Intelligent analysis method of building economy based on big data

Abstract

The present invention relates to the field of building economic technology, and in particular to a building economic intelligence analysis method based on big data, the method comprising: obtaining the location, height and area data of the building, as well as the name, distribution location, industry type, operating data and business distribution area data of the enterprises inside the building; cleaning, integrating and spatially processing the acquired data to construct a building economic database; obtaining the spatial aggregation characteristics of enterprises in different industries in the building; obtaining the aggregation characteristics of the business distribution area of ​​the enterprises in the building; constructing enterprise economic time series data; analyzing the spatial distribution change trend of the building economy; analyzing the relationship between the aggregation degree of enterprises in the building and the operating data; analyzing the relationship between the operating data of enterprises in the building and the business distribution area. The present invention optimizes the building layout according to the aggregation characteristics of the enterprises to promote the coordinated development of the industry; or formulates targeted support policies for the business aggregation areas to help the development of enterprises.

Description

Technical Field

[0001] The present invention relates to the field of building economic technology, and in particular to a building economic intelligent analysis method based on big data. Background Art

[0002] Building economy is a new economic form that has emerged in the development of China's urban economy in recent years. It is an economic form that takes commercial buildings, functional blocks and regional facilities as the main carriers, develops and rents buildings to attract various enterprises, thereby introducing tax sources and driving regional economic development. It is characterized by intensive and high density.

[0003] In the statistical process of building economic data, the patent with publication number CN115408450A discloses a method, device, equipment and medium for economic data statistics based on geographic information. The invention realizes the fusion of economic data, basic data and map by associating enterprise coordinates with economic data and basic information corresponding to each enterprise. When it is necessary to query the economic data of a certain area, the query area can be directly circled. At this time, the economic data of the query area can be displayed quickly and intuitively, which is more convenient. This method only has a good effect when querying the economic data of the corresponding area, and cannot meet the needs of analyzing the spatial distribution characteristics and aggregation characteristics of enterprises in buildings. In addition, the patent with authorization announcement number CN114077977B discloses a smart building management method, system and readable storage medium based on big data. The invention realizes the analysis and presentation of economic dynamics and building economic vitality by the regional economic monitoring platform based on the building enterprise portrait and the per-mu benefit index of the building, which can improve the detection accuracy of regional economy and building economic vitality. It only focuses on the electricity data information of enterprise users and the per-mu benefit index of the building, and mainly analyzes the dynamic operation data of the building itself to grasp the operation status of the building. Summary of the invention

[0004] The present invention identifies the distribution characteristics and spatial aggregation characteristics of different enterprises in a building, and analyzes the spatial distribution trend of the building economy, the relationship between the degree of enterprise aggregation and operating data, and the relationship between the operating data of enterprises in the building and the business distribution area by combining enterprise economic data. The building layout is optimized according to the aggregation characteristics of enterprises to promote the coordinated development of the industry; or targeted support policies are formulated for the business aggregation areas to help the development of enterprises.

[0005] The technical solution proposed by the present invention is: a building economic intelligence analysis method based on big data, the method comprising: Obtain the location, height and area data of the building, as well as the name, distribution location, industry type, operating data and business distribution area data of the enterprises inside the building; Clean, integrate and spatialize the acquired data to build a building economic database; Obtain the spatial clustering characteristics of enterprises in different industries within a building to analyze the spatial distribution pattern of enterprises within the building and the spatial correlation between different industries; Obtain the clustering characteristics of the business distribution areas of enterprises in the building to analyze the distribution patterns of business distribution areas among different enterprises and the spatial correlation between business distribution areas and building locations; Obtaining the business operation data of enterprises in the building within a preset time period from the building economic database to form the enterprise economic time series data; Combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building, the spatial distribution trend of the building economy is analyzed; Combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building, the relationship between the clustering degree of enterprises in the building and the operating data is analyzed; Combining the enterprise economic time series data and the clustering characteristics of the business distribution areas of enterprises in the building, the relationship between the operating data of enterprises in the building and the business distribution areas is analyzed.

