Power sale quantity dynamic deduction method considering domain knowledge, equipment and medium

Abstract

The invention relates to an electricity sale quantity dynamic deduction method and device considering domain knowledge, and a medium. The method comprises the following steps: collecting target domain electricity sale quantity data, constructing an industry relation adjacency matrix, and analyzing the association strength between industries; collecting electricity sale quantity data of upstream and downstream industries of an industrial chain in a target field to analyze an industrial chain upstream and downstream industry action relationship; constructing a feature extraction model, and extracting enterprise maintenance features, policy adjustment features and market quotation fluctuation features of the target domain; constructing a training set based on the data, and performing sample expansion on the training set; constructing an electricity selling boundary constraint based on power grid power supply and equipment physical characteristics; and constructing an electricity sale quantity prediction model based on the electricity sale boundary constraint, training the electricity sale quantity prediction model through the training set after sample expansion, and performing electricity sale quantity prediction of the target domain through the trained electricity sale quantity prediction model.

Description

Technical Field

[0001] The present invention relates to a method, device and medium for dynamically deducing power sales volume considering domain knowledge, and belongs to the technical field of power sales volume prediction. Background Art

[0002] Electricity sales forecasting is an important task in power system planning and operation. Commonly used methods include traditional statistical methods, machine learning methods, deep learning methods, and hybrid model methods. Traditional statistical methods such as time series analysis (ARIMA, exponential smoothing method) and regression analysis are suitable for scenarios with stable data and obvious linear relationships by modeling historical data. Machine learning methods such as support vector machines (SVM), decision trees (random forests, XGBoost), and artificial neural networks (ANN) can process nonlinear and multi-dimensional data, have strong adaptive capabilities, and are particularly suitable for the prediction of large-scale complex data.

[0003] Deep learning methods such as long short-term memory networks (LSTM), gated recurrent units (GRU), and Transformer models can capture long-term dependencies and global features in time series data, and are particularly suitable for time series prediction of electricity sales.

[0004] In addition, the hybrid model method combines multiple methods, such as first using a statistical model to predict trends, and then using a machine learning or deep learning model to optimize the residual part to further improve the prediction accuracy.

[0005] External factors that affect electricity sales, such as weather, economic level, population growth and holiday effects, are often taken into account in modeling. For example, modeling is carried out by combining temperature, seasonal changes in electricity demand, GDP and industrial structure data.

[0006] In recent years, the development of big data technology and cloud computing has also provided efficient data processing and computing support for electricity sales forecasting. In practical applications, ARIMA, LSTM or XGBoost are usually used for short-term forecasts, while regression analysis, economic models or deep learning methods are often used for medium- and long-term forecasts. Complex forecasting tasks often combine multiple models for hybrid forecasting to achieve higher accuracy.

[0007] Although the existing electricity sales forecasting methods have achieved remarkable results, they still have some shortcomings and limitations. Traditional statistical methods such as ARIMA and regression analysis rely on the stationarity and linearity assumptions of data, and are difficult to handle complex nonlinear relationships and sudden changes. Although machine learning and deep learning methods can model nonlinear problems, they have high requirements for data volume and quality, are easily affected by noise data, and have many model parameters, complex training processes, and are prone to overfitting. In addition, deep learning models such as LSTM have large computational complexity and low efficiency in long-term series prediction, and the "black box" characteristics of the model make it difficult to explain, affecting the credibility of practical applications. Although hybrid models can combine the advantages of multiple methods, they are complex in model design, difficult to tune parameters, and have high application costs. At the same time, the introduction of external factors such as weather, economic level, and policy changes can improve prediction accuracy, but data acquisition is difficult, and it is impossible to fully quantify and predict the impact of emergencies on electricity demand.

[0008] Therefore, how to improve the generalization ability, computational efficiency and interpretability of the model and solve the problems of incomplete and uncertain data remain important challenges in the field of electricity sales forecasting. Summary of the invention

[0009] In order to solve the above problems existing in the prior art, the present invention proposes a method, device and medium for dynamically deriving electricity sales amount taking into account domain knowledge.

