Battery Spare Part Prediction Method, Device, Computer Equipment and Storage Medium

Abstract

The present application relates to a battery spare part prediction method, device, computer device, and storage medium. The method includes: obtaining all data of a plurality of in-use battery packs to obtain full-scale data; wherein, the plurality of in-use battery packs are arranged in different electrical equipment; determining a failure change relationship according to the full-scale data; the failure change relationship includes the relationship between the failure probability of the battery pack and the change of the first survival duration; predicting the number of failures of the plurality of in-use battery packs within a preset future period according to the failure change relationship; and determining the number of battery pack spare parts required within the preset future period according to the number of failures. Using the present application can improve the accuracy of battery spare part prediction, thereby better supplying spare parts.

Description

Technical Field

[0001] This application relates to the technical field of spare part prediction, and particularly to a battery spare part prediction method, device, computer device, and storage medium. Background Art

[0002] In the automotive industry, spare part prediction is not only a core tool for supply chain management but also a strategic means to enhance the customer experience and brand competitiveness. Through scientific spare part prediction, enterprises can achieve efficient operation, reduce costs, and better respond to market demand changes. Especially in the wave of new energy and intelligentization, its importance is even more prominent.

[0003] Currently, for components with clear failure mechanisms such as bearings, the spare part prediction results are relatively accurate; however, for the spare part prediction of battery packs, there are problems with inaccurate prediction. Summary of the Invention

[0004] Based on the above problems, this application provides a battery spare part prediction method, device, computer device, and storage medium, which can improve the accuracy of battery spare part prediction and thus better supply spare parts.

[0005] In a first aspect, this application provides a battery spare part prediction method, which includes: obtaining all data of multiple in-use battery packs to obtain full-scale data; wherein, the multiple in-use battery packs are set in different electrical equipment; determining a failure change relationship according to the full-scale data; the failure change relationship includes the relationship between the failure probability of the battery pack and the change with the first survival duration; predicting the number of failures of the multiple in-use battery packs within a preset future period according to the failure change relationship; and determining the number of battery pack spare parts required within the preset future period according to the number of failures.

[0006] In the technical solution of the embodiments of this application, a corresponding failure change relationship is determined for the battery pack, and this failure change relationship can more accurately reflect the change of the failure probability of the battery pack with the first survival duration. Therefore, based on this failure change relationship for battery spare part prediction, the prediction accuracy can be improved, and thus better supply of spare parts can be achieved.

[0007] In some embodiments, determining the failure change relationship based on the full data includes: analyzing the failure causes of the battery pack according to the historical consumption data, coupling multiple factors affecting the survival duration of the battery pack based on the analysis results to obtain the corresponding relationship between the first survival duration and multiple influencing factors; calculating according to the corresponding relationship and the full data to obtain the sample survival durations of multiple sample battery packs and the sample failure probabilities corresponding to each sample survival duration; performing a fitting process on the multiple sample survival durations and multiple sample failure probabilities to obtain the failure change relationship. In the technical solution of the embodiments of the present application, the failure change relationship is established based on the first survival duration, and the first survival duration takes into account the failure mode of the sample battery pack, and has the characteristics of high fitting accuracy and strong interpretability. Therefore, the failure change relationship can more accurately reflect the corresponding relationship between the first survival duration and the failure probability of the battery pack, providing help for accurately predicting the spare part quantity subsequently.

[0008] In some embodiments, calculating according to the corresponding relationship and the full data to obtain the sample survival durations of multiple sample battery packs and the sample failure probabilities corresponding to each sample survival duration includes: determining the data of each influencing factor corresponding to each sample battery pack according to the full data; optimizing the importance weights of each influencing factor within a preset weight range; determining multiple optimized sample survival durations according to the optimized importance weights, sorting the multiple optimized sample survival durations, and calculating the sample failure probabilities corresponding to each optimized sample survival duration according to the sorting result. In the technical solution of the embodiments of the present application, according to the corresponding relationship between the first survival duration and the influencing factors, the sample survival durations and sample failure probabilities corresponding to multiple sample battery packs can be calculated, providing a data basis for subsequently fitting the failure change relationship.

[0009] In some embodiments, optimizing the importance weights of each influencing factor within a preset weight range includes: performing multiple rounds of assignment on the importance weights of each influencing factor within a preset weight range; after each round of assignment, calculating the sample survival durations of each sample battery pack and the sample failure probabilities corresponding to each sample survival duration according to the assigned importance weights, the data of each influencing factor, and the corresponding relationship; performing a fitting process on the multiple sample survival durations and multiple sample failure probabilities; determining the fitting error according to the fitting result and the sample failure probability of the sample battery pack; determining the optimized importance weights according to the multiple fitting errors calculated after multiple rounds of assignment. In the technical solution of the embodiments of the present application, optimizing the importance weights of each influencing factor can obtain importance weights that are more in line with the actual situation, thereby improving the accuracy of the first survival duration, and further improving the accuracy of predicting the failure probability of the in-use battery pack subsequently, as well as determining the failure quantity of the in-use battery pack and the spare part quantity of the battery pack.

[0010] In some embodiments, fitting the survival durations of multiple samples and the failure probabilities of multiple samples includes: performing a transformation process on a pre-established survival analysis model to obtain a linear relationship; the linear relationship uses the first logarithm of the sample failure probability as the independent variable and the second logarithm of the sample failure probability as the dependent variable; calculating the second logarithm and the first logarithm of the failure probability of each sample; performing a fitting process based on the calculated second logarithm and first logarithm, and determining the linear coefficient and the constant in the linear relationship according to the fitting result; and obtaining the model parameters in the survival analysis model according to the linear coefficient and the constant in the linear relationship. In the technical solution of the embodiments of the present application, fitting based on the second logarithm and the first logarithm of the sample failure probability can reduce the fitting difficulty and improve the fitting accuracy, so as to obtain a more accurate failure change relationship and improve the accuracy of battery spare part prediction.

[0011] In some embodiments, predicting the number of failures of multiple in-use battery packs within a preset future period according to the failure change relationship includes: predicting the failure probability of each in-use battery pack within the preset future period according to the failure change relationship; statistically analyzing the failure probabilities of multiple in-use battery packs to obtain the total failure probability; and determining the number of failures according to the total failure probability and the total number of in-use battery packs. In the technical solution of the embodiments of the present application, the failure probability of all in-use battery packs within the preset future period can be determined according to the failure change relationship, so as to accurately predict the number of failures and provide an accurate basis for determining the number of spare parts in the subsequent process.

