Building investment attraction intelligent matching method and system based on big data analysis

Abstract

The invention relates to the technical field of data analysis, in particular to a building investment attraction intelligent matching method and system based on big data analysis. According to the intelligent matching method based on multi-dimensional data analysis, the problems of inaccuracy and failure possibly caused by single feature matching in the prior art can be effectively solved, and different scenes and demand changes can be flexibly coped with by constructing the matching node network and performing dynamic adjustment according to multiple association values; according to the method and the system, the requirements of various types of buildings and tenants are met, the matching comprehensiveness and effectiveness are improved, historical information nodes in big data are used for extracting building dynamic information, and real-time analysis is performed in combination with tenant demand data. And through intelligent matching of multiple correlation values, a more accurate, flexible and efficient investment attracting solution is provided between the building and the tenant, and the method has remarkable technical advantages and practical value.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to an intelligent matching method and system for building investment promotion based on big data analysis. Background Art

[0002] The intelligent matching method for building investment promotion is a method that, through big data analysis and intelligent algorithms, helps commercial real estate developers or operators more efficiently screen and match suitable tenants, merchants, or partners during the investment promotion process. Specifically, the intelligent matching method is based on a large amount of building-related data (such as geographical location, rent, floor layout, traffic flow, target customer groups, etc.) and tenant demand data (such as industry type, business scale, lease requirements, etc.). Through data analysis and machine learning algorithms, it helps identify the most suitable matching relationship between tenants and commercial spaces, thereby improving the investment promotion efficiency and reducing the investment promotion cost.

[0003] In the prior art, the intelligent matching method for building investment promotion mainly analyzes the characteristics of buildings and the needs of potential tenants by using data analysis technology, and then uses machine learning and deep learning algorithms to find the most suitable match for each building and tenant, and uses historical data in big data for trend prediction and tenant demand prediction. However, due to the complex diversity between different buildings and tenants, the existing matching algorithms cannot well adapt to all scenarios, resulting in inaccurate or ineffective matching results. Summary of the Invention

[0004] The main object of the present invention is to provide an intelligent matching method for building investment promotion based on big data analysis, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes an intelligent matching method for building investment promotion based on big data analysis, including: Obtaining historical information nodes of each building, and obtaining historical dynamic information of the building according to the historical information nodes, wherein the historical dynamic information of the building includes a plurality of building environment data and building entity data; Obtaining demand information nodes of each tenant, and obtaining real-time dynamic information of the tenant according to the demand information nodes, wherein the real-time dynamic information of the tenant includes a plurality of tenant entity data and tenant demand data; Obtaining corresponding first demand correlation values according to each of the building environment data and tenant demand data, and obtaining corresponding fit correlation values according to each of the building environment data and tenant entity data; Obtaining corresponding second demand correlation values according to each of the building entity data and tenant demand data, and obtaining corresponding entity correlation values according to each of the building entity data and tenant entity data; Obtain corresponding matching values according to each first demand correlation value, fit correlation value, second demand correlation value, and entity correlation value, and associate and match the historical information nodes of the corresponding building with the demand information nodes of the tenant according to each of the matching values to construct a matching node network; Perform intelligent investment promotion matching for each building according to the matching node network.

[0006] Preferably, the step of obtaining the corresponding first demand correlation value according to each of the building environment data and tenant demand data includes: Obtain the surrounding pedestrian flow density and competition characteristic information of multiple shops within a preset regional area according to the building environment data, and obtain the shop rental level, shop competitiveness, and shop area according to each of the competition characteristic information; Obtain the individual commercial value of the corresponding shop according to each of the shop rental level, shop competitiveness, and shop area, and obtain the comprehensive commercial value according to multiple of the individual commercial values; Obtain the commercial competition intensity according to the comprehensive commercial value and the preset regional area; Obtain the industry code and tenant key financial indicators according to the tenant demand data, wherein the tenant key financial indicators include revenue growth rate, net profit rate, and R & D investment ratio; Obtain the tenant development index according to the revenue growth rate, net profit rate, and R & D investment ratio, and obtain the first demand correlation value according to the tenant development index, commercial competitiveness, industry code, and surrounding pedestrian flow density.

[0007] Preferably, the step of obtaining the corresponding fit correlation value according to each of the building environment data and tenant entity data includes: Obtain the air quality index, average day-night noise decibel number, and green space area within a preset regional area according to the building environment data; Obtain the noise pollution index according to the average day-night noise decibel number, and obtain the green space coverage index according to the green space area and the preset regional area; Obtain the environmental excellence index according to the air quality index, noise pollution index, and green space coverage index; Obtain the traffic density index, vehicle speed deviation degree, and interference characteristics according to the building environment data; Obtain the signal light influence coefficient and intersection influence coefficient within a preset traffic cycle according to the interference characteristics, and obtain the traffic signal interference coefficient according to the signal light influence coefficient and intersection influence coefficient; Obtain the traffic congestion index according to the traffic signal interference coefficient, vehicle speed deviation degree, and traffic density index; Obtain the commuting demand and environmental preferences according to the tenant entity data, and obtain the fitting correlation value according to the commuting demand, environmental preferences, environmental excellence index, and traffic congestion index.

[0008] Preferably, the step of obtaining the corresponding second demand correlation value according to each of the building entity data and tenant demand data includes: Obtain the building rent, building structure characteristics, and building layout characteristics according to the building entity data, and obtain the building height, building load-bearing capacity, and number of floors according to the building structure characteristics; Obtain the building structure expandability according to the building height, building load-bearing capacity, and number of floors; Obtain the total number of rooms in the building, the number of layoutable rooms in the building, the wall adaptability coefficient, and the space adjustment flexibility coefficient according to the building layout characteristics, and obtain the building layout expandability according to the total number of rooms in the building, the number of layoutable rooms in the building, the wall adaptability coefficient, and the space adjustment flexibility coefficient; Obtain the building space expandability according to the building layout expandability and the building structure expandability; Obtain the tenant rent demand and tenant expansion demand according to the tenant demand data, and obtain the second demand correlation value according to the tenant rent demand, tenant expansion demand, building space expandability, and building rent.

[0009] Preferably, the step of obtaining the corresponding entity correlation value according to each of the building entity data and tenant entity data includes: Obtain the tenant turnover growth rate, tenant leased area growth rate, and tenant number growth rate according to the tenant entity data, and obtain the comprehensive tenant growth rate according to the tenant turnover growth rate, tenant leased area growth rate, and tenant number growth rate; Obtain the building safety factor, fire protection facility coverage, access control system coverage, and power supply capacity according to the building entity data, and obtain the building safety facility coverage according to the fire protection facility coverage and access control system coverage; Obtain the safety facility requirements according to the tenant entity data, and obtain the tenant safety demand coefficient according to the safety facility requirements; Obtain the safety facility difference value according to the tenant safety demand coefficient and the building safety factor, and obtain the safety facility demand value according to the safety facility difference value, building safety facility coverage, and building safety factor; Obtain the entity correlation value according to the safety facility demand value and the comprehensive tenant growth rate.

