Multivariate spatial data management control method and device

Abstract

The invention discloses a multivariate spatial data management control method and device, and the method comprises the steps: obtaining multivariate spatial data in the operation process of a power system; performing correlation analysis on each external data and the power load data to obtain a correlation coefficient of each external data and the power load data; constructing a correlation coefficient matrix according to the correlation coefficient of each external data and the power load data; traversing the correlation coefficient matrix, and selecting external data of which the correlation coefficient is greater than or equal to a preset correlation threshold value to obtain an external data set mainly correlated with the power load data; and on the basis of the power demands of different power regions, predicting the power consumption required by the target power region according to the external data set mainly associated with the power load data so as to determine an optimal management scheme of the multivariate spatial data. According to the invention, the management efficiency and precision of the multivariate spatial data can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a multi-dimensional spatial data management control method and device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] The management of multivariate spatial data in the power industry is a complex but crucial task, involving a large amount of geographic information system (GIS) data, power facility operation status, environmental monitoring information, etc. The centralized management of these multivariate spatial data requires not only powerful data processing capabilities, but also the special needs of the power industry. At present, the existing technology has low efficiency and poor accuracy in the management of multivariate spatial data. Therefore, a method is urgently needed to solve the above problems. Summary of the invention

[0004] An embodiment of the present invention provides a multivariate spatial data management control method for improving the efficiency and accuracy of multivariate spatial data management. The method includes:

[0005] During the operation of the power system, multivariate spatial data is obtained; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data;

[0006] Perform correlation analysis on each external data and the power load data to obtain the correlation coefficient between each external data and the power load data;

[0007] According to the correlation coefficient between each external data and the power load data, a correlation coefficient matrix is ​​constructed; the correlation coefficient matrix is ​​traversed, and external data with a correlation coefficient greater than or equal to a preset correlation threshold are selected to obtain an external data set mainly associated with the power load data;

[0008] Based on the power demand of different power regions, the required power consumption of the target power region is predicted according to the external data set associated with the power load data master to determine the best management solution for multivariate spatial data.

[0009] The embodiment of the present invention further provides a multi-dimensional spatial data management control device for improving the efficiency and accuracy of multi-dimensional spatial data management, the device comprising:

[0010] The multivariate spatial data acquisition module is used to acquire multivariate spatial data during the operation of the power system; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data;

[0011] A correlation coefficient acquisition module is used to perform correlation analysis on each external data and the power load data to obtain the correlation coefficient between each external data and the power load data;

[0012] The main associated external data set acquisition module is used to construct a correlation coefficient matrix according to the correlation coefficient between each external data and the power load data; traverse the correlation coefficient matrix, select the external data with a correlation coefficient greater than or equal to a preset correlation threshold, so as to obtain the external data set mainly associated with the power load data;

[0013] The multivariate spatial data management module is used to predict the power consumption required in the target power area based on the power demand of different power areas and according to the external data set associated with the power load data master, so as to determine the best management plan for the multivariate spatial data.

[0014] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned multi-dimensional spatial data management control method when executing the computer program.

[0015] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the multi-dimensional spatial data management and control method is implemented.

[0016] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the multi-dimensional spatial data management control method is implemented.

[0017] In an embodiment of the present invention, during the operation of the power system, multivariate spatial data is obtained; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data; each external data is subjected to correlation analysis with the power load data to obtain the correlation coefficient between each external data and the power load data; a correlation coefficient matrix is ​​constructed according to the correlation coefficient between each external data and the power load data; the correlation coefficient matrix is ​​traversed to select external data with a correlation coefficient greater than or equal to a preset correlation threshold to obtain an external data set associated with the power load data; based on the power demand of different power areas, the power consumption required by the target power area is predicted according to the external data set associated with the power load data to determine the best management scheme for the multivariate spatial data. In the above process, the embodiment of the present invention calculates the correlation coefficient between the external data and the power load data, predicts the power consumption required by the target power area based on the correlation coefficient, and determines the best management scheme for the multivariate spatial data to optimize the power supply, thereby achieving the improvement of the efficiency and accuracy of the management of the multivariate spatial data during the operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0019] Figure 1 is a flow chart of a multivariate spatial data management control method according to an embodiment of the present invention;

