Intelligent data visualization system based on big data

Abstract

The invention discloses an intelligent data visualization system based on big data, and the system comprises a data collection module, a site evaluation module and an output module, and the site evaluation module is used for obtaining the utilization value and prediction prospect of a destination address through the information of the data collection module; according to the invention, the analysis result of the destination address is provided for the purchaser according to the section condition of the destination address, the commercial value evaluation module evaluates the commercial value generated by the commercial development near the destination address, the commercial value of the area near the destination address is visualized, and digitized multi-dimension is realized at the same time. The accuracy of the regional economy monitoring capability is improved; meanwhile, the purchase capacity near the destination address is evaluated through the market value evaluation module, the capacity of achieving production and conveying the goods to the nearby business district after the destination address is obtained and built into the plant is obtained, operation prospect data of destination address leasing is visualized, and the system has the advantages of being high in automation degree and high in detection capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing land management, and in particular to an intelligent data visualization system based on big data. Background Art

[0002] Nowadays, many people choose some factory land in the city for their manufacturing production needs, but on the one hand they need to consider the cost of building the factory, on the other hand they also need to consider a series of factors such as the local factory inventory and business prospects.

[0003] Although transportation costs are decreasing with the continuous development of the transportation industry, direct sales of goods within the city are still the best means of profit for the manufacturing industry. The ability of a city to produce and sell its own products is crucial to the operation of the manufacturing industry. In the existing technology, although many systems can evaluate the construction cost and quality of factory buildings, they cannot accurately digitize the commercial value of the factory buildings and the market prospects of the city where they are located. More often, they need to rely on manpower for market research. At the same time, the vacancy rate of standard factory buildings is very high, making it difficult to compare relevant data on the use prospects of various factory buildings. Therefore, it is necessary to design an intelligent data visualization system based on big data with a high degree of automation and strong detection capabilities. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent data visualization system based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent data visualization system based on big data, comprising a data acquisition module, a site evaluation module and an output module, wherein the data acquisition module is used to collect relevant information of the destination address and its surroundings; the site evaluation module is used to obtain the utilization value and predicted prospects of the destination address through the information of the data acquisition module; and the output module is used to output the evaluation results of the site evaluation module.

[0006] According to the above technical solution, the data collection module includes a site location information collection module and a historical information collection module. The site location information collection module is used to obtain the specific location of the destination address in the map software; the historical information collection module is used to obtain the factory information that existed in the destination address.

[0007] According to the above technical solution, the historical information collection module further includes a waste time statistics submodule and a revenue reference submodule. The waste time statistics submodule is used to count the time that the destination address has not been used; the revenue reference submodule is used to calculate the cumulative revenue value of the factories that existed before the destination address evaluated by the system.

[0008] According to the above technical solution, the site evaluation module includes a commercial value evaluation module and a market value evaluation module. The commercial value evaluation module obtains the commercial value of the factory by monitoring the flow of people around the destination address; the market value evaluation module is used to evaluate the business prospects of the destination factory.

[0009] According to the above technical solution, the output module includes a factory prospect prediction module and a factory comparison result module. The factory prospect prediction module is used to output the future operating prospects of the factory; the factory comparison result module is used to output a comparison result report of each vacant factory to be rented.

[0010] According to the above technical solution, the operation method of the intelligent data visualization system mainly includes the following steps:

[0011] Step S1: The visualization system obtains the image information inside and around the destination address through the camera, and obtains the location and related historical information of the destination address through the data acquisition module;

[0012] Step S2: The historical information collection module system obtains the reference revenue value of the factory in the past of the destination address through the information extracted by the data acquisition module;

[0013] Step S3: The commercial value assessment module analyzes the sales of takeaway restaurants in the "commercial circle" near the destination address and comprehensively assesses the commercial value of the destination address;

[0014] Step S4: The market value assessment module comprehensively assesses the market value of the area where the destination address is located based on the commodity flow information of the city where the destination address is located;

[0015] Step S5: The output module calculates the prospect prediction value H=Q+L of the destination address after the factory is built. If H>H1, the investment prospect of the site is determined. When 0<θ<100%, the information with a return of θ% and a risk of (100%-θ%) is output; when θ≥100%, the information that the investment prospect of the destination address is higher than the reference value by (θ%-100%) is output through the plant prospect prediction module, where H1 is the reference value stored in the database;

[0016] Step S6: The system calculates the predicted income E of the destination address after the factory is built in the future = reference income value * θ;

[0017] Step S7: The system obtains the factories that can be queried in other databases in the city, executes steps S1-6 and compares them, generates a comparison report and outputs the report through the factory comparison result module.

