Data information processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a data information processing method and device, a storage medium and electronic equipment. The method relates to the technical field of data processing, the technical field of electronics and other related technical fields, and comprises the following steps: classifying power grid net load sets of user clusters in a target power grid in a historical time period to obtain a plurality of power grid net load groups; performing feature extraction according to the power grid net load in each power grid net load group to obtain an elastic demand feature corresponding to each power grid net load group; and predicting the elastic demand coefficient of the target power grid on the target prediction day according to the elastic demand feature corresponding to each power grid net load group to obtain a target prediction result. Through the method and the device, the problem that the accuracy of determining the elastic demand coefficient is relatively low due to the fact that the demand response potential (namely, the elastic demand coefficient) of the power grid is evaluated manually based on the user electric meter data in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology, electronic technology, and other related technical fields, and more specifically, to a data information processing method and device, a storage medium, and an electronic device. Background Art

[0002] With the rapid development of the electricity market, demand response (DR) technology has gradually been widely used in power systems. Demand response can improve load characteristics, increase energy efficiency, reduce peak-to-valley differences, and promote the integration of new energy sources. Demand response can plan and adjust user electricity usage, allowing distribution companies to alleviate the volatility of intermittent distributed energy resources, thereby reducing peak loads and avoiding additional electricity charges for users. Therefore, evaluating demand response potential (also known as the elastic demand factor) has become a key measure to address the lack of flexibility in new power systems.

[0003] With the continuous development of renewable energy, the proportion of distributed photovoltaic installed capacity continues to rise. This increased penetration of photovoltaics also poses challenges to assessing demand response potential. Traditional methods manually assess the grid's demand response potential based on the net load power measured by customer meters. However, the invisibility of photovoltaic output and the inherent randomness of photovoltaic power generation reduce the predictability of net load power, posing a significant challenge to accurate demand response potential prediction.

[0004] Currently, no effective solution has been proposed to the problem that the accuracy of determining the elastic demand coefficient is relatively low due to manual evaluation of the demand response potential of the power grid (i.e., the elastic demand coefficient) based on user meter data in related technologies. Summary of the Invention

[0005] The main purpose of this application is to provide a data information processing method and device, a storage medium and an electronic device to solve the problem in the related art that the demand response potential of the power grid (that is, the elastic demand coefficient) is manually evaluated based on user meter data, resulting in a relatively low accuracy in determining the elastic demand coefficient.

[0006] To achieve the above-mentioned objectives, according to one aspect of the present application, a data information processing method is provided. The method comprises: classifying the net load set of a user cluster in a target power grid within a historical time period to obtain a plurality of net load groups; extracting features based on the net load in each net load group to obtain elastic demand features corresponding to each net load group; and predicting the elastic demand coefficient of the target power grid on a target prediction day based on the elastic demand features corresponding to each net load group to obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted based on the target prediction result.

[0007] Furthermore, the grid net load set of the user cluster in the target grid within the historical time period is classified to obtain multiple grid net load groups, including: for each initial grid net load in the grid net load set, determining the category information corresponding to each initial grid net load based on the size relationship between the preset net load threshold and the initial grid net load; classifying according to the category information corresponding to each initial grid net load to obtain multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category; obtaining the multiple grid net load groups based on the multiple grid net loads corresponding to the first category and the multiple grid net loads corresponding to the second category.

[0008] Furthermore, feature extraction is performed based on the grid net load in each grid net load group to obtain the elastic demand feature corresponding to each grid net load group, including: feature extraction of multiple grid net loads corresponding to the first category to obtain a first elastic demand feature; feature extraction of multiple grid net loads corresponding to the second category to obtain a second elastic demand feature; and determination of the elastic demand feature based on the first elastic demand feature and the second elastic demand feature.

[0009] Furthermore, feature extraction is performed on multiple grid net loads corresponding to the first category to obtain a first elastic demand feature, including: vectorizing the multiple grid net loads to obtain multiple feature vectors corresponding to the multiple grid net loads; calculating the multiple feature vectors by the Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple grid net loads; and calculating based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand feature.

[0010] Furthermore, calculating based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand characteristic includes: calculating based on the multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations, and multiple daily output maximum values ​​corresponding to the multiple photovoltaic output sequences; calculating based on the multiple photovoltaic parameters to obtain daily output estimation values ​​corresponding to the multiple photovoltaic output sequences; calculating based on the multiple power load sequences to obtain power load estimation values ​​corresponding to the multiple power load sequences; and determining the first elastic demand characteristic based on the daily output estimation values ​​and the power load estimation values.

[0011] Furthermore, based on the daily power estimate and the power load estimate, determining the first elastic demand characteristic includes: performing calculation based on the daily power estimate and the power load estimate to obtain a grid net load estimate; and performing feature fusion based on the grid net load estimate, the daily power estimate and the power load estimate to obtain the first elastic demand characteristic.

