t).

[0123] Therefore, the ordinate value of the hazard function h(t) is calculated from the survival function S(t) and its derivative S′(t). In practical applications, these data usually come from experimental observations or historical data and may be estimated using statistical models (such as parametric models, nonparametric models, or semiparametric models).

[0124] Similarly, you can import data into the lifelines library, a third-party Python package, to derive a hazard curve. NelsonAalenFitter is a class in the lifelines library that performs Nelson-Aalen estimation. The Nelson-Aalen estimator is a nonparametric method used to estimate the cumulative hazard function. Unlike the Kaplan-Meier estimator (used to estimate the survival function), the Nelson-Aalen estimator estimates the cumulative hazard function, which describes the cumulative risk of an event occurring before a given time point.

[0125] Core code: naf.fit(data['observed_time'], event_observed = data['event'], label = 'Predicted Vehicles (Nelson-Aalen)'), where data is a DataFrame containing 'observed_time' and 'event' columns for all vehicles based on the solid-state battery passenger car demonstration operation big data obtained through data processing; observed_time is the time when the individual vehicle was observed or tracked; event_observed is a Boolean or binary value indicating whether the event of interest (for example, death, failure, etc.) occurred at that time. 1 or True indicates that the event occurred, and 0 or False indicates that the individual was still alive at the end of the observation or no event occurred. Binary values ​​(0, 1) are used here.

[0126] See also Figure 6 , Figure 6 This is a diagram of a hazard curve. The center curve represents the cumulative risk predicted using the Nelson-Aalen method, which increases over time (x-axis, days). The shaded areas above and below the curve represent the actual cumulative risk, which may show different trends due to fluctuations in actual data.

[0127] The shaded upper bound represents the maximum possible cumulative hazard at a given point in time. This value is an upper bound based on actual observations or statistical model predictions, accounting for data variability, uncertainty, and possible extreme cases. In practical applications, the upper bound provides a worst-case estimate, indicating that the cumulative hazard is unlikely to exceed this level before a certain point in time, although this does not necessarily mean it is completely impossible. The shaded lower bound typically represents the starting value of the cumulative hazard, which is 0. In survival analysis, this means that at the beginning of observations (e.g., the date of purchase of a new car, the time when equipment is put into service), no event risk accumulates. From a technical perspective, the lower bound is not always strictly 0, especially in complex statistical procedures that may take into account prior information or baseline risk levels. However, in most cases, to simplify understanding and analysis, the cumulative hazard can be assumed to start at 0. The lower bound exists primarily to contrast with the upper bound, defining the range of possible variations in the cumulative hazard.

[0128] The shading of the upper and lower bounds allows for the expression of risk uncertainty. Together, these bounds form a confidence interval, reflecting the uncertainty of the cumulative risk estimate. The wider this interval, the greater the uncertainty in the estimate. Understanding the upper and lower bounds of the cumulative risk allows decision makers to better assess risk levels and develop appropriate risk mitigation measures or resource allocation strategies.

[0129] So far, survival analysis and hazard analysis have been performed on the target data to obtain the survival curve and hazard curve of the power battery respectively.

[0130] S13. Perform index item analysis on the survival curve and the danger curve respectively according to the performance evaluation period of the power battery to obtain the survival index and danger index of the power battery.

[0131] Specifically, survival indicators and risk indicators can be set separately based on analysis requirements. For example, indicators can be set using the slopes of the survival curve and risk curve, respectively. The slopes of the curves represent the changing characteristics of the power battery life as the battery is used. Taking the slope of the risk curve as an example, a larger slope indicates a higher cumulative risk; a smaller slope indicates a lower cumulative risk. It can be understood that both the survival curve and the risk curve provide an overall representation of the changing characteristics of the data, but the survival and risk indicators can be used to specify the performance evaluation results of the power battery during the performance evaluation period. The performance evaluation period can be freely set and can be a future period that needs to be predicted or a historical period that needs to be analyzed.