[0018] According to the above technical solution, step S2 further includes:

[0019] Step S21: The historical information collection module obtains the closing or relocation time of all n factories registered at the address in the past in the database, which are X1, X2, ..., X from the longest to the shortest year. n , and obtain their cumulative income values ​​as Y1, Y2, ...Y n , where the cumulative revenue value is the total turnover generated by the company to which the factory belongs during all business hours of the factory, and the unit of the cumulative revenue value is RMB;

[0020] Step S22: When n>3, remove the maximum and minimum cumulative revenues of n factories and calculate the cumulative average revenue of all remaining factories. The cumulative average income value at this time is recorded as the reference income value, where λ is the average annual currency inflation rate obtained by the system, 0≤λ≤0.2; when 0≤n≤3, the cumulative income of the factory closest to the present time is recorded. Denote as the reference return value, where Y p is the factory profit value closest to the present, and the value of p is 0, 1, 2 or 3.

[0021] According to the above technical solution, step S3 further includes:

[0022] Step S31: The business value assessment module obtains all m restaurants that can be delivered at the destination address through the food delivery platform, and obtains the total monthly sales volume of D1, D2, ..., D m , then the total daily sales of all restaurants near the destination address Where G is the total number of days in the previous month, then the takeaway expenditure in the area every day is M = αH, where α is the local per capita daily food consumption expenditure;

[0023] Step S32: Collect the comments section of the food delivery platform, count the number of good and bad reviews for each restaurant, and calculate the total number of good reviews for m food delivery restaurants as U1 and the total number of bad reviews as U2. The good review rate of food delivery restaurants in the region is the qualified rate of products in the region.

[0024] Step S33: Obtain a regional bird's-eye view with the destination address as the center and the farthest restaurant address distance collected by the system as the radius through map software, and mark the location points of all restaurants in the regional bird's-eye view through the restaurant positioning of the food delivery platform to obtain a discrete point map of food delivery restaurants in the region;

[0025] Step S34: Use an actual area of ​​π km 2The virtual circle is moved in the discrete point map, exhausting all positions in the discrete point map, and the position of the virtual circle is obtained under the condition of including as many discrete points as possible. The position is marked as the "business circle" and the square with a side length of 2 km and the center point of the virtual circle as the center is removed from the discrete point map;

[0026] Step S35: Continue to repeat step S43 until the number of discrete points contained in the obtained virtual circle is lower than the normal standard value of normal takeaway restaurants in the commercial street, and the system sets the number of all virtual circles obtained previously, that is, the number of "commercial circles" in the area, to R;

[0027] Step S36: Connect the center points of the R "commercial circles" to obtain the connection line graph of all the "commercial circles" in the area and obtain the graphic area of ​​the outermost connection line as S. If the destination address is not included in the connection line, the system determines that the destination address is remote; if the destination address is included in the connection line, the system calculates the commercial value of the destination address. Where γ1 is the unit conversion coefficient.

[0028] According to the above technical solution, step S4 further includes:

[0029] Step S41: The market value assessment module obtains the total volume of goods purchased in the business district of the destination address city from the relevant data in the city website. The total volume of goods purchased in the business district last year is Z1, and the total volume of goods purchased in the business district last year is Z2. The unit of freight volume is 10,000 tons. The growth rate of the total volume of goods purchased in the business district last year is When η≤0, the system determines that the local business district has the ability to connect with goods t=1; if 0<η<10%, the system determines that the local business district has the ability to connect with goods t=2; if η≥10%, the system determines that the local business district has the ability to connect with goods t=3;

[0030] Step S42: Evaluate the available area of ​​the destination address, and obtain the available volume of the destination address as V. Then the market value of the factory building at the destination address is L = γ2V t , where γ2 is the unit conversion coefficient.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention provides the buyer with the analysis results of the destination address through the location of the destination address, evaluates the commercial value generated by the commercial development near the destination address through the commercial value evaluation module, visualizes the commercial value of the area near the destination address from a micro perspective within the target address area, and realizes multi-dimensional data, thereby improving the accuracy of regional economic monitoring capabilities; at the same time, the market value evaluation module is used to evaluate the purchasing capacity near the destination address, and from the overall perspective of the entire city, the ability of the destination address to produce and deliver goods to nearby business districts after the factory is built is obtained, thereby intuitively reflecting the business prospects of the destination address and visualizing the business prospects data of the destination address lease. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 The present invention provides a technical solution: an intelligent data visualization system based on big data, comprising:

[0036] A data collection module, a site evaluation module and an output module. The data collection module is used to collect relevant information about the destination address and its surroundings; the site evaluation module is used to obtain the utilization value and predicted prospects of the destination address through the information of the data collection module; and the output module is used to output the evaluation results of the site evaluation module.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention provides the buyer with the analysis results of the destination address through the location of the destination address, evaluates the commercial value generated by the commercial development near the destination address through the commercial value evaluation module, visualizes the commercial value of the area near the destination address from a micro perspective within the target address area, and realizes multi-dimensional data, thereby improving the accuracy of regional economic monitoring capabilities; at the same time, the market value evaluation module is used to evaluate the purchasing capacity near the destination address, and from the overall perspective of the entire city, the ability of the destination address to produce and deliver goods to nearby business districts after the factory is built is obtained, thereby intuitively reflecting the business prospects of the destination address and visualizing the business prospects data of the destination address lease.