[0012] Furthermore, after predicting the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group and obtaining the target prediction result, the method also includes: judging whether the target prediction result is less than or equal to a preset threshold to obtain a judgment result; if the judgment result indicates that the target prediction result is less than or equal to the preset threshold, determining the target power supply strategy corresponding to the target power grid based on the target prediction result; and supplying power to the user cluster based on the target power supply strategy.

[0013] To achieve the above-mentioned purpose, according to another aspect of the present application, a data information processing device is provided. The device includes: a classification unit for classifying the net load set of user clusters in a target power grid within a historical time period to obtain multiple net load groups; an extraction unit for extracting features based on the net load in each net load group to obtain elastic demand features corresponding to each net load group; and a prediction unit for predicting the elastic demand coefficient of the target power grid on a target prediction day based on the elastic demand features corresponding to each net load group to obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted based on the target prediction result.

[0014] Furthermore, the classification unit includes: a first determination subunit, used to determine the category information corresponding to each initial grid net load in the grid net load set based on the size relationship between the preset net load threshold and the initial grid net load; a classification subunit, used to classify according to the category information corresponding to each initial grid net load, and obtain multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category; a second determination subunit, used to obtain the multiple grid net load groups based on the multiple grid net loads corresponding to the first category and the multiple grid net loads corresponding to the second category.

[0015] Furthermore, the extraction unit includes: a first extraction subunit, used to perform feature extraction on multiple grid net loads corresponding to the first category to obtain a first elastic demand feature; a second extraction subunit, used to perform feature extraction on multiple grid net loads corresponding to the second category to obtain a second elastic demand feature; and a third determination subunit, used to determine the elastic demand feature based on the first elastic demand feature and the second elastic demand feature.

[0016] Furthermore, the first extraction subunit includes: a vectorization module, used to vectorize the multiple grid net loads to obtain multiple characteristic vectors corresponding to the multiple grid net loads; a first calculation module, used to calculate the multiple characteristic vectors through the Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple grid net loads; a second calculation module, used to calculate based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand characteristics.

[0017] Furthermore, the second calculation module includes: a first calculation submodule, used to perform calculations based on the multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations and multiple daily output maximum values ​​corresponding to the multiple photovoltaic output sequences; a second calculation submodule, used to perform calculations based on the multiple photovoltaic parameters to obtain daily output estimation values ​​corresponding to the multiple photovoltaic output sequences; a third calculation submodule, used to perform calculations based on the multiple power load sequences to obtain power load estimation values ​​corresponding to the multiple power load sequences; a determination submodule, used to determine the first elastic demand characteristic based on the daily output estimation values ​​and the power load estimation values.

[0018] Furthermore, the determination submodule includes: a calculation submodule, which is used to calculate based on the daily power estimate value and the power load estimate value to obtain the net load estimate value of the power grid; and a fusion submodule, which is used to perform feature fusion based on the net load estimate value of the power grid, the daily power estimate value and the power load estimate value to obtain the first elastic demand feature.

[0019] Furthermore, the device also includes: a judgment unit, which is used to predict the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group, and after obtaining the target prediction result, judge whether the target prediction result is less than or equal to a preset threshold to obtain a judgment result; a determination unit, which is used to determine the target power supply strategy corresponding to the target power grid based on the target prediction result if the judgment result indicates that the target prediction result is less than or equal to the preset threshold; and a power supply unit, which is used to supply power to the user cluster based on the target power supply strategy.

[0020] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a computer-readable storage medium is provided, which stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the data information processing methods described above.

[0021] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is further provided, which includes one or more processors and a memory, and the memory is used to store the data information processing method described above implemented by one or more processors.

[0022] Through this application, the following steps are adopted: classifying the net load set of the user cluster in the target power grid within the historical time period to obtain multiple net load groups; extracting features based on the net load in each net load group to obtain the elastic demand features corresponding to each net load group; predicting the elastic demand coefficient of the target power grid on the target forecast day based on the elastic demand features corresponding to each net load group to obtain a target forecast result, wherein the power supply strategy for the user cluster is adjusted according to the target forecast result. Through this application, the problem of low accuracy in determining the elastic demand coefficient by manually evaluating the demand response potential of the power grid based on user meter data in the related art is solved. In this solution, the historical net load set of the user cluster in the target power grid is classified by scenario to obtain multiple net load groups. Then, data processing and feature extraction are performed on the net load group under each scenario to obtain the elastic demand features corresponding to the net load group under each scenario. Finally, the elastic demand coefficient of the target power grid on the target forecast day is predicted using the elastic demand features corresponding to the net load group under each scenario to obtain the target forecast result to adjust the power supply strategy for the user cluster. By performing scenario classification, data processing, and feature extraction on the historical grid net load set, user demand can be predicted more accurately, thereby precisely obtaining the elastic demand coefficient of the user cluster on the target forecast day, thereby optimizing the grid's power supply strategy, improving the grid's power supply efficiency, and enhancing the grid's reliability and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0024] Figure 1 is a flowchart of a data information processing method provided in an embodiment of the present application;

[0025] Figure 2 is a schematic diagram of a data information processing method provided according to an embodiment of the present application;

[0026] Figure 3 is a schematic diagram of a data information processing device provided according to an embodiment of the present application;

[0027] Figure 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display and analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0032] The present invention will be described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a data information processing method provided in accordance with an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0033] Step S101 : classifying the net load sets of user clusters in a target power grid within a historical time period to obtain a plurality of net load groups.