[0020] Figure 2 This is a flow chart of the preprocessing of raw power data in an embodiment of the present invention;

[0021] Figure 3 Schematic diagram of the hierarchical structure of the data lake in an embodiment of the present invention;

[0022] Figure 4 A flowchart of predicting power consumption in a target power area according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of a multivariate spatial data management control device in an embodiment of the present invention;

[0024] Figure 6 Schematic diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0026] Figure 1 Flow chart of a multivariate spatial data management control method according to an embodiment of the present invention, the method comprising:

[0027] Step 101, during the operation of the power system, multivariate spatial data is obtained; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data;

[0028] Step 102, performing correlation analysis on each external data and the power load data to obtain a correlation coefficient between each external data and the power load data;

[0029] Step 103, constructing a correlation coefficient matrix according to the correlation coefficient between each external data and the power load data; traversing the correlation coefficient matrix, selecting external data with a correlation coefficient greater than or equal to a preset correlation threshold, to obtain an external data set mainly associated with the power load data;

[0030] Step 104 , based on the power demand of different power areas, according to the external data set associated with the power load data, the power consumption required by the target power area is predicted to determine the best management solution for the multivariate spatial data.

[0031] Each step is described in detail below.

[0032] In step 101, during the operation of the power system, multivariate spatial data is obtained; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data.

[0033] Figure 2 This is a flow chart of the preprocessing of raw power data in an embodiment of the present invention. In one embodiment, before obtaining multivariate spatial data, the process includes:

[0034] Step 201, storing multivariate spatial data in a data lake;

[0035] Step 202: Use ETL tools to clean the original power data in the data lake;

[0036] Step 203, unifying the expression format of the cleaned original power data.

[0037] Among them, power facility data include the location and fixed parameters of substations, transmission lines, distribution networks and distribution facilities; power load data include power load data of different users; equipment operation data include operating parameters of substations, transmission lines, distribution networks and distribution facilities; environmental data include meteorological data, topographic data and soil data of the location of power facilities; inspection data include inspection images or other inspection record data of power facilities.

[0038] Figure 3 This is a schematic diagram of the hierarchical structure of the data lake in an embodiment of the present invention. In a specific embodiment, the data lake includes a core storage area, a data processing layer, a data warehouse layer, and a data service layer; wherein the core storage area is used for multivariate spatial data; the data processing layer is used to preprocess the stored multivariate spatial data; the data warehouse layer is used to convert the data format of the preprocessed spatial data and store the converted multivariate spatial data in a time series database; the data service layer is used to provide an API interface for data query and analysis. Through the "data lake", the multivariate spatial data of the power industry is stored and centrally managed. The core storage area can classify and store the original data to facilitate the traceability of power data and ensure the validity of the data; the data processing layer can preprocess the stored multivariate spatial data, perform denoising operations such as deduplication and data completion on the original data, and convert the multivariate spatial data of the power industry into unified structured data through the data warehouse layer, thereby improving the availability of multivariate spatial data and providing data support for further analysis and application of spatial data; the API interface provided by the data service layer enables the system to quickly query and retrieve the stored data. By querying the multivariate spatial data stored in the core storage area, it is necessary to add a unique identification mark (such as a timestamp) to each stored multivariate spatial data. The identification mark uniquely corresponds to the multivariate spatial data, and is finally stored in the time series database of the data warehouse layer together with the spatial data after preprocessing and optimization. During the query process, the original multivariate spatial data of the power system stored in the core storage area is queried and traced through the unique identification mark.