[0006] Preferably, the obtaining of spatial clustering characteristics of enterprises in different industries in a building comprises the following steps: Import building floor plans as the base map for spatial analysis; According to the distribution location information of the enterprises, each enterprise is located at a corresponding position on the building plan to form a point distribution map; the distribution location information of the enterprises includes the floor and room number of the enterprise; Add industry type attribute tags to each enterprise; Generate spatial aggregation maps of enterprises in different industries to show the spatial aggregation degree and distribution trend of different industries within buildings.

[0007] Preferably, generating a spatial aggregation map of enterprises in different industries for displaying the spatial aggregation degree and distribution trend of different industries in a building comprises the following steps: Import the kernel density analysis tool and set the search radius based on the building area data; According to the density value around each enterprise, a density raster layer is generated; each pixel value in the density raster layer represents the average density of enterprises in the corresponding building space, and the larger the pixel value, the higher the degree of enterprise aggregation; Convert the density raster layer into a heat map to show the degree of spatial aggregation in different areas through color gradients; Get the industry type attribute label of each enterprise; Identify the highest pixel value from the density raster layer, obtain the industry type attribute labels of all enterprises in the building space corresponding to the highest pixel value, and form a cluster feature set; The number of attribute labels of each industry type in the clustered feature set is counted and a bar chart is drawn to facilitate the analysis of the distribution trends of different industries in the corresponding building space.

[0008] Preferably, the obtaining of the aggregation characteristics of the business distribution areas of enterprises in the building comprises the following steps: Import digital maps of the area where the building is located; According to the location information of the business distribution area of ​​the enterprise, each business distribution area of ​​the enterprise is located at a corresponding position in the digital map to form a block distribution map; the location information of the business distribution area includes one or more of the city name, longitude and latitude range and land area covered by the business distribution area; Generate a business aggregation map for each enterprise to show the aggregation degree and overlapping areas of business distribution areas of different enterprises.

[0009] Preferably, the analyzing the distribution pattern of business distribution areas among different enterprises and the spatial correlation between business distribution areas and building locations comprises the following steps: Import the enterprise name information, business distribution area data and industry type attribute label information into the GIS software to generate a business distribution layer. Specifically, the business distribution area of ​​each enterprise is located in the corresponding location area in the digital map through the GIS software, and the corresponding location area is associated with the corresponding enterprise information; Use the intersection tool in the GIS software to calculate the common area between the business distribution areas of two or more enterprises in the business distribution layer, that is, the overlapping area; Generate an overlapping area distribution layer through the intersection tool to display the overlapping areas of business distribution areas between different enterprises; Calculate the area of ​​each overlapping region, and form a linked data set with the overlapping area and the corresponding enterprise name. ,in, Indicates The area of ​​the overlapping region, Indicates overlapping The name of the company and The name of the company, ; Calculate the area of ​​each enterprise's business distribution area , calculate the overlap area ratio , the overlapping area ratio value reflects the degree of concentration of enterprise businesses in the corresponding business distribution area; Calculate the distance between the business distribution areas of two or more enterprises without overlapping business distribution areas, which is used to analyze the cooperation potential between enterprises; Then the distance and the names of several companies with no business overlap are combined into a linked data set. ,in, Indicates Enterprises and The distance between the business distribution areas of an enterprise, Indicates The name of the company and The name of the company, , ; Obtain the location of the building, obtain the location of the area with the largest overlap ratio from the digital map, that is, the location of the area with the highest business concentration, calculate the distance between the building and the area with the highest business concentration, and form the associated data set with the building location, the location of the area with the highest concentration, and the distance between the building and the area with the highest business concentration. ,in Indicates the coordinates of the building in the digital map, Indicates the coordinates of the area with the highest degree of aggregation in the digital map, Indicates the distance between buildings and areas with the highest concentration of business.

[0010] Preferably, the step of acquiring the business operation data of enterprises in the building within a preset time period from the building economic database to form the business economic time series data comprises the following steps: Obtaining operating data of different enterprises within one year, wherein the operating data includes one or more of profit data, total turnover and inherent assets; The operating data of different enterprises are used to form the enterprise economic time series in chronological order. ,in Indicates Enterprise No. Annual operating data set, , represents the total number of enterprises; in , arrive Respectively represent Enterprise No. Operating data from the first month to the twelfth month of the year.