[0132] Exemplarily, step S13 includes sub-steps S13-1 to S13-4, which are described in detail as follows:

[0133] S13-1. Determine the median survival time, average survival time, and risk ratio of the power battery during the performance evaluation period based on the survival curve. The preset comparison items include a mileage comparison item and / or a fault type comparison item. The median survival time is the time point when the survival probability (or cumulative risk) drops to a threshold value. The threshold value can be set to 0.5. The median survival time can be determined based on the survival threshold to determine the time point corresponding to the survival probability. The average survival time is the average of the survival times of the power batteries on all vehicles. The average survival time can be directly calculated by averaging.

[0134] The hazard ratio is used in the Cox proportional hazards model to compare the risk ratio between two or more groups. This can be calculated by comparing mileage range or by grouping by fault type. For example, using mileage range (km) as an example, see Table 1 for the grouping results.

[0135] Table 1

[0136] Starting mileage Termination Mileage Group Number 0 10000 1 10001 20000 2 20001 30000 3 30001 40000 4 40001 50000 5 50001 60000 6 … … …

[0137] Table 1 includes multiple mileage intervals, which are estimated by the Cox model. The Cox proportional hazard model is a statistical method in survival analysis that is used to study how one or more predictor variables (also called covariates or risk factors) affect the time of occurrence of an event. Import the Python third-party package lifelines library. CoxPHFitter() is a class in the lifelines Python library that is used to fit the Cox proportional hazard model. Core code: from lifelines import CoxPHFitter

[0138] cph=CoxPHFitter() / / Initialize CoxPHFitter()

[0139] cph.fit(data,duration_col='observed_time',event_col='event',formula="group_num") / / Use CoxPHFitter to fit the model

[0140] The group number "group_num" represents the grouping based on the mileage interval. Furthermore, based on actual needs, you can perform risk ratio calculations on more groups to further identify the specific fault type by factors influencing the time of an event.

[0141] In this embodiment of the present invention, the risk ratio of the group is calculated based on the mileage interval of 10,000 km. Figure 7 , Figure 7 The calculated hazard ratio is shown in the middle box. cph.summary is a DataFrame returned after fitting the Cox Proportional Hazards Model in the lifelines library. It contains the model parameter estimation results and statistical test information.

[0142] The following is an analysis of this summary (or output page):

[0143] coef is the coefficient estimate for group_num, which represents the average change in the log hazard ratio when group_num increases by one unit (from group 1 to group 2, or from group 2 to group 3, etc.). In this example, coef is 0.065302, meaning that when group_num increases from 0 (group 1) to 10,000 km (group 2), the log hazard ratio increases by 0.065302 on average.

[0144] exp(coef) is an estimate of the hazard ratio (HR). It indicates the relative risk of one group compared to another. In this example, exp(coef) is 1.065302, meaning that the relative risk of group 2 is 1.065302 times that of group 1.

[0145] se(coef) is the standard error of the coefficient estimate, which indicates the uncertainty of the coefficient estimate. In this example, the standard error of the coefficient estimate is 0.772495.

[0146] coef lower 95% and coef upper 95% are the 95% confidence intervals for the coefficient estimate. These values ​​represent the lower and upper bounds of the 95% confidence interval (CI) for coef. In this example, the 95% CI for coef is (-1.450805, 1.577321). Since this interval includes 0, we cannot be certain that group_num has a significant effect on risk.

[0147] exp(coef)lower 95% and exp(coef)upper 95% are the 95% confidence intervals for the hazard ratio, the lower and upper limits of the 95% CI for the hazard ratio (0.234381, 4.841969).

[0148] cmp to indicates the baseline level to which this covariate is compared. Here, it is compared to 1 (i.e., group 1).

[0149] z is a statistic associated with the normal distribution (or standard normal distribution) that describes the number of standard deviations between an observation or sample mean and a theoretical value (such as the population mean). In this case, it is 0.081888.

[0150] p is the probability that the observed effect is due solely to random error. If the p-value is less than a certain significance level (such as 0.05), the null hypothesis (that the covariate has a significant relationship with the risk) is rejected. In this example, the p-value is 0.934736, which is greater than 0.05, so the null hypothesis (that there is no significant relationship between group_num and risk) cannot be rejected (but note that this could be due to small data or the absence of an effect).

[0151] log2(p) is the negative logarithm of the p-value and is sometimes used to quantify the strength of evidence.

