Energy storage device energy supply mode control method, device, equipment and storage medium

Abstract

The present application relates to a method, apparatus, device, and storage medium for controlling the energy supply mode of an energy storage device, and relates to the field of energy storage technology. The present application utilizes a big data acquisition method to obtain power supply distribution setting parameters for energy storage devices within a sub-service area where a target energy storage device is located. The power supply distribution setting parameters are used to obtain a target control parameter dataset that matches the target operating parameters of the target energy storage device. The target control parameter dataset is then modified using a default numerical threshold range to form a recommended energy supply mode. The recommended energy supply mode is then sent to the target energy storage device to provide a setting service and reduce maintenance costs.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a method, device, computer device, and storage medium for controlling an energy supply mode of an energy storage device. Background Art

[0002] When current energy storage devices are used for individual users, the power supply distribution method for the energy storage devices is mostly executed according to the default method or set according to the needs of individual users. The results of the default method or self-setting may deviate from the actual power supply load, resulting in a mismatch between the power supply method and the power supply load, causing unreasonable energy distribution. Individual users may also adjust the settings multiple times and the results may be inappropriate, requiring separate setting services, resulting in increased maintenance costs. Summary of the Invention

[0003] Based on this, a method, device, computer device and storage medium for controlling the energy supply mode of an energy storage device are provided to solve the technical problem that the current energy storage device sets the energy supply mode separately, resulting in unreasonable settings for some users, requiring separate setting services, and increasing maintenance costs.

[0004] In one aspect, a method for controlling an energy storage device supply mode is provided, the method comprising:

[0005] Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load;

[0006] Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods;

[0007] Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0008] Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode;

[0009] In response to power supply allocation being set for the target energy storage device or when a power supply allocation mode of the target energy storage device does not match a power supply load, the recommended energy supply mode is sent to the target energy storage device.

[0010] In one embodiment, obtaining a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load includes:

[0011] Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location;

[0012] Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

[0013] In one embodiment, parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes:

[0014] parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings;

[0015] The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters;

[0016] The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0017] In one embodiment, the energy storage device energy supply mode control method further includes:

[0018] The collected power distribution setting parameters are formed into a training set;

[0019] Constructing an MLP model, inputting the training set into the MLP model for training until convergence;

[0020] Using the trained MLP model to form a recommended energy supply mode for the target energy storage device;

[0021] The performance of the recommended energy supply mode is evaluated, and the energy supply mode selection method is updated by obtaining the data distribution change detection result to control the update of the MLP model.

[0022] In one embodiment, forming a training set from the collected power distribution setting parameters includes:

[0023] Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix;

[0024] Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector;

[0025] The feature vector is used as a training set.

[0026] In one embodiment, the using the trained MLP model to form a recommended energy supply mode for the target energy storage device includes:

[0027] Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch;

[0028] Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result;

[0029] The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

[0030] In one embodiment, the energy storage device energy supply mode control method further includes:

[0031] Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use;

[0032] The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0033] In another aspect, a device for controlling an energy supply mode of an energy storage device is provided, the device comprising:

[0034] A service area division module is used to obtain a regional map of the energy storage device distribution and divide the regional map into multiple sub-service areas according to regional location and power supply load;

[0035] A power supply parameter acquisition module is configured to obtain the sub-service area where the target energy storage device is located as the target area, collect power supply distribution setting parameters of the energy storage device in the target area, and analyze the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply load at different time periods;

[0036] a target control parameter value acquisition module, configured to analyze the operating condition of the target energy storage device to obtain target operating condition parameters, and acquire a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0037] a module for forming a recommended energy supply mode, configured to obtain a default value threshold range of each control parameter according to the target operating condition parameter, modify the target control parameter data set according to the default value threshold range of each control parameter, form a recommended energy supply mode, and store the recommended energy supply mode;

[0038] The mode sending module is used to send the recommended energy supply mode to the target energy storage device in response to power supply distribution setting for the target energy storage device or when the power supply distribution mode of the target energy storage device does not match the power supply load.