[0011] Preferably, the combining of enterprise economic time series data and spatial clustering characteristics of enterprises in different industries in a building to analyze the spatial distribution trend of building economy includes the following steps: Get the first Operating data for the year; Calculate the sum of monthly operating data of enterprises in each cluster area; According to the enterprises in each cluster area The sum of the monthly operating data within a year is used to draw an operating data curve chart to reflect the changes in operating data of different clustered areas in different buildings.

[0012] Preferably, combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building to analyze the relationship between the clustering degree of enterprises in the building and the operating data includes the following steps: Calculate the sum of the operating data of all enterprises in each cluster area to obtain the cluster area operating data set ;in Indicates The sum of the operating data of all enterprises in the cluster area; Using the clustered area business data set as the data source, draw a bar chart of the business data of different clustered areas; The pixel value of each clustering area is obtained, and the pixel value is associated with the corresponding histogram to reflect the relationship between the degree of clustering and the business data.

[0013] Preferably, combining the enterprise economic time series data and the clustering characteristics of the business distribution areas of the enterprises in the building, analyzing the relationship between the business data of the enterprises in the building and the business distribution areas includes the following steps: Extracting business data sets from corporate economic time series ; Calculate the number of The average distance from each enterprise to the corresponding business distribution range ;in, Indicates Enterprises to The distance between the business distribution areas, Indicates The total number of business distribution areas of an enterprise; Extracting parameters from joint dataset 3 ; Establish the Business-distance relationship model for an enterprise: ; in, represents the influence coefficient of aggregation degree, represents the distance influence coefficient, represents the distribution adjustment reference coefficient; if , indicating that the operating data of an enterprise is affected by the distance between the enterprise and the region with the highest degree of business concentration beyond the preset value; if , indicating that the business data of an enterprise is affected by the distance between the enterprise and the business distribution area beyond the preset value; In satisfying and Under the condition of , determine whether the The business distribution scope of an enterprise, or adjust the location of the enterprise.

[0014] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the building economic intelligence analysis method based on big data.

[0015] Beneficial effects of the present invention: 1. The present invention identifies the spatial clustering characteristics of enterprises in different industries within buildings and analyzes the spatial correlation between industries. For example, some industries may tend to cluster in corresponding areas due to supply chain relationships, technical exchanges or market demand, forming specific industrial clusters. This clustering phenomenon is of great significance for promoting the coordinated development of industries and enhancing regional competitiveness. Different spatial distribution patterns reflect the diversity and complexity of the building economy and have important reference value for formulating regional development plans and policies.

[0016] 2. In the present invention, the use of time series data to monitor the changing trend of the spatial distribution of the building economy can reveal the dynamic process and evolution of economic development. By comparing data at different time points, we can observe phenomena such as the migration, expansion or contraction of enterprises and industries, thereby evaluating policy impacts and market dynamics. For example, the implementation of a certain policy may lead to rapid growth or decline in a specific industry, which in turn affects its spatial distribution within the building; and changes in market dynamics, such as changes in consumer demand and the emergence of new technologies, may also trigger the re-layout of enterprises and industries. Therefore, dynamic change monitoring is of great significance for timely adjustment of policies and guiding the healthy development of industries.

[0017] 3. The present invention combines the enterprise economic time series data and the spatial aggregation characteristics of enterprises in different industries in the building to analyze the relationship between the aggregation degree of enterprises in the building and the operating data. For each aggregation area, the sum of the operating data (such as income, profit, etc.) of all enterprises in the area is calculated. This can be achieved by using the statistical function of GIS software or related data analysis tools, using the aggregation area operating data set as the data source to draw a bar chart of the operating data of different aggregation areas. This can be achieved by using data visualization tools (such as Excel, Tableau, etc.). The bar chart can intuitively display the operating data of different aggregation areas. The pixel value is associated with the corresponding bar chart, which can be achieved by adding a pixel value label to the bar chart or using color coding. According to the associated bar chart, the relationship between the aggregation degree and the operating data is analyzed, and it can be observed whether the corresponding operating data of the aggregation area with a higher pixel value is also higher, so as to judge the impact of the aggregation degree on the operating data. At the same time, other factors that may affect the operating data, such as industry type, market environment, etc., can be further explored. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the building economic intelligence analysis method based on big data of the present invention; Figure 2 A flow chart for analyzing the relationship between the business data of enterprises in a building and the business distribution areas in the present invention; Figure 3 This is a schematic diagram of the business distribution area distribution relationship of different enterprises in the present invention. DETAILED DESCRIPTION