[0039] In a specific embodiment, the data format conversion is performed on the preprocessed spatial data, which specifically includes the following steps: converting the structured SHP data in the multivariate spatial data into two-dimensional table data, parsing the GDB data in the multivariate spatial data to obtain the two-dimensional table data; extracting attribute features, spatial features and time features from the converted spatial data as index labels of the spatial data, and storing the preprocessed spatial data in a time series database according to the sequence of time features. The attribute features include the category or type name of the multivariate spatial data, the spatial features include the collection location of the multivariate spatial data, and the time features include the collection time of the multivariate spatial data.

[0040] In step 102, correlation analysis is performed on each external data and the power load data to obtain a correlation coefficient between each external data and the power load data.

[0041] In one embodiment, a correlation analysis is performed on each external data and the power load data to obtain a correlation coefficient between each external data and the power load data, including:

[0042] The correlation coefficient is calculated according to the following formula:

[0043]

[0044] Among them, ρ i represents the correlation coefficient between the ith external data and the power load data, Cov(Y,X i ) represents the i-th external data X i The covariance with the power load data Y, D(Y) represents the variance of the power load data, D(X i ) represents external data X i The variance of , g() represents the geometric mean operation.

[0045] In a specific embodiment, the correlation analysis process between external data and power load data is as follows:

[0046] 1) Use power load data as the dependent variable Y and other external data as the independent variable X i ;

[0047] 2) Extract dependent variable Y and independent variable X respectively i The data sequence of

[0048] 3) According to the dependent variable Y and independent variable X i The covariance between the dependent variable and multiple independent variables in the simplex space is calculated for the data series;

[0049] 4) According to the covariance obtained in step 3), calculate the correlation coefficient between the dependent variable and multiple independent variables:

[0050]

[0051] 5) According to the correlation coefficient obtained in step 4), the correlation relationship between the power load data and other external data is determined.

[0052] In step 103, a correlation coefficient matrix is ​​constructed according to the correlation coefficient between each external data and the power load data; the correlation coefficient matrix is ​​traversed to select external data with correlation coefficients greater than or equal to a preset correlation threshold to obtain an external data set mainly associated with the power load data.

[0053] In a specific embodiment, external data with a correlation coefficient greater than or equal to a preset correlation threshold is used as dominant characteristic data affecting the power load data.

[0054] In step 104, based on the power demand of different power areas, the power consumption required by the target power area is predicted according to the external data set associated with the power load data master to determine the best management solution for the multivariate spatial data.

[0055] Figure 4 The flowchart of predicting the power consumption of the target power area in an embodiment of the present invention, in one embodiment, based on the power demand of different power areas, according to the external data set associated with the power load data, predicting the power consumption of the target power area to determine the best management solution for spatial data, includes:

[0056] Step 401, according to the power demand of different power areas, the external data set associated with the power load data is input into the power load data prediction model to obtain the power load data prediction value of each power area; the power load data prediction model is obtained by training the LSTM neural network model based on the historical power load data prediction value and the historical external data associated with the power load data;

[0057] Step 402, based on the power load data prediction value, obtain the power demand of the target power area to determine the best management solution for the multivariate spatial data, wherein the multivariate spatial data includes relevant data during the operation of the power system in different power areas.

[0058] In a specific embodiment, the electricity demand and power consumption of the target power area are determined according to the predicted value of the power load data, so that the power supply situation of each power area can be clarified, and the multivariate spatial data in the data lake can be managed based on the power supply situation, so as to provide data support for formulating the best power generation management plan for different power areas, thereby optimizing the power supply of each power area.

[0059] The present invention also provides a multi-dimensional spatial data management control device, as described in the following embodiments. Since the principle of the device to solve the problem is similar to that of the multi-dimensional spatial data management control method, the implementation of the device can refer to the implementation of the multi-dimensional spatial data management control method, and the repeated parts will not be repeated.

