A method, device and equipment for positioning the regional industrial center of gravity

Abstract

The present application discloses a method, device and equipment for positioning the regional industrial focus, relating to the field of regional industrial positioning. The method includes collecting the longitude and latitude data and electricity consumption data of each sub-region within the target region; constructing an electricity consumption center of gravity recognition model; obtaining the electricity consumption center of gravity coefficients between any two sub-regions within the target region by using the electricity consumption center of gravity recognition model according to the longitude and latitude data and electricity consumption data of each sub-region, and forming an electricity consumption center of gravity coefficient matrix; binarizing the electricity consumption center of gravity coefficients in the electricity consumption center of gravity coefficient matrix; calculating the center of gravity index of each sub-region within the target region for the target industry by using the centrality analysis method according to the binarized electricity consumption center of gravity coefficient matrix; and selecting the sub-region with the largest center of gravity index as the center of gravity region of the target industry within the target region. The present application can accurately identify the regional industrial focus based on the electricity consumption data.

Description

Technical Field

[0001] This application relates to the field of regional industrial positioning, and particularly to a method, device, and equipment for positioning the center of gravity of regional industries. Background Art

[0002] With the rapid development of the global economy, the demand for electricity in various industries is increasing continuously, and electricity consumption data has gradually become an important tool for analyzing and predicting market trends. Especially in the context of accelerating urbanization, how to effectively identify the center of gravity of a specific industry and its distribution within a region by using electricity consumption data has become an urgent problem to be solved.

[0003] In many cases, traditional industrial gravity analysis mainly relies on macroeconomic data such as total economic volume, tax revenue, and employment situation. However, the acquisition of these data often has lag and regional asymmetry. Especially for in-depth analysis of specific industries or regions, there may be certain blind spots and deficiencies. Due to the direct and real-time characteristics of electricity consumption data, it can reflect the actual production and operation status of industries within a certain region. Therefore, its application in regional industrial analysis has gradually attracted attention. So in the prior art, the research on how to accurately identify the center of gravity of an industry by using electricity consumption data is relatively scattered, and there is a lack of an effective and systematic analysis method. Therefore, how to accurately locate the center of gravity city of an industry through electricity consumption data is still a key issue in the current data analysis field. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, and equipment for positioning the center of gravity of regional industries, which can accurately identify the center of gravity of regional industries based on electricity consumption data.

[0005] To achieve the above purpose, this application provides the following solutions.

[0006] In a first aspect, this application provides a method for positioning the center of gravity of regional industries, including: collecting the longitude and latitude data and electricity consumption data of each sub-region within the target region; constructing an electricity consumption center of gravity recognition model; according to the longitude and latitude data and electricity consumption data of each sub-region, using the electricity consumption center of gravity recognition model to obtain the electricity consumption center of gravity coefficients between any two sub-regions within the target region, and forming an electricity consumption center of gravity coefficient matrix; binarizing the electricity consumption center of gravity coefficients in the electricity consumption center of gravity coefficient matrix; according to the binarized electricity consumption center of gravity coefficient matrix, using the centrality analysis method to calculate the center of gravity index of each sub-region within the target region for the target industry; selecting the sub-region with the largest center of gravity index as the center of gravity region of the target industry within the target region.

[0007] Optionally, the electricity consumption center of gravity recognition model is as follows.

[0008] .

[0009] .

[0010] .

[0011] .

[0012] In the formula, is the power consumption gravity coefficient between sub-region and sub-region ; is the power consumption of the target industry in sub-region ; is the power consumption of the target industry in sub-region ; is the total power consumption of all industries in sub-region ; is the distance between sub-region and sub-region ; is the radius of the earth; is the radian difference between sub-region and sub-region ; is an intermediate variable; is the latitude of sub-region ; is the latitude of sub-region ; is the longitude of sub-region ; is the longitude of sub-region ; represents the multiplication sign.

[0013] Optionally, binarize the power consumption gravity coefficients in the power consumption gravity coefficient matrix, specifically including: if the power consumption gravity coefficient is greater than or equal to the average value of the power consumption gravity coefficients in the power consumption gravity coefficient matrix, set the power consumption gravity coefficient to 1; if the power consumption gravity coefficient is less than the average value of the power consumption gravity coefficients in the power consumption gravity coefficient matrix, set the power consumption gravity coefficient to 0.