[0019] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0020] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0021] refer to Figure 1 The technical solution provided by the present invention is: a building economic intelligence analysis method based on big data, comprising the following steps: 1. Obtain the location, height and area data of the building, as well as the name, distribution location, industry type, operating data and business distribution area data of the enterprises inside the building; 2. Clean, integrate and spatialize the acquired data to build a building economic database; specifically: Check and remove duplicate data, then process missing values, and then detect and process outliers. Use the SOL statement to integrate the data processed in the previous step. Select a spatial reference system for the building economics database, including the longitude and latitude coordinates or projection coordinates of the building. Use the relevant library functions in the GIS software to convert spatial data from different sources into the same spatial reference system. After associating non-spatial data with spatial data, import them into the database to form a building economics database. The non-spatial data includes company name, company room number and business data, and the spatial data includes longitude and latitude coordinates, building height and building area.

[0022] 3. Obtain the spatial clustering characteristics of enterprises in different industries in the building to analyze the spatial distribution pattern of enterprises in the building and the spatial correlation between different industries, which specifically includes the following steps: Import building floor plans into GIS software as the basic base map for spatial analysis; According to the distribution location information of the enterprises, each enterprise is located at a corresponding position on the building plan to form a point distribution map; the distribution location information of the enterprises includes the floor and room number of the enterprise; Add industry type attribute tags to each enterprise; Generate a spatial aggregation map of enterprises in different industries to show the spatial aggregation degree and distribution trend of different industries in the building, including the following steps: Call up the kernel density analysis tool in the GIS software and set the search radius based on the building area data; According to the density value around each enterprise, a density raster layer is generated; each pixel value in the density raster layer represents the average density of enterprises in the corresponding building space, and the larger the pixel value, the higher the degree of enterprise aggregation; Convert the density raster layer into a heat map to show the degree of spatial aggregation in different areas through color gradients; Get the industry type attribute label of each enterprise; Identify the highest pixel value from the density raster layer, obtain the industry type attribute labels of all enterprises in the building space corresponding to the highest pixel value, and form a cluster feature set; The number of attribute labels of each industry type in the clustered feature set is counted and a bar chart is drawn to facilitate the analysis of the distribution trends of different industries in the corresponding building space.

[0023] 4. Obtain the clustering characteristics of the business distribution areas of enterprises in the building to analyze the distribution patterns of business distribution areas between different enterprises and the spatial correlation between business distribution areas and building locations, specifically including the following steps: Import a digital map of the area where the building is located, and locate each business distribution area of ​​the enterprise at a corresponding position in the digital map according to the location information of the business distribution area of ​​the enterprise, so as to form a block distribution map; the location information of the business distribution area includes one or more of the city name, longitude and latitude range and land area covered by the business distribution area; generate a business aggregation map for each enterprise, which is used to display the aggregation degree and overlapping area of ​​the business distribution areas of different enterprises.

[0024] By importing digital maps and locating business distribution areas, the location information of the business distribution areas of enterprises can be collected and processed more accurately. This helps to improve the accuracy and reliability of analysis. Generating block distribution maps can intuitively show the spatial distribution of business distribution areas of different enterprises. This helps decision makers quickly understand the overall pattern and characteristics of business distribution. By counting indicators such as the number of enterprises and business scale in the business distribution area, the business aggregation degree of each enterprise can be accurately calculated. This helps to gain a deep understanding of the density and importance of the business distribution areas of different enterprises. By comparing the business aggregation maps of different enterprises, the overlapping areas of business distribution areas can be effectively identified. This helps to analyze the relevance and competitive relationship of the businesses between different enterprises, and provide a basis for formulating corresponding competitive strategies.

