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

Abstract

The invention relates to an energy storage equipment energy supply mode control method and device, equipment and a storage medium, and relates to the technical field of energy storage. The power supply distribution setting parameters of the energy storage equipment in the sub-service area where the target energy storage equipment is located are obtained in a big data collection mode, and the target control parameter data set matched with the target working condition parameters of the target energy storage equipment is obtained through the power supply distribution setting parameters; and the target control parameter data set is corrected by adopting 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 equipment to provide setting service, so that the maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a method, device, computer equipment 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 by default 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 but 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 equipment and storage medium for controlling the power supply mode of an energy storage device are provided to solve the technical problem that the current energy storage device sets the power supply mode separately, resulting in unreasonable settings for some users, requiring separate setting services, and causing increased maintenance costs.

[0004] On the one hand, a method for controlling an energy storage device energy supply mode is provided, the method comprising: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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.

[0005] In one embodiment, 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 locations and power supply loads includes: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to form multiple sub-service areas.

[0006] In one embodiment, the step of parsing the power distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters comprises: Parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0007] In one embodiment, the energy storage device energy supply mode control method further includes: The collected power distribution setting parameters are formed into a training set; Constructing an MLP model, and 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.

[0008] In one embodiment, forming a training set from the collected power distribution setting parameters comprises: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate a feature vector; The feature vector is used as a training set.

[0009] In one embodiment, the using the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical load prediction data is configured to form a recommended energy supply mode.

[0010] In one embodiment, the energy storage device energy supply mode control method further includes: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and the charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0011] On the other hand, a device for controlling energy supply mode of an energy storage device is provided, the device comprising: A service area division module 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; A power supply parameter acquisition module 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 analyze the power supply distribution setting parameters to obtain operating parameters and control parameter data sets corresponding to the operating parameters, wherein the operating parameters include power supply loads in different time periods; A target control parameter value acquisition module, used to analyze the operating condition of the target energy storage device to obtain target operating condition parameters, and to obtain a target control parameter data set matching 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 is used to obtain a default value 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 value threshold range of each control parameter to form 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 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.

[0012] 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: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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.

[0013] 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: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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.

[0014] The energy storage device power supply mode control method, device, computer device and storage medium described above obtain the power supply distribution setting parameters of the energy storage device in the sub-service area where the target energy storage device is located by utilizing big data collection, obtain the target control parameter data set that matches the target operating condition parameters of the target energy storage device using the power supply distribution setting parameters, and use the default numerical threshold range to correct the target control parameter data set to form a recommended power supply mode, and send the recommended power supply mode to the target energy storage device to provide setting services and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 A schematic diagram of a flow chart of a method for controlling an energy supply mode of an energy storage device in one embodiment of the present application; Figure 2 A logic diagram for forming a training set with collected power distribution setting parameters in one embodiment of the present application; Figure 3 This is a structural block diagram of a device for controlling energy supply mode of an energy storage device in one embodiment of the present application; Figure 4 This is a diagram of the internal structure of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] like Figure 1 As shown, an embodiment of the present invention creatively proposes a method for controlling an energy supply mode of an energy storage device, comprising the following steps: 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; Step S2, obtaining the sub-service area where the target energy storage device is located as the target area, collecting the power supply distribution setting parameters of the energy storage device in the target area, parsing the power supply distribution setting parameters to obtain operating parameters and a control parameter data set corresponding to the operating parameters, wherein the operating parameters include power supply loads in different time periods; 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 a control parameter data set corresponding to the operating condition parameters; Step S4, obtaining a default value 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 value threshold range of each control parameter to form a recommended energy supply mode and store it; Step S5, 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, sending the recommended power supply mode to the target energy storage device.

[0019] Among them, the power supply distribution setting parameters of the energy storage equipment in the sub-service area where the target energy storage equipment is located are obtained by utilizing the 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 equipment. 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 equipment to provide setting services and reduce maintenance costs.

[0020] 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 device in the target area are collected, so that the power load suitable for the corresponding geographical area can be achieved and the overall power consumption balance can be ensured. In addition, the environment and electrical appliances in various regions 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.

[0021] In this embodiment, the obtaining of a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional locations and power supply loads includes: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to form multiple sub-service areas.