[0012] In some embodiments, determining the number of failures according to the total failure probability and the total number of in-use battery packs includes: determining the failure probability range according to a pre-set confidence interval and the total failure probability; and determining the number of failures according to the failure probability range and the total number of in-use battery packs. In the technical solution of the embodiments of the present application, the pre-set confidence interval can expand the range of the number of failures, make the number of spare parts more flexible, so as to better supply spare parts and improve the user experience.

[0013] In some embodiments, determining the number of battery pack spare parts required within a preset future period according to the number of failures includes: performing a correction process on the number of failures according to the battery pack service information to obtain the number of battery pack spare parts required within the preset future period. In the technical solution of the embodiments of the present application, determining the number of spare parts by comprehensively considering failures and business scenarios reserves a margin for supplying spare parts, can reduce the risk of insufficient spare parts, so as to better supply spare parts and further improve the user experience.

[0014] In a second aspect, the present application also provides a battery spare part prediction device, which includes:

[0015] A data acquisition module, configured to acquire all data by obtaining various data of multiple in-use battery packs; wherein, the multiple in-use battery packs are arranged in different electrical devices;

[0016] A relationship determination module, configured to determine a failure change relationship according to all data; the failure change relationship includes the relationship between the failure probability of the battery pack and the change of the first survival duration.

[0017] A failure prediction module, configured to predict the number of failures of multiple in-use battery packs within a preset future period according to the failure change relationship.

[0018] A spare part determination module, configured to determine the number of battery pack spare parts required within a preset future period according to the number of failures.

[0019] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the method according to any one of the first aspects is implemented.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0021] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method according to any one of the first aspects is implemented. Description of the Drawings

[0022] By reading the detailed description of the optional embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the optional embodiments and are not considered to be a limitation of the present application. Moreover, in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0023] Figure 1 is a schematic diagram of the application environment of the battery spare part prediction method according to an embodiment of the present application;

[0024] Figure 2 is a schematic flowchart of the battery spare part prediction method according to an embodiment of the present application;

[0025] Figure 3 is a schematic flowchart of the step of determining the failure change relationship according to an embodiment of the present application;

[0026] Figure 4 is a schematic curve diagram of the failure change relationship according to an embodiment of the present application;

[0027] Figure 5 is a schematic flowchart of the step of calculating the sample survival duration and the sample failure probability according to an embodiment of the present application;

[0028] Figure 6It is a schematic flowchart of the importance weight optimization step in an embodiment of the present application;

[0029] Figure 7 It is a schematic flowchart of the fitting process step in an embodiment of the present application;

[0030] Figure 8 It is a schematic diagram of the fitting result in an embodiment of the present application;

[0031] Figure 9 It is a schematic flowchart of the predicted failure quantity step in an embodiment of the present application;

[0032] Figure 10 It is a schematic flowchart of the determined failure quantity step in an embodiment of the present application;

[0033] Figure 11 It is a structural block diagram of a battery spare part prediction device in an embodiment of the present application;

[0034] Figure 12 It is an internal structure diagram of a computer device in an embodiment of the present application. Detailed implementation manners

[0035] Hereinafter, embodiments of the technical solutions of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present application more clearly, and thus are only examples and cannot be used to limit the protection scope of the present application.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0037] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, "a plurality of" means more than two unless otherwise specifically defined.

[0038] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0039] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0040] In the description of the embodiments of the present application, the term "plurality" refers to two or more (including two). Similarly, "multiple groups" refers to two or more groups (including two groups), and "multiple pieces" refers to two or more pieces (including two pieces).

[0041] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", and "fixation" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific situations.

[0042] In the automotive industry, spare parts prediction is not only a core tool for supply chain management but also a strategic means to enhance the customer experience and brand competitiveness. Through scientific spare parts prediction, enterprises can achieve efficient operation, reduce costs, and better respond to changes in market demand. Especially in the wave of new energy and intelligentization, its importance is even more significant. Currently, for components with clear failure mechanisms such as bearings, the spare parts prediction results are relatively accurate; however, for the spare parts of battery packs, there are problems with inaccurate prediction.

[0043] To address the above problems, the embodiments of the present application provide a battery spare parts prediction method. This method obtains all the data of multiple in-use battery packs to obtain the full amount of data; determines the failure change relationship based on the full amount of data; the failure change relationship includes the relationship between the failure probability of the battery pack and the change with the first survival duration; according to the failure change relationship, predicts the number of failures of multiple in-use battery packs within a preset future period; and determines the number of battery pack spare parts required within the preset future period. In the technical solution of the embodiments of the present application, a corresponding failure change relationship is determined for the battery pack, and this failure change relationship can more accurately reflect the change of the failure probability of the battery pack with the first survival duration. Therefore, based on this failure change relationship for battery spare parts prediction, the prediction accuracy can be improved, thereby better supplying spare parts.

[0044] The battery spare parts prediction method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown, the application environment includes multiple terminals 102 and a server 104. The terminals 102 and the server 104 can communicate through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed on the cloud or other network servers. The terminals 102 can upload relevant data to the server 104 through the network, such as driving mileage, number of battery charges, battery health (State Of Health, SOH), throughput of battery power, off-line time of the battery pack, consumption time, consumption quantity, and geographical location at the time of consumption, etc. The server 104 collects the data, analyzes and processes the data to obtain the failure change relationship of the battery pack, and thus predicts the battery spare parts according to the failure change relationship.

[0045] Among them, the battery pack can be a component of various devices, and these devices can include but are not limited to vehicles, intelligent robots, drones, medical imaging devices, communication devices, etc. The above terminals 102 can be but are not limited to various personal computers, laptop computers, smart phones, tablet computers, controllers in devices, such as the vehicle controller and battery management system in a vehicle. The above server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0046] According to some embodiments of the present application, referring to Figure 2 , a method for predicting battery spare parts is provided. Taking the method applied to the Figure 1 server as an example for illustration, it can include the following steps:

[0047] Step 201, obtain all data of multiple in-use battery packs to obtain full data.