[0039] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0040] Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load;

[0041] Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods;

[0042] Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0043] Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode;

[0044] In response to power supply allocation being set for the target energy storage device or a mismatch between the power supply allocation mode and the power supply load of the target energy storage device, the recommended energy supply mode is sent to the target energy storage device.

[0045] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0046] Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load;

[0047] Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods;

[0048] Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0049] Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode;

[0050] In response to power supply allocation being set for the target energy storage device or a mismatch between the power supply allocation mode and the power supply load of the target energy storage device, the recommended energy supply mode is sent to the target energy storage device.

[0051] The above-mentioned energy storage device power supply mode control method, device, computer device and storage medium obtain the power supply distribution setting parameters of the energy storage devices within the sub-service area where the target energy storage device is located by using big data collection methods, use the power supply distribution setting parameters to obtain a target control parameter data set that matches the target operating parameters of the target energy storage device, and use a default numerical threshold range to correct the target control parameter data set to form a recommended power supply mode. The recommended power supply mode is sent to the target energy storage device to provide setting services and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 This is a flow chart of a method for controlling an energy supply mode of an energy storage device in one embodiment of the present application;

[0054] Figure 2 A logic diagram for forming a training set from collected power distribution setting parameters in one embodiment of the present application;

[0055] Figure 3 This is a structural block diagram of an energy storage device energy supply mode control device in one embodiment of the present application;

[0056] Figure 4 This is a diagram of the internal structure of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] like Figure 1 As shown, an embodiment of the present invention creatively proposes a method for controlling the energy supply mode of an energy storage device, comprising the following steps:

[0059] Step S1, obtaining a regional map of energy storage device distribution, and dividing the regional map into multiple sub-service areas according to regional location and power supply load;

[0060] Step S2: obtaining a sub-service area where a target energy storage device is located as a target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads at different time periods;

[0061] Step S3, analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and the control parameter data set corresponding to the operating condition parameters;

[0062] Step S4, obtaining a default numerical threshold range of each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range of each control parameter to form a recommended energy supply mode and store it;

[0063] Step S5: In response to power supply allocation being set for the target energy storage device or a mismatch between the power supply allocation mode and the power supply load of the target energy storage device, the recommended energy supply mode is sent to the target energy storage device.

[0064] Among them, the power supply distribution setting parameters of the energy storage devices in the sub-service area where the target energy storage device is located are obtained by using a big data collection method, and the power supply distribution setting parameters are used to obtain a target control parameter data set that matches the target operating parameters of the target energy storage device. The target control parameter data set is corrected using a default numerical threshold range to form a recommended energy supply mode, and the recommended energy supply mode is sent to the target energy storage device to provide setting services and reduce maintenance costs.

[0065] Specifically, by obtaining the sub-service area where the target energy storage device is located as the target area, the power supply distribution setting parameters of the energy storage devices in the target area are collected. This can achieve a power load suitable for the corresponding geographical area and ensure overall power balance. In addition, the environment and electrical appliances in each region have similar settings, so the power supply method of the target energy storage device needs to be adjusted based on the sub-service area where the target energy storage device is located.

[0066] In this embodiment, obtaining a regional map of energy storage device distribution and dividing the regional map into multiple sub-service areas according to regional location and power supply load includes:

[0067] Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location;

[0068] Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

[0069] The chessboard partitioning method is suitable for evenly distributed traversals, reducing overlap between adjacent coordinate points. Specifically, the coordinate matrix is treated as a black and white chessboard and then grouped by color. For example, in the first traversal of the black grid, all black grids satisfy (i+j) mod 2 = 0. In the second traversal of the white grid, all white grids satisfy (i+j) mod 2 = 1. Further expansion into N groups can be achieved by dividing the grid according to (i+j) mod N, ensuring an even distribution of points within each group. Here, mod is the remainder calculation.