[0034] Optionally, multiple initial grid net loads (i.e., grid net load sets) of user clusters in the target grid within a historical time period are obtained, and the scenarios are classified according to the size relationship between the initial grid net load and a preset net load threshold (the preset net load threshold is 0) to obtain multiple grid net load groups.

[0035] Step S102 , extracting features based on the net load of each net load group to obtain elastic demand features corresponding to each net load group.

[0036] Optionally, feature extraction is performed on the net load in each net load group to obtain elastic demand features corresponding to each net load group. The extracted elastic demand features are mainly divided into features that affect total daily electricity consumption (such as temperature information, seasonal tags, weekday tags, and weekend tags) and features that determine users' willingness to reduce electricity consumption (such as incentive prices, user baseline load, and DR period).

[0037] Step S103 , predicting the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group, and obtaining a target prediction result, wherein the power supply strategy for the user cluster is adjusted based on the target prediction result.

[0038] Optionally, a prediction model (e.g., machine learning models such as random forests and neural networks, or statistical models such as regression analysis) is used to predict the elastic demand coefficient of the target power grid on the target prediction day based on the input characteristics (i.e., elastic demand characteristics) to obtain a target prediction result.

[0039] It should be noted that after training the prediction model, the prediction model needs to be evaluated to ensure the accuracy of its prediction. The validation set data can be used for cross-validation to calculate the prediction error of the model, such as the mean square error (MSE) or mean absolute error (MAE).

[0040] In summary, the historical grid net load set of the user cluster in the target power grid is classified by scenario to obtain multiple grid net load groups. Then, data processing and feature extraction are performed on the grid net load group under each scenario to obtain the elastic demand characteristics corresponding to the grid net load group under each scenario. Finally, the elastic demand coefficient of the target grid on the target prediction day is predicted using the elastic demand characteristics corresponding to the grid net load group under each scenario to obtain the target prediction result to adjust the power supply strategy for the user cluster. By performing scenario classification, data processing and feature extraction on the historical grid net load set, user demand can be predicted more accurately, thereby accurately obtaining the elastic demand coefficient of the user cluster on the target prediction day, thereby optimizing the power supply strategy of the power grid, improving the power supply efficiency of the power grid, and enhancing the reliability and stability of the power grid.

[0041] Optionally, in the data information processing method provided in the embodiment of the present application, the grid net load set of the user cluster in the target power grid within the historical time period is classified to obtain multiple grid net load groups, including: for each initial grid net load in the grid net load set, determining the category information corresponding to each initial grid net load based on the size relationship between the preset net load threshold and the initial grid net load; classifying according to the category information corresponding to each initial grid net load to obtain multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category; obtaining multiple grid net load groups based on the multiple grid net loads corresponding to the first category and the multiple grid net loads corresponding to the second category.

[0042] In an optional embodiment, for a user cluster containing D demand response days (i.e., there are D days in the historical time period), the net load power data of each demand response day is extracted. d=1,2,…,D (i.e. each initial grid net load), and the net load power set of the user cluster on the demand response day is obtained (i.e., the net load of the power grid). (i.e. each initial net load in the net load set) is judged: if the net load power data (i.e., preset net load threshold), the demand response day d1 is classified as scenario 1 (i.e., category information); if the net load power data The demand response day d2 is classified as scenario 2 (ie, category information). The multiple net load power data corresponding to scenario 1 are used as the first grid net load group, and the multiple net load power data corresponding to scenario 2 are used as the second grid net load group.

[0043] By classifying the net load collection of user clusters in the target power grid within a historical time period, we can better understand and analyze the electricity consumption behavior and demand characteristics of user clusters, thereby more accurately predicting and controlling the load fluctuations of the power grid and improving the operating efficiency and stability of the power grid.

[0044] Optionally, in the data information processing method provided in the embodiment of the present application, feature extraction is performed based on the grid net load in each grid net load group to obtain the elastic demand feature corresponding to each grid net load group, including: feature extraction of multiple grid net loads corresponding to the first category to obtain a first elastic demand feature; feature extraction of multiple grid net loads corresponding to the second category to obtain a second elastic demand feature; and determination of the elastic demand feature based on the first elastic demand feature and the second elastic demand feature.

[0045] In an optional embodiment, net load decoupling is performed on the multiple net load power data corresponding to scenario one (i.e., the multiple grid net loads corresponding to the first category), and the future PV output, user load, and net load (i.e., the first elastic demand characteristic) are estimated based on the decoupling results. For the multiple net load power data corresponding to scenario two (i.e., the multiple grid net loads corresponding to the second category), the future net load (i.e., the second elastic demand characteristic) is estimated based only on the multiple net load power data. The elastic demand characteristic is extracted based on the PV output, user load, and net load corresponding to scenario one, and the net load corresponding to scenario two.