[0048] Among them, the in-use battery pack refers to the battery pack in a use state, and the full data includes various data of the battery pack, such as the number of battery charges, battery health, throughput of battery power, off-line time of the battery pack, used duration, etc. Multiple in-use battery packs are set in different electrical equipment. For example, the electrical equipment is a new energy vehicle, and one or more battery packs are set in each new energy vehicle.

[0049] In actual application, the server can communicate with each vehicle, obtain the data of the in-use battery packs from each vehicle, and thus obtain the full data. The server can also communicate with terminals such as smart phones and personal computers. The user inputs data in the terminal, and the server obtains the full data from the terminal.

[0050] Step 202, determine the failure change relationship according to the full data.

[0051] Among them, the first survival duration represents the absolute magnitude of a certain physical quantity accumulated by the battery pack from the time of manufacturing and offline to the moment of failure; for example, the first survival duration represents the number of charging times, throughput, and combined data of the number of charging times and throughput between the time of manufacturing and offline of the battery pack and the moment of failure. The second survival duration represents the absolute time length from the time of manufacturing and offline of the battery pack to the moment of failure.

[0052] The above failure change relationship includes the relationship between the failure probability of the battery pack and the first survival duration. For example, when the first generation duration is the number of charging times, the failure change relationship of the battery pack can include that the failure probability corresponding to the battery pack charging 30 times is 0.05%; the failure probability corresponding to the battery pack charging 500 times is 71%.

[0053] After obtaining the full amount of data, the server can couple different data in the full amount of data according to the failure mode of the battery pack to obtain the first survival duration corresponding to the battery pack. Then, according to the first survival durations of different battery packs, calculate the failure probabilities of each battery pack; according to the calculated multiple first survival durations and multiple failure probabilities, fit the failure change relationship of the battery pack.

[0054] Step 203, according to the failure change relationship, predict the number of failures of multiple in-use battery packs within a preset future period.

[0055] Among them, the preset future period is a preset time period to be predicted, such as next month, next week, etc.

[0056] According to the failure change relationship, the failure probabilities of each in-use battery pack within the preset future period can be predicted. Then, according to the failure probabilities of all in-use battery packs, determine the number of failures of multiple in-use battery packs within the preset future period.

[0057] For example, according to the failure change relationship, the failure probabilities of each in-use battery pack next month can be determined; according to the failure probabilities of all in-use battery packs, the number of failures of in-use battery packs next month can be determined.

[0058] Step 204, according to the number of failures, determine the number of battery pack spare parts required within the preset future period.

[0059] Determine the number of failures of multiple in-use battery packs within the preset future period, and the number of failures can be determined as the number of battery pack spare parts required within the preset future period. For example, the number of failures of M in-use battery packs next month is N, where M and N are natural numbers and M≥N, then the number of spare battery packs to be provided next month is N.

[0060] In the above embodiments, various data of multiple in-use battery packs are obtained to obtain full-scale data; a failure change relationship is determined based on the full-scale data; the failure change relationship includes the relationship between the failure probability of the battery pack and the change with the first survival duration; according to the failure change relationship, the number of failures of multiple in-use battery packs within a preset future period is predicted; according to the number of failures, the number of battery pack spare parts required within the preset future period is determined. In the technical solution of the embodiments of the present application, a corresponding failure change relationship is determined for the battery pack, and this failure change relationship can more accurately reflect the change of the failure probability of the battery pack with the first survival duration. Therefore, based on this failure change relationship for battery spare part prediction, the prediction accuracy can be improved, so as to better supply spare parts.

[0061] According to some embodiments of the present application, referring to Figure 3 , in the above embodiments, "determining the failure change relationship according to the full-scale data" may include the following steps:

[0062] Step 301, analyze the failure causes of the battery packs according to the historical consumption data, couple multiple factors affecting the survival duration of the battery packs based on the analysis results, and obtain the corresponding relationship between the first survival duration and multiple influencing factors.

[0063] Among them, the historical consumption data is the consumption data of the battery packs within the historical period. The historical consumption data may include the types, identifiers, consumption quantities, consumption times, geographical locations at the time of consumption, etc. of the battery packs consumed within the historical period. For example, within the historical period, the number of consumed battery packs is a, where a is a natural number; the consumption time of battery pack i is a certain date in a certain year and month, and i is the identifier of the battery pack, which can be a number, a letter, or a combination of numbers and letters; the geographical location is Pi, and Pi includes longitude and latitude.

[0064] It can be understood that if the reasons for the battery packs being consumed are different, that is, the failure reasons are different, then various failure reasons of the battery packs can be summarized according to the historical consumption data, so as to analyze various factors affecting the survival duration of the battery packs and determine the failure mode of the battery packs.

[0065] The server can establish the corresponding relationship between the first survival duration and multiple influencing factors according to the failure mode of the battery pack. Exemplarily, T = F1*X1 + F2*X2 + F3*X3, where T is the first survival duration corresponding to the battery pack, F1, F2, and F3 are the factors affecting the survival duration of the battery pack obtained by failure analysis for the battery pack, and X1, X2, and X3 are the importance weights corresponding to each influencing factor.

[0066] Step 302, calculate according to the corresponding relationship and the full-scale data to obtain the sample survival durations of multiple sample battery packs and the sample failure probabilities corresponding to each sample survival duration.

[0067] According to the full data, multiple sample battery packs can be selected from multiple in-use battery packs, and the data of each influencing factor corresponding to each sample battery pack can be determined. For each sample battery pack, by substituting the data of each influencing factor into the above corresponding relationship, the sample survival duration corresponding to the sample battery pack can be calculated. Then, by performing a normalization process on the sample survival durations of multiple sample battery packs, the normalization result corresponding to each sample battery pack can be determined as the sample failure probability corresponding to the sample battery pack.

[0068] Step 303: Perform a fitting process on multiple sample survival durations and multiple sample failure probabilities to obtain a failure change relationship.

[0069] The server can perform a fitting process on multiple sample survival durations and multiple sample failure probabilities by using a preset fitting algorithm. Exemplarily, the sample survival duration and the corresponding sample failure probability are used as a coordinate point. In this way, multiple sample survival durations and multiple sample failure probabilities can determine multiple coordinate points; a curve can be fitted according to the multiple coordinate points, and this curve can represent the failure change relationship.

