Regional industry gravity center analysis method and system based on industry electricity consumption
By analyzing the electricity consumption data between industries in the region, calculating the center degree and weight, and drawing a center of gravity correlation diagram, the problems of low drawing efficiency and low accuracy in the existing technology are solved, and a more accurate and efficient industrial center of gravity analysis is achieved.
Patent Information
- Application Number
- CN202411753340.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-02
- Publication Date
- 2025-04-25
AI Technical Summary
In the prior art, the drawing efficiency of regional industrial center of gravity relationship diagrams is low and has low accuracy, and depends on manual operations and traditional economic statistics, and is vulnerable to inflation, exchange rate fluctuations and tampering by illegal personnel.
By obtaining the daily electricity consumption data of the area to be analyzed, the industry classification and combined splitting of the electricity consumption label matrix are calculated, the distance and center degree between the electricity consumption label matrix is calculated, the weight and correction center degree are calculated using the Pagerank algorithm and the Pearson correlation coefficient, and the center degree is drawn.
The accuracy and drawing efficiency of the center of gravity relationship diagram are improved, the influence of human factors is reduced, and objective electricity consumption data is used as an analysis basis to provide a more reliable reference for industrial economic strategies.
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Figure CN119886520A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for analyzing regional industrial center of gravity based on industry electricity consumption. Background Art
[0002] The center of gravity correlation diagram of industries in a region is an important basis commonly used in urban planning and development analysis systems. Figure 2 In the prior art, the current center of gravity relationship Figure 1 Generally, it is drawn manually based on traditional economic statistics. On the one hand, the efficiency of manual drawing is low, and it relies on manual operation with a large subjective factor, which leads to low accuracy of the center of gravity correlation diagram; on the other hand, traditional economic statistics are generally economic figures directly related to currency. Such data are easily affected by factors such as inflation, exchange rate fluctuations and statistical caliber, and may be tampered with and fabricated by illegal personnel, causing interference to the statistical results.
[0003] The electricity consumption of industry users can directly reflect their production and operation conditions. At the same time, the correlation between industry electricity consumption data is an energy projection of the production and economic correlation between industries. By analyzing the correlation between electricity consumption between industries in the region, analyzing the economic correlation characteristics of regional industries, and then obtaining the regional industry center of gravity, it can provide energy reference and effect feedback for the formulation and implementation of government industrial economic strategies. This is also a means to fully explore the social and economic information carried by electricity consumption data and release the value of production factors of power and electricity data. However, if only the industry electricity consumption is used to measure the economic status of the industry in the region, it will seriously overestimate the high-energy consumption and low-value-added industries, and seriously underestimate the low-energy consumption and high-value-added industries. Summary of the invention
[0004] The purpose of an embodiment of the present invention is to provide a method and system for regional industrial center of gravity analysis based on industry electricity consumption. The method and system draw a center of gravity correlation diagram by combining the correlation of industry electricity consumption, thereby improving the accuracy of the center of gravity correlation diagram while combining objective data.
[0005] In order to achieve the above object, an embodiment of the present invention provides a method for analyzing the regional industrial center of gravity based on industry electricity consumption, including: Obtain daily electricity consumption data for the area to be analyzed; Categorize users to obtain multiple industry categories; Combining and splitting the daily electricity consumption data according to the industry categories to obtain an electricity consumption label matrix for each industry category; Calculating the distance between each two power consumption label matrices respectively; Calculate the centrality of each of the industry categories according to the distance; Calculate the weight of each of the said industry categories separately; Modifying the centrality by using the weight to obtain a modified centrality; Sorting the industry categories from large to small according to the modified centrality to obtain an industry category sequence about the regional center of gravity; Select the first N industry categories as the center of gravity analysis nodes; Draw a center of gravity association relationship diagram according to the center of gravity analysis node; The industry association characteristics of the area to be analyzed are determined according to the center of gravity association relationship diagram.