[0046] By extracting features of the grid net load in each grid net load group, the dimension and diversity of the data can be improved, so that the feature differences between different data can be captured more accurately, thereby improving the accuracy and generalization ability of the model.

[0047] Optionally, in the data information processing method provided in the embodiment of the present application, feature extraction is performed on multiple grid net loads corresponding to the first category to obtain the first elastic demand feature, including: vectorizing the multiple grid net loads to obtain multiple feature vectors corresponding to the multiple grid net loads; calculating the multiple feature vectors by the Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple grid net loads; and calculating based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand feature.

[0048] In an optional embodiment, each grid net load The composition of is represented linearly using eigenvectors. is the photovoltaic output curve of the user cluster during the T period on the demand response day d; is the user load curve of the user cluster during the T period on the demand response day d. The expressions of the photovoltaic output curve and the user load curve (i.e., the multiple characteristic vectors corresponding to the multiple grid net loads) are as follows:

[0049]

[0050] in, represents the photovoltaic output characteristic matrix on demand response day d, represents the user load characteristic matrix of demand response day d, is the coefficient vector, is the error vector.

[0051] For each grid net load Perform net load decoupling and construct the objective function Q d , objective function Q d The expression is as follows:

[0052]

[0053] in, represents the 2-norm, represents the photovoltaic output curve of the user cluster during the T period on the demand response day d, represents the user load curve of the user cluster during the T period on the demand response day d, represents the photovoltaic output characteristic matrix on demand response day d, represents the user load characteristic matrix of demand response day d, is the coefficient vector.

[0054] The objective function is to minimize the sum of squares of the estimated errors of photovoltaic output and user load. The constraints are that the photovoltaic output, user load and net load power satisfy the equation relationship, and the photovoltaic output and user load are positive. The Lagrange multiplier method is used to solve the objective function Q d , and obtain the decoupling results (i.e., multiple photovoltaic output sequences and multiple power load sequences).

[0055] By calculating multiple photovoltaic output sequences and multiple power load sequences, the first elastic demand characteristics can be obtained.

[0056] By quantizing the net load vectors of multiple power grids and calculating the photovoltaic output sequence and power load sequence through the Lagrange multiplier method, the expressive ability of the features can be improved, the actual situation of the power grid can be better reflected, and it is beneficial to subsequent analysis and prediction.

[0057] Optionally, in the data information processing method provided in the embodiment of the present application, calculating based on multiple photovoltaic output sequences and multiple power load sequences to obtain the first elastic demand characteristic includes: calculating based on multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations, and multiple daily output maximum values ​​corresponding to the multiple photovoltaic output sequences; calculating based on multiple photovoltaic parameters to obtain daily output estimation values ​​corresponding to the multiple photovoltaic output sequences; calculating based on multiple power load sequences to obtain power load estimation values ​​corresponding to the multiple power load sequences; and determining the first elastic demand characteristic based on the daily output estimation values ​​and the power load estimation values.

[0058] In an optional embodiment, the photovoltaic output characteristic index sequence is calculated based on multiple photovoltaic output sequences on the demand response day d to obtain the photovoltaic output characteristic index sequence

[0059] in, represents the average daily photovoltaic output (i.e. the average of multiple daily outputs), SD i Represents the daily photovoltaic output standard deviation (i.e., the standard deviation of multiple days' output), Represents the maximum daily photovoltaic output (i.e. the maximum output over multiple days).

[0060] Among them, the average daily photovoltaic output Daily photovoltaic output standard deviation SD i Heri photovoltaic maximum output The expression is as follows:

[0061]

[0062] Among them, I i,j is the photovoltaic output data, i = 1, 2, ..., T is the i-th day of historical data, and j = 1, 2, ..., 96 is the j-th time period in a day.

[0063] Photovoltaic output characteristic index vector based on each demand response day The clustering algorithm is used to divide the historical load days into four weather types. Based on the long short-term memory network, a photovoltaic output time series prediction model is constructed for each weather type to obtain the estimated value of daily photovoltaic output. (i.e., the estimated daily output value corresponding to multiple photovoltaic output sequences). Use the average method to estimate the load power data of multiple photovoltaic output sequences. Remove the days with the highest and lowest load levels from the Y historical load days, and leave the average value of the load of the middle X days as the load estimate. (i.e., the estimated power load values ​​corresponding to multiple power load sequences).

[0064] The estimated daily power output and the estimated power load are calculated to obtain a first elastic demand characteristic.

[0065] By calculating the daily output mean, standard deviation, and maximum values ​​of multiple photovoltaic output sequences, as well as the estimated power load values ​​of multiple power load sequences, the photovoltaic power generation and power load conditions can be estimated more accurately, thereby improving the accuracy of the prediction.