[0070] Among them, the grid block method is applicable to relatively regular matrices, such as m×n. Specifically, the coordinate matrix is divided into N groups of sub-blocks, and the size of each sub-block is approximately m×n.

[0071] Select sub-blocks in a checkerboard or hierarchical manner, i.e., regularly divide them into a grid numbered 1 to N. Number them in a "snake" or "spiral" fashion to reduce local deviations from adjacent blocks. Ensure that the centers of each sub-block are as evenly distributed as possible throughout the matrix.

[0072] In this embodiment, parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes:

[0073] parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings;

[0074] The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters;

[0075] The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0076] Understandably, the number and types of appliances used by users vary at different times, and power supply is divided into peak and off-peak periods, with electricity prices differing accordingly. Therefore, operating parameters are generated based on power supply loads at different times, combined with electricity price data for the corresponding periods. This accurately reflects actual power demand and the energy storage device's supply needs.

[0077] In this embodiment, the energy storage device energy supply mode control method further includes:

[0078] The collected power distribution setting parameters are formed into a training set;

[0079] Constructing an MLP model, inputting the training set into the MLP model for training until convergence;

[0080] Using the trained MLP model to form a recommended energy supply mode for the target energy storage device;

[0081] The performance of the recommended energy supply mode is evaluated, and the energy supply mode selection method is updated by obtaining the data distribution change detection result to control the update of the MLP model.

[0082] An MLP (Multilayer Perceptron) is a feedforward artificial neural network model consisting of an input layer, one or more hidden layers, and an output layer. Neurons in each layer are fully connected to neurons in the next layer, and activation functions introduce nonlinear transformations, enabling the learning of complex input-output relationships.

[0083] See also Figure 2 In this embodiment, forming a training set from the collected power distribution setting parameters includes:

[0084] Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix;

[0085] Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector;

[0086] The feature vector is used as a training set.

[0087] In this embodiment, the use of the trained MLP model to form a recommended energy supply mode for the target energy storage device includes:

[0088] Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch;

[0089] Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result;

[0090] The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

[0091] In this embodiment, the energy storage device energy supply mode control method further includes:

[0092] Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use;

[0093] The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0094] In the above-mentioned energy storage device power supply mode control method, the power supply distribution setting parameters of the energy storage devices within the sub-service area where the target energy storage device is located are obtained by using a big data acquisition method. The power supply distribution setting parameters are used to obtain a target control parameter data set that matches the target operating parameters of the target energy storage device. The target control parameter data set is corrected using a default numerical threshold range to form a recommended power supply mode. The recommended power supply mode is sent to the target energy storage device to provide a setting service and reduce maintenance costs.

[0095] In one embodiment, Figure 3 As shown, a device 10 for controlling energy storage device supply mode is provided, comprising: a service area division module 1, a power supply parameter acquisition module 2, a target control parameter value acquisition module 3, a recommended energy supply mode formation module 4, and a mode sending module 5.

[0096] The service area division module 1 is used to obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional location and power supply load.

[0097] The power supply parameter acquisition module 2 is used to obtain the sub-service area where the target energy storage device is located as the target area, collect the power supply distribution setting parameters of the energy storage device in the target area, and parse the power supply distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters. The operating parameters include the power supply load in different time periods.

[0098] The target control parameter value acquisition module 3 is used to analyze the operating condition of the target energy storage device to obtain target operating parameters, and obtain a target control parameter data set that matches the target operating parameters based on the operating parameters and the control parameter data set corresponding to the operating parameters.

[0099] The module 4 for forming the recommended energy supply mode is used to obtain a default numerical threshold range of each control parameter according to the target operating condition parameter, and to modify the target control parameter data set according to the default numerical threshold range of each control parameter to form and store the recommended energy supply mode.

[0100] The mode sending module 5 is configured to send the recommended energy supply mode to the target energy storage device in response to power supply allocation setting for the target energy storage device or when the power supply allocation mode of the target energy storage device does not match the power supply load.