[0010] The technical solution of the present invention is as follows: On the one hand, the present invention provides a method for dynamically deducing power sales volume considering domain knowledge, comprising the following steps: Collect electricity sales data from all industries in multiple regions in the target field, and build an industry relationship adjacency matrix based on the electricity sales data to analyze the correlation strength between industries; Collect electricity sales data of upstream and downstream industries in the target field industry chain to build an upstream and downstream relationship network model of the industry chain, and analyze the relationship between upstream and downstream industries in the industry chain; Construct a feature extraction model to extract enterprise maintenance features, policy adjustment features, and market fluctuation features in the target field; The training set is constructed based on the electricity sales data of all industries in multiple regions of the target field, the correlation strength between industries, the relationship between upstream and downstream industries in the industrial chain, the maintenance characteristics of enterprises, the characteristics of policy adjustments, and the characteristics of market fluctuations, and the samples of the training set are expanded; Establish power sales boundary constraints based on power grid supply and physical characteristics of equipment; A power sales prediction model is constructed based on the power sales boundary constraints, and the power sales prediction model is trained using the training set after sample expansion. The trained power sales prediction model is used to predict the power sales in the target area.

[0011] As a preferred embodiment of the present invention, the steps of constructing the industry relationship adjacency matrix are: Based on the electricity sales data of all industries in multiple regions in the target field, a characteristic matrix of electricity sales in different regions is constructed; Based on the characteristic matrix of electricity sales in different regions, the dynamic correlation coefficient between regions is defined, as shown in the following formula: ; in: Indicates the region and region The dynamic correlation coefficient between express Time Region Data samples in the electricity sales feature matrix; represents the hysteresis parameter; express Time Region Data samples in the electricity sales feature matrix; Indicates the region The standard deviation of the electricity sales characteristic matrix; Indicates the region The standard deviation of the electricity sales characteristic matrix; An industry association network is constructed based on the dynamic correlation coefficient between regions. The nodes in the industry association network represent an industry, and the edges represent the association between industries. Then, an industry relationship adjacency matrix is ​​constructed based on the industry association network, as shown in the following formula: ; in: Represents the nodes in the industry relationship adjacency matrix With Node The connection strength of the industry Industry The strength of association; represents the lag time interval; Indicates the start time; express Time Node With Node The dynamic correlation coefficient between corresponding regions.

[0012] As a preferred embodiment of the present invention, the specific steps of analyzing the relationship between upstream and downstream industries in the industrial chain are: Based on the electricity sales data of upstream and downstream industries in the target field industrial chain, a restricted model and an unrestricted model are constructed, as shown in the following formula: ; ; in: Representing a restricted model Output results at the moment; represents the total lag order of electricity sales demand in downstream industries; Indicates The lag order of electricity sales demand of downstream industries; express The electricity sales demand of downstream industries at the moment; Represents an unrestricted model Output results at the moment; represents the total lag order of electricity sales demand in the upstream industry; Indicates The lag order of electricity sales demand of upstream industries; express The electricity sales demand of upstream industries at all times; represents the random error term; Indicates The lag coefficient of electricity sales demand of downstream industries; Indicates The lag coefficient of electricity sales demand of upstream industries; The lagged parameter values ​​are calculated based on the residuals of the restricted model and the unrestricted model, as shown in the following formula: ; in: represents the residual sum of squares of the restricted model; represents the residual sum of squares of the unrestricted model; Based on the graph neural network, a network model of upstream and downstream relationships in the industrial chain is constructed. Each link in the upstream and downstream of the industrial chain is abstracted as a node, and the connection relationship between each link in the upstream and downstream of the industrial chain is abstracted as an edge. The upstream and downstream relationship network model of the industrial chain is specifically shown in the following formula: ; in: The network model of the upstream and downstream relationship of the industrial chain The node characteristics of the layer, including the connection strength between each node; represents the Laplacian matrix; represents the adjacency matrix, which is constructed based on the industry relationship adjacency matrix and obtained by substituting the calculated lag parameters; The network model of the upstream and downstream relationship of the industrial chain Node characteristics of the layer; The network model of the upstream and downstream relationship of the industrial chain The weight matrix of the layer; Represents the sigmoid function; Based on the final output of the upstream and downstream relationship network model of the industrial chain, the relationship between the upstream and downstream industries in the industrial chain is analyzed.