[0060] Figure 5 Schematic diagram of a multivariate spatial data management control device in an embodiment of the present invention, the device comprises:

[0061] The multivariate spatial data acquisition module 501 is used to acquire multivariate spatial data during the operation of the power system; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data;

[0062] A correlation coefficient acquisition module 502 is used to perform correlation analysis on each external data and the power load data to obtain a correlation coefficient between each external data and the power load data;

[0063] The main associated external data set acquisition module 503 is used to construct a correlation coefficient matrix according to the correlation coefficient between each external data and the power load data; traverse the correlation coefficient matrix, select the external data with a correlation coefficient greater than or equal to a preset correlation threshold, so as to obtain the external data set mainly associated with the power load data;

[0064] The multivariate spatial data management module 504 is used to predict the power consumption required by the target power area based on the power demand of different power areas according to the external data set associated with the power load data, so as to determine the best management solution for the multivariate spatial data.

[0065] In one embodiment, a data preprocessing module is further included, which is specifically used to:

[0066] Before acquiring multivariate spatial data, store the multivariate spatial data in the data lake;

[0067] Use ETL tools to clean the raw power data in the data lake;

[0068] Unify the expression format of the cleaned raw power data.

[0069] In one embodiment, the correlation coefficient acquisition module 502 is specifically used to:

[0070] The correlation coefficient is calculated according to the following formula:

[0071]

[0072] Among them, ρ i represents the correlation coefficient between the ith external data and the power load data, Cov(Y,X i ) represents the i-th external data X i The covariance with the power load data Y, D(Y) represents the variance of the power load data, D(X i ) represents external data X i The variance of , g() represents the geometric mean operation.

[0073] In one embodiment, the multivariate spatial data management module 504 is specifically used to:

[0074] According to the power demand of different power areas, the external data set associated with the power load data is input into the power load data prediction model to obtain the power load data prediction value of each power area; the power load data prediction model is obtained by training the LSTM neural network model based on the historical power load data prediction value and the historical external data associated with the power load data;

[0075] Based on the predicted value of power load data, the power demand of the target power area is obtained to determine the optimal management plan for multivariate spatial data.

[0076] An embodiment of the present invention further provides a computer device, Figure 6 This is a schematic diagram of a computer device in an embodiment of the present invention. The computer device 600 includes a memory 610, a processor 620, and a computer program 630 stored in the memory 610 and executable on the processor 620. When the processor 620 executes the computer program 630, the above-mentioned multi-dimensional spatial data management control method is implemented.

[0077] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the multi-dimensional spatial data management and control method is implemented.

[0078] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the multi-dimensional spatial data management control method is implemented.

[0079] In an embodiment of the present invention, during the operation of the power system, multivariate spatial data is obtained; the multivariate spatial data includes power load data and external data, and the external data includes: power facility data, equipment operation data, environmental data and inspection data; each external data is subjected to correlation analysis with the power load data to obtain the correlation coefficient between each external data and the power load data; a correlation coefficient matrix is ​​constructed according to the correlation coefficient between each external data and the power load data; the correlation coefficient matrix is ​​traversed to select external data with a correlation coefficient greater than or equal to a preset correlation threshold to obtain an external data set associated with the power load data; based on the power demand of different power areas, the power consumption required by the target power area is predicted according to the external data set associated with the power load data to determine the best management scheme for the multivariate spatial data. In the above process, the embodiment of the present invention calculates the correlation coefficient between the external data and the power load data, predicts the power consumption required by the target power area based on the correlation coefficient, and determines the best management scheme for the multivariate spatial data to optimize the power supply, thereby achieving the improvement of the efficiency and accuracy of the management of the multivariate spatial data during the operation of the power system.

[0080] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 processes in the flowchart and / or block diagram. 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.