[0066] Optionally, in the data information processing method provided in the embodiment of the present application, determining the first elastic demand feature based on the daily power estimate and the power load estimate includes: performing calculations based on the daily power estimate and the power load estimate to obtain a net load estimate of the power grid; and performing feature fusion based on the net load estimate of the power grid, the daily power estimate and the power load estimate to obtain the first elastic demand feature.

[0067] In an alternative embodiment, the load estimate is calculated Estimated daily photovoltaic output The difference between the two values ​​is used to obtain the net load power estimation result corresponding to the user cluster on the future demand response day. (i.e. estimated net load value of the power grid), net load power estimation result The expression is as follows:

[0068]

[0069] By fusing the estimated net load of the power grid, the estimated daily output and the estimated power load, the first elastic demand feature can be obtained.

[0070] The estimated value of the net load of the grid comprehensively considers the impact of photovoltaic power generation and power load, reflects the actual operation of the system more comprehensively, and helps to determine the elastic demand characteristics of the system.

[0071] Optionally, in the data information processing method provided in the embodiment of the present application, after predicting the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group and obtaining the target prediction result, the method also includes: judging whether the target prediction result is less than or equal to a preset threshold to obtain a judgment result; if the judgment result indicates that the target prediction result is less than or equal to the preset threshold, determining the target power supply strategy corresponding to the target power grid based on the target prediction result; and supplying power to the user cluster based on the target power supply strategy.

[0072] In an optional embodiment, the target prediction result is compared with a preset threshold. If the target prediction result is less than or equal to the preset threshold, it indicates that grid demand is relatively stable, and therefore a stable power supply strategy is selected to power the user cluster. If the target prediction result is greater than the preset threshold, it indicates that grid demand may fluctuate significantly, and some measures (such as adding backup power sources or adjusting power generation plans) are needed to address possible demand fluctuations. Based on the determined target power supply strategy, power is supplied to the user cluster, for example, by adjusting the operating status of power generation equipment and optimizing power scheduling.

[0073] Through the above steps, the elastic demand coefficient of the target power grid on the target prediction day can be effectively predicted, and the corresponding power supply strategy can be formulated according to the prediction results to ensure the stable operation of the power grid.

[0074] It should be noted that Figure 2 Schematic diagram of a data information processing method according to an embodiment of the present application. Figure 2 As shown, first, the scenarios are classified according to the size relationship between each initial grid net load in the grid net load set and the preset net load threshold, and multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category are obtained.

[0075] Then, feature extraction is performed on multiple net loads of the power grid corresponding to the first category to obtain a first elastic demand feature; feature extraction is performed on multiple net loads of the power grid corresponding to the second category to obtain a second elastic demand feature; and calculation is performed based on the first elastic demand feature and the second elastic demand feature to obtain the elastic demand feature.

[0076] Finally, the elastic demand coefficient of the target power grid on the target prediction day is predicted based on the elastic demand characteristics corresponding to each net load group of the power grid, and the target prediction result is obtained to adjust the power supply strategy for the user cluster.

[0077] The data information processing method provided in the embodiment of the present application is to classify the net load set of the user cluster in the target power grid within the historical time period to obtain multiple net load groups; extract the features of the net load in each net load group to obtain the elastic demand features corresponding to each net load group; predict the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand features corresponding to each net load group to obtain the target prediction result, wherein the power supply strategy for the user cluster is adjusted according to the target prediction result, which solves the problem that the accuracy of determining the elastic demand coefficient is relatively low by manually evaluating the demand response potential (i.e., elastic demand coefficient) of the power grid based on the user meter data in the related art. In summary, in this solution, the historical net load set of the user cluster in the target power grid is scene-classified to obtain multiple net load groups. Then, data processing and feature extraction are performed on the net load group under each scenario to obtain the elastic demand features corresponding to the net load group under each scenario. Finally, the elastic demand coefficient of the target power grid on the target prediction day is predicted using the elastic demand features corresponding to the net load group under each scenario to obtain the target prediction result to adjust the power supply strategy for the user cluster. By performing scenario classification, data processing, and feature extraction on the historical grid net load set, user demand can be predicted more accurately, thereby precisely obtaining the elastic demand coefficient of the user cluster on the target forecast day, thereby optimizing the grid's power supply strategy, improving the grid's power supply efficiency, and enhancing the grid's reliability and stability.

[0078] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0079] The present application also provides a data information processing device. It should be noted that the data information processing device of the present application can be used to execute the data information processing method provided in the present application. The data information processing device provided in the present application is introduced below.

[0080] Figure 3 Schematic diagram of a data information processing device according to an embodiment of the present application. Figure 3 As shown, the device includes: a classification unit 301, an extraction unit 302, and a prediction unit 303.

[0081] A classification unit 301 is configured to classify a set of net loads of user clusters in a target power grid within a historical time period to obtain a plurality of net load groups of the power grid;

[0082] An extraction unit 302 is configured to extract features based on the net load of each net load group to obtain elastic demand features corresponding to each net load group;

[0083] The prediction unit 303 is used to predict the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group, and obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted based on the target prediction result.