[0070] Refer to Figure 4 , in the coordinate system of the fitting curve, the x-axis is the sample survival duration, the y-axis is the sample failure probability, the curve in the figure is the fitted failure change relationship, the small dots in the figure are the coordinate points determined by the sample survival duration and the sample failure probability used for fitting, and the large dots in the figure are typical coordinate points.

[0071] In the above embodiment, the failure cause of the battery pack is analyzed based on historical consumption data, and multiple factors affecting the survival duration of the battery pack are coupled based on the analysis result to obtain the corresponding relationship between the first survival duration and multiple influencing factors; calculations are performed according to the corresponding relationship and the full data to obtain the sample survival durations of multiple sample battery packs and the sample failure probabilities corresponding to each sample survival duration; a fitting process is performed on multiple sample survival durations and multiple sample failure probabilities to obtain a failure change relationship. In the technical solution of the embodiment of the present application, the failure change relationship is established based on the first survival duration, and the first survival duration takes into account the failure mode of the sample battery pack and has the characteristics of high fitting accuracy and strong interpretability. Therefore, the failure change relationship can more accurately reflect the corresponding relationship between the first survival duration and the failure probability of the battery pack, providing help for accurately predicting the spare part quantity in the future.

[0072] According to some embodiments of the present application, refer to Figure 5 , in the above embodiment, "calculations are performed according to the corresponding relationship and the full data to obtain the sample survival durations of multiple sample battery packs and the sample failure probabilities corresponding to each sample survival duration" may include the following steps:

[0073] Step 401: Determine the data of each influencing factor corresponding to each sample battery pack according to the full data.

[0074] Exemplarily, 20 sample battery packs are selected according to the full data. The factors affecting the survival duration of the battery pack include battery health, number of charge cycles, and throughput of battery power. According to the full data, the battery health, number of charge cycles, and throughput of the 1st sample battery pack, the battery health, number of charge cycles, and throughput of the 2nd sample battery pack... the battery health, number of charge cycles, and throughput of the 20th sample battery pack can be determined.

[0075] Step 402: Optimize the importance weights of each influencing factor within the preset weight range.

[0076] To reduce the calculation, the importance weight can be set to vary between 0 and 1, that is, the preset weight range is 0 - 1. During the optimization process, the importance weights corresponding to each influencing factor can vary in steps of 0.05. To further reduce the calculation amount, X1 + X2 + X3 = 1 can be set.

[0077] Within the preset weight range, adjust the importance weights of each influencing factor, and calculate the sample survival duration and sample failure probability corresponding to each sample battery pack according to the adjusted importance weights, the data of each influencing factor, and the corresponding relationship. If the calculated sample survival duration and sample failure probability do not match the actual survival duration and failure probability, continue to adjust the importance weights of each influencing factor. Stop the optimization until the calculated sample survival duration and sample failure probability match the actual survival duration and failure probability, and determine the importance weights for calculating the above sample survival duration and sample failure probability as the optimized importance weights.

[0078] For example, for sample battery pack 1, determine its battery health F1, number of charge cycles F2, and throughput F3 according to the full data. Set the importance weight X1 to 0.05, X2 to 0.10, and X3 to 0.85, and then calculate the sample survival duration T according to the above corresponding relationship T = F1 * X1 + F2 * X2 + F3 * X3 1 and the sample failure probability F 1 . If the sample survival duration T 1 and the sample failure probability F 1 match the actual situation, then determine X1 = 0.05, X2 = 0.10, and X3 = 0.85 as the optimized importance weights. If the sample survival duration T 1 and the sample failure probability F 1 do not match the actual situation, then adjust X1 to 0.10, X2 to 0.15, and X3 to 0.75, and recalculate the sample survival duration T 2 and the sample failure probability F2 And so on, the optimized importance weights can be determined. And so on, the optimized importance weights corresponding to other sample battery packs can be calculated.

[0079] Step 403: Determine multiple optimized sample survival durations according to the optimized importance weights, sort the multiple optimized sample survival durations, and calculate the sample failure probabilities corresponding to the optimized sample survival durations according to the sorting results.

[0080] Determine the sample survival duration corresponding to the optimized importance weight as the optimized sample survival duration, and calculate the optimized sample survival duration for each sample battery pack. Then, sort the multiple optimized sample survival durations in ascending order, and calculate the sample failure probabilities corresponding to each sample battery pack according to the sorting results and the failure probability calculation formula. Among them, the failure probability calculation formula is F = i / (n + 1), where F is the sample failure probability, i is the sorted number, and n is the number of sample battery packs. Refer to the data of 6 sample battery packs in Table 1.

[0081] Table 1

[0082]

[0083] In the above embodiment, the data of each influencing factor corresponding to each sample battery pack are determined according to the full data; the importance weights of each influencing factor are optimized within the preset weight range; multiple optimized sample survival durations are determined according to the optimized importance weights, the multiple optimized sample survival durations are sorted, and the sample failure probabilities corresponding to the optimized sample survival durations are calculated according to the sorting results. In the technical solution of the embodiment of the present application, according to the corresponding relationship between the first survival duration and the influencing factors, the sample survival durations and sample failure probabilities corresponding to multiple sample battery packs can be calculated, providing a data basis for subsequent fitting of the failure change relationship.

[0084] According to some embodiments of the present application, referring to Figure 6 , "optimizing the importance weights of each influencing factor within the preset weight range" in the above embodiment may include the following steps:

[0085] Step 501: Assign values to the importance weights of each influencing factor in multiple rounds within the preset weight range.

[0086] Among them, the preset weight range can be 0 - 1, that is, assign values to the importance weights X1, X2, and X3 in multiple rounds between 0 and 1, and X1 + X2 + X3 = 1.

[0087] Exemplarily, in the first round of assignment, X1 is assigned a value of 0.05, X2 is assigned a value of 0.10, and X3 is assigned a value of 0.85. In the second round of assignment, X1 is assigned a value of 0.10, X2 is assigned a value of 0.15, and X3 is assigned a value of 0.75. In the third round of assignment, X1 is assigned a value of 0.15, X2 is assigned a value of 0.20, and X3 is assigned a value of 0.65. And so on, the importance weights after multiple rounds of assignment are obtained.