[0013] As a preferred embodiment of the present invention, the specific steps for extracting the enterprise maintenance characteristics, policy adjustment characteristics and market fluctuation characteristics are as follows: Build an enterprise maintenance feature extraction model based on machine learning model; The prior knowledge of the business personnel in the target field is collected. The prior knowledge of the business personnel in the target field specifically includes the enterprise operation data, equipment maintenance cycle, and equipment overhaul cycle in the target field. Based on the prior knowledge of the business personnel in the target field, the enterprise overhaul features are extracted through the enterprise overhaul feature extraction model, which is expressed as a mask matrix: ; in: represents the enterprise maintenance feature mask matrix; Indicates the start time; Indicates the end time; represents the indicator function; Indicates the number of enterprise maintenance features; Indicates Maintenance characteristics of each enterprise; represents the maintenance characteristic value of the enterprise; represents the threshold value of enterprise maintenance characteristic value; Collect policy adjustment data and market data in the target area, build an impact quantification model based on the machine learning model, and extract the policy adjustment characteristics and market fluctuation characteristics of the target area through the impact quantification model, as shown in the following formula: ; ; in: Indicates the characteristics of policy adjustments; express Policy adjustment data samples at each moment; It indicates the impact intensity corresponding to the policy adjustment data sample; represents the attenuation coefficient corresponding to the policy adjustment data sample; Indicates the characteristics of market fluctuations; express Market data samples at the moment; Indicates the impact intensity corresponding to the market data sample; Represents the attenuation coefficient corresponding to the market data sample.

[0014] As a preferred embodiment of the present invention, the specific steps of expanding the sample of the training set are: Perform translation, scaling, and interpolation operations on the training set to obtain a data-enhanced training set; A historical event database is constructed, which contains various types of historical impact events, and the impact of historical impact events on electricity sales is calculated, as shown in the following formula: ; in: express The impact of historical events on electricity sales forecast; express The target area is the electricity sales data of multiple regions and industries at all times; Indicates The impact coefficient of the historical impact event on the electricity sales data of multiple regions and industries in the target field; represents the influence function; Indicates The moment when a historically influential event occurred; Generate various types of historical impact events through simulation, and calculate the impact of the historical impact events generated by simulation on the electricity sales based on the above formula, and then assign weights to each historical impact event; The impact of historical events before and after the generation on electricity sales is included in the data-enhanced training set to obtain a training set containing influencing factors; Based on the noise perturbation, random Gaussian noise is generated for the training set containing the influencing factors to obtain the training set after noise generation; Different resampling frequencies are set according to the weight corresponding to each historical impact event in the training set after noise generation, and the remaining data in the training set are resampled at the time nodes corresponding to the historical impact events according to the resampling frequencies to obtain the training set after sample expansion.

[0015] As a preferred embodiment of the present invention, a boundary constraint for electricity sales is constructed based on power supply of the power grid and physical characteristics of the equipment, as shown in the following formula: ; ; ; ; in: Indicates the power grid The power generation capacity at any time; Indicates the power grid The power transmission capacity at any time; express The amount of electricity sold at the time; represents the capacity constraints of the power transmission lines of the power grid; Represents a power node The average voltage of Represents a power node The average voltage of Represents a power node With power node The line reactance between Represents a power node With power node The voltage phase angle difference between them.

[0016] As a preferred embodiment of the present invention, the following constraints are set on the basis of the power sales boundary constraints: Line loss constraints: ; in: Indicates power line loss; Indicates the power line current; Indicates the resistance of the power line; Indicates the power line loss threshold; Voltage stability constraints: ; in: Indicates the minimum allowable voltage of the power grid; Indicates the maximum allowable voltage of the power grid; Indicates the real-time voltage of the power grid; Transformer capacity constraints: ; in: Indicates transformer capacity; Represents the average transmission capacity of the power grid.