[0084] The data information processing device provided by the embodiment of the present application, the classification unit 301 classifies the grid net load set of the user cluster in the target power grid within the historical time period to obtain multiple grid net load groups; the extraction unit 302 extracts features based on the grid net load in each grid net load group to obtain the elastic demand features corresponding to each grid net load group; the prediction unit 303 predicts the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand features corresponding to each grid net load group to obtain the target prediction result, wherein the power supply strategy for the user cluster is adjusted according to the target prediction result, which solves the problem in the related art of manually evaluating the demand response potential (i.e., elastic demand coefficient) of the power grid based on user meter data, resulting in a relatively low accuracy in determining the elastic demand coefficient. In this solution, the historical grid net load set of the user cluster in the target power grid is scene-classified to obtain multiple grid net load groups. Then, data processing and feature extraction are performed on the grid net load group under each scenario to obtain the elastic demand features corresponding to the grid net load group under each scenario. Finally, the elastic demand coefficient of the target grid on the target forecast date is predicted using the elastic demand characteristics corresponding to the grid net load group in each scenario. This prediction yields the target forecast result, which is then used to adjust the power supply strategy for the user cluster. By performing scenario classification, data processing, and feature extraction on the historical grid net load collection, user demand can be more accurately predicted, resulting in a precise elastic demand coefficient for the user cluster on the target forecast date. This, in turn, optimizes the grid's power supply strategy, improves its power supply efficiency, and enhances its reliability and stability.

[0085] Optionally, in the data information processing device provided in the embodiment of the present application, the first determination subunit is used to determine, for each initial grid net load in the grid net load set, the category information corresponding to each initial grid net load based on the size relationship between a preset net load threshold and the initial grid net load; the classification subunit is used to classify according to the category information corresponding to each initial grid net load to obtain multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category; the second determination subunit is used to obtain multiple grid net load groups based on the multiple grid net loads corresponding to the first category and the multiple grid net loads corresponding to the second category.

[0086] Optionally, in the data information processing device provided in the embodiment of the present application, the extraction unit includes: a first extraction subunit, used to extract features of multiple power grid net loads corresponding to the first category to obtain a first elastic demand feature; a second extraction subunit, used to extract features of multiple power grid net loads corresponding to the second category to obtain a second elastic demand feature; and a third determination subunit, used to determine the elastic demand feature based on the first elastic demand feature and the second elastic demand feature.

[0087] Optionally, in the data information processing device provided in the embodiment of the present application, the first extraction subunit includes: a vectorization module, used to vectorize the multiple net loads of the power grid to obtain multiple characteristic vectors corresponding to the multiple net loads of the power grid; a first calculation module, used to calculate the multiple characteristic vectors through the Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple net loads of the power grid; a second calculation module, used to calculate based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand characteristic.

[0088] Optionally, in the data information processing device provided in the embodiment of the present application, the second calculation module includes: a first calculation submodule, used to perform calculations based on multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations and multiple daily output maximum values ​​corresponding to the multiple photovoltaic output sequences; a second calculation submodule, used to perform calculations based on multiple photovoltaic parameters to obtain daily output estimation values ​​corresponding to the multiple photovoltaic output sequences; a third calculation submodule, used to perform calculations based on multiple power load sequences to obtain power load estimation values ​​corresponding to the multiple power load sequences; a determination submodule, used to determine the first elastic demand characteristic based on the daily output estimation values ​​and the power load estimation values.

[0089] Optionally, in the data information processing device provided in the embodiment of the present application, the determination submodule includes: a calculation submodule, which is used to perform calculations based on the daily power estimate and the power load estimate to obtain the net load estimate of the power grid; and a fusion submodule, which is used to perform feature fusion based on the net load estimate of the power grid, the daily power estimate and the power load estimate to obtain the first elastic demand feature.

[0090] Optionally, in the data information processing device provided in the embodiment of the present application, the device also includes: a judgment unit, used to predict the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group, and after obtaining the target prediction result, judge whether the target prediction result is less than or equal to a preset threshold to obtain a judgment result; a determination unit, used to determine the target power supply strategy corresponding to the target power grid based on the target prediction result if the judgment result indicates that the target prediction result is less than or equal to the preset threshold; a power supply unit, used to supply power to the user cluster based on the target power supply strategy.

[0091] The data information processing device includes a processor and a memory. The above-mentioned classification unit 301, extraction unit 302, prediction unit 303, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize accurate processing of data information.

[0092] The processor contains a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels, and the kernel parameters can be adjusted to accurately process the data information.

[0093] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0094] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a data information processing method is implemented.

[0095] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a data information processing method when running.

[0096] like Figure 4 As shown, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: classifying the grid net load sets of user clusters in the target grid within a historical time period to obtain multiple grid net load groups; extracting features based on the grid net load in each grid net load group to obtain elastic demand features corresponding to each grid net load group; predicting the elastic demand coefficient of the target grid on a target prediction day based on the elastic demand features corresponding to each grid net load group to obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted based on the target prediction result.

