Power battery control strategy determination method and device, and equipment control method and device

Abstract

The invention discloses a power battery control strategy determination method and device, an equipment control method and device, computer equipment, a computer readable storage medium and a computer program product, and the power battery control strategy determination method comprises the steps: obtaining sampling data obtained through collection of a power battery, and determining features corresponding to different fault types according to the sampling data; according to the features corresponding to the fault types and the sampling data corresponding to the features, target features of the fault types are obtained through analysis; and determining a control strategy of the power battery according to the target characteristics of each fault type. The control strategy of the power battery better conforms to the actual working condition, the control strategy is more accurate, the fault occurrence probability is reduced, and the service life of the power battery is prolonged.

Description

Power battery control strategy determination method, equipment control method and device Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a power battery control strategy determination method, a device control method, a device, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Power batteries are the power sources that power tools. With the advancement of technology and society, the application of power batteries continues to expand. They are used not only in electric vehicles such as electric bicycles, electric motorcycles, and electric vehicles, but also in a variety of fields such as military equipment and aerospace. Traditional power battery control strategies are developed based on laboratory simulation and testing of the equipment used by power batteries, which makes it difficult to simulate real-world operating conditions. Improving the control accuracy of power batteries is a pressing issue.

[0003] Summary of the Invention

[0004] According to various embodiments of the present application, a power battery control strategy determination method, a device control method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product are provided.

[0005] In a first aspect, the present application provides a method for determining a power battery control strategy, comprising:

[0006] Acquire sampling data collected from the power battery, and determine characteristics corresponding to different fault types based on the sampling data;

[0007] Analyze and obtain target features of each fault type based on the features corresponding to each fault type and the sampling data corresponding to each feature;

[0008] Determine the control strategy of the power battery based on the target characteristics of each fault type.

[0009] The above-mentioned power battery control strategy determination method obtains sampled data from the power battery and determines the characteristics corresponding to different fault types based on the sampled data. Based on the characteristics corresponding to each fault type and the sampled data corresponding to each characteristic, the target characteristics of each fault type are analyzed. Based on the target characteristics of each fault type, a power battery control strategy is generated for updating the power battery control strategy. This ensures that the power battery control strategy is more consistent with actual operating conditions, making the control strategy more accurate, reducing the probability of faults, and extending the life of the power battery.

[0010] In one embodiment, determining the characteristics corresponding to different fault types based on the sampled data includes performing a differential analysis on the sampled data collected from the power battery, and determining the characteristics corresponding to the different fault types based on the differential analysis results. The differential analysis of the sampled data collected from the power battery enables accurate feature classification and determination of the characteristics corresponding to different fault types.

[0011] In one embodiment, the difference analysis includes at least one of statistical feature analysis, cross-feature analysis between variables, and temporal trend change feature analysis. Depending on actual needs, one or more of these can be selected for difference analysis.

[0012] In one embodiment, analyzing and obtaining target features for each fault type based on features corresponding to each fault type and sampled data corresponding to each feature includes: screening the features corresponding to each fault type based on the sampled data of the features corresponding to each fault type to obtain initial target features associated with each fault type; and determining the target features for each fault type based on the sampled data of the initial target features associated with each fault type and the fault prediction model corresponding to each fault type. The initial target features associated with each fault type are extracted through feature screening to avoid overlapping features with high correlation that affect processing efficiency. The target features for each fault type are then accurately obtained in combination with the fault prediction model.

[0013] In one embodiment, the method of screening the features corresponding to each fault type based on the sampled data of the features corresponding to each fault type to obtain the initial target features associated with each fault type includes: preprocessing the sampled data of the features corresponding to each fault type to obtain the preprocessed sampled data of each feature; and screening the features corresponding to each fault type based on the preprocessed sampled data of each feature to obtain the initial target features associated with each fault type. The sampled data is first preprocessed to ensure the accuracy and completeness of the data, and then feature screening is performed to determine the initial target features associated with each fault type, thereby improving the accuracy of feature association.

[0014] In one embodiment, the preprocessing includes at least one of data cleaning and data completion. One or more of data cleaning and data completion can be selected for data preprocessing according to actual needs.

[0015] In one embodiment, determining the target feature for each fault type based on the sampled data of the initial target feature associated with each fault type and the fault prediction model corresponding to each fault type includes: adding labels to the sampled data of the initial target feature associated with each fault type to generate training sample data for each fault type, wherein the labels include a faulty device label and a normal device label; performing model training based on the training sample data for each fault type to obtain a fault prediction model corresponding to each fault type; and determining the target feature for each fault type based on the fault prediction model corresponding to each fault type. Labeling the sampled data of the initial target feature associated with each fault type as training sample data for model training ensures the accuracy of the fault prediction model.

[0016] In one embodiment, after performing model training based on training sample data for each fault type to obtain a fault prediction model corresponding to each fault type, the method further includes: performing model evaluation on the fault prediction model, and selecting the fault prediction model that passes the evaluation as the final fault prediction model. Performing model evaluation on the trained fault prediction model and selecting the fault prediction model that passes the evaluation as the final fault prediction model better meets actual needs.

[0017] In one embodiment, evaluating the fault prediction model and selecting the fault prediction model that passes the evaluation as the final fault prediction model includes: performing a business evaluation on the fault prediction model according to preset business standards; performing a rationality evaluation on the fault prediction model that passes the business evaluation according to preset fault tree analysis results, and selecting the fault prediction model that passes the rationality evaluation as the final fault prediction model. Simultaneously performing both the business evaluation and the rationality evaluation on the fault prediction model, and selecting the fault prediction model that passes both evaluations as the final fault prediction model, improves model evaluation accuracy.