[0022] Among them, the chessboard block method is suitable for evenly distributed traversal to reduce the overlap of adjacent coordinate points. Specifically, the coordinate matrix is ​​regarded as a chessboard (black and white), and then grouped according to different colors. For example: the first time the black grid is traversed, all black grids are grids that satisfy (i+j) mod 2=0, the second time the white grid is traversed, all white grids are grids that satisfy (i+j) mod 2=1, and further expanded into N groups, which can be divided according to (i+j) mod N, and the points in each group are evenly distributed. Where mod is the remainder calculation.

[0023] Among them, the grid block method is suitable for a relatively regular matrix, 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.

[0024] Select sub-blocks in a chessboard or hierarchical manner, i.e., regularly divide into a grid, numbered 1 to N. Number in a "snake" or "spiral" manner to reduce local deviations from adjacent blocks. Make sure that the centers of each sub-block are as evenly distributed as possible throughout the matrix.

[0025] In this embodiment, 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, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0026] It is understandable that the number and types of electrical appliances used by users at different times vary, and power supply is divided into peak and off-peak periods, with different electricity price data at peak and off-peak periods. Therefore, the operating parameters are formed based on the power supply load at different times combined with the electricity price data at the corresponding time periods, which can accurately reflect the actual power demand and energy storage equipment supply demand.

[0027] In this embodiment, the energy storage device energy supply mode control method further includes: The collected power distribution setting parameters are formed into a training set; Constructing an MLP model, and 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.

[0028] MLP (Multilayer Perceptron) is a feedforward artificial neural network model consisting of an input layer, one or more hidden layers, and an output layer. The neurons in each layer are fully connected to the neurons in the next layer, and nonlinear transformations are introduced through activation functions, so that complex input and output relationships can be learned.

[0029] See also Figure 2 In this embodiment, forming a training set from the collected power distribution setting parameters includes: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate a feature vector; The feature vector is used as a training set.

[0030] In this embodiment, the use of the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical load prediction data is configured to form a recommended energy supply mode.

[0031] In this embodiment, the energy storage device energy supply mode control method further includes: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and the charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0032] In the above energy storage device energy supply mode control method, 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 utilizing 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 condition 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.

[0033] In one embodiment, Figure 3 As shown, a device 10 for controlling energy supply mode of energy storage equipment 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 forming module 4, and a mode sending module 5.

[0034] 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 locations and power supply loads.

[0035] 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, wherein the operating parameters include power supply loads in different time periods.

[0036] The target control parameter value acquisition module 3 is used to analyze the operating condition of the target energy storage device to obtain the target operating condition parameters, and to obtain the target control parameter data set matching the target operating condition parameters based on the operating condition parameters and the control parameter data set corresponding to the operating condition parameters.

[0037] The module 4 for forming the recommended energy supply mode is used to obtain a default value 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 value threshold range of each control parameter to form and store the recommended energy supply mode.

[0038] The mode sending module 5 is used 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.

[0039] In this embodiment, the obtaining of a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional locations and power supply loads includes: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to form multiple sub-service areas.

[0040] In this embodiment, 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, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0041] like Figure 2 , Figure 3 As shown, in this embodiment, the energy storage device supply mode control device 10 also includes: a feature processing module 6, an MLP model module 7 and a continuous learning module 8.

[0042] The feature processing module 6 is used to: form a training set from the collected power supply distribution setting parameters; 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.

[0043] 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 acquiring data distribution change detection results, and control the MLP model update.

[0044] In this embodiment, forming a training set from the collected power supply allocation setting parameters includes: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate a feature vector; The feature vector is used as a training set.

[0045] In this embodiment, the use of the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical load prediction data is configured to form a recommended energy supply mode.

[0046] In this embodiment, the energy storage device energy supply mode control method further includes: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and the charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0047] In the energy storage device supply mode control device, the power supply distribution setting parameters of the energy storage device 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 condition 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.

[0048] 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. Each module 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.

[0049] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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.

[0050] In one embodiment, the computer program further implements the following steps when executed by a processor: The obtaining of a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional locations and power supply loads comprises: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to form multiple sub-service areas.

[0051] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The step of parsing the power distribution setting parameters to obtain the operating condition parameters and the control parameter data set corresponding to the operating condition parameters comprises: Parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0052] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The collected power distribution setting parameters are formed into a training set; Constructing an MLP model, and 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.

[0053] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The step of forming a training set from the collected power supply distribution setting parameters comprises: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate a feature vector; The feature vector is used as a training set.

[0054] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The using the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical load prediction data is configured to form a recommended energy supply mode.

[0055] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and the charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0056] 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 mentioned above, which will not be repeated here.