[0038] The data collection module includes a site location information collection module and a historical information collection module. The site location information collection module is used to obtain the specific location of the destination address in the map software; the historical information collection module is used to obtain the factory information that existed in the destination address.

[0039] The historical information collection module further includes a waste time statistics submodule and a revenue reference submodule. The waste time statistics submodule is used to count the time that the destination address has not been used; the revenue reference submodule is used to calculate the cumulative revenue value of the factory that existed before the destination address evaluated by the system.

[0040] The site assessment module includes a commercial value assessment module and a market value assessment module. The commercial value assessment module obtains the commercial value of the factory by monitoring the flow of people around the destination address; the market value assessment module is used to evaluate the business prospects of the destination factory.

[0041] The output module includes a plant prospect prediction module and a plant comparison result module. The plant prospect prediction module is used to output the future business prospects of the plant; the plant comparison result module is used to output a comparison result report of each vacant plant to be leased.

[0042] In a preferred embodiment, the operation method of the intelligent data visualization system mainly includes the following steps:

[0043] Step S1: The visualization system obtains the image information inside and around the destination address through the camera, and obtains the location and related historical information of the destination address through the data acquisition module;

[0044] Step S2: The historical information collection module system obtains the reference revenue value of the factory in the past of the destination address through the information extracted by the data acquisition module;

[0045] Step S3: The commercial value assessment module analyzes the sales of takeaway restaurants in the "commercial circle" near the destination address and comprehensively assesses the commercial value of the destination address;

[0046] Step S4: The market value assessment module comprehensively assesses the market value of the area where the destination address is located based on the commodity flow information of the city where the destination address is located;

[0047] Step S5: The output module calculates the prospect prediction value H=Q+L of the destination address after the factory is built. If H>H1, the investment prospect of the site is determined. When 0<θ<100%, the information with a return of θ% and a risk of (100%-θ%) is output; when θ≥100%, the information that the investment prospect of the destination address is higher than the reference value by (θ%-100%) is output through the plant prospect prediction module, where H1 is the reference value stored in the database;

[0048] Step S6: The system calculates the predicted income E of the destination address after the factory is built in the future = reference income value * θ;

[0049] Step S7: The system obtains the factories that can be queried in other databases in the city, executes steps S1-6 and compares them, generates a comparison report and outputs the report through the factory comparison result module.

[0050] In this embodiment, step S2 further includes:

[0051] Step S21: The historical information collection module obtains the closing or relocation time of all n factories registered at the address in the past in the database, which are X1, X2, ..., X from the longest to the shortest year. n , and obtain their cumulative income values ​​as Y1, Y2, ...Y n , where the cumulative revenue value is the total turnover generated by the company to which the factory belongs during all business hours of the factory, and the unit of the cumulative revenue value is RMB;

[0052] Step S22: When n>3, remove the maximum and minimum cumulative revenues of n factories and calculate the cumulative average revenue of all remaining factories. The cumulative average income value at this time is recorded as the reference income value, where λ is the average annual monetary inflation rate obtained by the system. Inflation is calculated as the change in the weighted average of goods, 0≤λ≤0.2; when 0≤n≤3, the cumulative income of the factory closest to the present time is recorded. Denote as the reference return value, where Y p is the factory profit value closest to the present, and the value of p is 0, 1, 2 or 3.

[0053] Due to currency depreciation, there is a big difference between the factory's previous income and the current currency. By increasing the value of the income generated by the factory from the past to the present, the accuracy of the calculation of the factory's cumulative income value in the past is improved, and the data visualization system's data precision calculation is realized.

[0054] In this embodiment, step S3 further includes:

[0055] Step S31: The business value assessment module obtains all m restaurants that can be delivered at the destination address through the food delivery platform, and obtains the total monthly sales volume of D1, D2, ..., D m , then the total daily sales of all restaurants near the destination address Where G is the total number of days in the previous month, then the takeaway expenditure in the area every day is M = αH, where α is the local per capita daily food consumption expenditure;

[0056] By collecting the range of delivery restaurants collected by the food delivery platform as the evaluation criterion near the destination address, the real-time level of the system is effectively improved, and a suitable reference area is obtained for analysis at different addresses and in different areas.

