Power feature generation method and device of energy storage system, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power feature generation method and device of an energy storage system, computer equipment, a readable storage medium and a program product, and is applied to the technical field of data processing.The method comprises the steps that energy storage power data and configuration response information of the energy storage system are obtained, and multiple pieces of target energy storage power sub-data are selected from the energy storage power data, the configuration response information is used for representing the power configuration speed of the energy storage system; according to the configuration response information, wavelet transformation is carried out on the multiple pieces of target energy storage power sub-data, and at least one expansion coefficient and at least one pair of detail coefficients are obtained; a plurality of energy storage power characteristics are extracted from the at least one expansion coefficient and the at least one pair of detail coefficients, the energy storage power characteristics comprise at least one power characteristic value, and the power characteristic value is used for representing the operation state of an energy storage device in the energy storage system; and generating a target power feature of the energy storage system according to the plurality of energy storage power features. By adopting the method, the power characteristic generation accuracy of the energy storage system can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for generating power characteristics of an energy storage system. Background Art

[0002] With the widespread adoption of energy storage systems, the demand for their control is increasing. By constructing the power signature of an energy storage system, we can distinguish the sources of power fluctuations in different frequency bands or time periods. For example, short-term fluctuations may be caused by load changes, while long-term fluctuations may be related to the charging and discharging process of the energy storage device or the external grid status. This helps to better understand the operating characteristics of energy storage systems under different circumstances. The power signature of an energy storage system can help analyze the power requirements of the energy storage system at different time scales, thereby formulating more precise energy management strategies.

[0003] Currently, when generating the power characteristics of an energy storage system by using a preset transformation method (for example, Fourier transform, etc.), there is a situation where the speed of power configuration corresponding to the generation of the power characteristics of the energy storage system is relatively fixed, resulting in low accuracy in the generation of the power characteristics of the energy storage system. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for generating power characteristics of an energy storage system, which can improve the accuracy of power characteristics generation of the energy storage system, in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for generating a power signature of an energy storage system, comprising:

[0006] Acquiring energy storage power data and configuration response information of the energy storage system, and selecting a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system;

[0007] performing a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients;

[0008] Extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system;

[0009] A target power characteristic of the energy storage system is generated according to the multiple energy storage power characteristics.

[0010] In a second aspect, the present application further provides a power signature generating device for an energy storage system, comprising:

[0011] an acquisition module, configured to acquire energy storage power data and configuration response information of the energy storage system, and select a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system;

[0012] a transform module, configured to perform a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients;

[0013] an extraction module, configured to extract a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system;

[0014] A generating module is used to generate a target power characteristic of the energy storage system according to the multiple energy storage power characteristics.

[0015] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] Acquiring energy storage power data and configuration response information of the energy storage system, and selecting a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system;

[0017] performing a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients;

[0018] Extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system;

[0019] A target power characteristic of the energy storage system is generated according to the multiple energy storage power characteristics.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0021] Acquiring energy storage power data and configuration response information of the energy storage system, and selecting a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system;

[0022] performing a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients;

[0023] Extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system;

[0024] A target power characteristic of the energy storage system is generated according to the multiple energy storage power characteristics.

[0025] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0026] Acquiring energy storage power data and configuration response information of the energy storage system, and selecting a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system;

[0027] performing a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients;

[0028] Extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system;

[0029] A target power characteristic of the energy storage system is generated according to the multiple energy storage power characteristics.

[0030] The above-mentioned power characteristic generation method, device, computer equipment, computer-readable storage medium and computer program product of the energy storage system obtain energy storage power data and configuration response information of the energy storage system, and select multiple target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system; according to the configuration response information, the multiple target energy storage power sub-data are subjected to wavelet transform to obtain at least one expansion coefficient and at least one pair of detail coefficients; multiple energy storage power characteristics are extracted from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power characteristic includes at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system; based on the multiple energy storage power characteristics, the target power characteristics of the energy storage system are generated.

[0031] In this way, first, multiple target energy storage power sub-data are selected from the energy storage power data of the energy storage system, and wavelet transform is performed on the multiple target energy storage power sub-data based on the configuration response information of the energy storage system. This can make the coefficient change characteristics of at least one expansion coefficient and at least one pair of detail coefficients obtained match the power configuration speed of the energy storage system. At this time, when multiple energy storage power features are extracted from at least one expansion coefficient and at least one pair of detail coefficients, the energy storage power features can characterize the operating state of the energy storage device in the energy storage system. Therefore, based on the multiple energy storage power features, the target power features of the energy storage system are generated, so that the target power features can characterize the operating state of the energy storage system, and the corresponding power configuration speed is matched with the energy storage system, thereby improving the accuracy of the power feature generation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A diagram illustrating an application environment of a method for generating power characteristics of an energy storage system according to an embodiment;

[0034] Figure 2 1 is a flow chart of a method for generating power characteristics of an energy storage system in one embodiment;

[0035] Figure 3 A schematic diagram of a power linear graph generated in one embodiment;

[0036] Figure 4A flowchart illustrating the steps of performing wavelet transform on multiple target energy storage power sub-data according to configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients in one embodiment;

[0037] Figure 5 1. A flowchart illustrating steps for generating a target power characteristic of an energy storage system based on multiple energy storage power characteristics in one embodiment;

[0038] Figure 6 is a structural block diagram of a power signature generating device for an energy storage system in one embodiment;

[0039] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] 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.

