Regional industry gravity center positioning method, device and equipment

Abstract

The invention discloses a regional industry gravity center positioning method, device and equipment, and relates to the field of regional industry positioning. The method comprises the following steps: collecting latitude and longitude data and electricity consumption data of each sub-region in a target region; constructing a power consumption gravity center identification model; according to the latitude and longitude data and the electricity consumption data of each sub-region, utilizing an electricity consumption gravity center identification model to obtain an electricity consumption gravity center coefficient between any two sub-regions in the target region, and forming an electricity consumption gravity center coefficient matrix; carrying out binaryzation on power consumption gravity center coefficients in the power consumption gravity center coefficient matrix; according to the binarized power consumption gravity center coefficient matrix, calculating the gravity center index of each sub-region in the target region for the target industry by adopting a centrality analysis method; and selecting the sub-region with the maximum center-of-gravity index as the center-of-gravity region of the target industry in the target region. The regional industry gravity center can be accurately identified based on the electricity consumption data.

Description

Technical Field

[0001] The present application relates to the field of regional industrial positioning, and in particular to a method, device and equipment for positioning the center of gravity of a regional industry. Background Art

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

[0003] In many cases, traditional industry center of gravity analysis mainly relies on macroeconomic data such as economic aggregate, tax revenue, and employment. However, the acquisition of these data often has lags and regional asymmetry, especially for in-depth analysis of specific industries or regions, there may be certain blind spots and deficiencies. However, electricity consumption data, due to its direct and real-time characteristics, can reflect the actual production and operation status of an industry in a certain region, so its application in regional industry analysis has gradually attracted attention. Therefore, in the existing technology, the research on how to use electricity consumption data to accurately identify the center of gravity of a certain industry is still relatively scattered, and there is a lack of an effective and systematic analysis method. Therefore, how to accurately locate the center of gravity 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 locating the regional industrial center of gravity, which can accurately identify the regional industrial center of gravity based on electricity consumption data.

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

[0006] In the first aspect, the present application provides a method for locating the center of gravity of a region, including: collecting the longitude and latitude data and electricity consumption data of each sub-region within the target region; constructing a power consumption center of gravity identification model; based on the longitude and latitude data and electricity consumption data of each sub-region, using the power consumption center of gravity identification model, obtaining the power consumption center of gravity coefficient between any two sub-regions within the target region, and forming a power consumption center of gravity coefficient matrix; binarizing the power consumption center of gravity coefficient in the power consumption center of gravity coefficient matrix; based on the binarized power consumption center of gravity coefficient matrix, using the centrality analysis method, calculating 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 power consumption center of gravity identification model is as follows.

[0008] .

[0009] .

[0010] .

[0011] .

[0012] 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.

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

[0014] Optionally, based on the binarized electricity consumption gravity center coefficient matrix, a centrality analysis method is used to calculate the gravity center index of each sub-region in the target area for the target industry, specifically including: based on the binarized electricity consumption gravity center coefficient matrix, taking each sub-region as a node, and directly connecting two nodes with a binarized electricity consumption gravity center coefficient of 1 to form a target area network; wherein the distance between two directly connected nodes is 1; based on the target area network, respectively calculating the degree centrality, closeness centrality and betweenness centrality of each sub-region; normalizing the betweenness centrality of each sub-region; and determining the sum of the degree centrality, closeness centrality and normalized betweenness centrality of each sub-region as the gravity center index of each sub-region for the target industry.

[0015] Optionally, 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.

[0016] Optionally, 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.

[0017] Optionally, 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.

[0018] Optionally, 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.