[0025] The analysis process is as follows: Import the enterprise name information, business distribution area data and industry type attribute label information into the GIS software to generate a business distribution layer. Specifically, the business distribution area of ​​each enterprise is located in the corresponding location area in the digital map through the GIS software, and the corresponding location area is associated with the corresponding enterprise information; The intersection tool in the GIS software is used to calculate the common area between the business distribution areas of two or more enterprises in the business distribution layer, that is, the overlapping area; the intersection tool is used to generate an overlapping area distribution layer to display the overlapping area of ​​the business distribution areas of different enterprises; Calculate the area of ​​each overlapping region, and form a linked data set with the overlapping area and the corresponding enterprise name. ,in, Indicates The area of ​​the overlapping region, Indicates overlapping The name of the company and The name of the company, ; Calculate the area of ​​each enterprise's business distribution area , calculate the overlap area ratio The overlapping area ratio value reflects the degree of concentration of enterprise businesses in the corresponding business distribution area.

[0026] from Figure 3 It can be seen that the location relationship of the business distribution areas of the four enterprises, the location relationship between the building locations and the business distribution areas of the four enterprises, and the overlapping areas between the four enterprises. From the figure, the area with the highest business concentration can be identified.

[0027] Calculate the distance between the business distribution areas of two or more enterprises that do not overlap, and use it to analyze the cooperation potential between enterprises; the distance measurement process can be achieved using the distance measurement function of GIS software or related map service APIs. Enterprises that are closer may have higher cooperation potential because geographical proximity helps reduce cooperation costs and improve cooperation efficiency.

[0028] Then the distance and the names of several companies with no business overlap are combined into a linked data set. ,in, Indicates Enterprises and The distance between the business distribution areas of an enterprise, Indicates The name of the company and The name of the company, , ; Obtain the location of the building, obtain the location of the area with the largest overlap ratio from the digital map, that is, the location of the area with the highest business concentration, calculate the distance between the building and the area with the highest business concentration, and form the associated data set with the building location, the location of the area with the highest concentration, and the distance between the building and the area with the highest business concentration. ,in Indicates the coordinates of the building in the digital map, Indicates the coordinates of the area with the highest degree of aggregation in the digital map, It indicates the distance between buildings and the areas with the highest business concentration, which facilitates the query and analysis of the relationship between buildings and business active areas.

[0029] 5. Obtaining the business operation data of enterprises in the building within a preset time period from the building economic database to form the enterprise economic time series data, specifically including the following steps: Obtaining operating data of different enterprises within one year, wherein the operating data includes one or more of profit data, total turnover and inherent assets; The operating data of different enterprises are used to form the enterprise economic time series in chronological order. ,in Indicates Enterprise No. Annual operating data set, , represents the total number of enterprises; in , arrive Respectively represent Enterprise No. Operating data from the first month to the twelfth month of the year.

[0030] 6. Combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building, the spatial distribution trend of the building economy is analyzed, which specifically includes the following steps: Get the first Operating data for the year; Calculate the sum of monthly operating data of enterprises in each cluster area; According to the enterprises in each cluster area The sum of the monthly operating data within a year is used to draw an operating data curve chart to reflect the changes in operating data of different clustered areas in different buildings.

[0031] 7. Combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building, analyze the relationship between the clustering degree of enterprises in the building and the operating data, which specifically includes the following steps: Calculate the sum of the operating data of all enterprises in each cluster area to obtain the cluster area operating data set ;in Indicates The sum of the operating data of all enterprises in the cluster area; Taking the clustered area business data set as the data source, a bar chart of the business data of different clustered areas is drawn; the pixel value of each clustered area is obtained, and the pixel value is associated with the corresponding bar chart to reflect the relationship between the degree of clustering and the business data.

[0032] 8. Combining the enterprise economic time series data and the clustering characteristics of the business distribution areas of enterprises in the building, analyze the relationship between the business data of enterprises in the building and the business distribution areas, such as Figure 2 As shown, the specific steps include: Extracting business data sets from corporate economic time series ; Calculate the number of The average distance from each enterprise to the corresponding business distribution range ;in, Indicates Enterprises to The distance between the business distribution areas, Indicates The total number of business distribution areas of an enterprise; Extracting parameters from joint dataset 3 ; Establish the Business-distance relationship model for an enterprise: ; in, represents the influence coefficient of aggregation degree, represents the distance influence coefficient, represents the distribution adjustment reference coefficient; if , indicating that the operating data of an enterprise is affected by the distance between the enterprise and the region with the highest degree of business concentration beyond the preset value; if , indicating that the business data of an enterprise is affected by the distance between the enterprise and the business distribution area beyond the preset value; In satisfying and Under the condition of , indicating the The average distance from the building where the enterprise is located to the corresponding business distribution area is more than twice the distance from the area with the highest business concentration. The business distribution scope of an enterprise, or adjust the location of the enterprise.