[0041] It should be noted that the information involved in this application (for example, energy storage power data, configuration response information and device power information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the acquisition, transmission, storage, use and processing of relevant data are in compliance with the relevant provisions of national laws and regulations. The content pushed to the user (for example, at least one expansion coefficient, at least one pair of detail coefficients, energy storage power characteristics, target energy storage power sub-data and target power characteristics, etc.) can be rejected by the user or can be easily rejected by the user. In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0042] It's understandable that the target power signature of an energy storage system is intended to better schedule the system's charging, discharging, and stagnation times, optimizing the system's economic benefits and operational efficiency. By generating a target power signature for an energy storage system, potential instability factors, such as excessive instantaneous power fluctuations or sustained power fluctuations, can be identified in advance, enabling necessary control measures to be taken to enhance the system's control stability. Generating a target power signature for an energy storage system helps analyze the system's charging and discharging patterns, thereby inferring its future performance and lifespan. Because frequent, prolonged deep charging and discharging cycles can cause damage to the energy storage system, generating a target power signature for the energy storage system allows for earlier assessment of the system's health, thereby extending its service life. Furthermore, analyzing the power signature of an energy storage system can identify abnormalities during its operation. For example, abnormal power fluctuations in a specific frequency band may indicate a component failure or maintenance needs. The above analysis demonstrates that generating a target power signature for an energy storage system is closely linked to the system's normal operation, lifespan reduction, and control stability. Therefore, a method for accurately generating a power signature for an energy storage system is urgently needed.

[0043] The power characteristics generation method of the energy storage system provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the energy storage system 102 and the terminal 104 each communicate with the server 106 via a network. The data storage system can store data that the server 106 needs to process. The data storage system can be integrated with the server 106 or placed on a cloud or other network server. The server 106 receives the energy storage power data and configuration response information of the energy storage system 102 sent by the energy storage system 102, and selects multiple target energy storage power sub-data from the energy storage power data. Based on the configuration response information, the server 106 performs a wavelet transform on the multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients. The server 106 extracts multiple energy storage power features from the at least one expansion coefficient and at least one pair of detail coefficients, wherein the energy storage power feature includes at least one power feature value, which is used to characterize one of the charge state, discharge state, and stagnation state of the energy storage device in the energy storage system 102. The server 106 generates a target power feature of the energy storage system 102 based on the multiple energy storage power features. The server 106 can push at least one of the at least one expansion coefficient, the at least one pair of detail coefficients, the energy storage power feature, the target energy storage power sub-data, and the target power feature to the terminal 104. Terminal 104 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices may include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like. Server 106 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0044] In an exemplary embodiment, Figure 2 As shown, a method for generating power characteristics of an energy storage system is provided, and the method is applied to Figure 1 The server 106 in the example is used to illustrate the process, including the following steps 202 to 208.

[0045] Step 202 : Acquire energy storage power data and configuration response information of the energy storage system, and select multiple target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system.

[0046] The energy storage system in step 202 includes at least one energy storage device. Energy storage power data is used to characterize the operating power status of the energy storage system at multiple times (the time in this document can be a time point or a time period, without limitation). The energy storage power data may include multiple energy storage power sub-information, each of which includes a specific operating power value of the energy storage system at a specific time. The operating power value may be one of a first-type power characteristic value, a second-type power characteristic value, and a zero value. The first-type power characteristic value is used to characterize the charging state of the energy storage system, and the second-type power characteristic value is used to characterize the discharging state of the energy storage system. Specifically, when the first-type power characteristic value is positive, the second-type power characteristic value is negative; when the first-type power characteristic value is negative, the second-type power characteristic value is positive. For example, energy storage power sub-information a is (t1, p1). The energy storage power data may also include an energy storage system power curve, which characterizes the correspondence between time and the operating power value of the energy storage system. For example, with time as the horizontal axis and the operating power value of the energy storage system as the vertical axis, a curve drawn from multiple coordinate points (coordinate points consisting of multiple times and corresponding operating power values) serves as the power curve of the energy storage system.

[0047] The configuration response information in step 202 is used to characterize the energy storage system's configuration response capability. The configuration response information may include a configuration response frequency, which characterizes the speed at which the energy storage system's configuration power changes within a preset time period (the preset time period can be set by the user as needed and is the total time period consisting of the power configuration times corresponding to multiple target energy storage power sub-data). Specifically, the configuration response frequency is the ratio of the number of configuration power changes within the preset time period to the number of power configurations within the preset time period. For example, the configuration power within one minute is as follows: (1, 2, 2, 4, -1, -6, -2), and the configuration response frequency is 5 / 7 (5 changes per minute). A higher energy storage system configuration response frequency indicates a faster power configuration speed. The configuration response information may also include a configuration response interval, which characterizes the minimum time interval for the energy storage system to change its power configuration. A shorter configuration response interval indicates a faster power configuration speed.

[0048] Exemplarily, obtaining energy storage power data and configuration response information of the energy storage system includes: sending an energy storage configuration query request to the energy storage system, and obtaining energy storage power data and configuration response information of the energy storage system sent by the energy storage system after receiving the energy storage configuration query request.