[0019] In the second aspect, the present application provides a regional industrial center of 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] A collection module is used to collect the longitude and latitude data and electricity consumption data of each sub-region in the target area. A construction module is used to construct a power consumption gravity center identification model. A calculation module is used to obtain the power consumption gravity center coefficient between any two sub-regions in the target area based on the longitude and latitude data and electricity consumption data of each sub-region, and to form a power consumption gravity center coefficient matrix using the power consumption gravity center identification model. A binarization module is used to binarize the power consumption gravity center coefficient in the power consumption gravity center coefficient matrix. A centrality analysis module is used to calculate the gravity center index of each sub-region in the target area for the target industry based on the binarized power consumption gravity center coefficient matrix using the centrality analysis method. An identification module is used to select the sub-region with the largest gravity center index as the gravity center area of ​​the target industry in the target area.

[0021] In a third aspect, the present application 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 executes the computer program to implement any of the above-described methods for locating the regional industrial center of gravity.

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

[0023] This application provides a method, device and equipment for locating the center of gravity of regional industries. According to the latitude and longitude data and electricity consumption data of each sub-region, the power consumption center of gravity coefficient between any two sub-regions in the target region is obtained by using the constructed power consumption center of gravity identification model, and the center of gravity index of each sub-region in the target region for the target industry can be calculated by using the centrality analysis method. The sub-region with the largest center of gravity index is the center of gravity area of ​​the target industry in the target region. Therefore, this application provides an effective and systematic analysis method based on electricity consumption data, which can accurately identify the center of gravity of regional industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0026] Figure 2 A flowchart of a method for locating a regional industrial center of gravity is provided in accordance with an embodiment of the present application.

[0027] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] The regional industry center positioning method provided in the embodiment of the present application can be applied to 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 up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the longitude and latitude data and power consumption data of each sub-area in the target area to the server 104. After the server 104 receives the longitude and latitude data and power consumption data of each sub-area in the target area, the server 104 constructs a power consumption gravity center identification model for the longitude and latitude data and power consumption data of each sub-area in the target area; according to the longitude and latitude 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; the power consumption gravity center coefficient in the power consumption gravity center coefficient matrix is ​​binarized; according to the binarized power consumption gravity center coefficient matrix, the center of gravity index of each sub-area in the target area for the target industry is calculated by using the centrality analysis method; the sub-area with the largest gravity center index is selected as the gravity center area of ​​the target industry in the target area. The server 104 can feed back 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 by the server 104 or the terminal 102 alone, such as 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-area in the target area, or the server 104 can obtain the longitude and latitude data and power consumption data of each sub-area 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-area in the target area.

[0031] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0032] In an exemplary embodiment, Figure 2 As shown, a method for locating the center of gravity of a regional industry is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 206.

[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-region, the power consumption gravity center coefficient between any two sub-regions in the target region 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, 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; It can be seen that the electricity consumption data collected in the above step 201 includes the electricity consumption of the target industry and the electricity consumption of all industries in the sub-region.

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

[0048] Step 301: 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.

[0049] Step 302: 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.

[0050] In another exemplary embodiment of the present application, the core indicators of the centrality analysis method are comprehensively considered to identify the location of the industrial center of gravity area. The above step 205 can be replaced by the following steps 401 to 404.

[0051] Step 401: Based on the binarized power consumption gravity center coefficient matrix, each sub-region is used as a node, and two nodes whose binarized power consumption gravity center coefficient is 1 are directly connected to form a target area network; wherein the distance between two directly connected nodes is 1.

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

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

[0054] 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.

[0055] The calculation formula for closeness centrality is as follows: .

[0056] 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.

[0057] The calculation formula for betweenness centrality is as follows: .

[0058] 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.

[0059] The normalized formula is as follows: .

[0060] 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.

[0061] Step 403: Normalize the betweenness centrality of each sub-region.

[0062] Step 404: 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.

[0063] .

[0064] In the formula, For sub-region Center of gravity index for target industries.

[0065] In another exemplary embodiment of the present application, when each sub-region in the target region is a city, the present application uses the centrality analysis method to first collect the latitude and longitude data and electricity consumption data of each city in the target region; secondly, calculate the industrial power consumption center of gravity identification coefficient to compare the industrial center of gravity bias between cities; furthermore, binarize the calculation results; finally, identify the industrial center of gravity 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.