[0088] Step 502, after each round of assignment, according to the importance weights after assignment, the data of each influencing factor, and the corresponding relationship, calculate the sample survival duration corresponding to each sample battery pack and the sample failure probability corresponding to each sample survival duration.

[0089] After each round of assignment, according to the importance weights after assignment, the data of each influencing factor, and the above corresponding relationship T = F1*X1 + F2*X2 + F3*X3, calculate the sample survival duration and the sample failure probability.

[0090] Exemplarily, after the first round of assignment, according to X1 being 0.05, X2 being 0.10, X3 being 0.85, the battery health F1, the number of charge cycles F2, and the throughput of the battery power F3, calculate the sample survival duration T 1 and the sample failure probability F 1 . After the second round of assignment, according to X1 being 0.10, X2 being 0.15, X3 being 0.75, the battery health F1, the number of charge cycles F2, and the throughput of the battery power F3, calculate the sample survival duration T 2 and the sample failure probability F 2 . And so on, calculate multiple sample survival durations and multiple sample failure probabilities.

[0091] Step 503, perform fitting processing on multiple sample survival durations and multiple sample failure probabilities.

[0092] Take the sample survival duration and the corresponding sample failure probability as a coordinate point, then multiple coordinate points can be determined according to multiple sample survival durations and multiple sample failure probabilities. Perform fitting processing on multiple coordinate points to obtain a fitting result, and this fitting result can be represented by a fitting curve.

[0093] Step 504, determine the fitting error according to the fitting result and the sample failure probability of the sample battery pack.

[0094] The root mean square error can be used to calculate the fitting error, as shown in formula (1):

[0095] ------------------------------- (1)

[0096] where, e is the fitting error; i is the serial number of the sample battery pack, and n is the number of sample battery packs; is the sample failure probability before fitting of the sample battery pack; is the sample failure probability determined from the sample survival duration of the sample battery pack in the fitting curve.

[0097] Step 505: Determine the optimized importance weight according to multiple fitting errors calculated after multiple rounds of assignment.

[0098] For the sample survival duration and sample failure probability calculated after each round of assignment, a fitting result can be obtained; according to this fitting result, the fitting error can also be calculated. In this way, multiple fitting errors can be calculated after multiple rounds of assignment. The smallest fitting error among the multiple fitting errors is determined as the target fitting error, and the importance weight corresponding to the target fitting error is determined as the optimized importance weight.

[0099] In the above embodiments, multiple rounds of assignment are performed on the importance weights of each influencing factor within the preset weight range; after each round of assignment, according to the assigned importance weights, the data of each influencing factor and the corresponding relationship, calculate the sample survival duration corresponding to each sample battery pack and the sample failure probability corresponding to each sample survival duration; perform fitting processing on multiple sample survival durations and multiple sample failure probabilities; determine the fitting error according to the fitting result and the sample failure probability of the sample battery pack; determine the optimized importance weight according to multiple fitting errors calculated after multiple rounds of assignment. In the technical solution of the embodiments of the present application, by optimizing the importance weights of each influencing factor, more realistic importance weights can be obtained, thereby improving the accuracy of the first survival duration, and further improving the accuracy of the failure probability of the in-use battery pack, as well as the accuracy of the failure quantity and spare part quantity of the in-use battery pack.

[0100] According to some embodiments of the present application, with reference to Figure 7 in the above embodiments, "performing fitting processing on multiple sample survival durations and multiple sample failure probabilities" may include the following steps:

[0101] Step 601: Perform transformation processing on the pre-established survival analysis model to obtain a linear relationship.

[0102] Among them, the linear relationship takes the first logarithm of the sample failure probability as the independent variable and the second logarithm of the sample failure probability as the dependent variable.

[0103] Taking the Weibull distribution as an example, the pre-established survival analysis model is shown in formula (2):

[0104] ----------------------- (2)

[0105] Among them, N represents the lifespan, that is, the survival duration; F(N) represents the failure probability when the lifespan is N, and N 0 represents the minimum lifespan parameter, generally taken as 0; Na is a constant representing the characteristic lifespan parameter. When N = Na, F(Na) = 0.632.

[0106] Perform a transformation on the survival analysis model, as shown in formula (3):

[0107] ---------------------- (3)

[0108] Take the double logarithm of formula (3) to obtain formula (4):

[0109] ---------- (4)

[0110] Let N 0 = 0, then formula (5) is obtained:

[0111] ----------------- (5)

[0112] Among them, is the constant c, and b is the linear coefficient. Take as the independent variable x, as the dependent variable y, then , Formula (6) can be obtained:

[0113] --------------------------------- (6)

[0114] Step 602: Calculate the double logarithm and single logarithm of the failure probability of each sample.

[0115] According to Table 1 and the above formulas, the double logarithm lg(lg(1 / (1 - F(N))) and single logarithm lg(F(N)) of the failure probability F(N) of each sample can be calculated to obtain Table 2.

[0116] Table 2

[0117]

[0118] Step 603: Perform a fitting process based on the calculated double logarithm and single logarithm, and determine the linear coefficient and constant in the linear relationship according to the fitting result.

[0119] Determine the coordinate points according to x and y in Table 2, and then fit the relationship between the failure probability and the sample survival duration by means of function fitting, referring toFigure 8 According to the fitting result, the linear coefficient b = 1.323 and the constant c = 0.057 in the linear relationship formula.

[0120] Step 604: Obtain the model parameters in the survival analysis model according to the linear coefficient and the constant in the linear relationship formula.

[0121] Substitute the linear coefficient and the constant in the linear relationship formula into formula (6) and perform back-calculation to obtain the model parameter N in formula (2). a = 0.4823. Substitute the model parameter N a into formula (2), and then the failure change relationship can be obtained.

[0122] In the above embodiments, the pre-established survival analysis model is transformed to obtain a linear relationship formula; the natural logarithm of the second power and the natural logarithm of the first power of the failure probability of each sample are calculated; fitting processing is performed according to the calculated natural logarithm of the second power and the natural logarithm of the first power, and the linear coefficient and the constant in the linear relationship formula are determined according to the fitting result; the model parameters in the survival analysis model are obtained according to the linear coefficient and the constant in the linear relationship formula. In the technical solution of the embodiments of the present application, fitting according to the natural logarithm of the second power and the natural logarithm of the first power of the sample failure probability can reduce the fitting difficulty and improve the fitting accuracy, so as to obtain a more accurate failure change relationship and improve the accuracy of battery spare part prediction.