[0014] Optionally, according to the binarized power consumption gravity coefficient matrix, the centrality analysis method is adopted to calculate the gravity index of each sub-region in the target area for the target industry, which specifically includes: according to the binarized power consumption gravity coefficient matrix, each sub-region is used as a node, and two nodes with a binarized power consumption gravity coefficient of 1 are directly connected to form a target area network; wherein, the distance between two directly connected nodes is 1; according to the target area network, calculate the degree centrality, closeness centrality and betweenness centrality of each sub-region respectively; normalize the betweenness centrality of each sub-region; and determine the sum of the degree centrality, closeness centrality and normalized betweenness centrality of each sub-region as the gravity index of each sub-region for the target industry.

[0015] Optionally, the calculation formula of the degree centrality is: ; In the formula, is the degree centrality of sub-region ; is the number of sub-regions directly connected to sub-region ; is the total number of sub-regions in the target area.

[0016] Optionally, the calculation formula of the closeness centrality is: ; In the formula, is the closeness centrality of sub-region ; is the reciprocal of the shortest distance from sub-region to sub-region ; is the total number of sub-regions in the target area.

[0017] Optionally, the calculation formula of the betweenness centrality is: ; In the formula, is the betweenness centrality of sub-region ; is the number of times that sub-region serves as a bridge connecting the other two sub-regions in the shortest path between the other two sub-regions; if , and , and sub-region is on the shortest path between sub-region and sub-region , then it is determined that sub-region is a bridge connecting sub-region and sub-region ; represents that the binarized power consumption gravity coefficient between sub-region and sub-region is 0; represents sub-region and sub-region The binaryzied power consumption gravity coefficient between and sub-region is 1; Indicates sub-region and sub-region The binaryzied power consumption gravity coefficient between and sub-region is 1; Is sub-region and sub-region The number of shortest paths of and sub-region

[0018] Optionally, the normalization formula is: ; where Is the normalized betweenness centrality of sub-region ; Is the betweenness centrality of sub-region ; , Are the minimum and maximum values of the betweenness centrality respectively.

[0019] Second, this application provides a regional industrial gravity positioning device, including: a collection module, a construction module, a calculation module, a binarization module, a centrality analysis module, and an identification module.

[0020] The collection module is used to collect the longitude and latitude data and power consumption data of each sub-region in the target area. The construction module is used to construct a power consumption gravity identification model. The calculation module is used to obtain the power consumption gravity coefficient between any two sub-regions in the target area according to the longitude and latitude data and power consumption data of each sub-region, using the power consumption gravity identification model, and form a power consumption gravity coefficient matrix. The binarization module is used to binarize the power consumption gravity coefficient in the power consumption gravity coefficient matrix. The centrality analysis module is used to calculate the gravity index of each sub-region in the target area for the target industry by using the centrality analysis method according to the binarized power consumption gravity coefficient matrix. The identification module is used to select the sub-region with the largest gravity index as the gravity region of the target industry in the target area.

[0021] Third, this application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the regional industrial gravity positioning method described in any one of the above.

[0022] According to the specific embodiments provided by this application, this application has the following technical effects.

[0023] The present application provides a method, device and equipment for positioning the regional industrial center of gravity. According to the longitude and latitude data and power consumption data of each sub-region, using the constructed power consumption center of gravity identification model, the power consumption center of gravity coefficient between any two sub-regions within the target region is obtained, and the centrality analysis method is adopted to calculate the center of gravity index of each sub-region within the target region for the target industry. The sub-region with the largest center of gravity index is the center of gravity region of the target industry within the target region. Therefore, based on the power consumption data, the present application provides an effective and systematic analysis method, which can accurately identify the regional industrial center of gravity. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is an application environment diagram of a method for positioning the regional industrial center of gravity in an embodiment of the present application.

[0026] Figure 2 It is a flowchart of a method for positioning the regional industrial center of gravity provided in an embodiment of the present application.