[0010] Preferably, the step of associating and matching the historical information node of the corresponding building with the demand information node of the tenant according to each of the matching values to construct a matching node network includes: Obtain the matching values of each historical information node and multiple demand information nodes, and sort the multiple matching values in ascending order to obtain a matching value sorting table; Select the demand information node corresponding to the matching value ranked first in the matching value sorting table and the corresponding demand information node as the associated node; Until all historical information nodes and demand information nodes are traversed to obtain multiple associated nodes; Use the historical information node and demand information node corresponding to each associated node as an associated edge, and use the corresponding matching value as the edge weight. Construct a matching node network according to the multiple associated edges and edge weights.

[0011] This application also provides an intelligent matching system for building investment promotion based on big data analysis, including: The first acquisition module is used to acquire the historical information nodes of each building, and obtain the historical dynamic information of the building according to the historical information nodes, where the historical dynamic information of the building includes multiple building environment data and building entity data; The second acquisition module is used to acquire the demand information nodes of each tenant, and obtain the real-time dynamic information of the tenant according to the demand information nodes, where the real-time dynamic information of the tenant includes multiple tenant entity data and tenant demand data; The third acquisition module is used to obtain the corresponding first demand association value according to each building environment data and tenant demand data, and obtain the corresponding fit association value according to each building environment data and tenant entity data; The fourth acquisition module is used to obtain the corresponding second demand association value according to each building entity data and tenant demand data, and obtain the corresponding entity association value according to each building entity data and tenant entity data; The construction module is used to obtain the corresponding matching value according to each first demand association value, fit association value, second demand association value and entity association value, and perform association matching on the historical information node of the corresponding building and the demand information node of the tenant according to each matching value, and construct a matching node network; The matching module is used to perform intelligent investment promotion matching for each building according to the matching node network.

[0012] Preferably, the third acquisition module includes: The first acquisition unit is used to obtain the surrounding pedestrian flow density and the competition characteristic information of multiple shops within the preset area according to the building environment data, and obtain the shop rent level, shop competitiveness and shop area according to each competition characteristic information; The second acquisition unit is used to obtain the individual commercial value of the corresponding shop according to each shop rent level, shop competitiveness and shop area, and obtain the comprehensive commercial value according to the multiple individual commercial values; A third acquisition unit, configured to acquire the business competition intensity according to the comprehensive commercial value and the preset regional area; A fourth acquisition unit, configured to acquire the industry code and the tenant's key financial indicators according to the tenant demand data, where the tenant's key financial indicators include the revenue growth rate, the net profit rate, and the R & D investment ratio; A fifth acquisition unit, configured to acquire the tenant development index according to the revenue growth rate, the net profit rate, and the R & D investment ratio, and acquire a first demand correlation value according to the tenant development index, the business competitiveness, the industry code, and the surrounding pedestrian flow density.

[0013] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned intelligent matching method for building investment promotion based on big data analysis are implemented.

[0014] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned intelligent matching method for building investment promotion based on big data analysis are implemented.

[0015] The beneficial effects of the present invention are as follows: By comprehensively considering the historical dynamic information of the building and the real-time demand information of the tenant, and through the calculation of various correlation values such as the first demand correlation value and the fit correlation value, the present invention realizes the precise matching between the building and the tenant. The intelligent matching method based on multi-dimensional data analysis can effectively overcome the inaccuracy and failure problems that may be caused by single-feature matching in the prior art, and provide a more efficient and accurate investment promotion solution. By constructing a matching node network and dynamically adjusting according to various correlation values, it can flexibly respond to different scenarios and demand changes, adapt to various types of building and tenant needs, and improve the comprehensiveness and effectiveness of the matching. By using the historical information nodes in the big data to extract the building dynamic information and combining with the real-time analysis of the tenant demand data, the present invention can not only accurately predict the current demand, but also effectively predict the future trend based on the historical data, identify potential market changes and tenant demand changes in advance, so as to help the building management party make more scientific and forward-looking investment promotion decisions. Through intelligent data analysis and machine learning algorithms, it can quickly and accurately recommend the most suitable tenant for each building, saving the time and cost that rely heavily on manual judgment and experience in the traditional investment promotion method, and improving the efficiency and success rate of the investment promotion process. Through the calculation of multiple correlation values and matching strategies, it can continuously optimize the matching effect under the changeable market environment and different demand conditions, avoid the matching instability or failure problems that may be brought by a single algorithm, and enhance the adaptability and stability of the system. Through multi-dimensional data analysis, the combination of historical information and real-time dynamics, and the intelligent matching of multiple correlation values, the present invention provides a more accurate, flexible and efficient investment promotion solution between the building and the tenant, with significant technical advantages and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0017] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present invention.

[0018] Figure 3 It is a schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0019] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] As Figure 1 shown, the present application provides an intelligent matching method for building investment promotion based on big data analysis, including: S1. Obtain the historical information nodes of each building, and obtain the historical dynamic information of the building according to the historical information nodes. Among them, the historical dynamic information of the building includes multiple building environment data and building entity data; S2. Obtain the demand information nodes of each tenant, and obtain the real-time dynamic information of the tenant according to the demand information nodes. Among them, the real-time dynamic information of the tenant includes multiple tenant entity data and tenant demand data; S3. Obtain the corresponding first demand correlation value according to each of the building environment data and tenant demand data, and obtain the corresponding fit correlation value according to each of the building environment data and tenant entity data; S4. Obtain the corresponding second demand correlation value according to each of the building entity data and tenant demand data, and obtain the corresponding entity correlation value according to each of the building entity data and tenant entity data; S5. Obtain the corresponding matching value according to each first demand correlation value, fit correlation value, second demand correlation value and entity correlation value, and perform association matching between the historical information nodes of the corresponding building and the demand information nodes of the tenant according to each matching value to construct a matching node network; S6. Perform intelligent investment promotion matching for each building according to the matching node network.