[0101] In this embodiment, obtaining a regional map of energy storage device distribution and dividing the regional map into multiple sub-service areas according to regional location and power supply load includes:

[0102] Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location;

[0103] Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

[0104] In this embodiment, parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes:

[0105] parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings;

[0106] The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters;

[0107] The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0108] like Figure 2 、 Figure 3 As shown, in this embodiment, the energy storage device supply mode control device 10 further includes: a feature processing module 6, an MLP model module 7 and a continuous learning module 8.

[0109] The feature processing module 6 is used to: form a training set from the collected power supply distribution setting parameters;

[0110] The MLP model module 7 is used to: construct an MLP model, input the training set into the MLP model for training until convergence; and use the trained MLP model to form a recommended energy supply mode for the target energy storage device.

[0111] The continuous learning module 8 is used to perform performance evaluation on the recommended energy supply mode, update the energy supply mode selection method by obtaining the data distribution change detection result, and control the update of the MLP model.

[0112] In this embodiment, forming a training set from the collected power distribution setting parameters includes:

[0113] Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix;

[0114] Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector;

[0115] The feature vector is used as a training set.

[0116] In this embodiment, the use of the trained MLP model to form a recommended energy supply mode for the target energy storage device includes:

[0117] Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch;

[0118] Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result;

[0119] The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

[0120] In this embodiment, the energy storage device energy supply mode control method further includes:

[0121] Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use;

[0122] The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0123] In the above-mentioned energy storage device power supply mode control device, the power supply distribution setting parameters of the energy storage devices within the sub-service area where the target energy storage device is located are obtained by using a big data acquisition method. The power supply distribution setting parameters are used to obtain a target control parameter data set that matches the target operating parameters of the target energy storage device. The target control parameter data set is corrected using a default numerical threshold range to form a recommended power supply mode. The recommended power supply mode is sent to the target energy storage device to provide setting services and reduce maintenance costs.

[0124] For the specific definition of the energy storage device supply mode control device, please refer to the definition of the energy storage device supply mode control method above, which will not be repeated here. The various modules in the above-mentioned energy storage device supply mode control device can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0125] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0126] Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load;

[0127] Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods;

[0128] Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0129] Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode;

[0130] In response to power supply allocation being set for the target energy storage device or when a power supply allocation mode of the target energy storage device does not match a power supply load, the recommended energy supply mode is sent to the target energy storage device.

[0131] In one embodiment, the computer program further performs the following steps when executed by a processor:

[0132] The step of obtaining a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load includes:

[0133] Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location;

[0134] Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

[0135] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0136] The step of parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes:

[0137] parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings;

[0138] The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters;

[0139] The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0140] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0141] The collected power distribution setting parameters are formed into a training set;

[0142] Constructing an MLP model, inputting the training set into the MLP model for training until convergence;

[0143] Using the trained MLP model to form a recommended energy supply mode for the target energy storage device;

[0144] The performance of the recommended energy supply mode is evaluated, and the energy supply mode selection method is updated by obtaining the data distribution change detection result to control the update of the MLP model.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0146] The forming of a training set from the collected power supply distribution setting parameters includes:

[0147] Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix;

[0148] Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector;

[0149] The feature vector is used as a training set.

[0150] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0151] The forming of a recommended energy supply mode for the target energy storage device using the trained MLP model includes:

[0152] Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch;

[0153] Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result;

[0154] The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

[0155] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0156] Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use;

[0157] The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0158] For the specific limitations on the steps implemented when the computer program is executed by the processor, please refer to the limitations on the method for controlling the energy supply mode of the energy storage device above, which will not be repeated here.

[0159] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. 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 energy storage device power supply mode control data. The network 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 controlling the power supply mode of an energy storage device is implemented.

[0160] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part 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 component arrangement.