[0018] In one embodiment, the fault prediction model is an ensemble learning model; determining the target feature for each fault type based on the fault prediction model corresponding to each fault type includes determining the target feature for each fault type based on the feature importance of the ensemble learning model corresponding to each fault type. Determining the target feature for each fault type directly based on the feature importance of the ensemble learning model is simple, fast, and highly reliable.

[0019] In one embodiment, determining a power battery control strategy based on the target characteristics of each fault type includes: generating a preliminary revised power battery strategy based on the target characteristics of each fault type; simulating device operation based on the preliminary revised strategy to obtain simulated sampling data; and performing strategy evaluation based on the simulated sampling data and the fault prediction model to determine the power battery control strategy. After generating the preliminary revised power battery strategy based on the target characteristics of the fault type, the strategy is evaluated by simulating device operation to ensure that the power battery control strategy meets actual requirements.

[0020] In one embodiment, the policy evaluation includes a failure rate evaluation and / or a service evaluation. The revised policy can be evaluated for failure rate and / or service according to actual needs.

[0021] In one embodiment, the sampled data includes at least one of current data, temperature data, voltage data, and insulation data. It is practically necessary to select one or more of current data, temperature data, voltage data, and insulation data to analyze the characteristics of each fault type.

[0022] In the second aspect, the present application provides a device control method, including: determining a target revision strategy from the revision strategy of a power battery; the revision strategy of the power battery is obtained according to the above-mentioned power battery control strategy revision method; and sending the target revision strategy to a target device to update the control strategy of the power battery of the target device.

[0023] The above-mentioned device control method generates a revised strategy for the power battery according to the target characteristics of each fault type, so as to update the control strategy of the power battery, thereby making the control strategy of the power battery more in line with the actual working conditions, making the control strategy more accurate, reducing the probability of fault occurrence, and extending the life of the power battery.

[0024] In one embodiment, determining a target revision policy from the revision policies of the power battery includes: determining a target device associated with each revision policy based on an optimization goal of the revision policy; and using all revision policies associated with the target device as the target revision policy. Based on the optimization goal of the revision policy, all revision policies associated with the target device are used as the target revision policy to achieve active updating of the revision policy of the target device.

[0025] In one embodiment, determining the target revision policy from the revision policy of the power battery includes: based on an update request sent by a target device, obtaining, from the revision policy of the power battery, a revision policy associated with the target device and an optimization target, as the target revision policy. The policy update operation can be initiated by sending an update request via the target device as needed.

[0026] In a third aspect, the present application provides a device control method, including: obtaining a revision strategy of a power battery to be updated; the revision strategy of the power battery to be updated is obtained according to the above-mentioned power battery control strategy revision method; and updating the control strategy of the power battery of the device according to the revision strategy of the power battery to be updated.

[0027] The above-mentioned device control method generates a revised strategy for the power battery according to the target characteristics of each fault type, so as to update the control strategy of the power battery, thereby making the control strategy of the power battery more in line with the actual working conditions, making the control strategy more accurate, reducing the probability of fault occurrence, and extending the life of the power battery.

[0028] In one embodiment, obtaining the revision policy of the power battery to be updated includes: receiving a target revision policy from a server as the revision policy of the power battery to be updated; the target revision policy is obtained by the server based on all revision policies associated with a target device, and the target device is determined based on an optimization target of the revision policy. Based on the optimization target of the revision policy, all revision policies associated with the target device are used as the target revision policy, thereby actively updating the revision policy of the target device.

[0029] In one embodiment, obtaining the revision policy of the power battery to be updated includes: sending an update request to a server; receiving a target revision policy returned by the server in response to the update request as the revision policy of the power battery to be updated; the target revision policy is obtained by the server, based on the update request, from the revision policy of the power battery, by obtaining the revision policy associated with the optimization target and the target device. The policy update operation can be initiated by sending an update request through the target device as needed.

[0030] In a fourth aspect, the present application provides a power battery control strategy determination device, comprising:

[0031] A data acquisition module, configured to acquire sampled data collected from the power battery and determine characteristics corresponding to different fault types based on the sampled data;

[0032] A data analysis module is used to analyze and obtain target features of each fault type based on the features corresponding to each fault type and the sampled data corresponding to each feature;

[0033] The strategy revision module is used to determine the control strategy of the power battery according to the target characteristics of each fault type.

[0034] In a fifth aspect, the present application provides a device control apparatus, comprising:

[0035] a strategy determination module, configured to determine a target revision strategy from revision strategies of the power battery; the revision strategy of the power battery being obtained according to the power battery control strategy determination device;

[0036] The strategy sending module is used to send the target revised strategy to the target device to update the control strategy of the power battery of the target device.

[0037] In a sixth aspect, the present application provides a device control apparatus, comprising:

[0038] A strategy acquisition module, configured to acquire a revised strategy of the power battery to be updated; the revised strategy of the power battery to be updated is obtained by the power battery control strategy determination device described above;

[0039] The strategy update module is used to update the control strategy of the power battery of the device according to the revised strategy of the power battery to be updated.