[0022] As described in the above steps S1 - S6, the present invention obtains the historical information nodes of each building, and obtains multiple building environment data and building entity data of the historical dynamic information of the building according to the historical information nodes. By analyzing the historical information nodes of the building, the characteristics and past operation dynamics of each building can be understood in detail, which can provide more accurate data support for the investment promotion process. The historical dynamic data helps to reveal the long-term usage trends and demand changes of the building, so as to make more accurate predictions and adjustments for future investment promotion needs. The collection of building environment data and entity data makes the subsequent matching process more targeted, avoiding the limitations of relying on a single data source. By obtaining the demand information nodes of each tenant, and obtaining multiple tenant entity data and tenant demand data of the real-time dynamic information of the tenant according to the demand information nodes. By obtaining the tenant demand information nodes, the actual needs and dynamics of the tenant can be grasped in a timely manner, avoiding the use of outdated or inaccurate tenant information. The collection of real-time dynamic information ensures that flexible adjustments can be made according to the changes in tenant demands during the investment promotion process, enhancing the timeliness and adaptability of the entire system. By analyzing the tenant demand data and entity data, the potential demands of the tenant can be effectively identified, improving the matching accuracy, and thus enhancing the investment promotion effect; Obtain the corresponding first demand correlation value through each building environment data and tenant demand data. By establishing the correlation between building environment data and tenant demand data, the compatibility between the building and tenant demand can be accurately quantified, ensuring the scientificity and practicality of the matching process. This correlation value reflects the degree of adaptation between tenant demand and building environment, helps to find the building that best meets the actual needs of the tenant, improves the rental success rate, and reduces the vacancy rate. Obtain the corresponding compatibility correlation value according to each building environment data and tenant entity data. The calculation of the compatibility correlation value not only considers the building environment but also includes the entity data of the tenant (such as tenant type, industry attribute, etc.), thus improving the comprehensiveness of the matching. The compatibility correlation value can accurately reflect the possible synergy effect between the building and the tenant in actual operation, ensuring that the building selected by the tenant can better meet its operation needs, thereby reducing matching errors and increasing the rental rate of the building; Obtain the corresponding second demand correlation value through each building entity data and tenant demand data. The second demand correlation value further refines the connection between the building and tenant demand by combining the actual physical entity data of the building and the tenant demand data. The second demand correlation value helps with refined management, ensuring that the building can provide personalized space and services for the tenant, enhancing tenant satisfaction, and strengthening the competitiveness of the building. Obtain the corresponding entity correlation value according to each building entity data and tenant entity data. The entity correlation value reflects the actual matching degree between the building and the tenant in the physical space, including the compatibility between the building structure, size, location, etc. of the building and the actual business needs of the tenant (such as office area, location requirements, etc.). Through the calculation of the entity correlation value, more suitable tenants can be more accurately matched, avoiding resource waste and improving the operation efficiency of the building. Obtain the corresponding matching value through each first demand correlation value, compatibility correlation value, second demand correlation value, and entity correlation value. By comprehensively considering multiple correlation values (demand correlation value, compatibility correlation value, etc.), a multi-dimensional and all-round matching model can be provided, ensuring that the matching process is more accurate and scientific. By integrating multiple factors, the accuracy of the matching can be improved, ensuring the maximization of the compatibility between the building and the tenant, and enhancing the market response speed and competitiveness; According to each matching value, the historical information nodes of the corresponding building are associated and matched with the demand information nodes of the tenant, and a matching node network is constructed. By establishing the matching node network, the historical information of the building and the tenant and the real-time demand data are fully integrated, which not only enhances the intelligence of the matching process, but also greatly improves the flexibility and dynamic adaptability of the operation. The matching node network enables the building and the tenant to perform more efficient matching and docking, improves the rental rate of the building and reduces the interruption period or vacancy period. Through the matching node network, intelligent investment promotion matching is carried out for each building. Based on the intelligent investment promotion matching of the matching node network, an automated and precise investment promotion process can be realized, overcoming the interference of human factors in the traditional investment promotion process and avoiding the influence of empirical errors. Through intelligent matching, the optimal tenant can be quickly identified, the investment promotion efficiency can be improved, unnecessary investment promotion costs can be reduced, and real-time adjustment and optimization can be carried out according to market changes to ensure the high efficiency and long-term sustainability of the investment promotion process. It not only improves the efficiency and accuracy of investment promotion matching, but also strengthens the application of dynamic data to ensure that the matching results are more in line with actual needs, thereby improving the overall efficiency of building operation.

[0023] In one embodiment, step S3 of obtaining the corresponding first demand correlation value according to each of the building environment data and the tenant demand data includes: S31. Obtain the surrounding pedestrian flow density and the competition characteristic information of multiple shops within the preset area according to the building environment data, and obtain the shop rent level, shop competitiveness and shop area according to each competition characteristic information; S32. Obtain the individual commercial value of the corresponding shop according to the product of the standardized shop rent level, shop competitiveness and shop area, and obtain the comprehensive commercial value by weighted summation calculation after standardization according to multiple individual commercial values; S33. Obtain the commercial competition intensity according to the ratio of the comprehensive commercial value to the standardized preset area; S34. Obtain the industry code and the tenant's key financial indicators according to the tenant demand data, where the tenant's key financial indicators include the revenue growth rate, net profit rate and R & D investment ratio; S35. Obtain the tenant development index by weighted summation calculation after standardization according to the revenue growth rate, net profit rate and R & D investment ratio, and obtain the first demand correlation value by weighted summation after standardization according to the tenant development index, commercial competitiveness, industry code and surrounding pedestrian flow density.