[0066] This application uses electricity consumption data and centrality analysis methods to analyze electricity consumption data to understand the electricity consumption patterns and spillovers in industrial core areas, helping regional planners identify industrial core areas within the region, thereby optimizing resource allocation and improving the level of coordinated development between sub-regions within the target region. In addition, electricity consumption data reflects the electricity consumption of sub-regions, providing decision support for the power industry.

[0067] Furthermore, the method of the present application can also predict electricity demand, optimize electricity resource allocation, and improve electricity supply efficiency.

[0068] The following takes a city as an example to further illustrate the application of the method of this application.

[0069] (1) Calculate the distance between each city. The calculation results of the distance between each city are shown in Table 1.

[0070] Table 1 Distances between cities

[0071] (2) Calculate the power consumption center of gravity coefficient matrix. The power consumption center of gravity coefficient is shown in Table 2.

[0072] Table 2 Power consumption center of gravity coefficient

[0073] (3) The data is binarized. The binarized power consumption center of gravity coefficient is shown in Table 3.

[0074] Table 3 Binarized power consumption center of gravity coefficient

[0075] (4) Calculate the center of gravity index based on electricity consumption data. The center of gravity index is shown in Table 4.

[0076] Table 4 Center of gravity index

[0077] It can be seen that H City has the highest industrial focus index, and the development focus city of this industry is H City.

[0078] The beneficial effects of this application are: this application can better understand the electricity demand of various industries, identify the key cities for industrial development, and help decision makers to more accurately grasp the development status and future trends of regional industries.

[0079] Based on the same inventive concept, the embodiment of the present application also provides a regional industrial center of gravity positioning device for implementing the regional industrial center of gravity positioning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the regional industrial center of gravity positioning device provided below can refer to the limitations of the regional industrial center of gravity positioning method above, and will not be repeated here.

[0080] In an exemplary embodiment, a device for locating the center of gravity of a regional industry is provided, including: a collection module, a construction module, a calculation module, a binarization module, a centrality analysis module and an identification module.

[0081] A collection module is used to collect the longitude and latitude data and electricity consumption data of each sub-region in the target area. A construction module is used to construct a power consumption gravity center identification model. A calculation module is used to obtain the power consumption gravity center coefficient between any two sub-regions in the target area based on the longitude and latitude data and electricity consumption data of each sub-region, and to form a power consumption gravity center coefficient matrix using the power consumption gravity center identification model. A binarization module is used to binarize the power consumption gravity center coefficient in the power consumption gravity center coefficient matrix. A centrality analysis module is used to calculate the gravity center index of each sub-region in the target area for the target industry based on the binarized power consumption gravity center coefficient matrix using the centrality analysis method. An identification module is used to select the sub-region with the largest gravity center index as the gravity center area of ​​the target industry in the target area.

[0082] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to 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-area in the target area. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for locating the center of gravity of a regional industry is implemented.

[0083] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0084] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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 may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0085] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0086] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0087] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this 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-region, the power consumption gravity center coefficient between any two sub-regions in the target region 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; The sub-region with the largest centroid index is selected as the centroid area of ​​the target industry in the target area.

2. The method for locating the regional industrial center of gravity according to claim 1, characterized in that: 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.

3. 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.

4. The method for locating the regional industrial center of gravity according to claim 3, 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.

5. The method for locating the regional industrial center of gravity according to claim 4, 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.

6. The method for locating the regional industrial center of gravity according to claim 4, 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.

7. The method for locating the regional industrial center of gravity according to claim 4, 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.

8. The method for locating the regional industrial center of gravity according to claim 4, 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.

9. 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; The identification module is used to select the sub-region with the largest centroid index as the centroid region of the target industry in the target region.

10. 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 8.

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