[0040] In a seventh aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0041] In an eighth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0042] In a ninth aspect, the present application provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0043] The details of one or more embodiments of the present application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0045] FIG1 is a schematic diagram of an application environment of a method for determining a power battery control strategy according to an embodiment;

[0046] FIG2 is a flow chart of a method for determining a power battery control strategy in one embodiment;

[0047] FIG3 is a flow chart of analyzing and obtaining target features of each fault type based on features corresponding to each fault type and sampled data corresponding to each feature in one embodiment;

[0048] FIG4 is a flowchart of an embodiment of performing model training based on sampled data of initial target features associated with each fault type to obtain a fault prediction model corresponding to each fault type;

[0049] FIG5 is a flowchart of generating a revised strategy for a power battery according to target characteristics of each fault type in one embodiment;

[0050] FIG6 is a schematic diagram of step-charging control under the original strategy in one embodiment;

[0051] FIG7 is a schematic diagram of faulty vehicle ranking under the original strategy in one embodiment;

[0052] FIG8 is a schematic diagram of step-charging control under a new strategy in one embodiment;

[0053] FIG9 is a schematic diagram showing a comparison of faulty vehicle rankings under the original strategy and the new strategy in one embodiment;

[0054] FIG10 is a flow chart of a device control method according to one embodiment;

[0055] FIG11 is a flow chart of a device control method according to another embodiment;

[0056] FIG12 is a structural block diagram of a device for determining a power battery control strategy in one embodiment;

[0057] FIG13 is a block diagram of a device control apparatus according to an embodiment;

[0058] FIG14 is a structural block diagram of a device control device in another embodiment;

[0059] FIG15 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art 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-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0062] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0063] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0064] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0065] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0066] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0067] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0068] The power battery is the power source that provides the power source for the tool. Most of them adopt valve-sealed lead-acid batteries, open-type tubular lead-acid batteries and lithium iron phosphate batteries. They have the characteristics of high energy, high power and high energy density. The traditional power battery control strategy is formulated by combining laboratory simulation and testing of the equipment used by the power battery, and it is difficult to simulate the actual working conditions. Based on this, the present application provides a method for determining the power battery control strategy, which obtains the sampling data obtained by collecting data from the power battery and determines the characteristics corresponding to different fault types according to the sampling data. According to the characteristics corresponding to each fault type and the sampling data corresponding to each characteristic, the target characteristics of each fault type are analyzed and obtained. According to the target characteristics of each fault type, the control strategy of the power battery is determined for updating the control strategy of the power battery, so that the control strategy of the power battery is more in line with the actual working conditions, the control strategy is more accurate, the probability of fault occurrence is reduced, and the life of the power battery is extended.

[0069] The method for determining the power battery control strategy provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. The terminal 102 communicates with the server 104 through the 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 it can be placed on the cloud or other network servers. The terminal 102 collects the sampling data of the power battery and uploads it to the server 104. The server 104 analyzes and obtains the target characteristics of each fault type based on the characteristics corresponding to each fault type and the sampling data corresponding to each characteristic; based on the target characteristics of each fault type, the control strategy of the power battery is determined to be used for updating the control strategy of the power battery. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers. It should be noted that the power battery involved in the embodiments of the present application can be used in, but is not limited to, electrical equipment such as vehicles, ships or aircraft.

[0070] In one embodiment, a method for determining a power battery control strategy is provided, wherein the control strategy may be a charging control strategy, a thermal management strategy, a charge-discharge balancing control strategy, etc. As shown in FIG2 , the method includes:

[0071] Step S100: acquiring sampling data collected from the power battery, and determining features corresponding to different fault types based on the sampling data.

[0072] Among them, the server can communicate with the device using the power battery to obtain the sampled data. The device can be a vehicle, a ship or an aircraft, etc. For ease of understanding, the following explanation is given by taking the application of the power battery to a vehicle as an example. The server obtains the sampled data collected from the faulty vehicle and the normal vehicle, and determines the characteristics corresponding to the different fault types based on the sampled data. The number of fault types is not unique, and may include high-charge and low-discharge faults, abnormal insulation faults, etc. It can be understood that the characteristics corresponding to different fault types will also be different. The characteristics may be current, voltage, etc. Correspondingly, the sampled data may include current data, voltage data, etc. In one embodiment, the sampled data includes at least one of current data, temperature data, voltage data and insulation data. It may be actually necessary to select one or more of the current data, temperature data, voltage data and insulation data to analyze the characteristics of each fault type.

[0073] After acquiring the sampled data, the server performs data analysis in combination with the pre-saved mechanism analysis results and / or FTA (Fault Tree Analysis) analysis results to determine the characteristics corresponding to different fault types. Among them, the mechanism analysis results can characterize the cause of the vehicle failure. By combining the data of existing market vehicles and vehicle production processes, a forward analysis is performed based on different types of faults to determine the cause of the fault. Correspondingly, the FTA analysis results are analyzed through reverse deduction to determine what kind of fault will be caused when a problem occurs in the vehicle. In one embodiment, the characteristics corresponding to different fault types are determined based on the sampled data, including: performing a differential analysis on the sampled data collected from the power battery, and determining the characteristics corresponding to different fault types based on the differential analysis results. Based on the differential analysis of the sampled data collected from the power battery, feature classification can be accurately performed to determine the characteristics corresponding to different fault types.

[0074] Among them, the specific method of differential analysis is not unique. Differential analysis may include at least one of statistical feature analysis, cross-feature analysis between variables, and time series trend change feature analysis. One or more of statistical feature analysis, cross-feature analysis between variables, and time series trend change feature analysis can be selected for differential analysis based on actual needs. Specifically, statistical feature analysis can be to analyze whether there are significant differences in the statistical features (such as maximum value, mean, quantile, etc.) of each sampling data between faulty vehicles and normal vehicles; cross-feature analysis between variables can be to analyze the cross-features between variables, such as the difference in voltage when the current is greater than the current threshold; time series trend change feature analysis can be to analyze voltage consistency, self-discharge difference, etc. According to the correlation between the analyzed significant features and the fault type, corresponding to different fault types, determine what data differences exist between faulty vehicles and normal vehicles, thereby determining the characteristics corresponding to different fault types.

[0075] Step S200: Based on the features corresponding to each fault type and the sampled data corresponding to each feature, the target feature for each fault type is analyzed and obtained. The target feature refers to the most important feature with the highest correlation with the fault type, and can be one or more. After determining the features corresponding to each fault type, the server analyzes the actual sampled data for each feature to determine the target features for each fault type. This will then serve as a reference for revising policies for different fault types.