[0024] As described in the above steps S31 - S35, the present invention obtains the surrounding pedestrian flow density and the competitive characteristic information of multiple shops within a preset regional area through building environment data. The rent and competitiveness of building shops are usually affected by the surrounding environment, while traditional analysis methods often ignore dynamic environment data, resulting in the inability to accurately evaluate commercial value. By obtaining building environment data in real time and analyzing the surrounding pedestrian flow density, the actual situation can be dynamically reflected, and the competitive characteristics can be updated in real time, providing more accurate data support for shop leasing decisions, avoiding the information lag and prediction errors brought by traditional methods due to ignoring real-time environment changes; Obtain the shop rent level, shop competitiveness, and shop area according to each competitive characteristic information. Obtain the individual commercial value of the corresponding shop through the product of the standardized shop rent level, shop competitiveness, and shop area. Shop rent and competitiveness are often set according to some static parameters, without considering the dynamic changes of competitive characteristics, which may lead to inaccurate decisions in shop leasing. Closely linking shop rent, competitiveness, and shop area with competitive characteristics can help more accurately evaluate the commercial value of each shop under changing market conditions. By dynamically adjusting these parameters through competitive characteristics, the leasing decision can be made more flexible and forward-looking, improving the accuracy of rent pricing. The rent, competitiveness, and area among different shops are often in different dimensions or scales, and direct comparison may lead to errors. Through standardization processing, the deviation caused by inconsistent dimensions among different shops is eliminated, making the comparison among different shops fairer and more accurate. Calculating the individual commercial value through the product after standardization makes the method of comprehensively evaluating shop value more scientific and can eliminate external factor interference; Calculate the comprehensive commercial value by weighted summation after standardizing multiple individual commercial values. The commercial value of a single shop often cannot fully reflect the actual competition situation of shops in the building. Weighted summation can integrate the commercial values of multiple shops, making the final evaluation result more representative. By calculating the comprehensive commercial value, the competitiveness of shops inside the building can be accurately reflected, providing a basis for the building investment promotion decision. The weighted summation method ensures that the differences in the value contributions of different shops are reasonably reflected. Obtain the commercial competition intensity through the ratio of the comprehensive commercial value and the standardized preset regional area. The commercial competition intensity of a building is often affected by the size of the building and the number of shops. Traditional methods may ignore the differences in regional sizes. By calculating the standardized ratio of commercial value to regional area, the influence of different building area differences can be eliminated, ensuring the universality and comparability of the calculation of commercial competition intensity, and being able to provide more accurate competition analysis for investment promotion personnel to help them evaluate the investment promotion difficulty and potential of each area; Obtain the industry code and the revenue growth rate, net profit rate, and R & D investment ratio of the tenant's key financial indicators from the tenant demand data. After standardizing the revenue growth rate, net profit rate, and R & D investment ratio, perform a weighted sum to obtain the tenant development index. Existing demand matching methods usually only rely on industry statistical data and ignore the individual needs and financial health of tenants. By combining the financial indicators and industry codes of tenants, the needs of tenants can be understood from multiple dimensions, helping to analyze their leasing intentions and market adaptability. Financial data such as revenue growth rate, net profit rate, and R & D investment ratio can reflect the operating conditions and growth potential of tenants, providing higher predictability for investment promotion. There are significant differences in the development potential among different tenants. Traditional methods only rely on financial statements or static data and cannot accurately predict their future development. By calculating the tenant development index through weighted summation, various financial indicators of tenants can be comprehensively considered, providing a scientific assessment of the long-term development potential of tenants. This can not only screen out growing tenants but also provide data support during the investment promotion process, reducing the vacancy rate and the emergence of bad tenants; After standardizing the tenant development index, business competitiveness, industry code, and surrounding pedestrian flow density, perform a weighted sum to obtain the first demand correlation value. Traditional investment promotion matching methods are difficult to comprehensively consider data from multiple dimensions, resulting in unsatisfactory matching effects. By standardizing and weighted summing multi-dimensional data, a more accurate tenant and store matching result can be provided. The first demand correlation value can provide higher accuracy for investment promotion personnel, making the needs of tenants more compatible with the business competitiveness of the building and regional characteristics, thereby improving the success rate of business matching and reducing the vacancy risk.

[0025] In one embodiment, the step S3 of obtaining the corresponding fit correlation value according to each of the building environment data and tenant entity data includes: S36. Obtain the air quality index, the average day-night noise decibel number, and the green space area within the preset area according to the building environment data; S37. Obtain the noise pollution index according to the average day-night noise decibel number, and obtain the green space coverage index according to the ratio of the green space area to the preset area; S38. Calculate and obtain the excellent environment index by performing a weighted sum after standardizing the air quality index, noise pollution index, and green space coverage index; S39. Obtain the traffic density index, vehicle speed characteristics, and interference characteristics according to the building environment data. Obtain the real-time vehicle speed and the restricted vehicle speed according to the vehicle speed characteristics, and obtain the vehicle speed deviation degree according to the ratio of the difference between the restricted vehicle speed and the real-time vehicle speed to the restricted vehicle speed; S310. Obtain the signal light influence coefficient and intersection influence coefficient within a preset traffic cycle according to the interference characteristics, and obtain the traffic signal interference coefficient according to the ratio of the sum of the signal light influence coefficient and intersection influence coefficient to the standardized preset traffic cycle; S311. Obtain the traffic congestion index through weighted summation calculation after standardization based on the traffic signal interference coefficient, vehicle speed deviation degree, and traffic density index; S312. Obtain the commuting demand and environmental preference according to the tenant entity data, and obtain the fit correlation value through weighted summation calculation after standardization based on the commuting demand, environmental preference, environmental quality index, and traffic congestion index.

[0026] As described in the above steps S36 - S312, the present invention obtains the air quality index, the average day-night noise decibel number, and the green space area within a preset area through building environment data. By obtaining building environment data, including air quality, noise decibel number, and green space area, the environmental quality of the building can be comprehensively evaluated. By obtaining air quality and noise level data in real time and accurately, the living and working environment of the building can be clearly reflected, helping tenants make better decisions. The green space coverage rate can provide quantitative data on the greening degree of the building, thereby providing a healthier and more comfortable office or living environment for tenants and enhancing the competitiveness of the building. By obtaining these environmental data, the building management party can take targeted environmental improvement measures according to the feedback to improve the overall building quality; Obtain the noise pollution index through the average day-night noise decibel number. The noise pollution index is calculated based on the average day-night noise decibel number, which can quantify the noise pollution level. The day-night noise level is an important indicator for measuring the noise environment of a building. By calculating the noise pollution index, it is possible to more accurately identify which buildings have noise pollution problems. The noise pollution index can help tenants choose an environment that meets their needs. Especially for noise-sensitive tenants, it can ensure a quiet and comfortable working or living environment. Obtain the green space coverage index according to the ratio of the green space area to the preset area. Through the standardized green space coverage index, tenants can intuitively understand the green space resources of the building. Especially for tenants who pay attention to environmental protection and health, it provides an intuitive basis for selection. The increase in green space helps to improve the ecological environment of the building, enhance the happiness and work efficiency of tenants, and promote the high-value recognition of the building brand; The environmental excellence index is obtained by weighted summation calculation after standardizing the air quality index, noise pollution index, and green space coverage index. By combining multiple environmental factors, such as air quality, noise, and greening degree, a comprehensive environmental assessment index is derived, which can comprehensively reflect the environmental quality of the building. Buildings with a high environmental excellence index can attract more tenants seeking high-quality office and living environments, improving the rental rate and rent level. The traffic density index, vehicle speed characteristics, and interference characteristics are obtained through building environmental data. By accurately obtaining the traffic density index, vehicle speed characteristics, and interference characteristics, it can help tenants understand the traffic conditions around the building, predict the convenience or difficulty of commuting, and analyze factors such as vehicle speed deviation and traffic signal interference coefficient, which helps tenants avoid high-traffic congestion areas when choosing a building and improve the commuting experience of tenants. The real-time vehicle speed and restricted vehicle speed are obtained according to the vehicle speed characteristics; The vehicle speed deviation is obtained according to the ratio between the difference between the restricted vehicle speed and the real-time vehicle speed and the restricted vehicle speed, which can help tenants understand the traffic flow conditions of the surrounding roads and predict changes in commuting time. Tenants can make a more reasonable commuting plan based on the vehicle speed deviation, avoiding traffic jams during peak hours. A high vehicle speed deviation may mean a greater risk of traffic congestion or traffic accidents. Through this data, the risk for tenants when choosing a location can be effectively reduced. The signal light influence coefficient and intersection influence coefficient within a preset traffic cycle are obtained through the interference characteristics, and the traffic signal interference coefficient is obtained according to the ratio between the sum of the signal light influence coefficient and the intersection influence coefficient and the standardized preset traffic cycle. The signal light influence and the interference coefficient of the intersection can help tenants better understand the traffic flow and signal light scheduling situation and choose a more suitable commuting route. Tenants are more sensitive to the impact of the surrounding traffic. Reducing traffic signal interference helps improve the work efficiency and living comfort of tenants; The traffic congestion index is obtained by weighted summation calculation after standardizing the traffic signal interference coefficient, vehicle speed deviation, and traffic density index. By calculating the traffic congestion index, tenants can obtain quantitative data on traffic congestion, optimize their commuting time, and choose a suitable building, understand the traffic congestion situation, and can provide suggestions for tenants to avoid high-congestion areas in advance, reducing commuting pressure and improving the quality of life of tenants. The commuting demand and environmental preferences are obtained through tenant entity data, and the fit correlation value is obtained by weighted summation calculation after standardizing the commuting demand, environmental preferences, environmental excellence index, and traffic congestion index. According to the commuting demand and environmental preferences of tenants, integrating environmental and traffic factors, the fit correlation value is calculated to help tenants find the building that best meets their needs, better adapting to the diversity and complexity between tenants and buildings, avoiding problems of matching failure or inaccuracy in the prior art, and providing more accurate and personalized building rental recommendations.