[0057] 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 through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, 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 through a network connection. When the computer program is executed by the processor, a method for controlling the energy supply mode of an energy storage device is implemented.

[0058] 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0059] 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, wherein the processor implements the following steps when executing the computer program: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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.

[0060] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: The obtaining of a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional locations and power supply loads comprises: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to form multiple sub-service areas.

[0061] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: The step of parsing the power distribution setting parameters to obtain the operating condition parameters and the control parameter data set corresponding to the operating condition parameters comprises: Parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0062] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: The collected power distribution setting parameters are formed into a training set; Constructing an MLP model, and 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.

[0063] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: The step of forming a training set from the collected power supply distribution setting parameters comprises: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate a feature vector; The feature vector is used as a training set.

[0064] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: The using the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical load prediction data is configured to form a recommended energy supply mode.

[0065] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and the charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0066] 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 mentioned above, which will not be repeated here.

[0067] In one embodiment, 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: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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.

[0068] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The obtaining of a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional locations and power supply loads comprises: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to form multiple sub-service areas.

[0069] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The step of parsing the power distribution setting parameters to obtain the operating condition parameters and the control parameter data set corresponding to the operating condition parameters comprises: Parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output mode are combined to form a control parameter data set corresponding to the operating condition parameters.

[0070] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The collected power distribution setting parameters are formed into a training set; Constructing an MLP model, and 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.

[0071] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The step of forming a training set from the collected power supply distribution setting parameters comprises: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate a feature vector; The feature vector is used as a training set.

[0072] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: The using the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical load prediction data is configured to form a recommended energy supply mode.

[0073] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and the charging mode or the sleep standby mode corresponds to the time period of external network power supply.

[0074] 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 mentioned above, which will not be repeated here.

[0075] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0076] The technical features of the above embodiments may 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.

[0077] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for controlling energy supply mode of energy storage equipment, characterized in that: include: Obtain a regional map of energy storage device distribution, and divide the regional map into multiple sub-service areas according to regional locations and power supply loads; Acquire 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, wherein the operating 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 value 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 value threshold range of 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 obtaining of a regional map of energy storage device distribution and dividing the regional map into a plurality of sub-service areas according to regional locations and power supply loads comprises: Dividing the regional map into a plurality of geographical distribution areas of equal area by chessboard block method or grid block method according to the regional location; The power supply load distribution information of two adjacent geographical distribution areas is obtained, and the first distance area between the two adjacent geographical distribution areas is used as a transition area. Within the transition area, the same power supply load areas are merged into the geographical distribution area with the largest proportion to 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 the operating condition parameters and the control parameter data set corresponding to the operating condition parameters comprises: Parsing the power distribution setting parameters, performing data preprocessing to obtain key parameters, and classifying the key parameters into numerical features and category features, wherein the numerical features include electrical load data and power distribution setting data, and the category features include a power output 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 the electricity price data of the corresponding time periods to form operating condition parameters; The power supply distribution setting data and the power supply output 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, and 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 step of forming a training set from the collected power supply distribution setting parameters comprises: Acquire the numerical features and category features of the power supply distribution setting parameters, perform standardization processing on the numerical features to form a standardized processing result, and perform one-hot encoding processing on the category features to convert them into a one-hot encoding matrix; Calculate derived features according to the standardized processing result and the one-hot encoding matrix, merge the features obtained by calculation, ensure the consistency of feature order, and generate 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 using the trained MLP model to form a recommended energy supply mode for the target energy storage device 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 load prediction data is input into the output layer of the MLP model, and a power supply distribution method corresponding to the electrical 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: Acquire expected operating condition parameters of the target energy storage device, set a plurality of selectable power supply output modes according to the expected operating condition parameters, and set the target energy storage device to select an adaptive power supply output mode according to the real-time operating condition parameters when in use; The target energy storage device is set to set the time period for power supply of the energy storage device and external network based on the electricity price data, and the corresponding power supply output mode is selected according to the time period. The power supply output 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, select the start time of the charging mode, and 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 energy storage equipment, characterized in that: The device comprises: A service area division module 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; A power supply parameter acquisition module 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 analyze the power supply distribution setting parameters to obtain operating parameters and control parameter data sets corresponding to the operating parameters, wherein the operating parameters include power supply loads in different time periods; A target control parameter value acquisition module, used to analyze the operating condition of the target energy storage device to obtain target operating condition parameters, and to obtain a target control parameter data set matching 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 is used to obtain a default value 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 value threshold range of each control parameter to form 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 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.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: 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.

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