[0161] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0162] Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load;

[0163] Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods;

[0164] Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0165] Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode;

[0166] In response to power supply allocation being set for the target energy storage device or when a power supply allocation mode of the target energy storage device does not match a power supply load, the recommended energy supply mode is sent to the target energy storage device.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] The step of obtaining a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load includes:

[0169] Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location;

[0170] Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

[0171] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0172] The step of parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes:

[0173] parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings;

[0174] The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters;

[0175] The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0176] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0177] The collected power distribution setting parameters are formed into a training set;

[0178] Constructing an MLP model, inputting the training set into the MLP model for training until convergence;

[0179] Using the trained MLP model to form a recommended energy supply mode for the target energy storage device;

[0180] The performance of the recommended energy supply mode is evaluated, and the energy supply mode selection method is updated by obtaining the data distribution change detection result to control the update of the MLP model.

[0181] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0182] The forming of a training set from the collected power supply distribution setting parameters includes:

[0183] Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix;

[0184] Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector;

[0185] The feature vector is used as a training set.

[0186] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0187] The forming of a recommended energy supply mode for the target energy storage device using the trained MLP model includes:

[0188] Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch;

[0189] Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result;

[0190] The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

[0191] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0192] Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use;

[0193] The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0194] For the specific limitations on the steps implemented when the processor executes the computer program, please refer to the limitations on the method for controlling the energy supply mode of the energy storage device above, which will not be repeated here.

[0195] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0196] Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load;

[0197] Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods;

[0198] Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters;

[0199] Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode;

[0200] In response to power supply allocation being set for the target energy storage device or when a power supply allocation mode of the target energy storage device does not match a power supply load, the recommended energy supply mode is sent to the target energy storage device.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0202] The step of obtaining a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load includes:

[0203] Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location;

[0204] Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

[0205] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0206] The step of parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes:

[0207] parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings;

[0208] The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters;

[0209] The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0210] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0211] The collected power distribution setting parameters are formed into a training set;

[0212] Constructing an MLP model, inputting the training set into the MLP model for training until convergence;

[0213] Using the trained MLP model to form a recommended energy supply mode for the target energy storage device;

[0214] The performance of the recommended energy supply mode is evaluated, and the energy supply mode selection method is updated by obtaining the data distribution change detection result to control the update of the MLP model.

[0215] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0216] The forming of a training set from the collected power supply distribution setting parameters includes:

[0217] Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix;

[0218] Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector;

[0219] The feature vector is used as a training set.

[0220] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0221] The forming of a recommended energy supply mode for the target energy storage device using the trained MLP model includes:

[0222] Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch;

[0223] Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result;

[0224] The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

[0225] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0226] Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use;

[0227] The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0228] For the specific limitations on the steps implemented when the computer program is executed by the processor, please refer to the limitations on the method for controlling the energy supply mode of the energy storage device above, which will not be repeated here.

[0229] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0230] 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.

[0231] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for controlling energy supply mode of an energy storage device, characterized in that: include: Obtaining a regional map of energy storage device distribution, and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load; Obtaining the sub-service area where the target energy storage device is located as the target area, collecting power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply loads in different time periods; Analyzing the operating condition of the target energy storage device to obtain target operating condition parameters, and obtaining a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters; Obtaining a default numerical threshold range for each control parameter according to the target operating condition parameter, and modifying the target control parameter data set according to the default numerical threshold range for each control parameter to form and store a recommended energy supply mode; In response to power supply allocation being set for the target energy storage device or when a power supply allocation mode of the target energy storage device does not match a power supply load, the recommended energy supply mode is sent to the target energy storage device.

2. The energy storage device energy supply mode control method according to claim 1, characterized in that: The step of obtaining a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional location and power supply load includes: Dividing the regional map into a plurality of geographical distribution areas of equal area by a chessboard block method or a grid block method according to the regional location; Obtain power supply load distribution information of two adjacent geographical distribution areas, use the first distance area between the two adjacent geographical distribution areas as a transition area, merge the same power supply load areas into the geographical distribution area with the largest proportion within the transition area, and form multiple sub-service areas.