[0027] In one embodiment, step S4 of obtaining the corresponding second demand correlation value according to each of the building entity data and the tenant demand data includes: S41. Obtain the building rent, building structure characteristics, and building layout characteristics according to the building entity data, and obtain the building height, building load-bearing capacity, and number of floors according to the building structure characteristics; S42. Obtain the building structure expandability according to the ratio between the product of the standardized building height and the standardized building load-bearing capacity and the standardized number of floors; S43. Obtain the total number of rooms in the building, the number of layout-able rooms in the building, the wall adaptability coefficient, and the space adjustment flexibility coefficient according to the building layout characteristics, and obtain the building layout expandability according to the product of the ratio between the number of layout-able rooms in the building and the total number of rooms in the building multiplied by the product of the wall adaptability coefficient and the space adjustment flexibility coefficient; S44. Obtain the building space expandability according to the sum of the building layout expandability and the building structure expandability; S45. Obtain the tenant rent demand and tenant expansion demand according to the tenant demand data, and obtain the second demand correlation value through weighted summation calculation after standardization according to the tenant rent demand, tenant expansion demand, building space expandability, and building rent.

[0028] As described in the above steps S41 - S45, the present invention obtains the building rent, building structure characteristics, and building layout characteristics through the building entity data. By obtaining the entity data of the building (such as rent, structure characteristics, and layout characteristics), it is possible to comprehensively understand all aspects of the building, which helps to deeply analyze the multi-dimensional characteristics of the building and provides comprehensive data support for the subsequent matching process. Traditional building investment promotion matching often relies on limited data or subjective judgment. Obtaining more detailed building structure and layout data can greatly improve the accuracy of matching, reduce information errors, and obtain the building height, building load-bearing capacity, and number of floors according to the building structure characteristics. Understanding the height, load-bearing capacity, and number of floors of the building can help accurately predict the scope of use of the building. In particular, obtaining the building load-bearing capacity can help determine whether the building is suitable for certain tenants with heavy equipment or special needs. The factors such as the height, number of floors, and load-bearing capacity of the building affect the utilization rate and adaptability of the space. Accurately obtaining this information can better recommend the most suitable building for tenants and improve the rationality and efficiency of the building use space; The scalability of the building structure is obtained by the ratio between the product of the standardized building height and the standardized building load-bearing capacity and the standardized number of floors. Through the calculation of the standardized data, the scalability of the building structure can be quantified, which provides a comparable numerical basis for intelligent matching, transforms the traditional rule of thumb into a mathematical model, improves the objectivity and accuracy of matching. Using the structure scalability index, the most suitable building for the tenant can be more precisely matched, reducing the deviation of manual judgment. The total number of rooms in the building, the number of layoutable rooms in the building, the wall adaptability coefficient, and the space adjustment flexibility coefficient are obtained through the building layout characteristics. Obtaining information such as the number of rooms and the number of layoutable rooms can accurately evaluate the space adjustment flexibility of the building and provide a basis for customizing the space configuration for the tenant. The introduction of the flexibility coefficient further improves the space adaptability, which enables the building to provide more personalized space options for tenants with different needs during the rental process and meet the diverse market demands; The layout scalability of the building is obtained by multiplying the ratio between the number of layoutable rooms in the building and the total number of rooms in the building by the product of the wall adaptability coefficient and the space adjustment flexibility coefficient. Through the calculation of the second demand correlation value, the layout scalability of the building can be objectively evaluated, thus ensuring the adaptability of different tenants to the building space requirements. The quantified layout scalability can provide a more demand-compliant space option for the tenant, avoiding mismatches, strengthening the refined management of the internal space of the building, making the use of the building more efficient, and being able to flexibly adjust and optimize the layout according to actual needs. The building space scalability is obtained by the sum of the building layout scalability and the building structure scalability. The calculation of the building space scalability combines multiple factors of the building structure and layout, comprehensively evaluating the space potential of the building, which enables the use efficiency and flexibility of the building to reach the optimum, greatly improving the actual utilization rate of the space. This comprehensive evaluation helps to select the most suitable building space in multiple scenarios and adapt to the needs of different tenants; Obtain the tenant's rent demand and tenant expansion demand through tenant demand data, and calculate the second demand correlation value through weighted summation after standardization based on the tenant's rent demand, tenant expansion demand, building space scalability, and building rent. By obtaining the tenant's rent demand and expansion demand, it is possible to more accurately understand the actual needs of the tenant, avoid matching mistakes caused by information asymmetry, quantify the tenant's needs, especially the expansion demand, which can provide a more clear goal for the subsequent matching process, reduce the uncertainty in the leasing process, and comprehensively standardize and weight-sum the tenant's needs and the characteristics of the building to accurately calculate the second demand correlation value, which is an important basis for optimizing the matching. The second demand correlation value provides decision support for the automated system, making each match more in line with the tenant's needs and the actual conditions of the building. Through the method of weighted summation, various factors are effectively integrated, significantly improving the matching degree between the tenant and the building, optimizing the resource allocation in the building investment promotion process, and avoiding the mismatch or waste phenomenon caused by too rough matching methods.