[0017] As a preferred embodiment of the present invention, the power sales boundary constraint, line loss constraint, voltage stability constraint and transformer capacity constraint are used as the prior information of the power sales forecasting model, and the loss function of the power sales forecasting model is for: ; in: represents the loss function of the data part of the electricity sales prediction model; represents the boundary constraint loss function of electricity sales; represents the voltage stability constraint loss function; represents the line loss constraint loss function; represents the transformer capacity constraint loss function; , , , Represents the weight parameter.

[0018] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0019] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0020] The present invention has the following beneficial effects: By integrating multi-source data, physical constraints and key event information, the present invention can comprehensively capture the complex factors affecting electricity sales, accurately identify long-term trends, seasonality and sudden fluctuations in time series, significantly improve the accuracy of prediction results and the stability in different scenarios, help power companies optimize resource allocation, identify potential supply and demand imbalance risks in advance, improve the scientificity and efficiency of power dispatch, and thus achieve safe and stable operation of the power system and maximize economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0024] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0026] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0027] Embodiment 1: See also Figure 1 , a method for dynamic deduction of electricity sales volume considering domain knowledge, comprising the following steps: Collect electricity sales data from all industries in multiple regions in the target field, and build an industry relationship adjacency matrix based on the electricity sales data to analyze the correlation strength between industries; Collect electricity sales data of upstream and downstream industries in the target field industry chain to build an upstream and downstream relationship network model of the industry chain, and analyze the relationship between upstream and downstream industries in the industry chain; Construct a feature extraction model to extract enterprise maintenance features, policy adjustment features, and market fluctuation features in the target field; The training set is constructed based on the electricity sales data of all industries in multiple regions of the target field, the correlation strength between industries, the relationship between upstream and downstream industries in the industrial chain, the maintenance characteristics of enterprises, the characteristics of policy adjustments, and the characteristics of market fluctuations, and the samples of the training set are expanded; Establish power sales boundary constraints based on power grid supply and physical characteristics of equipment; A power sales prediction model is constructed based on the power sales boundary constraints, and the power sales prediction model is trained using the training set after sample expansion. The trained power sales prediction model is used to predict the power sales in the target area.

[0028] As a preferred implementation of this embodiment, the steps for constructing the industry relationship adjacency matrix are: Based on the electricity sales data of all industries in multiple regions in the target field, a characteristic matrix of electricity sales in different regions is constructed; Based on the characteristic matrix of electricity sales in different regions, the dynamic correlation coefficient between regions is defined as follows: ; in: Indicates the region and region The dynamic correlation coefficient between express Time Region Data samples in the electricity sales feature matrix; It represents the hysteresis parameter, which is used to analyze the transmission lag of electricity sales; express Time Region Data samples in the electricity sales feature matrix; Indicates the region The standard deviation of the electricity sales characteristic matrix; Indicates the region The standard deviation of the electricity sales characteristic matrix; An industry association network is constructed based on the dynamic correlation coefficient between regions. The nodes in the industry association network represent an industry, and the edges represent the association between industries. Then, an industry relationship adjacency matrix is ​​constructed based on the industry association network, as shown in the following formula: ; in: Represents the nodes in the industry relationship adjacency matrix With Node The connection strength of the industry Industry The strength of association; represents the lag time interval; Indicates the start time; express Time Node With Node Dynamic correlation coefficient between corresponding regions; Through multi-dimensional cross-comparison, the interdependence and potential rules of electricity sales in different regions and industries are revealed, and the spatial distribution and evolution trend of the correlation between electricity sales in different regions and industries are clearly displayed, thus providing support for the optimal allocation of power resources.