[0152] S13-2. Determine the cumulative risk value and / or curve slope of the power battery during the performance evaluation period based on the risk curve. The cumulative risk value describes the cumulative risk of damage or death to a person or object before a specific moment. The cumulative risk is the integral of the risk over time, reflecting the cumulative risk from the starting point to the current time point t. The slope of the curve characterizes the changing characteristics of the cumulative risk.

[0153] S13-3. Characterize the survival indicators of the power battery based on the median survival time, average survival time, and hazard ratio under the preset comparison items. Each survival indicator can be assigned a corresponding label, and the corresponding median survival time, average survival time, and hazard ratio can be entered under the corresponding label to facilitate staff to read the data.

[0154] S13-4. Characterize the risk index of the power battery based on the cumulative risk value and / or curve slope. Similarly, a label can be created using a template for characterizing risk indexes, and the corresponding cumulative risk value and / or curve slope can be entered under the label.

[0155] So far, the survival indicators and danger indicators of power batteries have been obtained.

[0156] S14. Determine the performance of the power battery during the performance evaluation period based on the survival index and the danger index.

[0157] Specifically, the survival and hazard indicators can directly reflect the performance of a power battery during the performance evaluation period. For example, if a power battery's survival probability is less than 0.4 after 1,000 days of use, it is highly susceptible to failure, presents a high operational risk, and exhibits poor performance. The performance of the power battery can be scored, for example, on a scale of 1-10. A score of 8-10 indicates good performance during the performance evaluation period; conversely, a score below 5 indicates poor performance during the performance evaluation period.

[0158] By evaluating power battery performance, vehicle operations can be optimized. For example, if the performance evaluation period falls within the operational management period, management strategies for operational vehicles can be generated for subsequent management periods. Based on these strategies, maintenance and replacement schedules for power batteries on operational vehicles can be determined. These strategies can guide battery maintenance and replacement schedules, reducing operational risks and costs and providing a scientific basis for decision-makers. Furthermore, performance evaluations can also quantify the strengths and weaknesses of power battery technology, providing a basis for decision-making in product development and market promotion.

[0159] Based on the same technical concept as the evaluation method, the embodiment of the present invention also provides a vehicle battery performance evaluation device, please refer to Figure 8 , Figure 8The diagram below shows the structure of the evaluation device. The evaluation device includes:

[0160] An acquisition module 801 is used to acquire target data of multiple vehicles on a big data platform, wherein the target data is data on changes in the performance of the vehicle's power battery during the vehicle's historical driving missions;

[0161] A first obtaining module 802 is configured to perform survival analysis and hazard analysis on target data of multiple vehicles to obtain a survival curve and a hazard curve of the power battery;

[0162] A second obtaining module 803 is configured to perform index item analysis on the survival curve and the danger curve according to the performance evaluation period of the power battery to obtain a survival index and a danger index of the power battery;

[0163] The first determination module 804 is configured to determine the performance of the power battery during the performance evaluation period according to the survival index and the danger index.

[0164] In an optional embodiment, the acquisition module includes:

[0165] The first acquisition submodule is used to extract the data of historical driving tasks on the big data platform according to the preset statistical table template to obtain the data statistical table of each vehicle;

[0166] A second obtaining submodule is used to obtain, based on the data represented by the data statistics table of each vehicle, first data indicating that the driving time of each vehicle varies with the performance of the power battery, and second data indicating that the mileage of each vehicle varies with the performance of the power battery;

[0167] The third obtaining submodule is configured to obtain target data of each vehicle according to the first data and the second data.

[0168] In an optional embodiment, the third obtaining submodule includes:

[0169] a first pre-processing unit, configured to clean the first data and the second data of each vehicle to remove duplicate data and invalid data;

[0170] a second preprocessing unit, configured to perform missing value filling on the first data and the second data after data cleaning, to obtain the first data and the second data after data filling;

[0171] The third preprocessing unit is used to perform normalization processing on the first data and the second data after completing the data filling to obtain target data of the corresponding vehicle.