[0076] Step S300: Determine the control strategy for the power battery based on the target characteristics of each fault type. After determining the target characteristics for each fault type, the server weakens or strengthens the corresponding characteristics of the vehicle's current control strategy based on the target characteristics, and obtains a revised strategy as the new control strategy for the power battery. The revised strategy is imported into market vehicles to reduce the market failure rate. The obtained revised strategy can be used directly to update the control strategy of the power battery, or it can be used to perform a strategy evaluation on the revised strategy, and the revised strategy that passes the strategy evaluation is used to update the control strategy of the power battery.

[0077] Furthermore, there's more than one way to update the power battery control strategy based on a revised strategy. The server can determine the vehicle to which the revised strategy applies based on the revised strategy's optimization objective, combined with the vehicle's region of use or age. The server then sends all revised strategies associated with the target vehicle to the target vehicle to update the power battery control strategy. For example, if the revised strategy's optimization objective is charging current, the revised strategy is considered applicable to all vehicles in the project and implemented accordingly. If the revised strategy's optimization objective is thermal management enablement, the revised strategy is applicable only to vehicles used in tropical regions. Furthermore, upon receiving an update request from a target vehicle, the server can filter the revised strategies associated with the target vehicle and send them to the target vehicle to update the power battery control strategy.

[0078] The above-mentioned power battery control strategy determination method obtains sampled data from the power battery and determines the characteristics corresponding to different fault types based on the sampled data. Based on the characteristics corresponding to each fault type and the sampled data corresponding to each characteristic, the target characteristics of each fault type are analyzed. Based on the target characteristics of each fault type, a power battery control strategy is generated for updating the power battery control strategy. This ensures that the power battery control strategy is more consistent with actual operating conditions, making the control strategy more accurate, reducing the probability of faults, and extending the life of the power battery.

[0079] In one embodiment, as shown in FIG3 , step S200 includes step S210 and step S220 .

[0080] Step S210: Based on the sampling data of the features corresponding to each fault type, the features corresponding to each fault type are screened to obtain the initial target features associated with each fault type. Specifically, after determining the features corresponding to each fault type, a machine learning algorithm (such as a random forest algorithm, an XGBoost algorithm, etc.) can be used in combination with the corresponding sampling data to screen the features, and obtain the core features associated with the fault type as the initial target features, so as to reduce the features with high linear correlation and avoid data overlap. It can be understood that for different fault types, the core features obtained by screening will also be different. Taking the high-charge and low-discharge fault as an example, the core features may include the change in the cell voltage ranking during charging, the current and voltage fitting K value during discharge (characterizing the difference in the internal parameters of the cell), the voltage difference during charging, etc.

[0081] Step S220: determining the target feature of each fault type based on the sampled data of the initial target feature associated with each fault type and the fault prediction model corresponding to each fault type.

[0082] Among them, after determining the initial target features associated with each fault type, the server can determine the training sample data in combination with the initial target features to perform model training and obtain the fault prediction model corresponding to each fault type. It is understood that the type of fault prediction model is not unique. For example, it can be selected but not limited to an integrated learning model. In practice, a specific model can be selected based on the amount of data. After determining the fault prediction model corresponding to each fault type, the target features of the corresponding fault type can be determined by combining the parameters of the fault prediction model. It is understood that in other embodiments, the fault prediction model corresponding to each fault type can be obtained by pre-training based on historical data. The server combines the sample data of the initial target features associated with each fault type and directly calls the corresponding fault prediction model to determine the target features of the corresponding fault type.

[0083] In this embodiment, initial target features associated with each fault type are extracted through feature screening to avoid overlapping features with high correlation affecting processing efficiency, and then the target features of each fault type are accurately obtained in combination with the fault prediction model.

[0084] In one embodiment, step S210 includes: preprocessing the sampling data of the features corresponding to each fault type to obtain the preprocessed sampling data of each feature; screening the features corresponding to each fault type based on the preprocessed sampling data of each feature to obtain the initial target features associated with each fault type.

[0085] It can be understood that there is no single way to pre-process the sampled data. In this embodiment, the pre-processing includes at least one of data cleaning and data completion. One or more of data cleaning and data completion can be selected for data pre-processing according to actual needs. Taking the simultaneous data cleaning and data completion as an example, data cleaning is to eliminate abnormal values ​​in the sampled data that exceed the set threshold. For example, if the sampled current data exceeds 2000A, it can be considered an abnormal value. Data completion is to perform analogical analysis in combination with other existing battery cell data, and to complete the invalid collected data through existing parameters. After performing data cleaning and data completion simultaneously, a fault feature database is constructed based on the sampled data of each feature after pre-processing, and the features corresponding to each fault type are extracted from the fault feature database for screening to obtain the initial target features associated with each fault type.

[0086] In this embodiment, the sampled data is first preprocessed to ensure the accuracy and completeness of the data, and then feature screening is performed to determine the initial target features associated with each fault type to improve the accuracy of feature association.

[0087] In one embodiment, as shown in FIG4 , step S220 includes step S221 , step S222 , and step S224 .

[0088] Step S221: adding labels to the sampled data of the initial target features associated with each fault type to generate training sample data for each fault type.

[0089] Among them, the labels include faulty device labels and normal device labels. The specific types of faulty device labels and normal device labels are not unique either, and can be numbers, symbols, or codes. In this embodiment, the faulty device label is 0, and the normal device label is 1. Specifically, after determining the initial target features associated with each fault type, the sampled data of the initial target features are labeled in combination with the data obtained from the faulty vehicle and the normal vehicle, and training sample data of each fault type are generated. Taking a certain fault type as an example, 10 initial target features related to the fault type are extracted to form a data set. The data set contains the sampled data of the 10 initial target features collected from the faulty vehicle and the normal vehicle. The label 1 is added to the sampled data of the normal vehicle, and the label 0 is added to the sampled data of the faulty vehicle, thereby obtaining 11 training sample data of the fault type.