[0123] According to some embodiments of the present application, referring to Figure 9 , in the above embodiments, "predict the number of failures of multiple in-use battery packs within a preset future period according to the failure change relationship" may include the following steps:

[0124] Step 701: Predict the failure probability of each in-use battery pack within a preset future period according to the failure change relationship.

[0125] For each in-use battery pack, its failure probability can be determined in the failure change relationship according to its current survival duration and the preset future period.

[0126] Exemplarily, the preset future period is next month, and this preset future period is equivalent to 100; the current survival duration of the first in-use battery pack is 100, then the failure probability corresponding to the survival duration of 200 can be determined in the failure change relationship. The current survival duration of the second in-use battery pack is 150, then the failure probability corresponding to the survival duration of 250 can be determined in the failure change relationship. And so on, determine the failure probability corresponding to each in-use battery pack.

[0127] Step 702: Statistically analyze the failure probabilities of multiple in-use battery packs to obtain the total failure probability.

[0128] Sum the failure probabilities of multiple in-use battery packs to obtain the total failure probability. For example, if the failure probability of the first in-use battery pack is f1, the failure probability of the second in-use battery pack is f2... and the failure probability of the jth in-use battery pack is fj, then the total failure probability f0 = f1 + f2... + fj.

[0129] Step 703, determine the number of failures according to the total failure probability and the total number of in-use battery packs.

[0130] Calculate the product of the total failure probability and the total number of in-use battery packs to obtain the number of failures. For example, the number of failures , where f0 is the total failure probability and j is the total number of in-use battery packs.

[0131] In the above embodiments, predict the failure probability of each in-use battery pack at a preset future time period according to the failure change relationship; count the failure probabilities of multiple in-use battery packs to obtain the total failure probability; determine the number of failures according to the total failure probability and the total number of in-use battery packs. In the technical solution of the embodiments of the present application, according to the failure change relationship, the failure probability of all in-use battery packs at a preset future time period can be determined, so as to accurately predict the number of failures, providing an accurate basis for determining the number of battery pack spare parts subsequently.

[0132] According to some embodiments of the present application, referring to Figure 10 , in the above embodiments, "determine the number of failures according to the total failure probability and the total number of in-use battery packs" may include the following steps:

[0133] Step 801, determine the failure probability range according to the preset confidence interval and the total failure probability.

[0134] In practical applications, in order to improve the prediction accuracy, a confidence interval can be preset. After determining the total failure probability, broaden the total failure probability to a failure probability range according to the preset confidence interval.

[0135] For example, if the preset confidence interval is 95% and the total failure probability is f0, the failure probability range can be 95%*f0~f0, or it can also be 95%*f0~105%*f0.

[0136] It should be noted that the preset confidence interval is not limited to the above example, and the determination method of the failure probability range is not limited to the above example either, and can be set according to the actual situation.

[0137] Step 802, determine the number of failures according to the failure probability range and the total number of in-use battery packs.

[0138] Based on the failure probability range and the total number of in-use battery packs, the range of the number of failures can be calculated. For example, if the failure probability range is 95%*f0~f0 and the total number of in-use battery packs is j, the minimum number of failures is , and the maximum number of failures is .

[0139] It should be noted that different confidence intervals result in different failure probability ranges, and the calculated minimum and maximum numbers of failures are also different.

[0140] In the above embodiments, the failure probability range is determined according to the preset confidence interval and the total failure probability; the number of failures is determined according to the failure probability range and the total number of in-use battery packs. In the technical solution of the embodiments of the present application, the preset confidence interval can expand the range of the number of failures, making the spare part quantity more flexible, so as to better supply spare parts and improve the user experience.

[0141] According to some embodiments of the present application, in the above embodiments, "determining the number of battery pack spare parts required in a preset future period according to the number of failures" may include: correcting the number of failures according to the battery pack service information to obtain the number of battery pack spare parts required in a preset future period.

[0142] Among them, the battery pack service information includes at least one of raw material procurement of the battery pack, production cycle of production scheduling, transfer cycle, and delivery time-consuming.

[0143] When predicting battery spare parts, not only the number of failures needs to be considered, but also the actual business scenario needs to be considered. For example, the time of raw material procurement and the production cycle time of production scheduling need to be considered. Considering the above factors comprehensively, the number of failures is corrected to obtain the number of battery pack spare parts required in a preset future period.

[0144] For example, if it is predicted that the number of failures of in-use battery packs next month is N, considering the raw material procurement time of the battery pack, the production cycle of the battery pack, the transfer cycle of the battery pack, etc., a certain number of additional spare parts need to be provided, then the number of failures N is corrected to obtain the number of spare parts of the battery pack next month as B, and B is greater than N.

[0145] In the above embodiments, the number of failures is corrected according to the battery pack service information to obtain the number of battery pack spare parts required in a preset future period. In the technical solution of the embodiments of the present application, the spare part quantity is determined by comprehensively considering failures and business scenarios, leaving a margin for spare part supply, which can reduce the risk of insufficient spare parts, so as to better supply spare parts and further improve the user experience.

[0146] According to some embodiments of the present application, a method for predicting battery spare parts is provided, and this method is applied to Figure 1Taking the computer device in [[]] as an example for illustration, the following steps may be included:

[0147] Step 1: Obtain all data of multiple in-use battery packs to get full data.

[0148] Step 2: Analyze the failure causes of the battery packs according to historical consumption data, couple multiple factors affecting the survival duration of the battery packs based on the analysis results, and obtain the corresponding relationship between the first survival duration and multiple influencing factors.

[0149] Step 3: Determine the data of each influencing factor corresponding to each sample battery pack according to the full data.

[0150] Step 4: Assign importance weights to each influencing factor in a preset weight range for multiple rounds; after each round of assignment, calculate the sample survival duration corresponding to each sample battery pack and the sample failure probability corresponding to each sample survival duration according to the assigned importance weights, the data of each influencing factor, and the corresponding relationship; perform fitting processing on the sample survival duration and the sample failure probability; determine the fitting error according to the fitting result and the sample failure probability of the sample battery pack; determine the optimized importance weights according to the multiple fitting errors calculated after multiple rounds of assignment.