[0027] Figure 3 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0029] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0030] The method for positioning the regional industrial center of gravity provided in the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the longitude and latitude data and power consumption data of each sub-region in the target area to the server 104. After receiving the longitude and latitude data and power consumption data of each sub-region in the target area, for the longitude and latitude data and power consumption data of each sub-region in the target area, the server 104 constructs a power consumption center of gravity recognition model; according to the longitude and latitude data and power consumption data of each sub-region, using the power consumption center of gravity recognition model, obtain the power consumption center of gravity coefficient between any two sub-regions in the target area, and form a power consumption center of gravity coefficient matrix; binarize the power consumption center of gravity coefficients in the power consumption center of gravity coefficient matrix; according to the binarized power consumption center of gravity coefficient matrix, adopt the centrality analysis method to calculate the center of gravity index of each sub-region in the target area for the target industry; select the sub-region with the largest center of gravity index as the center of gravity area of the target industry in the target area. The server 104 can feedback the obtained center of gravity area of the target industry in the target area to the terminal 102. In addition, in some embodiments, the regional industry center of gravity positioning method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform regional industry center of gravity positioning for the longitude and latitude data and power consumption data of each sub-region in the target area, or the server 104 can obtain the longitude and latitude data and power consumption data frequency of each sub-region in the target area from the data storage system, and perform regional industry center of gravity positioning for the longitude and latitude data and power consumption data of each sub-region in the target area.

[0031] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0032] In an exemplary embodiment, as Figure 2 shown, a regional industry center of gravity positioning method is provided. This method is executed by a computer device, and can be specifically executed independently by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in

[0033] Step 201: Collect the longitude and latitude data and power consumption data of each sub-area in the target area.

[0034] Step 202: Construct a power consumption center of gravity identification model.

[0035] Step 203: Based on the latitude and longitude data and power consumption data of each sub-area, the power consumption gravity center coefficient between any two sub-areas in the target area is obtained by using the power consumption gravity center identification model, and a power consumption gravity center coefficient matrix is ​​formed.

[0036] Step 204: Binarizing the power consumption centroid coefficients in the power consumption centroid coefficient matrix.

[0037] Step 205: Based on the binarized power consumption gravity coefficient matrix, a centrality analysis method is used to calculate the gravity index of each sub-region in the target region for the target industry.

[0038] Step 206: Select the sub-region with the largest centroid index as the centroid region of the target industry in the target region.

[0039] The implementation of steps 201 to 206 above aims to solve the problem of difficulty in locating the center of gravity of the industry. Electricity consumption shows a certain degree of correlation in space. This correlation is reflected in the present application in that electricity consumption in the center of gravity of the industry has a large spillover effect on other regions. Electricity consumption data can reflect the electricity consumption of sub-regions and is an important basis for judging the competitiveness of urban industries. Therefore, analyzing the spillover effects between sub-regions based on electricity consumption data can reflect the center of gravity of the industry in a certain area. By analyzing the spillover effects of each sub-region on other sub-regions in industrial development, the center of gravity of industrial development is located to promote coordinated development between sub-regions.

[0040] This application collects and processes the electricity consumption data of each sub-area in the target area, and combines it with industry characteristics to calculate the center of gravity area of ​​a specific industry in the target area.

[0041] In another exemplary embodiment of the present application, a power consumption center of gravity identification model is constructed: two sub-areas are randomly selected and subregions , by calculating the sub-region Electricity consumption in sub-areas and subregions The proportion of total electricity consumption, taking into account sub-regions The electricity consumption in the sub-area The proportion of electricity consumption in the entire industry is used to compare the center of gravity bias of the industry between the two sub-regions. Since the remote geographical location will have a negative impact on the centrality of the sub-region, the inverse of the geographical distance is introduced. The power consumption center of gravity identification model is as follows.

[0042] 。

[0043] 。

[0044] 。

[0045] 。

[0046] In the formula, is the power consumption gravity coefficient between sub-region and sub-region ; is the power consumption of the target industry in sub-region ; is the power consumption of the target industry in sub-region ; is the total power consumption of all industries in sub-region ; is the distance between sub-region and sub-region ; is the radius of the earth; is the radian difference between sub-region and sub-region ; is an intermediate variable; is the latitude of sub-region ; is the latitude of sub-region ; is the longitude of sub-region ; is the longitude of sub-region ; represents the multiplication sign. It can be seen that the power consumption data collected in step 201 above includes the power consumption of the target industry and the total power consumption of all industries in the sub-region.