[0006] Optionally, obtaining daily electricity consumption data of the area to be regulated includes: For each user, daily electricity consumption data for at least one year in the past is obtained.
[0007] Optionally, respectively calculating the distance between each two of the power consumption label matrices includes: The distance is calculated according to formula (1): , (1) in, Indicates The power consumption label matrix and The distance between the power consumption label matrices, For the The power consumption label matrix and Pearson correlation coefficient between the power consumption label matrices.
[0008] Optionally, calculating the centrality of each of the industry categories according to the distance includes: The centrality is calculated using the Pagerank algorithm.
[0009] Optionally, a weight is calculated for each of the industry categories separately, including: The weight is calculated according to formula (2): , (2) in, For the The weight of the electricity consumption label matrix, For the A power consumption label matrix, The date.
[0010] Optionally, the centrality is modified by using the weight to obtain a modified centrality, including: The corrected centrality is determined according to formula (3): , (3) in, is the modified centrality, is the centrality, is the weight.
[0011] Optionally, select the top N industry categories as the center of gravity analysis nodes, including: The two industry categories whose distance is less than or equal to the preset value are regarded as first-order connected industry nodes; The two industry categories connected by the first-order connected industry nodes are used as second-order connected industry nodes.
[0012] Optionally, drawing a center of gravity association relationship diagram according to the center of gravity analysis node includes: The Python open source toolkit networkx is used to draw the centroid association relationship diagram.
[0013] On the other hand, the present invention also provides a regional industrial center of gravity analysis system based on industry electricity consumption, the system comprising a processor, and the processor is configured to execute any of the methods described above.
[0014] Through the above technical solution, the present invention provides a method and system for analyzing the regional industrial center of gravity based on industry electricity consumption. The method and system use the correlation of industry electricity consumption as the basis for drawing the center of gravity correlation diagram, and use the objective data of industry electricity consumption as a reference for the correlation. Compared with the prior art, the method and system provided by the present invention overcome the commonly used artificial drawing method of using economic data as a reference, and improve the accuracy and efficiency of drawing the center of gravity correlation diagram.
[0015] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 is a flow chart of a method for analyzing regional industrial center of gravity based on industry electricity consumption according to an embodiment of the present invention; Figure 2 is an example diagram of a center of gravity association relationship diagram according to an embodiment of the present invention; Figure 3 This is an example diagram of the basis for industry classification according to one embodiment of the present invention. DETAILED DESCRIPTION
[0017] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.
[0018] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.
[0019] like Figure 1 The figure is a flow chart of a method for analyzing the regional industrial center of gravity based on industry electricity consumption according to one embodiment of the present invention. Figure 1 In, the method may include: In step S10, daily electricity consumption data of the area to be analyzed is obtained; In step S11, users are classified to obtain multiple industry categories; In step S12, the daily electricity consumption data is combined and split according to the industry category to obtain the electricity consumption label matrix of each industry category; In step S13, the distance between every two power consumption label matrices is calculated respectively; In step S14, the centrality of each industry category is calculated based on the distance; In step S15, the weight of each industry category is calculated respectively; In step S16, the centrality is corrected using the weight to obtain a corrected centrality; In step S17, the industry categories are sorted from large to small according to the modified centrality to obtain an industry category sequence about the regional center of gravity; In step S18, the first N industry categories are selected as the center of gravity analysis nodes; In step S19, a center of gravity association relationship diagram is drawn according to the center of gravity analysis node; In step S20, the industry association characteristics of the area to be analyzed are determined according to the center of gravity association relationship diagram.
[0020] In this Figure 1 In the method shown, step S10 can be used to obtain daily electricity consumption data of the area to be analyzed. Specifically, in this embodiment, step S10 can be to obtain daily electricity consumption data of at least one year in the past for each user.
[0021] Step S11 can be used to classify users to obtain multiple industry categories. The basis for classifying users can be various forms known to those skilled in the art. In one example of the present invention, the basis for classifying users can be to determine the industry label of each user according to the policy document "National Economic Industry Classification 2017 (GB / T 4754-2017)", wherein the granularity of industry classification is not limited, but should be guaranteed to be at the same level (such as category, major category, medium category, etc.), and then the classification is performed in combination with the industry label of each user.