[0090] Step S222: Model training is performed based on the training sample data for each fault type to obtain a fault prediction model corresponding to each fault type. Specifically, after obtaining the training sample data for each fault type, model training is performed based on the training sample data for each fault type, thereby establishing a corresponding fault prediction model for each fault type. The fault prediction model can be used to score the failure probability of vehicles on the market (e.g., a failure probability of 0.5, 0.8, etc.) to assess the vehicle failure probability.

[0091] Step S224: Determine the target feature of each fault type based on the fault prediction model corresponding to each fault type. After determining the fault prediction model corresponding to each fault type, directly determine the target feature of the corresponding fault type in combination with the parameters of the fault prediction model.

[0092] In this embodiment, labels are added to the sampled data of the initial target features associated with each fault type as training sample data for model training to ensure the accuracy of the fault prediction model.

[0093] In one embodiment, the fault prediction model is an ensemble learning model. Step S224 includes determining target features for each fault type based on the feature importance of the ensemble learning model corresponding to each fault type. Based on the feature importance (Shapley value, etc.) provided by the ensemble learning model, one or more important features (e.g., current, voltage, etc.) associated with the corresponding fault type can be obtained as the target features for the corresponding fault type. Determining the target features for each fault type directly based on the feature importance of the ensemble learning model is simple, fast, and highly reliable.

[0094] Furthermore, in one embodiment, continuing to refer to FIG4 , after step S222, step S220 further includes step S223: performing model evaluation on the fault prediction model, and using the fault prediction model that passes the evaluation as the final fault prediction model. Similarly, the method of model evaluation is not unique and can be set according to actual needs. For example, the model evaluation may include one or more of business evaluation and rationality evaluation. The fault prediction model that passes the evaluation is used as the final fault prediction model, and is used as the target feature for subsequently determining the relevant fault type. Correspondingly, in step S224, the target feature of the corresponding fault type is determined based on the final fault prediction model. In this embodiment, the trained fault prediction model is model evaluated, and the one that passes the evaluation is used as the final fault prediction model, which is more in line with actual needs. It can be understood that in other embodiments, the fault prediction model may not be model evaluated, and all the fault prediction models obtained through training are subsequently used to determine the target features of the corresponding fault type.

[0095] In one embodiment, step S223 includes: performing a business evaluation on the fault prediction model according to a preset business standard; performing a rationality evaluation on the fault prediction model that passes the business evaluation according to a preset fault tree analysis result, and using the fault prediction model that passes the rationality evaluation as the final fault prediction model.

[0096] Specifically, business evaluation criteria can be pre-set to assess whether the fault prediction model meets business requirements, such as cost compliance. Taking the example of a fault prediction model using an ensemble learning model, after the ensemble learning model passes the business evaluation, the rationality of the target features is confirmed by combining the feature importance determined by the ensemble learning model with the FTA analysis results. For example, if the FTA analysis results indicate that overcharging is more important than floating charging, the ensemble learning model should reach a similar conclusion. After both the business evaluation and rationality assessment pass, the fault prediction model is saved as the final fault prediction model.

[0097] In this embodiment, the fault prediction model is subjected to both business evaluation and rationality evaluation, and a model that passes both evaluations is used as the final fault prediction model, thereby improving the accuracy of the model evaluation.

[0098] In one embodiment, as shown in FIG5 , step S300 includes steps S310 to S330 .

[0099] Step S310: Generate a preliminary revised strategy for the power battery based on the target characteristics of each fault type. Specifically, the correspondence between the type of target characteristic and the adjustment method (weakening or enhancing) can be saved in advance. For example, if the target characteristic is current, it can be considered that the larger the current, the less safe it is, and the adjustment method is to reduce the current. After determining the target characteristics of the fault type, the strategy is revised on the basis of the original strategy of the vehicle in combination with the target characteristics, and a preliminary revised strategy for the power battery is generated by weakening or enhancing the corresponding characteristics (such as charging rate, charging switching point, thermal management start-up conditions, balancing management conditions, etc.). Taking the revision of the charging control strategy as an example, the target characteristic corresponding to the charging fault is the charging rate, then the parameters in the strategy are adjusted to reduce the charging rate to obtain a preliminary revised strategy.

[0100] Step S320: Simulate device operation according to the preliminary revised strategy to obtain simulated sampled data. Specifically, simulate vehicle operation according to the revised preliminary revised strategy and obtain simulated sampled data related to the corresponding fault type to obtain a changed dataset, so that the changed dataset can simulate the changed preliminary revised strategy.

[0101] Step S330: A strategy evaluation is performed based on the simulated sampling data and the fault prediction model to determine the power battery control strategy. Specifically, data from the fault type-related change dataset is imported into the corresponding fault prediction model. The model's prediction results are evaluated, for example, to determine the cost of the strategy change and the potential reduction in market failure rates. The impact of the preliminary revised strategy on faults is analyzed to determine its reasonableness. The strategy that passes the evaluation is then selected as the power battery control strategy.

[0102] In this embodiment, after generating a preliminary revised strategy for the power battery based on the target characteristics of the fault type, the strategy is evaluated by simulating device operation to ensure that the control strategy for the power battery meets actual requirements.

[0103] In one embodiment, the strategy evaluation includes a failure rate evaluation and / or a business evaluation. The revised strategy can be selected to undergo a failure rate evaluation and / or a business evaluation based on actual needs. Specifically, after the data in the change data set related to the fault type is imported into the corresponding fault prediction model, the failure rate is determined to be reduced based on the prediction results of the fault prediction model. The preliminary revised strategy can be considered feasible and the failure rate evaluation is passed. Furthermore, a business demand analysis is performed on the preliminary revised strategy that has passed the failure rate evaluation in combination with preset business standards. If the preliminary revised strategy can reduce the failure rate and the cost is within a controllable range, the strategy revision is considered reasonable and can be used as a control strategy for the power battery.