[0047] In another exemplary embodiment of the present application, the power consumption gravity coefficient is binarized so as to use the same scale range to calculate degree centrality, closeness centrality, and betweenness centrality in the subsequent centrality analysis method. The binarized power consumption gravity coefficient matrix is used as the basis for identifying the industrial gravity center. Then step 204 above can be replaced by the following steps 301 to 302.

[0048] Step 301: If the power consumption gravity coefficient is greater than or equal to the average value of the power consumption gravity coefficients in the power consumption gravity coefficient matrix, set the power consumption gravity coefficient to 1.

[0049] Step 302: If the power consumption gravity coefficient is less than the average value of the power consumption gravity coefficients in the power consumption gravity coefficient matrix, set the power consumption gravity coefficient to 0.

[0050] In another exemplary embodiment of the present application, considering the core indicators of the centrality analysis method comprehensively, the landing points of the industrial gravity areas are identified. The above step 205 can be replaced by the following steps 401 to 404.

[0051] Step 401: According to the binary power consumption gravity coefficient matrix, taking each sub-region as a node, and directly connecting the two nodes with a binary power consumption gravity coefficient of 1, to form a target area network; wherein, the distance between the two directly connected nodes is 1.

[0052] Step 402: According to the target area network, calculate the degree centrality, closeness centrality and betweenness centrality of each sub-region respectively.

[0053] Exemplarily, the calculation formula of the degree centrality is as follows:

[0054] 。

[0055] In the formula, is the degree centrality of sub-region ; is the number of sub-regions directly connected to sub-region ; is the total number of sub-regions in the target area.

[0056] The calculation formula of the closeness centrality is as follows:

[0057] 。

[0058] In the formula, is the closeness centrality of sub-region ; is the reciprocal of the shortest distance from sub-region to sub-region ; is the total number of sub-regions in the target area.

[0059] The calculation formula of the betweenness centrality is as follows:

[0060] 。

[0061] In the formula, is the betweenness centrality of sub-region ; is the number of times that sub-region serves as a bridge connecting the other two sub-regions in the shortest path between the other two sub-regions; if , and , and if the sub-region is on the shortest path between the sub-region and the sub-region , then it is determined that the sub-region is the bridge between the series sub-region and the sub-region ; indicates that the binarized power consumption gravity coefficient between the sub-region and the sub-region is 0; indicates that the binarized power consumption gravity coefficient between the sub-region and the sub-region is 1; indicates that the binarized power consumption gravity coefficient between the sub-region and the sub-region is 1; is the number of shortest paths between the sub-region and the sub-region .

[0062] The normalization formula is as follows:

[0063] .

[0064] In the formula, is the normalized betweenness centrality of the sub-region ; is the betweenness centrality of the sub-region ; , are the minimum and maximum values of the betweenness centrality respectively.

[0065] Step 403: Normalize the betweenness centrality of each sub-region.

[0066] Step 404: Determine the sum of the degree centrality, closeness centrality and normalized betweenness centrality of each sub-region as the gravity index of each sub-region for the target industry.

[0067] .

[0068] In the formula, is the gravity index of the sub-region for the target industry.

[0069] In another exemplary embodiment of the present application, when each sub-region within the target region is a city, the present application utilizes the centrality analysis method. First, it collects the longitude and latitude data and electricity consumption data of each city within the target region; secondly, it calculates the industrial electricity consumption gravity center identification coefficient to compare the industrial gravity center bias between cities; furthermore, it performs binary processing on the calculation results; finally, it identifies the industrial gravity center city through centrality analysis. The present application can not only improve the accuracy and real-time performance of identification, but also provide strong data support for industrial policy formulation and regional development.

[0070] The present application uses electricity consumption data and the centrality analysis method. By analyzing the electricity consumption data, it understands the electricity consumption patterns and spillovers in the industrial gravity center region, helps regional planners identify the industrial gravity center region within the region, thereby optimizing resource allocation and enhancing the coordinated development level among sub-regions within the target region. And the electricity consumption data reflects the electricity consumption situation of the sub-region, providing decision-making support for the power industry.