[0033] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the building economic intelligence analysis method based on big data.

[0034] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical cable, RF, etc., or any suitable combination of the foregoing.

[0035] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0036] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be deformed or modified in any way.

Claims

1. A building economic intelligence analysis method based on big data, characterized in that: The method comprises: Obtain the location, height and area data of the building, as well as the name, distribution location, industry type, operating data and business distribution area data of the enterprises inside the building; Clean, integrate and spatialize the acquired data to build a building economic database; Obtain the spatial clustering characteristics of enterprises in different industries within a building to analyze the spatial distribution pattern of enterprises within the building and the spatial correlation between different industries; Obtain the clustering characteristics of the business distribution areas of enterprises in the building to analyze the distribution patterns of business distribution areas among different enterprises and the spatial correlation between business distribution areas and building locations; Obtaining the business operation data of enterprises in the building within a preset time period from the building economic database to form the enterprise economic time series data; Combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building, the spatial distribution trend of the building economy is analyzed; Combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building, the relationship between the clustering degree of enterprises in the building and the operating data is analyzed; Combining the enterprise economic time series data and the clustering characteristics of the business distribution areas of enterprises in the building, the relationship between the operating data of enterprises in the building and the business distribution areas is analyzed.

2. The building economic intelligence analysis method based on big data according to claim 1 is characterized in that: The step of obtaining the spatial clustering characteristics of enterprises in different industries in a building includes the following steps: Import building floor plans as the base map for spatial analysis; According to the distribution location information of the enterprises, each enterprise is located at a corresponding position on the building plan to form a point distribution map; the distribution location information of the enterprises includes the floor and room number of the enterprise; Add industry type attribute tags to each enterprise; Generate spatial aggregation maps of enterprises in different industries to show the spatial aggregation degree and distribution trend of different industries within buildings.

3. The building economic intelligence analysis method based on big data according to claim 2 is characterized in that: The generating of the spatial aggregation diagram of enterprises in different industries, which is used to display the spatial aggregation degree and distribution trend of enterprises in different industries in the building, includes the following steps: Import the kernel density analysis tool and set the search radius based on the building area data; According to the density value around each enterprise, a density raster layer is generated; each pixel value in the density raster layer represents the average density of enterprises in the corresponding building space, and the larger the pixel value, the higher the degree of enterprise aggregation; Convert the density raster layer into a heat map to show the degree of spatial aggregation in different areas through color gradients; Get the industry type attribute label of each enterprise; Identify the highest pixel value from the density raster layer, obtain the industry type attribute labels of all enterprises in the building space corresponding to the highest pixel value, and form a cluster feature set; The number of attribute labels of each industry type in the clustered feature set is counted and a bar chart is drawn to facilitate the analysis of the distribution trends of different industries in the corresponding building space.

4. The building economic intelligence analysis method based on big data according to claim 1 is characterized in that: The step of obtaining the clustering characteristics of the enterprise business distribution area in the building includes the following steps: Import digital maps of the area where the building is located; According to the location information of the business distribution area of ​​the enterprise, each business distribution area of ​​the enterprise is located at a corresponding position in the digital map to form a block distribution map; the location information of the business distribution area includes one or more of the city name, longitude and latitude range and land area covered by the business distribution area; Generate a business aggregation map for each enterprise to show the aggregation degree and overlapping areas of business distribution areas of different enterprises.