[0022] Step S13 can be used to calculate the distance between each two power consumption label matrices. The specific method for calculating the distance can be various forms known to those skilled in the art. In one example of the present invention, the distance can be calculated using formula (1): , (1) in, Indicates The power consumption label matrix and The distance between the power consumption label matrices, For the The power consumption label matrix and Pearson correlation coefficient between the power consumption label matrices.
[0023] Step S14 can be used to calculate the centrality of each industry category based on the distance. The specific method for calculating the centrality can be various forms known to those skilled in the art. In one example of the present invention, the centrality can be calculated using the Pagerank algorithm.
[0024] Step S15 can be used to calculate the weight of each industry category. The specific method for calculating the weight can be various forms known to those skilled in the art. In one example of the present invention, the weight can be calculated according to formula (2): , (2) in, For the The weight of the electricity consumption label matrix, For the A power consumption label matrix, The date.
[0025] Step S16 may be used to modify the centrality using weights to obtain a modified centrality. The specific method for modifying the centrality may be in various forms known to those skilled in the art. In one example of the present invention, the modified centrality may be calculated according to formula (3): , (3) in, To correct the centrality, is the centrality, is the weight.
[0026] Step S18 is used to select the first N industry categories as the center of gravity analysis nodes. The specific value of the number N can be determined by those skilled in the art in combination with the scale and area of the area to be analyzed. In one example of the present invention, the value of the number N can be 5. In order to further determine the correlation between every two industry categories, in one example of the present invention, two industry categories whose distance is less than or equal to a preset value can be used as first-order connected industry nodes, and two industry categories connected by the first-order connected industry nodes can be used as second-order connected industry nodes.
[0027] Step S19 can be used to draw a center of gravity association relationship diagram based on the center of gravity analysis node. In one example of the present invention, the center of gravity association relationship diagram can be drawn using the python open source toolkit networkx. Specifically, the center of gravity industry node can be drawn first, and then the first-order connected industry nodes can be drawn on its periphery according to the node association situation, and finally the second-order connected industry nodes can be drawn on the periphery of the first-order connected industry nodes. Different sizes can be used to represent different levels of nodes, and different colors can be used to represent different industry classifications. The example diagram drawn is as follows Figure 2 shown.
[0028] On the other hand, the present invention also provides a regional industry center of gravity analysis system based on industry electricity consumption, the system comprising a processor, the processor being configured to execute any of the above methods. Specifically, the method may include the following steps: In step S10, daily electricity consumption data of the area to be analyzed is obtained; In step S11, users are classified to obtain multiple industry categories; In step S12, the daily electricity consumption data is combined and split according to the industry category to obtain the electricity consumption label matrix of each industry category; In step S13, the distance between every two power consumption label matrices is calculated respectively; In step S14, the centrality of each industry category is calculated based on the distance; In step S15, the weight of each industry category is calculated respectively; In step S16, the centrality is corrected using the weight to obtain a corrected centrality; In step S17, the industry categories are sorted from large to small according to the modified centrality to obtain an industry category sequence about the regional center of gravity; In step S18, the first N industry categories are selected as the center of gravity analysis nodes; In step S19, a center of gravity association relationship diagram is drawn according to the center of gravity analysis node; In step S20, the industry association characteristics of the area to be analyzed are determined according to the center of gravity association relationship diagram.
[0029] In this Figure 1 In the method shown, step S10 can be used to obtain daily electricity consumption data of the area to be analyzed. Specifically, in this embodiment, step S10 can be to obtain daily electricity consumption data of at least one year in the past for each user.