[0071] Furthermore, the method of the present application can also predict electricity demand, optimize the allocation of power resources, and improve power supply efficiency.

[0072] Taking the sub-region as a city as an example, the method of the present application is further illustrated with application examples below.

[0073] (1) Calculate the distances between cities. The calculation results of the distances between cities are shown in Table 1.

[0074] Table 1 Distances between cities

[0075]

[0076] (2) Calculate the matrix of electricity consumption gravity center coefficients. The electricity consumption gravity center coefficients are shown in Table 2.

[0077] Table 2 Electricity consumption gravity center coefficients

[0078]

[0079] (3) Binary process the data. The binary electricity consumption gravity center coefficients obtained are shown in Table 3.

[0080] Table 3 Binary electricity consumption gravity center coefficients

[0081]

[0082] (4) Calculate the gravity center index based on electricity consumption data. The gravity center indexes are shown in Table 4.

[0083] Table 4 Gravity center indexes

[0084]

[0085] It can be seen that the industrial gravity center index of City H is the highest, and the development gravity center city of this industry is City H.

[0086] The beneficial effects of this application are as follows: This application can better understand the electricity consumption demands of various industries, identify the gravity center cities of industrial development, and help decision-makers more accurately grasp the development status and future trends of regional industries.

[0087] Based on the same inventive concept, the embodiment of this application also provides a regional industrial gravity center positioning device for implementing the above-mentioned regional industrial gravity center positioning method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following regional industrial gravity center positioning device can refer to the limitations on the regional industrial gravity center positioning method in the above text, and will not be elaborated here.

[0088] In an exemplary embodiment, a regional industrial gravity center positioning device is provided, including: a collection module, a construction module, a calculation module, a binarization module, a centrality analysis module, and an identification module.

[0089] The collection module is used to collect the longitude and latitude data and electricity consumption data of each sub-region within the target region. The construction module is used to construct an electricity consumption gravity center identification model. The calculation module is used to obtain the electricity consumption gravity center coefficient between any two sub-regions within the target region by using the electricity consumption gravity center identification model based on the longitude and latitude data and electricity consumption data of each sub-region, and form an electricity consumption gravity center coefficient matrix. The binarization module is used to binarize the electricity consumption gravity center coefficients in the electricity consumption gravity center coefficient matrix. The centrality analysis module is used to calculate the gravity center index of each sub-region within the target region for the target industry by using the centrality analysis method based on the binarized electricity consumption gravity center coefficient matrix. The identification module is used to select the sub-region with the largest gravity center index as the gravity center region of the target industry within the target region.

[0090] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the longitude and latitude data and power consumption data of each sub-region in the target area. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for positioning the industrial center of gravity of a region.

[0091] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0092] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0093] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0094] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0095] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for locating the regional industrial center of gravity, characterized in that: include: Collect the latitude and longitude data and electricity consumption data of each sub-area in the target area; Construct a model for identifying the center of gravity of electricity consumption; According to the latitude and longitude data and power consumption data of each sub-area, the power consumption gravity center coefficient between any two sub-areas in the target area is obtained by using the power consumption gravity center identification model, and a power consumption gravity center coefficient matrix is ​​formed; Binarizing the power consumption gravity center coefficient in the power consumption gravity center coefficient matrix; According to the binary power consumption gravity coefficient matrix, the gravity index of each sub-region in the target area for the target industry is calculated by using the centrality analysis method; Select the sub-region with the largest center of gravity index as the center of gravity of the target industry in the target region; The power consumption center of gravity identification model is: ； ； ； ； In the formula, For sub-region and subregions The power consumption center of gravity coefficient between Target industries In sub-area electricity consumption; Target industries In sub-area electricity consumption; For sub-region Electricity consumption of the entire industry; For sub-region and subregions The distance between is the radius of the Earth; It is a sub-region and subregions The difference in arc between is an intermediate variable; For sub-region Latitude; For sub-region Latitude; For sub-region longitude; For sub-region longitude; Represents the multiplication sign.