[0097] Optionally, the grid net load set of the user cluster in the target grid within the historical time period is classified to obtain multiple grid net load groups, including: for each initial grid net load in the grid net load set, determining the category information corresponding to each initial grid net load based on the size relationship between the preset net load threshold and the initial grid net load; classifying according to the category information corresponding to each initial grid net load to obtain multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category; obtaining multiple grid net load groups based on the multiple grid net loads corresponding to the first category and the multiple grid net loads corresponding to the second category.

[0098] Optionally, feature extraction is performed based on the grid net load in each grid net load group to obtain the elastic demand feature corresponding to each grid net load group, including: feature extraction of multiple grid net loads corresponding to the first category to obtain a first elastic demand feature; feature extraction of multiple grid net loads corresponding to the second category to obtain a second elastic demand feature; and determining the elastic demand feature based on the first elastic demand feature and the second elastic demand feature.

[0099] Optionally, feature extraction is performed on multiple grid net loads corresponding to the first category to obtain the first elastic demand feature, including: vectorizing the multiple grid net loads to obtain multiple feature vectors corresponding to the multiple grid net loads; calculating the multiple feature vectors by the Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple grid net loads; and calculating based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand feature.

[0100] Optionally, calculating based on multiple photovoltaic output sequences and multiple power load sequences to obtain the first elastic demand characteristic includes: calculating based on multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations, and multiple daily output maximum values ​​corresponding to the multiple photovoltaic output sequences; calculating based on multiple photovoltaic parameters to obtain daily output estimates corresponding to the multiple photovoltaic output sequences; calculating based on multiple power load sequences to obtain power load estimates corresponding to the multiple power load sequences; and determining the first elastic demand characteristic based on the daily output estimates and the power load estimates.

[0101] Optionally, determining the first elastic demand characteristic based on the daily power estimate and the power load estimate includes: performing calculations based on the daily power estimate and the power load estimate to obtain a net load estimate of the power grid; and performing feature fusion based on the net load estimate of the power grid, the daily power estimate and the power load estimate to obtain the first elastic demand characteristic.

[0102] Optionally, after predicting the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group and obtaining the target prediction result, the method also includes: judging whether the target prediction result is less than or equal to a preset threshold to obtain a judgment result; if the judgment result indicates that the target prediction result is less than or equal to the preset threshold, determining the target power supply strategy corresponding to the target power grid based on the target prediction result; and supplying power to the user cluster based on the target power supply strategy.

[0103] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0104] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: classifying the grid net load sets of user clusters in the target grid within a historical time period to obtain multiple grid net load groups; extracting features based on the grid net load in each grid net load group to obtain the elastic demand characteristics corresponding to each grid net load group; predicting the elastic demand coefficient of the target grid on the target prediction day based on the elastic demand characteristics corresponding to each grid net load group to obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted based on the target prediction result.

[0105] Optionally, the grid net load set of the user cluster in the target grid within the historical time period is classified to obtain multiple grid net load groups, including: for each initial grid net load in the grid net load set, determining the category information corresponding to each initial grid net load based on the size relationship between the preset net load threshold and the initial grid net load; classifying according to the category information corresponding to each initial grid net load to obtain multiple grid net loads corresponding to the first category and multiple grid net loads corresponding to the second category; obtaining multiple grid net load groups based on the multiple grid net loads corresponding to the first category and the multiple grid net loads corresponding to the second category.

[0106] Optionally, feature extraction is performed based on the grid net load in each grid net load group to obtain the elastic demand feature corresponding to each grid net load group, including: feature extraction of multiple grid net loads corresponding to the first category to obtain a first elastic demand feature; feature extraction of multiple grid net loads corresponding to the second category to obtain a second elastic demand feature; and determining the elastic demand feature based on the first elastic demand feature and the second elastic demand feature.

[0107] Optionally, feature extraction is performed on multiple grid net loads corresponding to the first category to obtain the first elastic demand feature, including: vectorizing the multiple grid net loads to obtain multiple feature vectors corresponding to the multiple grid net loads; calculating the multiple feature vectors by the Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple grid net loads; and calculating based on the multiple photovoltaic output sequences and the multiple power load sequences to obtain the first elastic demand feature.

[0108] Optionally, calculating based on multiple photovoltaic output sequences and multiple power load sequences to obtain the first elastic demand characteristic includes: calculating based on multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations, and multiple daily output maximum values ​​corresponding to the multiple photovoltaic output sequences; calculating based on multiple photovoltaic parameters to obtain daily output estimates corresponding to the multiple photovoltaic output sequences; calculating based on multiple power load sequences to obtain power load estimates corresponding to the multiple power load sequences; and determining the first elastic demand characteristic based on the daily output estimates and the power load estimates.

[0109] Optionally, determining the first elastic demand characteristic based on the daily power estimate and the power load estimate includes: performing calculations based on the daily power estimate and the power load estimate to obtain a net load estimate of the power grid; and performing feature fusion based on the net load estimate of the power grid, the daily power estimate and the power load estimate to obtain the first elastic demand characteristic.