[0151] Step 5: Determine multiple optimized sample survival durations according to the optimized importance weights, sort the multiple optimized sample survival durations, and calculate the sample failure probability corresponding to each optimized sample survival duration according to the sorting result.

[0152] Step 6: Perform transformation processing on the pre-established survival analysis model to obtain a linear relationship; the linear relationship takes the first logarithm of the sample failure probability as the independent variable and the second logarithm of the sample failure probability as the dependent variable; calculate the second logarithm and the first logarithm of each sample failure probability; perform fitting processing according to the calculated second logarithm and first logarithm, and determine the linear coefficient and the constant in the linear relationship according to the fitting result; obtain the model parameters in the survival analysis model according to the linear coefficient and the constant in the linear relationship, substitute the model parameters into the survival analysis model, and obtain the failure change relationship.

[0153] Step 7: Predict the failure probability of each in-use battery pack within a preset future period according to the failure change relationship; count the failure probabilities of multiple in-use battery packs to obtain the total failure probability; determine the failure probability range according to the preset confidence interval and the total failure probability; determine the number of failures according to the failure probability range and the total number of in-use battery packs.

[0154] Step 8: Correct the number of failures according to the battery pack business information to obtain the number of battery pack spare parts required within the preset future period.

[0155] In the above embodiments, a corresponding failure change relationship is determined for the battery pack, and this failure change relationship can more accurately reflect the change of the failure probability of the battery pack with the first survival duration. Therefore, based on this failure change relationship, battery spare part prediction can be performed, which can improve the prediction accuracy and thus better supply spare parts.

[0156] It should be understood that although the steps in the above flowcharts are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above flowcharts may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0157] Based on the same inventive concept, an embodiment of the present application also provides a battery spare part prediction device for implementing the battery spare part prediction method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery spare part prediction device provided below can refer to the limitations on the battery spare part prediction method in the above text, and will not be repeated here.

[0158] According to some embodiments of the present application, with reference to Figure 11 , the present application provides a battery spare part prediction device, and this device includes:

[0159] A data acquisition module 901, configured to acquire various data of multiple in-use battery packs to obtain full-scale data; wherein, the multiple in-use battery packs are arranged in different electrical equipment;

[0160] A relationship determination module 902, configured to determine a failure change relationship according to the full-scale data; the failure change relationship includes the relationship between the failure probability of the battery pack and the change of the first survival duration;

[0161] A failure prediction module 903, configured to predict the number of failures of multiple in-use battery packs within a preset future period according to the failure change relationship;

[0162] A spare part determination module 904, configured to predict the number of failures of multiple in-use battery packs within a preset future period according to the number of failures.

[0163] In some embodiments, the relationship determination module 902 is specifically configured to analyze the failure cause of the battery pack according to the historical consumption data, couple multiple factors affecting the survival duration of the battery pack based on the analysis result, and obtain the corresponding relationship between the first survival duration and multiple influencing factors; calculate according to the corresponding relationship and the full data to obtain the sample survival durations of multiple sample battery packs and the sample failure probabilities corresponding to each sample survival duration; perform a fitting process on the multiple sample survival durations and multiple sample failure probabilities to obtain the failure change relationship.

[0164] In some embodiments, the relationship determination module 902 is specifically configured to determine the data of each influencing factor corresponding to each sample battery pack according to the full data; optimize the importance weights of each influencing factor within a preset weight range; determine multiple optimized sample survival durations according to the optimized importance weights, sort the multiple optimized sample survival durations, and calculate the sample failure probabilities corresponding to each optimized sample survival duration according to the sorting result.

[0165] In some embodiments, the relationship determination module 902 is specifically configured to perform multiple rounds of assignment of the importance weights of each influencing factor within a preset weight range; after each round of assignment, calculate the sample survival durations corresponding to each sample battery pack and the sample failure probabilities corresponding to each sample survival duration according to the assigned importance weights, the data of each influencing factor, and the corresponding relationship; perform a fitting process on the multiple sample survival durations and multiple sample failure probabilities; determine the fitting error according to the fitting result and the sample failure probability of the sample battery pack; determine the optimized importance weights according to the multiple fitting errors calculated after multiple rounds of assignment.

[0166] In some embodiments, the relationship determination module 902 is specifically configured to perform a transformation process on a pre-established survival analysis model to obtain a linear relationship; the linear relationship uses the first logarithm of the sample failure probability as the independent variable and the second logarithm of the sample failure probability as the dependent variable; calculate the second logarithm and the first logarithm of each sample failure probability; perform a fitting process according to the calculated second logarithm and first logarithm, and determine the linear coefficient and the constant in the linear relationship according to the fitting result; obtain the model parameters in the survival analysis model according to the linear coefficient and the constant in the linear relationship.

[0167] In some embodiments, the failure prediction module 903 is specifically configured to predict the failure probabilities of each in-use battery pack within a preset future period according to the failure change relationship; count the failure probabilities of multiple in-use battery packs to obtain the total failure probability; determine the failure quantity according to the total failure probability and the total quantity of the in-use battery packs.

[0168] In some embodiments, the failure prediction module 903 is specifically configured to determine a failure probability range according to a preset confidence interval and a total failure probability; and determine a failure quantity according to the failure probability range and the total number of in-use battery packs.

[0169] In some embodiments, the spare part determination module 904 is specifically configured to perform a correction process on the failure quantity according to the battery pack service information to obtain the quantity of battery pack spare parts required within a preset future time period.

[0170] Each module in the above battery spare part prediction device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the electronic device in the form of hardware or be independent of the processor, or can be stored in the memory in the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0171] According to some embodiments of the present application, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 12 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a battery spare part prediction method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0172] Those skilled in the art can understand, Figure 12The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0173] According to some embodiments of the present application, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory including instructions, and the above instructions can be executed by a processor of an electronic device to complete the above method. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0174] According to some embodiments of the present application, there is also provided a computer program product. When the computer program is executed by a processor, the above method can be implemented. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, part or all of the above method can be implemented according to the process or function described in the embodiments of the present application.