[0082] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] 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 instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multivariate spatial data management and control method, characterized in that: include: Acquire multivariate spatial data during power system operation; Multivariate spatial data includes power load data and external data. External data includes: power facility data, equipment operation data, environmental data and inspection data; Perform correlation analysis on each external data and the power load data to obtain the correlation coefficient between each external data and the power load data; According to the correlation coefficient between each external data and the power load data, a correlation coefficient matrix is ​​constructed; the correlation coefficient matrix is ​​traversed, and external data with a correlation coefficient greater than or equal to a preset correlation threshold are selected to obtain an external data set mainly associated with the power load data; Based on the power demand of different power regions, the required power consumption of the target power region is predicted according to the external data set associated with the power load data master to determine the best management solution for multivariate spatial data.

2. The method according to claim 1, characterized in that Before acquiring multivariate spatial data include: Storing multivariate spatial data in a data lake; Use ETL tools to clean the raw power data in the data lake; Unify the expression format of the cleaned raw power data.

3. The method according to claim 1, characterized in that Perform correlation analysis on each external data and the power load data to obtain the correlation coefficient between each external data and the power load data, including: The correlation coefficient is calculated according to the following formula: Among them, ρ i represents the correlation coefficient between the ith external data and the power load data, Cov(Y,X i ) represents the i-th external data X i The covariance with the power load data Y, D(Y) represents the variance of the power load data, D(X i ) represents external data X i The variance of , g() represents the geometric mean operation.

4. The method according to claim 1, characterized in that Based on the power demand of different power areas, the power consumption of the target power area is predicted according to the external data set associated with the power load data master to determine the best management plan for spatial data, including: According to the power demand of different power areas, the external data set associated with the power load data is input into the power load data prediction model to obtain the power load data prediction value of each power area; the power load data prediction model is obtained by training the LSTM neural network model based on the historical power load data prediction value and the historical external data associated with the power load data; Based on the predicted value of power load data, the power demand of the target power area is obtained to determine the optimal management plan for multivariate spatial data.

5. A multi-dimensional spatial data management and control device, characterized in that: include: A multivariate spatial data acquisition module is used to acquire multivariate spatial data during the operation of the power system; Multivariate spatial data includes power load data and external data. External data includes: power facility data, equipment operation data, environmental data and inspection data; A correlation coefficient acquisition module is used to perform correlation analysis on each external data and the power load data to obtain the correlation coefficient between each external data and the power load data; The main associated external data set acquisition module is used to construct a correlation coefficient matrix according to the correlation coefficient between each external data and the power load data; traverse the correlation coefficient matrix, select the external data with a correlation coefficient greater than or equal to a preset correlation threshold, so as to obtain the external data set mainly associated with the power load data; The multivariate spatial data management module is used to predict the power consumption required in the target power area based on the power demand of different power areas and according to the external data set associated with the power load data master, so as to determine the best management plan for the multivariate spatial data.

6. The device according to claim 5, characterized in that It also includes a data preprocessing module, which is specifically used to: Before acquiring multivariate spatial data, store the multivariate spatial data in the data lake; Use ETL tools to clean the raw power data in the data lake; Unify the expression format of the cleaned raw power data.

7. The device according to claim 5, characterized in that The correlation coefficient acquisition module is specifically used for: The correlation coefficient is calculated according to the following formula: Among them, ρ i represents the correlation coefficient between the ith external data and the power load data, Cov(Y,X i ) represents the i-th external data X i The covariance with the power load data Y, D(Y) represents the variance of the power load data, D(X i ) represents external data X i The variance of , g() represents the geometric mean operation.

8. The device according to claim 5, characterized in that Multivariate spatial data management module, specifically used for: According to the power demand of different power areas, the external data set associated with the power load data is input into the power load data prediction model to obtain the power load data prediction value of each power area; the power load data prediction model is obtained by training the LSTM neural network model based on the historical power load data prediction value and the historical external data associated with the power load data; Based on the predicted value of power load data, the power demand of the target power area is obtained to determine the optimal management plan for multivariate spatial data.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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