[0110] Optionally, after predicting the elastic demand coefficient of the target power grid on the target prediction day based on the elastic demand characteristics corresponding to each power grid net load group and obtaining the target prediction result, the method also includes: judging whether the target prediction result is less than or equal to a preset threshold to obtain a judgment result; if the judgment result indicates that the target prediction result is less than or equal to the preset threshold, determining the target power supply strategy corresponding to the target power grid based on the target prediction result; and supplying power to the user cluster based on the target power supply strategy.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0119] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A data information processing method, characterized in that: include: Classifying the net load sets of the user clusters in the target power grid within the historical time period to obtain multiple net load groups of the power grid; Extracting features based on the net load of each net load group of the power grid to obtain elastic demand features corresponding to each net load group of the power grid; The elastic demand coefficient of the target power grid on the target prediction day is predicted according to the elastic demand characteristics corresponding to each power grid net load group to obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted according to the target prediction result.

2. The method according to claim 1, characterized in that: The net load set of the user cluster in the target power grid in the historical time period is classified to obtain multiple net load groups of the power grid including: For each initial grid net load in the grid net load set, determining category information corresponding to each initial grid net load according to a size relationship between a preset net load threshold and the initial grid net load; Classify according to the category information corresponding to each initial power grid net load to obtain a plurality of power grid net loads corresponding to the first category and a plurality of power grid net loads corresponding to the second category; The plurality of grid net load groups are obtained according to the plurality of grid net loads corresponding to the first category and the plurality of grid net loads corresponding to the second category.

3. The method according to claim 2, characterized in that According to the net load of each net load group of the power grid, the characteristics of elastic demand corresponding to each net load group of the power grid are extracted, including: Extracting features of a plurality of power grid net loads corresponding to the first category to obtain a first elastic demand feature; Extracting features of a plurality of power grid net loads corresponding to the second category to obtain a second elastic demand feature; The elastic demand characteristic is determined according to the first elastic demand characteristic and the second elastic demand characteristic.

4. The method according to claim 3, characterized in that Feature extraction is performed on multiple power grid net loads corresponding to the first category to obtain first elastic demand features including: Vectorizing the plurality of power grid net loads to obtain a plurality of feature vectors corresponding to the plurality of power grid net loads; Calculating the multiple eigenvectors by Lagrange multiplier method to obtain multiple photovoltaic output sequences and multiple power load sequences corresponding to the multiple grid net loads; The first elastic demand characteristic is obtained by performing calculations based on the multiple photovoltaic output sequences and the multiple power load sequences.

5. The method according to claim 4, characterized in that The first elastic demand characteristics are obtained by calculating according to the multiple photovoltaic output sequences and the multiple power load sequences, including: Calculating according to the multiple photovoltaic output sequences to obtain multiple photovoltaic parameters, wherein the multiple photovoltaic parameters at least include: multiple daily output means, multiple daily output standard deviations, and multiple daily output maximums corresponding to the multiple photovoltaic output sequences; Calculating according to the plurality of photovoltaic parameters to obtain daily output estimation values ​​corresponding to the plurality of photovoltaic output sequences; Calculating according to the multiple power load sequences to obtain power load estimation values ​​corresponding to the multiple power load sequences; The first elastic demand characteristic is determined according to the daily power output estimate and the power load estimate.

6. The method according to claim 5, characterized in that Determining the first elastic demand characteristic according to the daily power output estimate and the power load estimate includes: Calculate the estimated value of net load of the power grid according to the estimated value of daily power generation and the estimated value of power load; The first elastic demand feature is obtained by performing feature fusion based on the estimated value of the net load of the power grid, the estimated value of the daily power output and the estimated value of the power load.

7. The method according to claim 1, characterized in that After predicting the elastic demand coefficient of the target power grid on the target prediction day according to the elastic demand characteristics corresponding to each power grid net load group to obtain the target prediction result, the method further includes: Determine whether the target prediction result is less than or equal to a preset threshold, and obtain a determination result; If the judgment result indicates that the target prediction result is less than or equal to the preset threshold, determining the target power supply strategy corresponding to the target power grid according to the target prediction result; Power is supplied to the user cluster according to the target power supply strategy.

8. A data information processing device, characterized in that: include: A classification unit, used for classifying a set of net loads of a user cluster in a target power grid within a historical time period to obtain a plurality of net load groups of the power grid; An extraction unit, used to extract features according to the net load of each net load group of the power grid, and obtain elastic demand features corresponding to each net load group of the power grid; A prediction unit is used to predict the elastic demand coefficient of the target power grid on a target prediction day according to the elastic demand characteristics corresponding to each power grid net load group, and obtain a target prediction result, wherein the power supply strategy for the user cluster is adjusted according to the target prediction result.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the storage medium is controlled to execute the data information processing method according to any one of claims 1 to 7 on a device.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data information processing method described in any one of claims 1 to 7.

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