[0175] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0176] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0177] The above-described embodiments merely represent several implementation manners of the present application, facilitating the specific and detailed understanding of the technical solution of the present application. However, it should not be construed as a limitation on the protection scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all fall within the protection scope of the present application. It should be understood that the technical solutions obtained by those skilled in the art through logical analysis, reasoning or limited experiments based on the technical solution provided by the present application are all within the protection scope of the appended claims of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the content of the appended claims, and the description and drawings can be used to explain the content of the claims.

Claims

1. A battery spare parts prediction method, characterized in that: The method comprises: Acquire various data of multiple battery packs in use to obtain full data; wherein the multiple battery packs in use are arranged in different power-consuming devices; Determine a failure change relationship according to the full amount of data; the failure change relationship includes a relationship between a failure probability of the battery pack and a first survival time; Predicting the number of failures of the plurality of battery packs in use within a preset future period according to the failure variation relationship; Determining the number of battery pack spare parts required within the preset future period according to the number of failures; The failure change relationship is calculated based on the corresponding relationship between the first survival time and multiple influencing factors and the full amount of data to obtain sample survival times of multiple sample battery packs and sample failure probabilities corresponding to each of the sample survival times, and is determined based on the sample failure probabilities; The process of calculating the sample failure probability includes: optimizing the importance weight of each of the influencing factors within a preset weight range; The optimizing the importance weight of each of the influencing factors within a preset weight range includes: Perform multiple rounds of assigning importance weights of each of the influencing factors within the preset weight range; After each round of assignment, the sample survival time corresponding to each sample battery pack and the sample failure probability corresponding to each sample survival time are calculated according to the assigned importance weight, the data of each influencing factor and the corresponding relationship; Performing fitting processing on the survival time of the plurality of samples and the failure probability of the plurality of samples; Determine a fitting error according to the fitting result and the sample failure probability of the sample battery pack; The importance weight after the optimization is determined according to the multiple fitting errors calculated after multiple rounds of assignment.

2. The method according to claim 1, characterized in that The determining the failure change relationship according to the full amount of data includes: Analyzing the failure cause of the battery pack according to the historical consumption data, coupling multiple factors affecting the battery pack life span based on the analysis result, and obtaining a corresponding relationship between the first life span and multiple influencing factors; Calculating according to the corresponding relationship and the full amount of data to obtain sample survival times of multiple sample battery packs and sample failure probabilities corresponding to the sample survival times; Fitting is performed on multiple sample survival times and multiple sample failure probabilities to obtain the failure change relationship.

3. The method according to claim 2, characterized in that The calculation is performed according to the corresponding relationship and the full amount of data to obtain the sample survival time of multiple sample battery packs and the sample failure probability corresponding to each of the sample survival time, including: Determine data of each influencing factor corresponding to each of the sample battery packs according to the full amount of data; Optimizing the importance weight of each of the influencing factors within a preset weight range; The survival times of multiple optimized samples are determined according to the importance weights after optimization, the survival times of the multiple optimized samples are sorted, and the sample failure probability corresponding to each of the optimized sample survival times is calculated according to the sorting result.

4. The method according to any one of claims 2 to 3, characterized in that: The fitting process of the survival time of the plurality of samples and the failure probability of the plurality of samples comprises: Transforming the pre-established survival analysis model to obtain a linear relationship; the linear relationship uses the first logarithm of the sample failure probability as an independent variable and the second logarithm of the sample failure probability as a dependent variable; Calculating the quadratic logarithm and the linear logarithm of the failure probability of each of the samples; Performing fitting processing according to the calculated quadratic logarithm and linear logarithm, and determining the linear coefficient and constant in the linear relationship equation according to the fitting result; The model parameters in the survival analysis model are obtained according to the linear coefficients and constants in the linear relationship.

5. The method according to claim 1, characterized in that The predicting, based on the failure variation relationship, the number of failures of the plurality of battery packs in use within a preset future period of time includes: Predicting the failure probability of each of the in-use battery packs within the preset future period according to the failure change relationship; Counting the failure probabilities corresponding to the multiple battery packs in use to obtain a total failure probability; The failure quantity is determined based on the total failure probability and the total quantity of the battery packs in use.

6. The method according to claim 5, characterized in that The determining the failure quantity according to the total failure probability and the total quantity of the battery packs in use includes: Determining a failure probability range according to a preset confidence interval and the total failure probability; The failure quantity is determined based on the failure probability range and the total quantity of the battery packs in use.

7. The method according to claim 1, characterized in that The determining, based on the number of failures, the number of battery pack spare parts required in the preset future period includes: The failure quantity is corrected according to the battery pack business information to obtain the required number of battery pack spare parts in the preset future time period.

8. A battery spare parts prediction device, characterized in that: The device comprises: A data acquisition module, used to acquire various data of a plurality of battery packs in use to obtain full data; wherein the plurality of battery packs in use are arranged in different electrical devices; A relationship determination module, configured to determine a failure change relationship based on the full amount of data; the failure change relationship includes a relationship between a failure probability of a battery pack and a first survival time; A failure prediction module, used to predict the number of failures of the plurality of battery packs in use within a preset future period according to the failure change relationship; A spare parts determination module, used to determine the number of battery pack spare parts required in the preset future period according to the number of failures; The failure change relationship is calculated based on the corresponding relationship between the first survival time and multiple influencing factors and the full amount of data to obtain sample survival times of multiple sample battery packs and sample failure probabilities corresponding to each of the sample survival times, and is determined based on the sample failure probabilities; The process of calculating the sample failure probability includes: optimizing the importance weight of each of the influencing factors within a preset weight range; The optimizing the importance weight of each of the influencing factors within a preset weight range includes: Perform multiple rounds of assigning importance weights of each of the influencing factors within the preset weight range; After each round of assignment, the sample survival time corresponding to each sample battery pack and the sample failure probability corresponding to each sample survival time are calculated according to the assigned importance weight, the data of each influencing factor and the corresponding relationship; Performing fitting processing on the survival time of the plurality of samples and the failure probability of the plurality of samples; Determine a fitting error according to the fitting result and the sample failure probability of the sample battery pack; The importance weight after the optimization is determined according to the multiple fitting errors calculated after multiple rounds of assignment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images