[0049] Exemplarily, selecting a plurality of target energy storage power sub-data from the energy storage power data includes: obtaining a data selection quantity, and selecting a plurality of target energy storage power sub-data from the energy storage power data according to the data selection quantity.

[0050] Further, as an embodiment, obtaining the number of data selections includes: obtaining the number of data selections set by the user.

[0051] As another embodiment, obtaining the number of data to be selected includes: obtaining operating characteristics of the energy storage system, and determining the number of data to be selected based on the operating characteristics.

[0052] In this way, it can be ensured that the time distribution corresponding to the power characteristics of the generated energy storage system meets user expectations.

[0053] As one embodiment, determining the number of data to be selected based on operating characteristics includes: extracting periodic change features in the operating characteristics, and determining the number of data to be selected based on the periodic change features, wherein the more frequent the periodic changes represented by the periodic change features, the greater the number of data to be selected.

[0054] In this way, considering that the energy storage power data of the energy storage system is essentially the charging and discharging conditions of the energy storage system, the energy storage power change of the energy storage system is a time-series change. Therefore, by analyzing the periodic change characteristics in the operating characteristics of the energy storage system, it is possible to ensure that the power characteristics of the constructed energy storage system can represent the frequency change conditions of the periodic energy storage system.

[0055] As another embodiment, determining the number of data to be selected based on the operating characteristics includes: extracting power distribution features from the operating characteristics, and determining the number of data to be selected based on the power distribution features.

[0056] In this way, the amount of data selection is determined based on the power distribution characteristics, thereby ensuring that the power characteristics of the constructed energy storage system can characterize the frequency change status of the complete energy storage system as much as possible.

[0057] As another embodiment, obtaining the number of data to be selected includes: determining the number of data to be selected according to a processing number rule corresponding to the wavelet transform, wherein the processing number rule corresponding to the wavelet transform is specifically a power of 2 (for example, 2 n ).

[0058] In this way, it can be ensured that the multiple target energy storage power sub-data obtained by selecting the data selection quantity are compatible with the wavelet transform, thereby avoiding the need to remove too much target energy storage power sub-data to ensure the normal operation of the subsequent wavelet transform.

[0059] Furthermore, according to the processing quantity rule corresponding to the wavelet transform, the number of data to be selected is determined, including: obtaining the total data quantity of the energy storage power data, and determining the number of data to be selected according to the total data quantity and the processing quantity rule corresponding to the wavelet transform, wherein the number of data to be selected is determined to be a number close to the total data quantity and meeting the processing quantity rule corresponding to the wavelet transform, for example, the total data quantity is 12, and 2 3<12<2 4 , so the number of data selected can be 2 3 =8, or 2 4 =16.

[0060] In this way, it is ensured that the multiple target energy storage power sub-data obtained by selecting the data selection quantity can match the wavelet transform and select as many target energy storage power sub-data as possible.

[0061] Exemplarily, based on the number of data selected, multiple target energy storage power sub-data are selected from the energy storage power data, including: when the number of data selected is not greater than the total information number, uniformly selecting multiple target energy storage power sub-data from the energy storage power data, the number of which is equal to the number of data selected; when the number of data selected is greater than the total information selected, uniformly padding zeros in the energy storage power data to obtain multiple target energy storage power sub-data equal to the number of data selected.

[0062] Step 204 : performing wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients.

[0063] Exemplarily, step 204 includes: determining the number of transformation rounds according to the configuration response information; performing wavelet transformation on multiple target energy storage power sub-data according to the number of transformation rounds to obtain at least one expansion coefficient and at least one pair of detail coefficients.

[0064] Step 206 : extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power feature includes at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system.

[0065] Exemplarily, step 206 includes: screening at least one pair of detail coefficients obtained in the last round of wavelet transform from at least one pair of detail coefficients; extracting multiple power sub-features from at least one expansion coefficient and all detail coefficients obtained in the last round of wavelet transform, respectively, wherein the number of features of the power sub-features corresponds to the number of transformation rounds.

[0066] The more transformation rounds there are, the more features there are in the power sub-features. All expansion coefficients include at least one expansion coefficient obtained in each round of wavelet transformation.

[0067] As an embodiment, power sub-features are extracted from all detail coefficients, including: extracting power sub-features from two detail coefficients belonging to the same time interval in each round of wavelet transform process, specifically, the operating power values ​​corresponding to the time interval are two detail coefficients, and the operating power values ​​corresponding to the remaining time intervals are 0.

[0068] For example, the detail coefficients obtained from the first round of wavelet transform include (7, -7, -2, 2). At this time, one power sub-feature extracted from the detail coefficients obtained from the first round of wavelet transform includes (7, -7, 0, 0), and another power sub-feature extracted includes (0, 0, -2, 2).

[0069] As an embodiment, multiple power sub-features are extracted from at least one expansion coefficient obtained in the last round of wavelet transform, including: using each detail coefficient obtained in the last round of wavelet transform as the operating power value of the time interval corresponding to the detail coefficient.

[0070] Step 208: Generate a target power characteristic of the energy storage system based on the multiple energy storage power characteristics.