3. The energy storage device energy supply mode control method according to claim 1, characterized in that: The step of parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters includes: parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, the numerical features including electrical load data and power distribution setting data, and the category features including an energy supply mode based on electrical load settings; The electrical load data is counted according to time to form power supply loads in different time periods, and the power supply loads in different time periods are combined with electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the energy supply mode are combined to form a control parameter data set corresponding to the operating condition parameters.

4. The energy storage device energy supply mode control method according to claim 3, characterized in that: Also includes: The collected power distribution setting parameters are formed into a training set; Constructing an MLP model, inputting the training set into the MLP model for training until convergence; Using the trained MLP model to form a recommended energy supply mode for the target energy storage device; The performance of the recommended energy supply mode is evaluated, and the energy supply mode selection method is updated by obtaining the data distribution change detection result to control the update of the MLP model.

5. The energy storage device energy supply mode control method according to claim 4, characterized in that: The forming of a training set from the collected power supply distribution setting parameters includes: Acquire numerical features and categorical features of the power distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the categorical features to convert them into a one-hot encoding matrix; Calculating derived features based on the normalization result and the one-hot encoding matrix, merging the features obtained by the calculation to ensure feature order consistency, and generating a feature vector; The feature vector is used as a training set.

6. The energy storage device energy supply mode control method according to claim 5, characterized in that: The forming of a recommended energy supply mode for the target energy storage device using the trained MLP model includes: Inputting the feature vector into the input layer of the MLP model, and using a feature separator to classify the feature vector into a general load feature branch and a dedicated load feature branch; Inputting the general load feature branch and the special load feature branch into the feature merging layer of the MLP model to perform feature merging, and obtaining electrical appliance load prediction data according to the feature merging result; The electrical appliance load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical appliance load prediction data is configured to form a recommended energy supply mode.

7. The energy storage device energy supply mode control method according to claim 1, characterized in that: Also includes: Obtaining expected operating condition parameters of the target energy storage device, setting a plurality of selectable energy supply modes according to the expected operating condition parameters, and setting the target energy storage device to select an adaptive energy supply mode according to the real-time operating condition parameters when in use; The target energy storage device power supply time period and the external network power supply time period are set based on the electricity price data, and the corresponding energy supply mode is selected according to the time period. The energy supply mode includes a low-voltage load power supply mode, a high-voltage load power supply mode, a charging mode, and a sleep standby mode. The charging mode is to charge the target energy storage device during the low electricity price period, calculate the charging time according to the remaining power of the target energy storage device, and select the start time of the charging mode. The charging mode or the sleep standby mode corresponds to the time period of external network power supply.

8. A device for controlling energy supply mode of an energy storage device, characterized in that: The device comprises: A service area division module is used to obtain a regional map of the energy storage device distribution and divide the regional map into multiple sub-service areas according to regional location and power supply load; A power supply parameter acquisition module is configured to obtain the sub-service area where the target energy storage device is located as the target area, collect power supply distribution setting parameters of the energy storage device in the target area, and analyze the power supply distribution setting parameters to obtain operating condition parameters and a control parameter data set corresponding to the operating condition parameters, wherein the operating condition parameters include power supply load at different time periods; a target control parameter value acquisition module, configured to analyze the operating condition of the target energy storage device to obtain target operating condition parameters, and acquire a target control parameter data set that matches the target operating condition parameters based on the operating condition parameters and a control parameter data set corresponding to the operating condition parameters; a module for forming a recommended energy supply mode, configured to obtain a default value threshold range of each control parameter according to the target operating condition parameter, modify the target control parameter data set according to the default value threshold range of each control parameter, form a recommended energy supply mode, and store the recommended energy supply mode; The mode sending module is used to send the recommended energy supply mode to the target energy storage device in response to power supply distribution setting for the target energy storage device or when the power supply distribution mode of the target energy storage device does not match the power supply load.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are 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 steps of the method according to any one of claims 1 to 7 are implemented.

Images