[0172] In an optional embodiment, the first obtaining module includes:

[0173] a fourth obtaining submodule, configured to obtain target faults of the plurality of vehicles based on data features represented by target data of the plurality of vehicles, wherein the target faults are faults related to the performance of the power battery;

[0174] The first determination submodule is used to determine the survival probability of the power battery under different usage cycles based on the target faults of multiple vehicles and a preset survival function;

[0175] A fifth obtaining submodule is used to obtain a survival curve of the power battery according to the corresponding change relationship between the service life of the power battery and the survival probability;

[0176] The sixth obtaining submodule is used to obtain the danger curve of the power battery according to the survival curve and the preset danger function.

[0177] In an optional embodiment, the fourth obtaining submodule includes:

[0178] a determination unit configured to determine corresponding characteristic data in the target data based on failure items associated with the performance of the power battery in different component systems of each vehicle, wherein the component systems include a transmission system, a braking system, an electrical system, a cooling system, and a battery system;

[0179] The obtaining unit is used to collect statistics on the characteristic data of all fault items of each vehicle to obtain target faults of multiple vehicles.

[0180] In an optional embodiment, the second obtaining module includes:

[0181] A second determination submodule is configured to determine, based on the survival curve, the median survival time, the average survival time, and the risk ratio of the power battery during the performance evaluation period under preset comparison items, wherein the preset comparison items include a mileage comparison item and / or a fault type comparison item;

[0182] A third determination submodule is configured to determine the cumulative risk value and / or the slope of the curve of the power battery during the performance evaluation period according to the risk curve;

[0183] The first characterization submodule is used to characterize the survival indicators of the power battery based on the median survival time, mean survival time and the risk ratio under the preset comparison items;

[0184] The second characterization submodule is used to characterize the danger index of the power battery according to the cumulative risk value and / or the curve slope.

[0185] In an optional embodiment, the evaluation device further includes:

[0186] An output module, used for outputting the management strategy of the operating vehicle in the subsequent management period when the performance evaluation period is the operation management period;

[0187] The second determination module is used to determine the maintenance time and replacement time of the power battery on the operating vehicle according to the management strategy.

[0188] Based on the same technical concept as the evaluation method, an embodiment of the present invention also provides an electronic device, including a processor and a memory, wherein the memory is coupled to the processor and stores instructions. When the instructions are executed by the processor, the electronic device executes the steps of any one of the evaluation methods.

[0189] Based on the same technical concept as the evaluation method, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any one of the evaluation methods when executed by a processor.

[0190] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0191] By acquiring target data from multiple vehicles on a big data platform, which tracks changes in the performance of the vehicles' power batteries during their historical driving missions, survival analysis and hazard analysis can be performed on the target data of multiple vehicles to obtain the survival curve and hazard curve of the power batteries. Indicator analysis of the survival curve and hazard curve is then performed based on the performance evaluation period of the power batteries to obtain survival indicators and hazard indicators of the power batteries. Based on the survival indicators and hazard indicators, the performance of the power batteries during the performance evaluation period is determined. This technical solution implements survival and hazard analysis based on big data related to power battery performance, accurately statistics the performance change characteristics of the power batteries during use, realizes a scientific and comprehensive evaluation of the power battery performance, and improves the accuracy of the power battery performance evaluation.

[0192] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0193] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (modules, systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0194] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0196] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0197] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the performance of a vehicle battery, characterized in that: The method comprises: Acquire target data of multiple vehicles on a big data platform, wherein the target data is data on changes in the performance of the power batteries of the vehicles during the vehicles' historical driving tasks; performing survival analysis and hazard analysis on the target data of the plurality of vehicles respectively to obtain a survival curve and a hazard curve of the power battery; performing an index item analysis on the survival curve and the danger curve respectively according to a performance evaluation period of the power battery to obtain a survival index and a danger index of the power battery; determining, according to the survival index and the danger index, the performance of the power battery during the performance evaluation period; The performing index item analysis on the survival curve and the danger curve respectively according to the performance evaluation period of the power battery to obtain the survival index and the danger index of the power battery includes: determining, according to the survival curve, the median survival time, the average survival time, and the hazard ratio of the power battery under preset comparison items during the performance evaluation period, wherein the preset comparison items include a mileage comparison item and / or a fault type comparison item; determining, according to the risk curve, a cumulative risk value and / or a curve slope of the power battery during the performance evaluation period; Characterizing a survival indicator of the power battery according to the median survival time, the mean survival time, and the hazard ratio under the preset comparison item; The risk index of the power battery is characterized according to the cumulative risk value and / or the slope of the curve.