[0071] The target power feature in step 208 is used to characterize the target charge and discharge power distribution of each energy storage device at each time. The target power feature may include the target characteristic value of each energy storage device (corresponding to each type of energy storage device or each energy storage device mentioned above) at each time. The target power feature may also include multiple decomposed target power linear graphs, each target power linear graph corresponding to one energy storage device, and each power linear graph characterizes the correspondence between time and the target characteristic value of the corresponding energy storage device. For example, with time as the horizontal axis and the target characteristic value of the corresponding energy storage device as the vertical axis, a line drawn by multiple coordinate points (coordinate points consisting of multiple times and corresponding characteristic values) is used as a power linear graph. The power linear graph can be a broken line graph or a curve graph, without limitation herein. The target characteristic value can be one of the first type of power characteristic value, the second type of power characteristic value, and zero.

[0072] As an embodiment, step 208 includes: determining each power characteristic value in each energy storage power characteristic as an operating power value of each energy storage device at each time.

[0073] As another embodiment, step 208 includes: generating a plurality of power linear graphs according to each power characteristic value of each energy storage power characteristic, wherein each point in each power linear graph corresponds to each power characteristic value of each energy storage power characteristic.

[0074] Optionally, the distribution of multiple energy storage power characteristics is as follows: , and the time interval corresponding to multiple target energy storage power sub-data is 1 / 9, and the range is 0-1, for example, then the generated power linear graph can refer to Figure 3 .

[0075] Optionally, after step 208, the method further includes: controlling the energy storage system according to the target power characteristics of the energy storage system.

[0076] Furthermore, the energy storage system is controlled according to the target power characteristics of the energy storage system. Specifically, each energy storage device in the energy storage system is controlled according to the charge and discharge power distribution of each energy storage device represented by the target power characteristics of the energy storage system.

[0077] In the above-mentioned method for generating power characteristics of the energy storage system, multiple target energy storage power sub-data are first selected from the energy storage power data of the energy storage system, and wavelet transform is performed on the multiple target energy storage power sub-data based on the configuration response information of the energy storage system. This can make the coefficient change characteristics of at least one expansion coefficient and at least one pair of detail coefficients obtained match the power configuration speed of the energy storage system. At this time, when extracting multiple energy storage power characteristics from at least one expansion coefficient and at least one pair of detail coefficients, the energy storage power characteristics can characterize the operating state of the energy storage device in the energy storage system. Therefore, the target power characteristics of the energy storage system are then generated based on the multiple energy storage power characteristics, so that the target power characteristics can characterize the operating state of the energy storage system, and the corresponding power configuration speed is matched with the energy storage system, thereby improving the accuracy of the power characteristic generation of the energy storage system.

[0078] In an exemplary embodiment, Figure 4 As shown, a method for accurately performing wavelet transform is provided. According to the configuration response information, wavelet transform is performed on multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients, including steps 302 to 304.

[0079] Step 302 : Determine the number of transformation rounds according to the number of data selected from a plurality of target energy storage power sub-data and configuration response information.

[0080] Exemplarily, step 302 includes: evaluating the configuration response requirement type of the energy storage system according to the configuration response information; and determining the number of transformation rounds according to the response requirement speed represented by the configuration response requirement type and the number of data selected from multiple target energy storage power sub-data.

[0081] The faster the response demand speed represented by the configuration response demand type is, the fewer the number of transformation rounds is.

[0082] Furthermore, based on the configuration response information, the configuration response requirement type of the energy storage system is evaluated, including: extracting statistical characteristic values ​​of the configuration response sub-information of each energy storage device in the energy storage system; and evaluating the configuration response requirement type of the energy storage system based on the statistical characteristic values, wherein the statistical characteristic value can be a minimum value, an average value, or a median value.

[0083] The configuration response sub-information may include one of the number of configuration power changes and the configuration response frequency.

[0084] It can be understood that when the statistical characteristic value is the minimum value, the number of transformation rounds can be guaranteed to be as large as possible. That is, the power configuration corresponding to the multiple energy storage power characteristics extracted from the coefficients obtained by the transformation with the number of transformation rounds changes slowly. In this case, most of the energy storage power characteristics may need to be further split according to the power configuration speed of the corresponding energy storage device to obtain split power characteristics with relatively faster power configuration changes; when the statistical characteristic value is the average value or the median value, the power configuration corresponding to the multiple energy storage power characteristics extracted from the coefficients obtained by the transformation with the number of transformation rounds changes quickly. In this case, a small part of the energy storage power characteristics may need to be further split according to the power configuration speed of the corresponding energy storage device to obtain split power characteristics with relatively faster power configuration changes.

[0085] Optionally, the statistical characteristic value can be determined according to the number of device types of energy storage devices included in the energy storage system. Specifically, when the number of device types is less than a preset number threshold, the minimum value is determined as the statistical characteristic value; when the number of device types is not less than the preset number threshold, the average value or median value is determined as the statistical characteristic value.

[0086] In this way, when the energy storage system includes a small number of energy storage device types (one type of configuration response sub-information corresponds to one type of energy storage device), a larger number of transformation rounds is set to ensure that the power configuration corresponding to the multiple extracted energy storage power characteristics is transformed slowly. Even if most of the energy storage power characteristics need to be split, the processing volume involved is not large due to the small number of energy storage devices, and the accuracy of the target power characteristics finally generated can also be guaranteed. When the energy storage system includes a large number of energy storage devices, a relatively small number of transformation rounds is set to ensure that the power configuration corresponding to the multiple extracted energy storage power characteristics is transformed quickly, thereby minimizing the number of energy storage power characteristics that need to be split, thereby ensuring the efficiency of generating the target power characteristics.