[0029] As a preferred implementation of this embodiment, the specific steps of analyzing the relationship between upstream and downstream industries in the industrial chain are: Based on the electricity sales data of upstream and downstream industries in the target field industry chain, a restricted model (which does not consider the impact of upstream enterprises) and an unrestricted model (which considers the impact of upstream enterprises) are constructed, as shown in the following formula: ; ; in: Representing a restricted model Output results at the moment; represents the total lag order of electricity sales demand in downstream industries; Indicates The lag order of electricity sales demand of downstream industries; express The electricity sales demand of downstream industries at the moment; Represents an unrestricted model Output results at the moment; represents the total lag order of electricity sales demand in the upstream industry; Indicates The lag order of electricity sales demand of upstream industries; express The electricity sales demand of upstream industries at all times; represents the random error term; Indicates The lag coefficient of the electricity sales demand of the downstream industries indicates the lag effect of the downstream electricity sales demand itself; Indicates The lag coefficient of the electricity sales demand of the upstream industry indicates the lag effect of the electricity sales demand of the upstream industry itself; The lagged parameter values ​​are calculated based on the residuals of the restricted model and the unrestricted model, as shown in the following formula: ; in: represents the residual sum of squares of the restricted model; represents the residual sum of squares of the unrestricted model; Based on the graph neural network, a network model of upstream and downstream relationships in the industrial chain is constructed. Each link in the upstream and downstream of the industrial chain is abstracted as a node, and the connection relationship between each link in the upstream and downstream of the industrial chain is abstracted as an edge. The upstream and downstream relationship network model of the industrial chain is specifically shown in the following formula: ; in: The network model of the upstream and downstream relationship of the industrial chain The node characteristics of the layer, including the connection strength between each node; represents the Laplacian matrix; represents the adjacency matrix, which is constructed based on the industry relationship adjacency matrix and obtained by substituting the calculated lag parameters; The network model of the upstream and downstream relationship of the industrial chain Node characteristics of the layer; The network model of the upstream and downstream relationship of the industrial chain The weight matrix of the layer; Represents the sigmoid function; With the help of graph neural networks, the traditional input-output table is upgraded to a dynamic and learnable graph structure, which further captures the potential dependency patterns in time series data and automatically identifies which changes in upstream links have the greatest impact on downstream, as well as the duration and magnitude of the impact; Based on the final output of the upstream and downstream relationship network model of the industrial chain, the relationship between the upstream and downstream industries in the industrial chain is analyzed.

[0030] As a preferred implementation of this embodiment, the specific steps for extracting the enterprise maintenance characteristics, policy adjustment characteristics and market fluctuation characteristics are as follows: Build an enterprise maintenance feature extraction model based on machine learning model; The prior knowledge of the business personnel in the target field is collected. The prior knowledge of the business personnel in the target field specifically includes the enterprise operation data, equipment maintenance cycle, and equipment overhaul cycle in the target field. Based on the prior knowledge of the business personnel in the target field, the enterprise overhaul features are extracted through the enterprise overhaul feature extraction model, which is expressed as a mask matrix: ; in: represents the enterprise maintenance feature mask matrix; Indicates the start time; Indicates the end time; represents the indicator function; Indicates the number of enterprise maintenance characteristics; Indicates Maintenance characteristics of each enterprise; represents the maintenance characteristic value of the enterprise; represents the threshold value of enterprise maintenance characteristic value; Collect policy adjustment data and market data (such as changes in commodity prices) in the target area, build an impact quantification model based on the machine learning model, and use the impact quantification model to extract the policy adjustment characteristics and market fluctuation characteristics of the target area, as shown in the following formula: ; ; in: Indicates the characteristics of policy adjustments; express Policy adjustment data samples at each moment; It indicates the impact intensity corresponding to the policy adjustment data sample; represents the attenuation coefficient corresponding to the policy adjustment data sample; Indicates the characteristics of market fluctuations; express Market data samples at the moment; Indicates the impact intensity corresponding to the market data sample; Represents the attenuation coefficient corresponding to the market data sample.