[0104] Traditional power battery control strategies are developed based on laboratory simulation and testing of the equipment used in power batteries. This approach has the following problems:

[0105] 1. Laboratory simulation and vehicle testing are difficult to simulate real working conditions;

[0106] 2. Only the initial stage of the power battery is simulated. This method is difficult to simulate after the power battery ages.

[0107] 3. If testing the aged power battery, this method is extremely costly;

[0108] 4. A unified strategy is used for the same project, and the strategy cannot be revised for a single vehicle.

[0109] The power battery control strategy determination method provided in this application is based on big data to revise the power battery control strategy of in-service new energy vehicles. By revising the strategy of in-service new energy vehicles, the control strategy is made more precise, the probability of failure is reduced, and the power battery life is extended without compromising the user experience. In addition, it can also be used to guide the formulation of power battery control strategies in the incremental market, reduce the cost of simulated vehicle testing, and shorten the vehicle development cycle. It can also be used to customize the control strategy for vehicles in the existing market to achieve differentiated control of the vehicles.

[0110] The following example illustrates the revised charging rate: The original charging strategy for electric vehicles assumes a stepped charging method, with the current design shown in Figure 6. Based on the charging conditions, operating condition characteristics are extracted and modeled. In a specific project, there are nine faulty vehicles. In the fault prediction model, the fault risk threshold is determined as score_threshold. The final ranking of the faulty vehicles is shown in Figure 7.

[0111] The charging strategy is changed by using the power battery control strategy determination method provided by this application as shown in Figure 8. The charging current before and after the change has almost no effect on the charging time (the current mean remains almost unchanged). New features are re-extracted for the new strategy, and the new features are re-predicted. The final ranking of faulty vehicles is shown in Figure 9. The strategy change has reduced the failure rate of most vehicles, and the number of vehicles that require market investigation (exceeding the risk threshold score_threshold) has been greatly reduced, reducing the cost of troubleshooting.

[0112] In one embodiment, as shown in FIG10 , a device control method is further provided, including:

[0113] Step S410: Determine a target revision strategy from the revision strategies of the power battery. The revision strategy of the power battery is obtained according to the above-mentioned power battery control strategy determination method. Specifically, after obtaining the revision strategy for each fault type, the server determines the target revision strategy from the revision strategies.

[0114] Step S420: Send the target revised policy to the target device to update the control policy of the power battery of the target device. The server sends the target revised policy to the target vehicle to update the control policy of the power battery of the target vehicle.

[0115] In one embodiment, step S410 includes: determining target devices associated with each revision policy based on an optimization goal of the revision policy; and using all revision policies associated with the target device as target revision policies. Based on the optimization goal of the revision policy, all revision policies associated with the target device are used as target revision policies to proactively update the revision policies of the target device.

[0116] In another embodiment, step S410 includes: obtaining, from the power battery revision policy, a revision policy associated with the optimization target and the target device based on an update request sent by the target device, as the target revision policy. The policy update operation can be initiated by sending an update request via the target device based on actual needs.

[0117] It can be understood that the specific steps of the above-mentioned device control method are explained in detail in the above-mentioned power battery control strategy determination method, and will not be repeated here.

[0118] The above-mentioned device control method generates a revised strategy for the power battery according to the target characteristics of each fault type, so as to update the control strategy of the power battery, thereby making the control strategy of the power battery more in line with the actual working conditions, making the control strategy more accurate, reducing the probability of fault occurrence, and extending the life of the power battery.

[0119] In one embodiment, as shown in FIG11 , a device control method is further provided, including:

[0120] Step S510: obtaining a revised strategy of the power battery to be updated; the revised strategy of the power battery to be updated is obtained according to the above-mentioned power battery control strategy determination method.

[0121] Step S520: Update the control strategy of the power battery of the device according to the revised strategy of the power battery to be updated. Specifically, the target vehicle obtains the revised strategy of the power battery to be updated issued by the server and updates the control strategy of the power battery of the vehicle.

[0122] In one embodiment, step S510 includes receiving a target revision policy sent by a server as the revision policy for the power battery to be updated; the target revision policy is obtained by the server based on all revision policies associated with a target device, and the target device is determined based on an optimization target of the revision policy. Based on the optimization target of the revision policy, all revision policies associated with the target device are used as the target revision policy, thereby actively updating the revision policy of the target device.

[0123] In another embodiment, step S510 includes: sending an update request to a server; receiving a target revision policy returned by the server in response to the update request as the revision policy of the power battery to be updated; the target revision policy is obtained by the server, based on the update request, by obtaining the revision policy associated with the optimization target and the target device from the revision policy of the power battery. The update request can be sent via the target device to initiate the policy update operation as needed.

[0124] It can be understood that the specific steps of the above-mentioned device control method are explained in detail in the above-mentioned power battery control strategy determination method, and will not be repeated here.

[0125] The above-mentioned device control method generates a revised strategy for the power battery according to the target characteristics of each fault type, so as to update the control strategy of the power battery, thereby making the control strategy of the power battery more in line with the actual working conditions, making the control strategy more accurate, reducing the probability of fault occurrence, and extending the life of the power battery.

[0126] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0127] Based on the same inventive concept, embodiments of the present application also provide a power battery control strategy determination device for implementing the power battery control strategy determination method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more power battery control strategy determination device embodiments provided below can be found in the limitations of the power battery control strategy determination method described above and will not be repeated here.