[0087] As one embodiment, the number of transformation rounds is determined based on the response demand speed represented by the configuration response demand type and the data selection number of multiple target energy storage power sub-data, including: determining the transformation round number conditions corresponding to the multiple target energy storage power sub-data based on the data selection number of the multiple target energy storage power sub-data, wherein the transformation round number conditions are used to represent the maximum number of transformation rounds corresponding to the multiple target energy storage power sub-data; determining the transformation round number based on the response demand speed represented by the configuration response demand type and the transformation round number conditions.

[0088] The condition for the number of transformation rounds may be a range consisting of the maximum number of transformation rounds, or may be the maximum number of transformation rounds directly. The faster the response demand speed represented by the configuration response demand type, the smaller the number of transformation rounds determined.

[0089] Furthermore, based on the response demand speed represented by the configuration response demand type and the condition for the number of transformation rounds, the number of transformation rounds is determined, including: determining the power configuration change information corresponding to each round number included in the condition for the number of transformation rounds, and selecting the number of transformation rounds whose corresponding power configuration change information matches the response demand speed represented by the configuration response demand type from the round numbers included in the condition for the number of transformation rounds.

[0090] Step 304 : performing at least one round of wavelet transform on the plurality of target energy storage power sub-data according to the number of transformation rounds, to obtain at least one expansion coefficient and at least one pair of detail coefficients.

[0091] The number of rounds of wavelet transformation on the target energy storage power sub-data is consistent with the number of transformation rounds.

[0092] Exemplarily, step 304 includes: a wavelet transform step: performing wavelet transform on multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients of the current round; updating the multiple target energy storage power sub-data according to at least one expansion coefficient of the current round; accumulating the number of rounds of the wavelet transform process, and when the number of rounds is inconsistent with the number of transformation rounds, returning to execute the wavelet transform step until the number of rounds is consistent with the number of transformation rounds, and ending the wavelet transform process.

[0093] Furthermore, a wavelet transform is performed on the multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients of the current round, including: determining the average value between every two adjacent data in the multiple target energy storage power sub-data as the at least one expansion coefficient of the current round; and determining the two mean differences between every two adjacent data in the partial data (half of the two differences between every two adjacent data is the mean difference, for example, 5 and 9 are adjacent, then the absolute mean difference is (5-9) / 2=-2, and (9-5) / 2=2) as a pair of detail coefficients in the time interval corresponding to the two adjacent data.

[0094] For example, the target energy storage power sub-data includes (5, 9, 1, 3), at least one expansion coefficient of the current round obtained by wavelet transform includes: (7, 2), and at least one pair of detail coefficients of the current round obtained by wavelet transform includes (-2, 2, -1, 1).

[0095] In this embodiment, the number of transformation rounds is determined based on the number of data selected from multiple target energy storage power sub-data and the configuration response information. Considering that the number of data selected affects the number of rounds that can be performed on the wavelet transform, and the configuration response information affects the configuration response requirements of the energy storage system, the determined number of transformation rounds is ensured to meet both the wavelet transform requirements and the configuration response requirements of the energy storage system. The expansion coefficients and detail coefficients obtained in the wavelet transform process based on the number of transformation rounds meet both the wavelet transform requirements and the configuration response requirements of the energy storage system, thereby improving the accuracy of the wavelet transform.

[0096] In an exemplary embodiment, Figure 5 As shown, a method for accurately generating a target power characteristic is provided, and a target power characteristic of an energy storage system is generated based on multiple energy storage power characteristics, including steps 402 to 408.

[0097] Step 402: Obtain device power information of each type of energy storage device in the energy storage system, wherein each type of energy storage device includes at least one energy storage device.

[0098] Different types of energy storage devices correspond to different device power information, and the device power information is used to represent the rated power value of the energy storage device.

[0099] Exemplarily, step 402 includes: obtaining device power information of each energy storage device in the energy storage system, classifying multiple energy storage devices with the same device power information into the same category, and classifying each energy storage device with different device power information from other energy storage devices into a category.

[0100] For example, an energy storage system includes energy storage devices A, B, C, D, and E. The device power information of the five energy storage devices is a, b, c, d, and b, respectively. Then, energy storage device A is classified into category ①, energy storage devices B and E are classified into category ②, energy storage device C is classified into category ③, and energy storage device D is classified into category ④.

[0101] Step 404 : Allocate a plurality of power sub-signatures to energy storage devices in the energy storage system according to the device power information, wherein each type of energy storage device is allocated one power sub-signature.

[0102] As one embodiment, step 404 includes: obtaining maximum power characteristic values ​​corresponding to multiple power sub-characteristics, and assigning each power sub-characteristic to an energy storage device in the energy storage system based on the maximum power characteristic values ​​corresponding to the multiple power sub-characteristics and the rated power value represented by the device power information, wherein the rated power value represented by the device power information of each type of energy storage device is closest to the maximum power characteristic value corresponding to the assigned power sub-characteristic.

[0103] Step 406 : For each power sub-feature, if the feature change information of the power sub-feature does not match the corresponding configuration response sub-information of at least one energy storage device, the power sub-feature is split according to the corresponding configuration response sub-information of at least one energy storage device.