[0030] Step S11 can be used to classify users to obtain multiple industry categories. The basis for classifying users can be various forms known to those skilled in the art. In one example of the present invention, the basis for classifying users can be to determine the industry label of each user according to the policy document "National Economic Industry Classification 2017 (GB / T 4754-2017)", wherein the granularity of industry classification is not limited, but should be guaranteed to be at the same level (such as category, major category, medium category, etc.), and then the classification is performed in combination with the industry label of each user.
[0031] Step S13 can be used to calculate the distance between each two power consumption label matrices. The specific method for calculating the distance can be various forms known to those skilled in the art. In one example of the present invention, the distance can be calculated using formula (1): , (1) in, Indicates The power consumption label matrix and The distance between the power consumption label matrices, For the The power consumption label matrix and Pearson correlation coefficient between the power consumption label matrices.
[0032] Step S14 can be used to calculate the centrality of each industry category based on the distance. The specific method for calculating the centrality can be various forms known to those skilled in the art. In one example of the present invention, the centrality can be calculated using the Pagerank algorithm.
[0033] Step S15 can be used to calculate the weight of each industry category. The specific method for calculating the weight can be various forms known to those skilled in the art. In one example of the present invention, the weight can be calculated according to formula (2): , (2) in, For the The weight of the electricity consumption label matrix, For the A power consumption label matrix, The date.
[0034] Step S16 may be used to modify the centrality using weights to obtain a modified centrality. The specific method for modifying the centrality may be in various forms known to those skilled in the art. In one example of the present invention, the modified centrality may be calculated according to formula (3): , (3) in, To correct the centrality, is the centrality, is the weight.
[0035] Step S18 is used to select the first N industry categories as the center of gravity analysis nodes. The specific value of the number N can be determined by those skilled in the art in combination with the scale and area of the area to be analyzed. In one example of the present invention, the value of the number N can be 5. In order to further determine the correlation between every two industry categories, in one example of the present invention, two industry categories whose distance is less than or equal to a preset value can be used as first-order connected industry nodes, and two industry categories connected by the first-order connected industry nodes can be used as second-order connected industry nodes.
[0036] Step S19 can be used to draw a center of gravity association relationship diagram based on the center of gravity analysis node. In one example of the present invention, the center of gravity association relationship diagram can be drawn using the python open source toolkit networkx. Specifically, the center of gravity industry node can be drawn first, and then the first-order connected industry nodes can be drawn on its periphery according to the node association situation, and finally the second-order connected industry nodes can be drawn on the periphery of the first-order connected industry nodes. Different sizes can be used to represent different levels of nodes, and different colors can be used to represent different industry classifications. The example diagram drawn is as follows Figure 2 shown.
[0037] In addition, in one embodiment of the present invention, 17 industry categories (uncommon categories, such as international organizations, are merged into other categories) in 19 cities in Shandong Province can be analyzed, and the electricity correlation between different cities and different industry categories can be calculated two by two. On this basis, the centrality of each city and industry can be analyzed. Electricity data: daily electricity consumption of various industries in various cities in Shandong Province from January 1, 2019 to December 31, 2020. The basis for the corresponding industry classification is as follows Figure 3 shown.
[0038] By adopting the method provided by the present invention, the top five cities in terms of centrality can be obtained as follows: 1. Qingdao real estate industry; 2. Jinan real estate industry; 3. Linyi real estate industry; 4. Qingdao business service industry; 5. Yantai real estate industry. Preliminary conclusion: The centrality ranking is basically consistent with the actual urban development level; the service industry represented by the real estate industry has a relatively core position in Shandong's national economy; the high centrality of the real estate industry may be the result of overheated real estate in this period of time.
[0039] In combination with the analysis of step S20, the analysis results are as follows: 1. Most industry nodes are clustered due to their industry attributes, while the influence of regional attributes is relatively weak.