[0031] As a preferred implementation of this embodiment, the specific steps of expanding the sample of the training set are: Perform translation, scaling, and interpolation operations on the training set to obtain a data-enhanced training set; A historical event database is constructed, which contains various types of historical impact events (such as equipment failures, maintenance plans, natural disasters, policy adjustments, and abnormal market fluctuations). The impact of historical impact events on electricity sales is calculated as shown in the following formula: ; in: express The impact of historical events on electricity sales forecast; express The target area is the electricity sales data of multiple regions and industries at all times; Indicates The impact coefficient of the historical impact event on the electricity sales data of multiple regions and industries in the target field; represents the influence function; Indicates The moment when a historically influential event occurred; Generate various types of historical impact events through simulation, and calculate the impact of the historical impact events generated by simulation on the electricity sales based on the above formula, and then assign weights to each historical impact event; For some rare but important events (such as extreme weather or large-scale power outages), a rule-driven approach is used to generate synthetic data to supplement the deficiencies of the training set; The impact of historical events before and after the generation on electricity sales is included in the data-enhanced training set to obtain a training set containing influencing factors; Based on the noise perturbation, random Gaussian noise is generated for the training set containing the influencing factors to obtain the training set after noise generation; Different resampling frequencies are set according to the weight corresponding to each historical impact event in the training set after noise generation, and the remaining data in the training set are resampled at the time nodes corresponding to the historical impact events according to the resampling frequencies to obtain the training set after sample expansion.

[0032] As a preferred implementation of this embodiment, a power sales boundary constraint is constructed based on power supply from the power grid and physical characteristics of the equipment, as shown in the following formula: ; ; ; ; in: Indicates the power grid The power generation capacity at any time; Indicates the power grid The power transmission capacity at any time; express The amount of electricity sold at the time; represents the capacity constraints of the power transmission lines of the power grid; Represents a power node The average voltage of Represents a power node The average voltage of Represents a power node With power node The line reactance between Represents a power node With power node The voltage phase angle difference between them.

[0033] As a preferred implementation of this embodiment, the following constraints are set based on the power sales boundary constraints: Line loss constraints: ; in: Indicates power line loss; Indicates the power line current; Indicates the resistance of the power line; Indicates the power line loss threshold; Voltage stability constraints: ; in: Indicates the minimum permissible voltage of the power grid; Indicates the maximum allowable voltage of the power grid; Indicates the real-time voltage of the power grid; Transformer capacity constraints: ; in: Indicates transformer capacity; Represents the average transmission capacity of the power grid.

[0034] As a preferred implementation of this embodiment, the power sales boundary constraint, line loss constraint, voltage stability constraint and transformer capacity constraint are used as the prior information of the power sales forecasting model, and the loss function of the power sales forecasting model is for: ; in: represents the loss function of the data part of the electricity sales prediction model; represents the boundary constraint loss function of electricity sales; represents the voltage stability constraint loss function; represents the line loss constraint loss function; represents the transformer capacity constraint loss function; , , , represents the weight parameter; The data part loss function is specifically: ; in: express The actual value of electricity sales at the moment; Identifies the electricity sales forecast value of the electricity sales forecast model at time t.

[0035] Embodiment 2: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0036] Embodiment three: This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0037] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0038] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0040] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0041] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for dynamically deducing electricity sales volume considering domain knowledge, characterized in that: The following steps are involved: Collect electricity sales data from all industries in multiple regions in the target field, and build an industry relationship adjacency matrix based on the electricity sales data to analyze the correlation strength between industries; Collect electricity sales data from upstream and downstream industries in the target industry chain, build a network model of upstream and downstream relationships in the industry chain, and analyze the relationship between upstream and downstream industries in the industry chain; Construct a feature extraction model to extract enterprise maintenance features, policy adjustment features, and market fluctuation features in the target field; The training set is constructed based on the electricity sales data of all industries in multiple regions of the target field, the correlation strength between industries, the relationship between upstream and downstream industries in the industrial chain, the maintenance characteristics of enterprises, the characteristics of policy adjustments, and the characteristics of market fluctuations, and the samples of the training set are expanded; Establish power sales boundary constraints based on power grid supply and physical characteristics of equipment; A power sales prediction model is constructed based on the power sales boundary constraints, and the power sales prediction model is trained using the training set after sample expansion. The trained power sales prediction model is used to predict the power sales in the target area.