2. The method for evaluating vehicle battery performance according to claim 1, wherein: The step of obtaining target data of multiple vehicles on the big data platform includes: Extracting the data of the historical driving tasks on the big data platform according to a preset statistical table template to obtain a data statistical table for each vehicle; Obtaining, based on the data represented by the data statistics table for each vehicle, first data indicating a change in the driving time of each vehicle as a function of the power battery performance, and second data indicating a change in the mileage of each vehicle as a function of the power battery performance; Target data of each vehicle is obtained according to the first data and the second data.

3. The method for evaluating vehicle battery performance according to claim 2, wherein: The step of obtaining target data of each vehicle according to the first data and the second data includes: Performing data cleaning on the first data and the second data of each vehicle to remove duplicate data and invalid data; Filling missing values ​​on the first data and the second data after data cleaning to obtain the first data and the second data after data filling; The first data and the second data after data filling are normalized to obtain target data corresponding to the vehicle.

4. The method for evaluating vehicle battery performance according to claim 1, wherein: The performing survival analysis and hazard analysis on the target data of the plurality of vehicles respectively to obtain a survival curve and a hazard curve of the power battery includes: Obtaining target faults of the multiple vehicles according to data features represented by target data of the multiple vehicles, wherein the target faults are faults related to the performance of the power batteries; determining, based on the target faults of the multiple vehicles and a preset survival function, the survival probability of the power battery under different usage cycles; Obtaining a survival curve of the power battery according to a corresponding change relationship between the service life of the power battery and the survival probability; A danger curve of the power battery is obtained according to the survival curve and a preset danger function.

5. The method for evaluating vehicle battery performance according to claim 4, wherein: The obtaining target faults of the multiple vehicles according to data features represented by the target data of the multiple vehicles includes: Determining corresponding characteristic data in the target data based on fault items associated with the performance of the power battery in different component systems on each vehicle, wherein the component systems include a transmission system, a braking system, an electrical system, a cooling system, and a battery system; Statistics are collected on the characteristic data of all fault items of each vehicle to obtain target faults of the multiple vehicles.

6. The method for evaluating vehicle battery performance according to claim 1, wherein: After determining the usage performance of the power battery during the performance evaluation period based on the survival indicator and the danger indicator, the method further includes: When the performance evaluation period is an operation management period, outputting a management strategy for the operation vehicle in a subsequent management period; The maintenance time and replacement time of the power battery on the operating vehicle are determined according to the management strategy.

7. A vehicle battery performance evaluation device, characterized in that: The device comprises: An acquisition module is used to acquire target data of multiple vehicles on a big data platform, wherein the target data is data on changes in the performance of the power batteries of the vehicles during the execution of historical driving tasks; A first obtaining module is configured to perform survival analysis and hazard analysis on the target data of the plurality of vehicles to obtain a survival curve and a hazard curve of the power battery; a second obtaining module, configured to perform an index item analysis on the survival curve and the danger curve respectively according to a performance evaluation period of the power battery, so as to obtain a survival index and a danger index of the power battery; a first determining module, configured to determine the performance of the power battery during the performance evaluation period according to the survival indicator and the danger indicator; The second obtaining module is specifically configured to: determining, according to the survival curve, the median survival time, the average survival time, and the hazard ratio of the power battery under preset comparison items during the performance evaluation period, wherein the preset comparison items include a mileage comparison item and / or a fault type comparison item; determining, according to the risk curve, a cumulative risk value and / or a curve slope of the power battery during the performance evaluation period; Characterizing a survival indicator of the power battery according to the median survival time, the mean survival time, and the hazard ratio under the preset comparison item; The risk index of the power battery is characterized according to the cumulative risk value and / or the slope of the curve.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein the memory is coupled to the processor and stores instructions, and when the instructions are executed by the processor, the electronic device executes the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images