[0104] Among them, the feature change information in step 406 is used to characterize the speed of change of the feature value of the power sub-feature; the feature change information includes the feature change frequency and the feature change interval duration. The feature change frequency is used to characterize the speed of change of each power feature value in the power sub-feature, specifically, the ratio of the number of changes of each power feature value in the power sub-feature to the number of power feature values ​​contained in the power sub-feature. For example, the power sub-feature includes (1, 2, 2, 4, -1, -6, -2), and the feature change frequency is 5 / 7 (changed 5 times in this time period). The higher the feature change frequency, the faster the feature value change speed of the power sub-feature represented by the feature change information; the feature change interval duration is used to characterize the shortest event interval between changes in the power feature value in the power sub-feature. The shorter the feature change interval duration, the faster the feature value change speed of the power sub-feature represented by the feature change information.

[0105] Optionally, the method further includes: if the speed difference between the characteristic value change speed represented by the characteristic change information of the power sub-feature and the power configuration speed represented by the configuration response sub-information of at least one corresponding energy storage device is greater than a preset speed difference threshold, then determining that the characteristic change information of the power sub-feature does not match the configuration response sub-information of the at least one corresponding energy storage device; if the speed difference between the characteristic value change speed represented by the characteristic change information of the power sub-feature and the power configuration speed represented by the configuration response sub-information of each corresponding energy storage device is not greater than the preset speed difference threshold, then determining that the characteristic change information of the power sub-feature matches the configuration response sub-information of each corresponding energy storage device.

[0106] Exemplarily, the power sub-feature is split according to the configuration response sub-information of at least one correspondingly assigned energy storage device, including: determining the number of splits and split feature change information according to the configuration response sub-information of each correspondingly assigned energy storage device, wherein the number of splits is less than or equal to the number of correspondingly assigned energy storage devices; identifying power change feature information of the power sub-feature, wherein the power change feature information is used to characterize at least one of a stagnation state distribution and a charge-discharge state distribution of the power sub-feature; and splitting the power sub-feature according to the power change feature information, the number of splits and the split feature change information.

[0107] The characteristic change information of the power sub-features before and after the split is consistent. The characteristic change information refers to at least one of the ratio of the zero-value interval and the ratio of the charge-discharge interval. The split characteristic change information is characteristic change information of each split power sub-feature obtained by the split, and the characteristic change information of each split power sub-feature obtained by the split is matched with the configuration response sub-information of each corresponding energy storage device.

[0108] For example, the power sub-feature includes (1, 1, 1, 1, -1, -1, -1, -1). At this time, the charge and discharge interval ratio of the power sub-feature is 1:1, the feature change frequency is 1 / 8 (changes once within this period), and the corresponding assigned energy storage devices A and B have a configuration response frequency of 3 / 8 (changes three times within this period). The decomposed features can be (1, 1, 0, 0, -1, -1, 0, 0) and (0, 0, 1, 1, 0, 0, -1, -1).

[0109] Step 408 : Determine the multiple power sub-signatures that respectively match the configuration response sub-information of the corresponding energy storage devices as target power signatures of the energy storage system.

[0110] As an embodiment, step 408 includes: determining the power characteristic value in the power sub-characteristic that matches the configuration response sub-information of each energy storage device as the operating power value of each energy storage device at each time.

[0111] As another embodiment, step 408 includes: generating multiple power linear graphs according to the power characteristic values ​​in the power sub-characteristics that match the configuration response sub-information of each energy storage device, wherein each point in each power linear graph corresponds to each power characteristic value in each power sub-characteristic that matches the configuration response sub-information of each energy storage device.

[0112] In this embodiment, considering that device power information can characterize the power operating capability of the energy storage device, multiple power sub-features are respectively assigned to the energy storage devices based on the device power information, so that the power sub-feature obtained by correspondingly assigning each energy storage device can be adapted to the power operating capability of the energy storage device. That is, each energy storage device can successfully configure the power characteristic value of the corresponding assigned power sub-feature. For power sub-features whose characteristic change information of the power sub-feature does not match the configuration response sub-information of at least one correspondingly assigned energy storage device, further feature splitting is performed, so that the split power sub-features can meet the power configuration requirements more quickly, so that each power sub-feature is matched with the corresponding energy storage device, thereby improving the accuracy of generating the target power feature.

[0113] As a detailed embodiment, energy storage power data and configuration response information of the energy storage system are obtained, and multiple target energy storage power sub-data are selected from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system; statistical characteristic values ​​of the configuration response sub-information of each energy storage device in the energy storage system are extracted; based on the statistical characteristic values, the configuration response requirement type of the energy storage system is evaluated; the number of transformation rounds is determined based on the response requirement speed characterized by the configuration response requirement type and the number of data selected from the multiple target energy storage power sub-data; based on the number of transformation rounds, at least one round of wavelet transformation is performed on the multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients; at least one pair of detail coefficients obtained in the last round of wavelet transformation are screened from the at least one pair of detail coefficients; and multiple power sub-features are extracted from the at least one pair of detail coefficients obtained in the last round of wavelet transformation and all the expansion coefficients, respectively, wherein the number of features of the power sub-features corresponds to the number of transformation rounds, and the energy storage power feature includes at least one power characteristic value, which is used to characterize the operating status of the energy storage device in the energy storage system.