[0040] 2. The same industries cluster into islands. The island in the center is composed of #11 real estate and #12 business services. The islands extend in three directions, including (from the center to the edge): #5 Construction, #4 Electricity, heat, gas and water production and supply, #3 Manufacturing; - #10 Finance, #14 Water Conservancy, Environment and Public Facilities Management, #15 Residential Services, Repairs and Other Services, #16 Culture, Education, Health, Sports and Entertainment; #8 Catering and accommodation, #6 Wholesale and retail, #7 Transportation, warehousing and postal services, #9 Information transmission, software and information technology services; 3. From the perspective of electricity consumption, it can be seen that the distribution characteristics of Shandong's industry center of gravity are service-oriented. The closer the industry characteristics are to business services and residential services, the higher the centrality.
[0041] 4. In the focus analysis based on correlation, #3 manufacturing industry is at the edge, which means that Shandong, as a major manufacturing province, has relatively "independent" electricity consumption in the manufacturing industry, and its electricity consumption pattern is different from that of other industries.
[0042] Through the above technical solution, the present invention provides a method and system for analyzing the regional industrial center of gravity based on industry electricity consumption. The method and system use the correlation of industry electricity consumption as the basis for drawing the center of gravity correlation diagram, and use the objective data of industry electricity consumption as a reference for the correlation. Compared with the prior art, the method and system provided by the present invention overcome the commonly used artificial drawing method of using economic data as a reference, and improve the accuracy and efficiency of drawing the center of gravity correlation diagram.
[0043] 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. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0044] 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 generate 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.
[0045] 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 comprising 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.
[0046] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0047] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0048] 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.
[0049] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape 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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0050] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0051] The above are only 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 modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A method for analyzing regional industrial center of gravity based on industry electricity consumption, characterized in that: The method comprises: Obtain daily electricity consumption data for the area to be analyzed; Categorize users to obtain multiple industry categories; Combining and splitting the daily electricity consumption data according to the industry categories to obtain an electricity consumption label matrix for each industry category; Calculating the distance between each two power consumption label matrices respectively; Calculate the centrality of each of the industry categories according to the distance; Calculate the weight of each of the said industry categories separately; Modifying the centrality by using the weight to obtain a modified centrality; Sorting the industry categories from large to small according to the modified centrality to obtain an industry category sequence about the regional center of gravity; Select the first N industry categories as the center of gravity analysis nodes; Draw a center of gravity association relationship diagram according to the center of gravity analysis node; The industry association characteristics of the area to be analyzed are determined according to the center of gravity association relationship diagram.
2. The method according to claim 1, characterized in that Obtain daily electricity consumption data for the area to be regulated, including: For each user, daily electricity consumption data for at least one year in the past is obtained.
3. The method according to claim 1, characterized in that Calculating the distance between each two of the power consumption label matrices respectively includes: The distance is calculated according to formula (1), ，（1） in, Indicates The power consumption label matrix and The distance between the power consumption label matrices, For the The power consumption label matrix and Pearson correlation coefficient between the power consumption label matrices.
4. The method according to claim 1, characterized in that: The centrality of each of the industry categories is calculated based on the distance, including: The centrality is calculated using the Pagerank algorithm.
5. The method according to claim 1, characterized in that Calculate the weights for each of the industry categories separately, including: The weight is calculated according to formula (2): ，（2） in, For the The weight of the electricity consumption label matrix, For the A power consumption label matrix, The date.
6. The method according to claim 1, characterized in that The centrality is modified by using the weight to obtain a modified centrality, including: The corrected centrality is determined according to formula (3): ，（3） in, is the modified centrality, is the centrality, is the weight.
7. The method according to claim 1, characterized in that Select the top N industry categories as the center of gravity analysis nodes, including: The two industry categories whose distance is less than or equal to the preset value are regarded as first-order connected industry nodes; The two industry categories connected by the first-order connected industry nodes are used as second-order connected industry nodes.
8. The method according to claim 1, characterized in that Drawing a center of gravity association relationship diagram according to the center of gravity analysis node includes: The Python open source toolkit networkx is used to draw the centroid association relationship diagram.
9. A regional industry center of gravity analysis system based on industry electricity consumption, characterized in that: The system comprises a processor configured to execute the method according to any one of claims 1 to 8.