[0128] In one embodiment, a power battery control strategy determination device is further provided, wherein the control strategy may be a charging control strategy, a thermal management strategy, a charge-discharge balancing control strategy, etc. As shown in FIG12 , the device includes: a data acquisition module 100, a data analysis module 200, and a strategy revision module 300, wherein:

[0129] The data acquisition module 100 is used to acquire sampled data collected from the power battery and determine features corresponding to different fault types based on the sampled data.

[0130] The data analysis module 200 is used to analyze and obtain target features of each fault type based on the features corresponding to each fault type and the sampling data corresponding to each feature.

[0131] The strategy revision module 300 is used to determine the control strategy of the power battery according to the target characteristics of each fault type.

[0132] In one embodiment, the data acquisition module 100 performs a difference analysis on the sampled data collected from the power battery, and determines the characteristics corresponding to different fault types according to the difference analysis results.

[0133] In one embodiment, the data analysis module 200 screens the features corresponding to each fault type based on the sampling data of the features corresponding to each fault type to obtain the initial target features associated with each fault type; and determines the target features of each fault type based on the sampling data of the initial target features associated with each fault type and the fault prediction model corresponding to each fault type.

[0134] In one embodiment, the data analysis module 200 preprocesses the sampling data of the features corresponding to each fault type to obtain the preprocessed sampling data of each feature; based on the preprocessed sampling data of each feature, the features corresponding to each fault type are screened to obtain the initial target features associated with each fault type.

[0135] In one embodiment, the data analysis module 200 adds labels to the sampling data of the initial target features associated with each fault type to generate training sample data for each fault type, where the labels include faulty device labels and normal device labels; performs model training based on the training sample data for each fault type to obtain a fault prediction model corresponding to each fault type; and determines the target features of each fault type based on the fault prediction model corresponding to each fault type.

[0136] In one embodiment, the data analysis module 200 performs model evaluation on the fault prediction model, and uses the fault prediction model that passes the evaluation as the final fault prediction model.

[0137] In one embodiment, the data analysis module 200 performs a business evaluation on the fault prediction model according to a preset business standard; performs a rationality evaluation on the fault prediction model that passes the business evaluation according to a preset fault tree analysis result, and uses the fault prediction model that passes the rationality evaluation as the final fault prediction model.

[0138] In one embodiment, the data analysis module 200 determines the target feature of each fault type according to the feature importance of the ensemble learning model corresponding to each fault type.

[0139] In one embodiment, the strategy revision module 300 generates a preliminary revised strategy for the power battery based on the target characteristics of each fault type; simulates device operation according to the preliminary revised strategy to obtain simulated sampling data; and performs strategy evaluation based on the simulated sampling data and the fault prediction model to obtain a control strategy for the power battery.

[0140] Each module in the above-mentioned power battery control strategy determination device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor of the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0141] In one embodiment, as shown in FIG13 , a device control apparatus is further provided, including: a policy determination module 410 and a policy sending module 420 , wherein:

[0142] The strategy determination module 410 is used to determine a target revision strategy from the revision strategies of the power battery; the revision strategy of the power battery is obtained according to the power battery control strategy determination device described above.

[0143] The policy sending module 420 is configured to send the target revised policy to the target device to update the control policy of the power battery of the target device.

[0144] In one embodiment, the policy determination module 410 determines the target device associated with each revision policy according to the optimization target of the revision policy, and uses all revision policies associated with the target device as target revision policies.

[0145] In one embodiment, the policy determination module 410 obtains the revision policy associated with the optimization target and the target device from the revision policy of the power battery according to the update request sent by the target device, as the target revision policy.

[0146] It can be understood that the specific principles of the above-mentioned equipment control device have been explained in the above-mentioned power battery control strategy determination device, and will not be repeated here.

[0147] In one embodiment, as shown in FIG14 , a device control apparatus is further provided, including: a policy acquisition module 510 and a policy update module 520 , wherein:

[0148] The strategy acquisition module 510 is used to acquire the revised strategy of the power battery to be updated; the revised strategy of the power battery to be updated is obtained by the power battery control strategy determination device mentioned above.

[0149] The strategy updating module 520 is configured to update the control strategy of the power battery of the device according to the revised strategy of the power battery to be updated.

[0150] In one embodiment, the policy acquisition module 510 receives the target revision policy sent by the server as the revision policy of the power battery to be updated; the target revision policy is obtained by the server based on all revision policies associated with the target device, and the target device is determined based on the optimization target of the revision policy.

[0151] In one embodiment, the policy acquisition module 510 sends an update request to the server; receives the target revision policy returned by the server in response to the update request as the revision policy of the power battery to be updated; the target revision policy is obtained by the server according to the update request, obtaining the revision policy associated with the optimization target and the target device from the revision policy of the power battery.

[0152] It can be understood that the specific principles of the above-mentioned equipment control device have been explained in the above-mentioned power battery control strategy determination device, and will not be repeated here.

[0153] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 15. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for determining a power battery control strategy or a device control method is implemented.

[0154] Those skilled in the art will understand that the structure shown in FIG15 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0156] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0157] In one embodiment, a computer program product is also provided, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0158] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0159] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0160] The above embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for determining a power battery control strategy, characterized in that: include: Acquire sampling data collected from the power battery, and determine characteristics corresponding to different fault types based on the sampling data; Analyze and obtain target features of each fault type based on the features corresponding to each fault type and the sampling data corresponding to each feature; Determine the control strategy of the power battery based on the target characteristics of each fault type.

2. The method according to claim 1, characterized in that Determining features corresponding to different fault types based on the sampled data includes: Perform difference analysis on the sampled data collected from the power battery, and determine the characteristics corresponding to different fault types based on the difference analysis results.

3. The method according to claim 2, characterized in that The difference analysis includes at least one of statistical feature analysis, cross-feature analysis between variables, and time series trend change feature analysis.