[0114] Furthermore, device power information of each type of energy storage device in the energy storage system is obtained, wherein each type of energy storage device includes at least one energy storage device; according to the device power information, multiple power sub-features are respectively assigned to the energy storage devices in the energy storage system, wherein each type of energy storage device is assigned one power sub-feature; for each power sub-feature, if the feature change information of the power sub-feature does not match the configuration response sub-information of at least one correspondingly assigned energy storage device, the number of splits and the split feature change information are determined according to the configuration response sub-information of each correspondingly assigned energy storage device, wherein the number of splits is less than or equal to the number of correspondingly assigned energy storage devices; power change feature information of the power sub-feature is identified, wherein the power change feature information is used to characterize at least one of the stagnation state distribution and the charge and discharge state distribution of the power sub-feature; according to the power change feature information, the power sub-feature is split according to the split number and the split feature change information; and multiple power sub-features that respectively match the configuration response sub-information of the corresponding energy storage devices are determined as the target power features of the energy storage system.

[0115] In this way, first, multiple target energy storage power sub-data are selected from the energy storage power data of the energy storage system, and wavelet transform is performed on the multiple target energy storage power sub-data based on the configuration response information of the energy storage system. This can make the coefficient change characteristics of at least one expansion coefficient and at least one pair of detail coefficients obtained match the power configuration speed of the energy storage system. At this time, when multiple energy storage power features are extracted from at least one expansion coefficient and at least one pair of detail coefficients, the energy storage power features can characterize the operating state of the energy storage device in the energy storage system. Therefore, based on the multiple energy storage power features, the target power features of the energy storage system are generated, so that the target power features can characterize the operating state of the energy storage system, and the corresponding power configuration speed is matched with the energy storage system, thereby improving the accuracy of the power feature generation of the energy storage system.

[0116] Furthermore, the number of transformation rounds is determined based on the number of data selected from multiple target energy storage power sub-data and the configuration response information. Considering that the number of data selected affects the number of rounds that can be performed on the wavelet transform, and the configuration response information affects the configuration response requirements of the energy storage system, the number of transformation rounds determined is such that it meets both the wavelet transform requirements and the configuration response requirements of the energy storage system. The expansion coefficients and detail coefficients obtained in the wavelet transform process based on the number of transformation rounds meet both the wavelet transform requirements and the configuration response requirements of the energy storage system, thereby improving the accuracy of the wavelet transform. Furthermore, considering that the device power information can characterize the power operation capability of the energy storage device, the device power information is used to characterize the power operation capability of the energy storage device. Based on the device power information, multiple power sub-features are respectively assigned to energy storage devices, so that the power sub-feature obtained by the corresponding assignment to each energy storage device can be adapted to the power operating capability of the energy storage device. That is, each energy storage device can successfully configure the power characteristic value of the corresponding assigned power sub-feature. For power sub-features whose characteristic change information of the power sub-feature does not match the configuration response sub-information of at least one corresponding assigned energy storage device, further feature splitting is performed, so that the split power sub-features can meet the power configuration requirements more quickly, so that each power sub-feature is matched with the corresponding energy storage device, thereby improving the generation accuracy of the target power feature.

[0117] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0118] Based on the same inventive concept, embodiments of the present application also provide a power signature generation device for an energy storage system for implementing the aforementioned method for generating a power signature for an energy storage system. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of the power signature generation device for one or more energy storage systems provided below can be found in the limitations of the method for generating a power signature for an energy storage system described above and will not be further elaborated here.

[0119] In an exemplary embodiment, Figure 6 As shown, a power signature generation device 600 for an energy storage system is provided, comprising: an acquisition module 602, a transformation module 604, an extraction module 606 and a generation module 608, wherein:

[0120] An acquisition module 602 is configured to acquire energy storage power data and configuration response information of the energy storage system, and select multiple target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system;

[0121] A transformation module 604 is configured to perform a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients;

[0122] An extraction module 606 is configured to extract a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system;

[0123] The generating module 608 is configured to generate a target power characteristic of the energy storage system according to the plurality of energy storage power characteristics.

[0124] In one embodiment, the transformation module 604 is also used to determine the number of transformation rounds based on the data selection quantity and configuration response information of multiple target energy storage power sub-data; based on the number of transformation rounds, at least one round of wavelet transformation is performed on the multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients.

[0125] In one embodiment, the configuration response information includes configuration response sub-information of each energy storage device in the energy storage system; the transformation module 604 is further used to extract statistical characteristic values ​​of the configuration response sub-information of each energy storage device in the energy storage system; based on the statistical characteristic values, the configuration response requirement type of the energy storage system is evaluated; and the number of transformation rounds is determined based on the response requirement speed represented by the configuration response requirement type and the number of data selected from multiple target energy storage power sub-data.

[0126] In one embodiment, the configuration response information includes configuration response sub-information of each energy storage device in the energy storage system; the extraction module 606 is further used to screen at least one pair of detail coefficients obtained in the last round of wavelet transform from at least one pair of detail coefficients; and extract multiple power sub-features from the at least one pair of detail coefficients and all expansion coefficients obtained in the last round of wavelet transform, respectively, wherein the number of features of the power sub-features corresponds to the number of transformation rounds.