2. The method for locating the regional industrial center of gravity according to claim 1, characterized in that: Binarizing the power consumption gravity center coefficient in the power consumption gravity center coefficient matrix specifically includes: If the power consumption gravity center coefficient is greater than or equal to the average value of the power consumption gravity center coefficients in the power consumption gravity center coefficient matrix, the power consumption gravity center coefficient is set to 1; If the power consumption gravity center coefficient is less than the average value of the power consumption gravity center coefficients in the power consumption gravity center coefficient matrix, the power consumption gravity center coefficient is set to 0.

3. The method for locating the regional industrial center of gravity according to claim 2, characterized in that: According to the binary power consumption gravity coefficient matrix, the centrality analysis method is used to calculate the gravity index of each sub-region in the target area for the target industry, including: According to the binarized power consumption gravity center coefficient matrix, each sub-region is taken as a node, and two nodes with a binarized power consumption gravity center coefficient of 1 are directly connected to form a target area network; wherein the distance between two directly connected nodes is 1; According to the target area network, the degree centrality, closeness centrality and betweenness centrality of each sub-area are calculated respectively; Normalize the betweenness centrality of each sub-region; The sum of the degree centrality, closeness centrality and normalized betweenness centrality of each sub-region is determined as the center of gravity index of each sub-region for the target industry.

4. The method for locating the regional industrial center of gravity according to claim 3, characterized in that: The calculation formula of the degree centrality is: ； In the formula, For sub-region The degree centrality of For sub-region The number of directly connected sub-regions; is the total number of sub-regions in the target region.

5. The method for locating the regional industrial center of gravity according to claim 3, characterized in that: The calculation formula of the closeness center is: ； In the formula, For sub-region The closeness centrality of For sub-region To sub-area The reciprocal of the shortest distance; is the total number of sub-regions in the target region.

6. The method for locating the regional industrial center of gravity according to claim 3, characterized in that: The calculation formula of the betweenness centrality is: ； In the formula, For sub-region The betweenness centrality of For sub-region The number of times it is a bridge connecting two other sub-areas on the shortest path between the other two sub-areas; if , and , and the sub-region In sub-area and subregions The shortest path of It is a concatenated sub-region and subregions bridges; Indicates sub-area and subregions The centroid coefficient of electricity consumption after binarization is 0; Indicates sub-area and subregions The centroid coefficient of electricity consumption after binarization is 1; Indicates sub-area and subregions The centroid coefficient of electricity consumption after binarization is 1; For sub-region and subregions The number of shortest paths.

7. The method for locating the regional industrial center of gravity according to claim 3, characterized in that: The normalized formula is: ； In the formula, For sub-region Normalized betweenness centrality; For sub-region The betweenness centrality of , are the minimum and maximum values ​​of betweenness centrality respectively.

8. A device for locating the center of gravity of a regional industry, characterized in that: The regional industrial center of gravity positioning device comprises: A collection module, used to collect the latitude and longitude data and power consumption data of each sub-area in the target area; A building module for building a power consumption center of gravity identification model; A calculation module, for obtaining the power consumption gravity center coefficient between any two sub-areas in the target area according to the latitude and longitude data and power consumption data of each sub-area and using the power consumption gravity center identification model, and forming a power consumption gravity center coefficient matrix; A binarization module, used for binarizing the power consumption gravity center coefficient in the power consumption gravity center coefficient matrix; The centrality analysis module is used to calculate the centrality index of each sub-region within the target region for the target industry based on the binarized power consumption centrality coefficient matrix using the centrality analysis method; An identification module is used to select the sub-region with the largest center of gravity index as the center of gravity region of the target industry in the target region; The power consumption center of gravity identification model is: ； ； ； ； In the formula, For sub-region and subregions The power consumption center of gravity coefficient between Target industries In sub-area electricity consumption; Target industries In sub-area electricity consumption; For sub-region Electricity consumption of the entire industry; For sub-region and subregions The distance between is the radius of the Earth; It is a sub-region and subregions The difference in arc between is an intermediate variable; For sub-region Latitude; For sub-region Latitude; For sub-region longitude; For sub-region longitude; Represents the multiplication sign.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for locating the regional industrial center of gravity as described in any one of claims 1 to 7.

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