2. According to claim 1, a method for dynamically deducing power sales volume considering domain knowledge is characterized in that: The steps for constructing the industry relationship adjacency matrix are: Based on the electricity sales data of all industries in multiple regions in the target field, a characteristic matrix of electricity sales in different regions is constructed; Based on the characteristic matrix of electricity sales in different regions, the dynamic correlation coefficient between regions is defined as follows: ； in: Indicates the region and region The dynamic correlation coefficient between express Time Region Data samples in the electricity sales feature matrix; represents the hysteresis parameter; express Time Region Data samples in the electricity sales feature matrix; Indicates the region The standard deviation of the electricity sales characteristic matrix; Indicates the region The standard deviation of the electricity sales characteristic matrix; An industry association network is constructed based on the dynamic correlation coefficient between regions. The nodes in the industry association network represent an industry, and the edges represent the association between industries. Then, an industry relationship adjacency matrix is ​​constructed based on the industry association network, as shown in the following formula: ； in: Represents the nodes in the industry relationship adjacency matrix With Node The connection strength of the industry With the industry The strength of association; represents the lag time interval; Indicates the start time; express Time Node With Node The dynamic correlation coefficient between corresponding regions.

3. According to claim 2, a method for dynamically deducing power sales volume considering domain knowledge is characterized in that: The specific steps for analyzing the relationship between upstream and downstream industries in the industrial chain are: Based on the electricity sales data of upstream and downstream industries in the target field industrial chain, a restricted model and an unrestricted model are constructed, as shown in the following formula: ； ； in: Representing a restricted model Output results at the moment; It represents the total lag order of electricity sales demand in downstream industries; Indicates The lag order of electricity sales demand of downstream industries; express The electricity sales demand of downstream industries at the moment; Represents an unrestricted model Output results at the moment; represents the total lag order of electricity sales demand in the upstream industry; Indicates The lag order of electricity sales demand of upstream industries; express The electricity sales demand of upstream industries at all times; represents the random error term; Indicates The lag coefficient of electricity sales demand of downstream industries; Indicates The lag coefficient of electricity sales demand of upstream industries; The lagged parameter values ​​are calculated based on the residuals of the restricted model and the unrestricted model, as shown in the following formula: ； in: represents the residual sum of squares of the restricted model; represents the residual sum of squares of the unrestricted model; Based on the graph neural network, a network model of upstream and downstream relationships in the industrial chain is constructed. Each link in the upstream and downstream of the industrial chain is abstracted as a node, and the connection relationship between each link in the upstream and downstream of the industrial chain is abstracted as an edge. The upstream and downstream relationship network model of the industrial chain is specifically shown in the following formula: ； in: The network model of the upstream and downstream relationship of the industrial chain The node characteristics of the layer, including the connection strength between each node; represents the Laplacian matrix; represents the adjacency matrix, which is constructed based on the industry relationship adjacency matrix and obtained by substituting the calculated lag parameters; The network model of the upstream and downstream relationship of the industrial chain Node characteristics of the layer; The network model of the upstream and downstream relationship of the industrial chain The weight matrix of the layer; Represents the sigmoid function; Based on the final output of the upstream and downstream relationship network model of the industrial chain, the relationship between the upstream and downstream industries in the industrial chain is analyzed.