[0127] In one embodiment, the configuration response information includes configuration response sub-information of each energy storage device in the energy storage system; the generation module 608 is further used to obtain device power information of each type of energy storage device in the energy storage system, wherein each type of energy storage device includes at least one energy storage device; according to the device power information, a plurality of power sub-features are respectively assigned to the energy storage devices in the energy storage system, wherein each type of energy storage device is assigned one power sub-feature; for each power sub-feature, if the feature change information of the power sub-feature does not match the configuration response sub-information of the corresponding assigned at least one energy storage device, the power sub-feature is split according to the configuration response sub-information of the corresponding assigned at least one energy storage device; and the plurality of power sub-features that respectively match the configuration response sub-information of the corresponding energy storage device are determined as the target power features of the energy storage system.

[0128] In one embodiment, the generation module 608 is further used to determine the number of splits and split feature change information based on the configuration response sub-information of each correspondingly assigned energy storage device, wherein the number of splits is less than or equal to the number of correspondingly assigned energy storage devices; identify power change feature information of the power sub-feature, wherein the power change feature information is used to characterize at least one of the stagnation state distribution and the charge and discharge state distribution of the power sub-feature; and split the power sub-feature according to the power change feature information, the number of splits and the split feature change information.

[0129] Each module in the power signature generation device for the energy storage system described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0130] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, and the wireless communication can be achieved via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for generating power characteristics of an energy storage system. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0131] Those skilled in the art will understand that Figure 7 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.

[0132] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0133] 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 steps in the above-mentioned method embodiments are implemented.

[0134] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0135] 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, database or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0136] The technical features of the above embodiments can be combined arbitrarily. In order 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 application.

[0137] The above embodiments merely illustrate 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 may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for generating power characteristics of an energy storage system, characterized in that: The method comprises: Acquiring energy storage power data and configuration response information of the energy storage system, and selecting a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system; performing a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients; Extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system; A target power characteristic of the energy storage system is generated according to the multiple energy storage power characteristics.

2. The method according to claim 1, characterized in that The step of performing a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients includes: Determining the number of transformation rounds according to the number of data selected from the plurality of target energy storage power sub-data and the configuration response information; According to the number of transformation rounds, at least one round of wavelet transformation is performed on the multiple target energy storage power sub-data to obtain at least one expansion coefficient and at least one pair of detail coefficients.

3. The method according to claim 2, characterized in that The configuration response information includes configuration response sub-information of each energy storage device in the energy storage system; Determining the number of transformation rounds according to the number of data selected from the plurality of target energy storage power sub-data and the configuration response information includes: Extracting statistical characteristic values ​​of configuration response sub-information of each energy storage device in the energy storage system; evaluating, based on the statistical characteristic values, a configuration response requirement type of the energy storage system; The number of transformation rounds is determined according to the response demand speed represented by the configuration response demand type and the number of data selected from the multiple target energy storage power sub-data.

4. The method according to claim 1, wherein The extracting a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients comprises: Filtering at least one expansion coefficient obtained in the last round of wavelet transform process from the at least one expansion coefficient; A plurality of power sub-features are extracted from at least one expansion coefficient and all the detail coefficients obtained in the last round of wavelet transform, respectively, wherein the number of features of the power sub-features corresponds to the number of transform rounds.

5. The method according to claim 1, wherein The configuration response information includes configuration response sub-information of each energy storage device in the energy storage system; and generating a target power characteristic of the energy storage system according to the multiple energy storage power characteristics includes: Acquiring device power information of each type of energy storage device in the energy storage system, wherein each type of energy storage device includes at least one energy storage device; According to the device power information, the plurality of power sub-signatures are respectively assigned to energy storage devices in the energy storage system, wherein each type of energy storage device is assigned one power sub-signature; For each of the power sub-features, if the feature change information of the power sub-feature does not match the correspondingly assigned configuration response sub-information of at least one energy storage device, splitting the power sub-feature according to the correspondingly assigned configuration response sub-information of at least one energy storage device; A plurality of power sub-signatures respectively matching the configuration response sub-information of the corresponding energy storage devices are determined as target power signatures of the energy storage system.

6. The method according to claim 5, characterized in that The splitting of the power sub-features according to the correspondingly allocated configuration response sub-information of at least one energy storage device includes: Determining the number of splits and split feature change information based on the configuration response sub-information of each correspondingly allocated energy storage device, wherein the number of splits is less than or equal to the number of correspondingly allocated energy storage devices; Identifying power change characteristic information of the power sub-characteristic, wherein the power change characteristic information is used to characterize at least one of a stagnation state distribution and a charge-discharge state distribution of the power sub-characteristic; The power sub-features are split according to the power change feature information, the split quantity and the split feature change information.

7. A power characteristic generating device for an energy storage system, characterized in that: The device comprises: an acquisition module, configured to acquire energy storage power data and configuration response information of the energy storage system, and select a plurality of target energy storage power sub-data from the energy storage power data, wherein the configuration response information is used to characterize the power configuration speed of the energy storage system; a transform module, configured to perform a wavelet transform on the plurality of target energy storage power sub-data according to the configuration response information to obtain at least one expansion coefficient and at least one pair of detail coefficients; an extraction module, configured to extract a plurality of energy storage power features from the at least one expansion coefficient and the at least one pair of detail coefficients, wherein the energy storage power features include at least one power characteristic value, and the power characteristic value is used to characterize the operating state of the energy storage device in the energy storage system; A generating module is used to generate a target power characteristic of the energy storage system according to the multiple energy storage power characteristics.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. 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 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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