5. The building economic intelligence analysis method based on big data according to claim 4 is characterized in that: The analysis of the distribution pattern of business distribution areas between different enterprises and the spatial correlation between business distribution areas and building locations includes the following steps: Import the enterprise name information, business distribution area data and industry type attribute label information into the GIS software to generate a business distribution layer. Specifically, the business distribution area of ​​each enterprise is located in the corresponding location area in the digital map through the GIS software, and the corresponding location area is associated with the corresponding enterprise information; Use the intersection tool in the GIS software to calculate the common area between the business distribution areas of two or more enterprises in the business distribution layer, that is, the overlapping area; Generate an overlapping area distribution layer through the intersection tool to display the overlapping areas of business distribution areas between different enterprises; Calculate the area of ​​each overlapping region, and form a linked data set with the overlapping area and the corresponding enterprise name. ,in, Indicates The area of ​​the overlapping region, Indicates overlapping The name of the company and The name of the company, ; Calculate the area of ​​each enterprise's business distribution area , calculate the overlap area ratio , the overlapping area ratio value reflects the concentration degree of enterprise business in the corresponding business distribution area; Calculate the distance between the business distribution areas of two or more enterprises without overlapping business distribution areas, which is used to analyze the cooperation potential between enterprises; Then the distance and the names of several companies with no business overlap are combined into a linked data set. ,in, Indicates Enterprises and The distance between the business distribution areas of an enterprise, Indicates The name of the company and The name of the company, , ; Obtain the location of the building, obtain the location of the area with the largest overlap ratio from the digital map, that is, the location of the area with the highest business concentration, calculate the distance between the building and the area with the highest business concentration, and form the associated data set with the building location, the location of the area with the highest concentration, and the distance between the building and the area with the highest business concentration. ,in Indicates the coordinates of the building in the digital map, Indicates the coordinates of the area with the highest degree of aggregation in the digital map, Indicates the distance between buildings and areas with the highest concentration of business.

6. The building economic intelligence analysis method based on big data according to claim 1 is characterized in that: The step of obtaining the business operation data of enterprises in the building within a preset time period from the building economic database to form the business economic time series data includes the following steps: Obtaining operating data of different enterprises within one year, wherein the operating data includes one or more of profit data, total turnover and inherent assets; The operating data of different enterprises are used to form the enterprise economic time series in chronological order. ,in Indicates Enterprise No. Annual operating data set, , represents the total number of enterprises; in , arrive Respectively represent Enterprise No. Operating data from the first month to the twelfth month of the year.

7. The building economic intelligence analysis method based on big data according to claim 6 is characterized in that: The method of combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building includes the following steps: Get the first Operating data for the year; Calculate the sum of monthly operating data of enterprises in each cluster area; According to the enterprises in each cluster area The sum of the monthly operating data within a year is used to draw an operating data curve chart to reflect the changes in operating data of different clustered areas in different buildings.

8. The building economic intelligence analysis method based on big data according to claim 7 is characterized in that: The method of combining the enterprise economic time series data and the spatial clustering characteristics of enterprises in different industries in the building to analyze the relationship between the clustering degree of enterprises in the building and the operating data includes the following steps: Calculate the sum of the operating data of all enterprises in each cluster area to obtain the cluster area operating data set ;in Indicates The sum of the operating data of all enterprises in the cluster area; Using the clustered area business data set as the data source, draw a bar chart of the business data of different clustered areas; The pixel value of each clustering area is obtained, and the pixel value is associated with the corresponding histogram to reflect the relationship between the degree of clustering and the business data.

9. The building economic intelligence analysis method based on big data according to claim 7 is characterized in that: The method of combining the enterprise economic time series data and the clustering characteristics of the business distribution areas of the enterprises in the building to analyze the relationship between the business data of the enterprises in the building and the business distribution areas includes the following steps: Extracting business data sets from corporate economic time series ; Calculate the number of The average distance from each enterprise to the corresponding business distribution range ;in, Indicates Enterprises to The distance between the business distribution areas, Indicates The total number of business distribution areas of an enterprise; Extracting parameters from joint dataset 3 ; Establish the Business-distance relationship model for an enterprise: ; in, represents the influence coefficient of aggregation degree, represents the distance influence coefficient, represents the distribution adjustment reference coefficient; if , indicating that the operating data of an enterprise is affected by the distance between the enterprise and the region with the highest degree of business concentration beyond the preset value; if , indicating that the business data of an enterprise is affected by the distance between the enterprise and the business distribution area beyond the preset value; In satisfying and Under the condition of , determine whether the The business distribution scope of an enterprise, or adjust the location of the enterprise.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the building economic intelligence analysis method based on big data as described in any one of claims 1 to 9.

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