[0029] In one embodiment, step S4 of obtaining the corresponding entity correlation value according to each of the building entity data and tenant entity data includes: S46. Obtain the tenant turnover growth rate, tenant leased area growth rate, and tenant number growth rate according to the tenant entity data, and calculate the comprehensive tenant growth rate through weighted summation after standardization based on the tenant turnover growth rate, tenant leased area growth rate, and tenant number growth rate; S47. Obtain the building safety factor, fire protection facility coverage, access control system coverage, and power supply capacity according to the building entity data, and obtain the building safety facility coverage according to the fire protection facility coverage and access control system coverage; S48. Obtain the safety facility requirements according to the tenant entity data, and obtain the tenant safety demand coefficient according to the safety facility requirements; S49. Obtain the safety facility difference value according to the tenant safety demand coefficient and the building safety factor, and obtain the safety facility demand value according to the difference between the sum of the building safety facility coverage and the building safety factor and the safety facility difference value; S410. Calculate the entity correlation value through weighted summation after standardization based on the safety facility demand value and the comprehensive tenant growth rate.

[0030] As described in the above steps S46 - S410, the present invention obtains the tenant turnover growth rate, the tenant leased area growth rate, and the tenant quantity growth rate through tenant entity data. By obtaining the turnover growth rate, leased area growth rate, and quantity growth rate of tenants, the business expansion of tenants can be quantified. This indicator can help identify tenants with rapid business growth, accurately predict future tenant needs, and facilitate the building management party to anticipate the types of potential tenants. Different from traditional single rent data analysis, this method evaluates the growth of tenants from multiple dimensions, can better reflect the operating conditions of tenants, and provides more accurate data support for building investment promotion; The comprehensive tenant growth rate is obtained by weighted summation calculation after standardization based on the tenant turnover growth rate, the tenant leased area growth rate, and the tenant quantity growth rate. Standardization processing can eliminate the dimensional differences between data in different dimensions (such as turnover, area, quantity), enabling them to be fairly compared in the same evaluation system. Weighted summation calculation can assign different weights to different factors, reflecting their relative importance in the comprehensive evaluation, so as to obtain a more accurate and representative comprehensive tenant growth rate, which can improve the accuracy of investment promotion decisions and ensure that the building management party can give priority to tenants with greater growth potential during investment promotion. The building safety factor, fire protection facility coverage, access control system coverage, and power supply capacity are obtained through building entity data. Building safety is one of the key factors for tenants to choose an office or commercial space. By extracting indicators such as safety factor, fire protection facility coverage, and access control system coverage from building entity data, the safety guarantee level of the building can be comprehensively evaluated. These data will help the building management party identify the deficiencies in the safety facilities of the building, and then provide a safer and more stable office environment for tenants, enhancing the attractiveness of the building; The building safety facility coverage is obtained based on the fire protection facility coverage and the access control system coverage. This step further refines the coverage of the building safety facilities. By calculating the overall safety facility coverage through the coverage of the fire protection and access control systems, the safety protection level of the building can be more accurately evaluated. This method can make targeted improvement suggestions according to the specific safety facility conditions of different buildings, avoid the deviation that may exist in single - dimension safety assessment, and improve the accuracy of the building safety assessment. The safety facility requirements are obtained through tenant entity data, and the tenant safety demand coefficient is obtained according to the safety facility requirements. The safety facility requirements of tenants reflect their safety needs in a specific environment. By analyzing the specific safety needs of tenants, these needs can be quantified and converted into a safety demand coefficient, so that the building management party can better understand the special safety requirements of each tenant, and thus provide safety facilities that better meet the needs of each tenant, improve tenant satisfaction, and enhance the competitiveness of the building; Obtain the safety facility difference value through the tenant safety requirement coefficient and the building safety coefficient. The safety facility difference value reveals the matching degree between the current building and the tenant's safety requirements by comparing the differences between the tenant's requirements and the safety facilities actually provided by the building. Through the calculation of this difference value, the weak links of the building in terms of safety facilities can be accurately identified, and the building can be improved in a timely manner to avoid potential safety problems caused by insufficient safety facilities. At the same time, it can also enhance the tenant's satisfaction and trust in the building. And obtain the safety facility requirement value according to the difference between the sum of the building safety facility coverage and the building safety coefficient and the safety facility difference value. By comprehensively considering the difference values of the building safety facility coverage, safety coefficient, and tenant requirements, the requirement value of the safety facilities is further quantified. Through this comprehensive evaluation, the advantages, disadvantages, and deficiencies of the current safety facilities in the building can be clearly understood, and an accurate safety facility improvement plan can be proposed. The safety facility requirement value can be used as a basis for the building management party to further optimize the safety facility configuration to ensure the perfect fit between the safety level of the building and the tenant's requirements; Calculate the entity association value by weighted summation after standardizing the safety facility requirement value and the comprehensive tenant growth rate. Combining the safety facility requirement value and the comprehensive tenant growth rate, and performing weighted summation after standardization, an entity association value reflecting the comprehensive matching degree between the building and the tenant can be obtained. Through the entity association value, the building management party can more clearly understand the matching degree between the safety requirements of each tenant and the existing facilities in the building, so as to optimize the tenant matching strategy. Thus, not only the safety facilities of the building are concerned, but also the tenant growth potential is comprehensively considered, improving the accuracy of the matching between the building and the tenant. The present invention comprehensively analyzes the tenant and the building from multiple dimensions (such as turnover, rental area, safety facilities, etc.), can provide a more comprehensive and accurate matching. Through standardization processing and weighted calculation, the comparability and rationality of various indicators are improved, ensuring the scientificity and accuracy of the matching algorithm. By particularly focusing on the matching between the building safety facilities and the tenant safety requirements, the problem of insufficient consideration of safety in the prior art is solved, and a comprehensive decision-making support tool can be provided for the building management party, which helps to optimize the investment promotion strategy and improve the tenant satisfaction.

[0031] In one embodiment, step S5 of associating and matching the historical information nodes of the corresponding building with the demand information nodes of the tenant according to each of the matching values to construct a matching node network includes: S51. Obtain the matching values of each historical information node and multiple demand information nodes, and sort the multiple matching values in descending order to obtain a matching value sorting table; S52. Select the demand information node corresponding to the matching value ranked first in the matching value sorting table and the corresponding demand information node as the associated nodes; S53. Until all historical information nodes and requirement information nodes are traversed to obtain multiple associated nodes; S54. Take the historical information node and requirement information node corresponding to each of the associated nodes as an associated edge, and take the corresponding matching value as the edge weight. Construct a matching node network according to the multiple associated edges and edge weights.