4. The method for dynamically deducing power sales volume considering domain knowledge according to claim 3 is characterized in that: The specific steps for extracting the enterprise maintenance characteristics, policy adjustment characteristics and market fluctuation characteristics are as follows: Build an enterprise maintenance feature extraction model based on machine learning model; The prior knowledge of the business personnel in the target field is collected. The prior knowledge of the business personnel in the target field specifically includes the enterprise operation data, equipment maintenance cycle, and equipment overhaul cycle in the target field. Based on the prior knowledge of the business personnel in the target field, the enterprise overhaul features are extracted through the enterprise overhaul feature extraction model, which is expressed as a mask matrix: ； in: represents the enterprise maintenance feature mask matrix; Indicates the start time; Indicates the end time; represents the indicator function; Indicates the number of enterprise maintenance features; Indicates Maintenance characteristics of each enterprise; represents the maintenance characteristic value of the enterprise; Indicates the threshold value of enterprise maintenance characteristic value; Collect policy adjustment data and market data in the target area, build an impact quantification model based on the machine learning model, and extract the policy adjustment characteristics and market fluctuation characteristics of the target area through the impact quantification model, as shown in the following formula: ； ； in: Indicates the characteristics of policy adjustments; express Policy adjustment data samples at each moment; It indicates the impact intensity corresponding to the policy adjustment data sample; represents the attenuation coefficient corresponding to the policy adjustment data sample; Indicates the characteristics of market fluctuations; express Market data samples at the moment; Indicates the impact intensity corresponding to the market data sample; Represents the attenuation coefficient corresponding to the market data sample.

5. According to claim 1, a method for dynamically deducing power sales volume considering domain knowledge is characterized in that: The specific steps of expanding the sample of the training set are: Perform translation, scaling, and interpolation operations on the training set to obtain a data-enhanced training set; A historical event database is constructed, which contains various types of historical impact events, and the impact of historical impact events on electricity sales is calculated, as shown in the following formula: ； in: express The impact of historical events on electricity sales forecast; express The target area is the electricity sales data of multiple regions and industries at all times; Indicates The impact coefficient of the historical impact event on the electricity sales data of multiple regions and industries in the target field; represents the influence function; Indicates The moment when a historically influential event occurred; Generate various types of historical impact events through simulation, and calculate the impact of the historical impact events generated by simulation on the electricity sales based on the above formula, and then assign weights to each historical impact event; The impact of historical events before and after the generation on electricity sales is included in the data-enhanced training set to obtain a training set containing influencing factors; Based on the noise perturbation, random Gaussian noise is generated for the training set containing the influencing factors to obtain the training set after noise generation; Different resampling frequencies are set according to the weight corresponding to each historical impact event in the training set after noise generation, and the remaining data in the training set are resampled at the time nodes corresponding to the historical impact events according to the resampling frequencies to obtain the training set after sample expansion.

6. The method for dynamically deducing power sales volume considering domain knowledge according to claim 1, characterized in that: The boundary constraints for electricity sales are constructed based on the power supply of the power grid and the physical characteristics of the equipment, as shown in the following formula: ； ； ； ； in: Indicates the power grid The power generation capacity at any time; Indicates the power grid The power transmission capacity at any time; express The amount of electricity sold at the time; represents the power transmission line capacity constraints of the power grid; Represents a power node The average voltage of Represents a power node The average voltage of Represents a power node With power node The line reactance between Represents a power node With power node The voltage phase angle difference between them.

7. The method for dynamically deducing power sales volume considering domain knowledge according to claim 6 is characterized in that: The following constraints are set based on the power sales boundary constraints: Line loss constraints: ； in: Indicates power line loss; Indicates the power line current; Indicates the resistance of the power line; Indicates the power line loss threshold; Voltage stability constraints: ； in: Indicates the minimum permissible voltage of the power grid; Indicates the maximum allowable voltage of the power grid; Indicates the real-time voltage of the power grid; Transformer capacity constraints: ； in: Indicates transformer capacity; Represents the average transmission capacity of the power grid.

8. The method for dynamically deducing power sales volume considering domain knowledge according to claim 7 is characterized in that: Taking the power sales boundary constraint, line loss constraint, voltage stability constraint and transformer capacity constraint as the prior information of the power sales forecasting model, the loss function of the power sales forecasting model is for: ； in: represents the loss function of the data part of the electricity sales prediction model; represents the boundary constraint loss function of electricity sales; represents the voltage stability constraint loss function; represents the line loss constraint loss function; represents the transformer capacity constraint loss function; , , , Represents the weight parameter.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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