4. The method according to claim 1, wherein The target features of each fault type are obtained by analyzing the features corresponding to each fault type and the sampling data corresponding to each feature, including: Based on the sampling data of the features corresponding to each fault type, the features corresponding to each fault type are screened to obtain the initial target features associated with each fault type; The target feature of each fault type is determined based on the sampling data of the initial target feature associated with each fault type and the fault prediction model corresponding to each fault type.

5. The method according to claim 4, characterized in that The sampling data of the features corresponding to each fault type is used to screen the features corresponding to each fault type to obtain the initial target features associated with each fault type, including: Preprocessing the sampling data of the features corresponding to each fault type to obtain the preprocessed sampling data of each feature; According to the preprocessed sampling data of each feature, the features corresponding to each fault type are screened to obtain the initial target features associated with each fault type.

6. The method according to claim 5, characterized in that The preprocessing includes at least one of data cleaning and data completion.

7. The method according to claim 4, characterized in that The method of determining the target feature of each fault type based on the sampled data of the initial target feature associated with each fault type and the fault prediction model corresponding to each fault type includes: Add labels to the sampled data of the initial target features associated with each fault type to generate training sample data for each fault type, wherein the labels include a faulty device label and a normal device label; Perform model training based on the training sample data of each fault type to obtain the fault prediction model corresponding to each fault type; According to the fault prediction model corresponding to each fault type, the target characteristics of each fault type are determined.

8. The method according to claim 7, characterized in that After the model training is performed based on the training sample data of each fault type to obtain the fault prediction model corresponding to each fault type, the method further includes: performing model evaluation on the fault prediction model, and using the fault prediction model that passes the evaluation as the final fault prediction model.

9. The method according to claim 8, characterized in that The method of performing model evaluation on the fault prediction model and using the fault prediction model that passes the evaluation as the final fault prediction model includes: Conduct business evaluation of the fault prediction model based on preset business standards; According to the preset fault tree analysis results, the fault prediction model that passes the business evaluation is evaluated for rationality, and the fault prediction model that passes the rationality evaluation is used as the final fault prediction model.

10. The method according to claim 7, characterized in that The fault prediction model is an integrated learning model; the target features of each fault type are determined based on the fault prediction model corresponding to each fault type, including: According to the feature importance of the ensemble learning model corresponding to each fault type, the target feature of each fault type is determined.

11. The method according to claim 4, characterized in that Determining the control strategy of the power battery according to the target characteristics of each fault type includes: Generate a preliminary revision strategy for the power battery based on the target characteristics of each fault type; simulating equipment operation according to the preliminary revised strategy to obtain simulated sampling data; A strategy evaluation is performed based on the simulated sampling data and the fault prediction model to obtain a control strategy for the power battery.

12. The method according to claim 11, characterized in that The policy evaluation includes failure rate evaluation and / or service evaluation.

13. The method according to any one of claims 1 to 12, characterized in that: The sampled data includes at least one of current data, temperature data, voltage data and insulation data.

14. A device control method, characterized in that: include: Determining a target revision strategy from a revision strategy of a power battery; wherein the revision strategy of the power battery is obtained according to the method according to any one of claims 1 to 13; The target revision strategy is sent to a target device to update a control strategy of a power battery of the target device.

15. The method according to claim 14, characterized in that Determining the target revision strategy from the revision strategy of the power battery includes: Determining target devices associated with each revision strategy according to the optimization goal of the revision strategy; All revision policies associated with the target device are used as target revision policies.

16. The method according to claim 14, characterized in that Determining the target revision strategy from the revision strategy of the power battery includes: According to the update request sent by the target device, a revision policy in which the optimization target is associated with the target device is obtained from the revision policy of the power battery as the target revision policy.

17. A device control method, characterized in that: include: Obtain the revision strategy of the power battery to be updated; The revision strategy of the power battery to be updated is obtained according to the method according to any one of claims 1 to 13; The control strategy of the power battery of the device is updated according to the revised strategy of the power battery to be updated.

18. The method according to claim 17, characterized in that The obtaining of the revision strategy of the power battery to be updated includes: The target revision strategy sent by the server is received as the revision strategy of the power battery to be updated; the target revision strategy is obtained by the server according to all revision strategies associated with the target device, and the target device is determined according to the optimization target of the revision strategy.

19. The method according to claim 17, wherein The obtaining of the revision strategy of the power battery to be updated includes: Send update request to the server; Receive the target revision policy returned by the server in response to the update request as the revision policy of the power battery to be updated; the target revision policy is obtained by the server according to the update request, obtaining the revision policy that associates the optimization target with the target device from the revision policy of the power battery.

20. A power battery control strategy determination device, characterized in that: include: A data acquisition module, configured to acquire sampled data collected from the power battery and determine characteristics corresponding to different fault types based on the sampled data; A data analysis module is used to analyze and obtain target features of each fault type based on the features corresponding to each fault type and the sampled data corresponding to each feature; The strategy revision module is used to determine the control strategy of the power battery according to the target characteristics of each fault type.

21. A device control device, characterized in that: include: a strategy determination module, configured to determine a target revision strategy from a revision strategy of a power battery; the revision strategy of the power battery is obtained by the apparatus according to claim 20; The strategy sending module is used to send the target revised strategy to the target device to update the control strategy of the power battery of the target device.

22. A device control device, characterized in that: include: A strategy acquisition module, used to obtain the revised strategy of the power battery to be updated; The revision strategy of the power battery to be updated is obtained by the device according to claim 20; The strategy update module is used to update the control strategy of the power battery of the device according to the revised strategy of the power battery to be updated.

23. 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 steps of the method according to any one of claims 1 to 19 are implemented.

24. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 19 are implemented.

25. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 19 are implemented.

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