[0032] As described in the above steps S51 - S54, the present invention obtains the matching values of each historical information node and multiple requirement information nodes, sorts the multiple matching values in descending order to obtain a matching value sorting table. By sorting the matching values, it can effectively determine which requirement information nodes best meet the requirements of the historical information nodes. This sorting mechanism provides a clear priority for subsequent node selection, making the matching result more accurate and reasonable, avoiding the interference of irrelevant or non - priority matches to the result, and significantly improving the accuracy of the matching. By selecting the requirement information node corresponding to the matching value ranked first in the matching value sorting table and the corresponding requirement information node as the associated node until all historical information nodes and requirement information nodes are traversed to obtain multiple associated nodes, by preferentially selecting the node corresponding to the matching value ranked first, it ensures that each selection is based on the current optimal match, maximizing the optimization of the matching process. The process of gradually traversing all historical information nodes and requirement information nodes helps to find the most suitable matching node pairs in a wide selection space, thus reducing the risk of missing potential suitable matches; By taking the historical information node and requirement information node corresponding to each associated node as an associated edge, and taking the corresponding matching value as the edge weight, constructing a matching node network according to the multiple associated edges and edge weights, structuring the matching result into graphical associated edges and weights not only simplifies the result representation, but also provides a clear structure for subsequent data analysis and optimization. By introducing edge weights, the algorithm can make trade - offs according to the priorities of different matches, accurately reflecting the influence of each matching node, further improving the accuracy and flexibility of the matching algorithm. Constructing the matching node network helps to globally display the relationships between multiple historical information nodes and requirement information nodes, forming a network structure for convenient further analysis and optimization. By considering the relationships and weights between all nodes, it enhances the global nature and scalability of the algorithm, enabling it to adapt to more complex and diverse application scenarios. The network structure is also convenient for dynamic adjustment and optimization, enabling this method to continuously provide accurate matching results as the requirements and historical information are updated; By sorting the matching values, optimizing node association, and introducing weight calculation, the problems of inaccurate matching and inability to adapt to diverse requirements in the prior art are overcome. By combining historical data and demand information, this method can dynamically and precisely adjust the matching strategy to ensure the maximum adaptability and optimality of the matching results, better meeting the diverse needs of buildings and tenants in complex scenarios, thereby achieving efficient and accurate intelligent matching.

[0033] As Figure 2 shown, this application also provides a building investment promotion intelligent matching system based on big data analysis, including: The first acquisition module is used to acquire the historical information nodes of each building and obtain the building historical dynamic information according to the historical information nodes, where the building historical dynamic information includes multiple building environment data and building entity data; The second acquisition module is used to acquire the demand information nodes of each tenant and obtain the tenant real-time dynamic information according to the demand information nodes, where the tenant real-time dynamic information includes multiple tenant entity data and tenant demand data; The third acquisition module is used to obtain the corresponding first demand association value according to each building environment data and tenant demand data, and obtain the corresponding fit association value according to each building environment data and tenant entity data; The fourth acquisition module is used to obtain the corresponding second demand association value according to each building entity data and tenant demand data, and obtain the corresponding entity association value according to each building entity data and tenant entity data; The construction module is used to obtain the corresponding matching value according to each first demand association value, fit association value, second demand association value, and entity association value, and perform association matching on the historical information nodes of the corresponding building and the demand information nodes of the tenant according to each matching value to construct a matching node network; The matching module is used to perform intelligent investment promotion matching for each building according to the matching node network.

[0034] In one embodiment, the third acquisition module includes: The first acquisition unit is used to obtain the surrounding pedestrian flow density and the competition characteristic information of multiple shops within the preset regional area according to the building environment data, and obtain the shop rent level, shop competitiveness, and shop area according to each competition characteristic information; The second acquisition unit is used to obtain the individual commercial value of the corresponding shop according to each shop rent level, shop competitiveness, and shop area, and obtain the comprehensive commercial value according to multiple individual commercial values; The third acquisition unit is used to obtain the commercial competition intensity according to the comprehensive commercial value and the preset regional area; A fourth acquisition unit, configured to acquire an industry code and tenant key financial indicators according to the tenant demand data, where the tenant key financial indicators include a revenue growth rate, a net profit rate, and a research and development investment ratio; A fifth acquisition unit, configured to acquire a tenant development index according to the revenue growth rate, the net profit rate, and the research and development investment ratio, and acquire a first demand correlation value according to the tenant development index, the business competitiveness, the industry code, and the surrounding pedestrian flow density.

[0035] It should be noted that each module and unit in the intelligent matching system for building investment promotion based on big data analysis corresponds one by one to the steps in the method for intelligent matching of building investment promotion based on big data analysis.

[0036] As Figure 3 shown, the present application further provides a computer device, which may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer design 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 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 all data required for the process of the method for intelligent matching of building investment promotion based on big data analysis. The network 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, it implements the method for intelligent matching of building investment promotion based on big data analysis.

[0037] Those skilled in the art can understand that Figure 3 the structure shown in

[0038] 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.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0040] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article or method comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, device, article or method comprising that element.

[0041] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall equally be included in the patent protection scope of the present invention.

Claims

1. An intelligent matching method for building investment promotion based on big data analysis, characterized in that, Including: Obtain the historical information nodes of each building, and obtain the historical dynamic information of the building according to the historical information nodes, where the historical dynamic information of the building includes a plurality of building environment data and building entity data; Obtain the demand information nodes of each tenant, and obtain the real-time dynamic information of the tenant according to the demand information nodes, where the real-time dynamic information of the tenant includes a plurality of tenant entity data and tenant demand data; Obtain the corresponding first demand correlation value according to each of the building environment data and tenant demand data, and obtain the corresponding fit correlation value according to each of the building environment data and tenant entity data; Obtain the corresponding second demand correlation value according to each of the building entity data and tenant demand data, and obtain the corresponding entity correlation value according to each of the building entity data and tenant entity data; Obtain the corresponding matching value according to each first demand correlation value, fit correlation value, second demand correlation value and entity correlation value, and perform associated matching on the historical information nodes of the corresponding building and the demand information nodes of the tenant according to each of the matching values to construct a matching node network; Perform intelligent investment promotion matching for each building according to the matching node network.

2. The intelligent matching method for building investment promotion based on big data analysis according to claim 1, wherein The step of obtaining the corresponding first demand correlation value according to each of the building environment data and tenant demand data includes: Obtain the surrounding pedestrian flow density and the competition characteristic information of multiple shops within the preset regional area according to the building environment data, and obtain the shop rent level, shop competitiveness and shop area according to each of the competition characteristic information; Obtain the individual commercial value of the corresponding shop according to each of the shop rent level, shop competitiveness and shop area, and obtain the comprehensive commercial value according to the multiple individual commercial values; Obtain the commercial competition intensity according to the comprehensive commercial value and the preset regional area; Obtain the industry code and the tenant's key financial indicators according to the tenant demand data, where the tenant's key financial indicators include revenue growth rate, net profit rate and R & D investment ratio; Obtain the tenant development index according to the revenue growth rate, net profit rate and R & D investment ratio, and obtain the first demand correlation value according to the tenant development index, commercial competitiveness, industry code and surrounding pedestrian flow density.

3. The intelligent matching method for building investment promotion based on big data analysis according to claim 1, characterized in that The step of obtaining the corresponding fit correlation value according to each of the building environment data and tenant entity data includes: Obtain the air quality index, the average day-night noise decibel number and the green space area within the preset regional area according to the building environment data; Obtain the noise pollution index according to the average day-night noise decibel number, and obtain the green space coverage index according to the green space area and the preset regional area; Obtain the environmental excellence index according to the air quality index, noise pollution index and green space coverage index; Obtain the traffic density index, vehicle speed deviation degree and interference characteristics according to the building environment data; Obtain the signal light influence coefficient and intersection influence coefficient within the preset traffic cycle according to the interference characteristics, and obtain the traffic signal interference coefficient according to the signal light influence coefficient and intersection influence coefficient; Obtain the traffic congestion index according to the traffic signal interference coefficient, vehicle speed deviation degree and traffic density index; Obtain the commuting demand and environmental preference according to the tenant entity data, and obtain the fit correlation value according to the commuting demand, environmental preference, environmental quality index, and traffic congestion index.

4. The intelligent matching method for building investment promotion based on big data analysis according to claim 1, characterized in that The step of obtaining the corresponding second demand correlation value according to each building entity data and tenant demand data includes: Obtain the building rent, building structure characteristics, and building layout characteristics according to the building entity data, and obtain the building height, building load-bearing capacity, and number of floors according to the building structure characteristics; Obtain the building structure expandability according to the building height, building load-bearing capacity, and number of floors; Obtain the total number of rooms in the building, the number of layoutable rooms in the building, the wall adaptability coefficient, and the space adjustment flexibility coefficient according to the building layout characteristics, and obtain the building layout expandability according to the total number of rooms in the building, the number of layoutable rooms in the building, the wall adaptability coefficient, and the space adjustment flexibility coefficient; Obtain the building space expandability according to the building layout expandability and the building structure expandability; Obtain the tenant rent demand and tenant expansion demand according to the tenant demand data, and obtain the second demand correlation value according to the tenant rent demand, tenant expansion demand, building space expandability, and building rent.

5. The intelligent matching method for building investment promotion based on big data analysis according to claim 1, wherein The step of obtaining the corresponding entity correlation value according to each building entity data and tenant entity data includes: Obtain the tenant turnover growth rate, tenant leased area growth rate, and tenant number growth rate according to the tenant entity data, and obtain the comprehensive tenant growth rate according to the tenant turnover growth rate, tenant leased area growth rate, and tenant number growth rate; Obtain the building safety factor, fire protection facility coverage, access control system coverage, and power supply capacity according to the building entity data, and obtain the building safety facility coverage according to the fire protection facility coverage and access control system coverage; Obtain the safety facility requirements according to the tenant entity data, and obtain the tenant safety demand coefficient according to the safety facility requirements; Obtain the safety facility difference value according to the tenant safety demand coefficient and the building safety factor, and obtain the safety facility demand value according to the safety facility difference value, building safety facility coverage, and building safety factor; Obtain the entity correlation value according to the safety facility demand value and the comprehensive tenant growth rate.

6. The intelligent matching method for building investment promotion based on big data analysis according to claim 1, characterized in that The step of associating and matching the historical information nodes of the corresponding building with the demand information nodes of the tenant according to each matching value to construct a matching node network includes: Obtain the matching values of each historical information node and multiple demand information nodes, and sort the multiple matching values in descending order to obtain a matching value sorting table; Select the demand information node corresponding to the matching value ranked first in the matching value sorting table and the corresponding demand information node as the associated node; Until all historical information nodes and demand information nodes are traversed to obtain multiple associated nodes; Use the historical information node and demand information node corresponding to each associated node as the associated edge, and use the corresponding matching value as the edge weight, and construct a matching node network according to the multiple associated edges and edge weights.

7. An intelligent matching system for building investment promotion based on big data analysis, characterized in that, Include: The first acquisition module is used to acquire the historical information nodes of each building and obtain the historical dynamic information of the building according to the historical information nodes, where the historical dynamic information of the building includes a plurality of building environment data and building entity data; The second acquisition module is used to acquire the demand information nodes of each tenant and obtain the real-time dynamic information of the tenant according to the demand information nodes, where the real-time dynamic information of the tenant includes a plurality of tenant entity data and tenant demand data; The third acquisition module is used to obtain the corresponding first demand correlation value according to each of the building environment data and tenant demand data, and obtain the corresponding fit correlation value according to each of the building environment data and tenant entity data; The fourth acquisition module is used to obtain the corresponding second demand correlation value according to each of the building entity data and tenant demand data, and obtain the corresponding entity correlation value according to each of the building entity data and tenant entity data; The construction module is used to obtain the corresponding matching value according to each first demand correlation value, fit correlation value, second demand correlation value and entity correlation value, and perform associated matching on the historical information nodes of the corresponding building and the demand information nodes of the tenant according to each matching value to construct a matching node network; The matching module is used to perform intelligent investment promotion matching for each building according to the matching node network.

8. The intelligent matching system for building investment promotion based on big data analysis according to claim 7, characterized in that The third acquisition module includes: The first acquisition unit is used to obtain the surrounding pedestrian flow density and the competition characteristic information of multiple shops within a preset regional area according to the building environment data, and obtain the shop rent level, shop competitiveness and shop area according to each competition characteristic information; The second acquisition unit is used to obtain the individual commercial value of the corresponding shop according to each of the shop rent level, shop competitiveness and shop area, and obtain the comprehensive commercial value according to the multiple individual commercial values; The third acquisition unit is used to obtain the commercial competition intensity according to the comprehensive commercial value and the preset regional area; The fourth acquisition unit is used to obtain the industry code and the tenant's key financial indicators according to the tenant demand data, where the tenant's key financial indicators include revenue growth rate, net profit rate and R & D investment ratio; The fifth acquisition unit is used to obtain the tenant development index according to the revenue growth rate, net profit rate and R & D investment ratio, and obtain the first demand correlation value according to the tenant development index, commercial competitiveness, industry code and surrounding pedestrian flow density.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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