[0057] Step S32: Collect the comments section of the food delivery platform, count the number of good and bad reviews for each restaurant, and calculate the total number of good reviews for m food delivery restaurants as U1 and the total number of bad reviews as U2. The good review rate of food delivery restaurants in the region is the qualified rate of products in the region.

[0058] People's evaluation of the quality of takeout on the food delivery platform can intuitively reflect the product quality of restaurants in the area, thereby deeply evaluating the commercial value of the area.

[0059] Step S33: Obtain a regional bird's-eye view with the destination address as the center and the farthest restaurant address distance collected by the system as the radius through map software, and mark the location points of all restaurants in the regional bird's-eye view through the restaurant positioning of the food delivery platform to obtain a discrete point map of food delivery restaurants in the region;

[0060] Step S34: Use an actual area of ​​π km 2 The virtual circle is moved in the discrete point map, exhausting all positions in the discrete point map, and the position of the virtual circle is obtained under the condition of including as many discrete points as possible. The position is marked as the "business circle" and the square with a side length of 2 km and the center point of the virtual circle as the center is removed from the discrete point map;

[0061] The location of the factory is an important criterion for measuring its commercial value. Since the concentration areas of takeaway restaurants are all commercially developed areas, the position of the virtual circle obtained by including as many discrete points as possible can effectively obtain the place where the takeaway restaurants are most concentrated in the area.

[0062] Step S35: Continue to repeat step S43 until the number of discrete points contained in the obtained virtual circle is lower than the normal standard value of normal takeaway restaurants in the commercial street, and the system sets the number of all virtual circles obtained previously, that is, the number of "commercial circles" in the area, to R;

[0063] Step S36: Connect the center points of the R "commercial circles" to obtain the connection line graph of all the "commercial circles" in the area and obtain the graphic area of ​​the outermost connection line as S. If the destination address is not included in the connection line, the system determines that the destination address is remote; if the destination address is included in the connection line, the system calculates the commercial value of the destination address. Where γ1 is the unit conversion coefficient.

[0064] Since the destination address is at the center of the regional map, the larger the graphic area of ​​the outermost connecting line composed of the "business circles" near the destination address, the farther the average distance between each "business circle" and the destination address is, and the smaller the commercial value of the destination address is; the more "business circles" there are near the destination address, the more developed the economy of the region where the destination address is located, and the greater the commercial value of the destination address.

[0065] The commercial value assessment module detects takeout restaurants near the destination address, and digitizes the commercial value generated in the "business circle" area from two perspectives: the number of daily sales orders and the quality of goods of the takeout restaurants. This not only quantifies the prosperity of the area through the number of diners per takeout order, but also quantifies the management capacity and market monitoring capacity of the area by evaluating the quality of the restaurants. While visualizing the commercial value of the area near the destination address, it realizes the multi-dimensionality of data and improves the accuracy of regional economic monitoring capabilities. At the same time, the takeout sales situation in the area can be used to reflect the operating status of the entire city's business district, reflecting the development of the city from a macro perspective.

[0066] In this embodiment, step S4 further includes:

[0067] Step S41: The market value assessment module obtains the total volume of goods purchased in the business district of the destination address city from the relevant data in the city website. The total volume of goods purchased in the business district last year is Z1, and the total volume of goods purchased in the business district last year is Z2. The unit of freight volume is 10,000 tons. The growth rate of the total volume of goods purchased in the business district last year is When η≤0, the system determines that the local business district has the ability to connect with goods t=1; if 0<η<10%, the system determines that the local business district has the ability to connect with goods t=2; if η≥10%, the system determines that the local business district has the ability to connect with goods t=3;

[0068] Different sales strategies and investment tendencies within a city play an important role in the development of business districts in different regions. The market value assessment module can intuitively reflect the development of the city's self-production and self-marketing industry chain by comparing the purchase volume of nearby business districts in the city last year and the year before, and determine whether the distribution status and production capacity in the target address area match. At the same time, it predicts the future direction of urban operations and improves the accuracy of data visualization.

[0069] Step S42: Evaluate the available area of ​​the destination address, and obtain the available volume of the destination address as V. Then the market value of the factory building at the destination address is L = γ2V t , where γ2 is the unit conversion coefficient.

[0070] With the continuous updating and development of productivity and production tools, if the city's internal ability to digest commodities has not improved compared with the past, then the circulation speed of commodities produced by factories will not be able to go further; and if the city's internal ability to digest commodities has developed steadily compared with the past, then the circulation of commodities produced by factories will not be hindered, and the market value of factories will increase exponentially.

[0071] By analyzing the market value of the destination address, we can obtain the capacity of the destination address to produce and circulate goods after the factory is built, which directly reflects the business prospects of the destination address. While realizing data visualization, we can also evaluate the city's self-selling capacity, providing a favorable basis for urban development monitoring.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0073] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Intelligent data visualization system based on big data, characterized by: It includes a data acquisition module, a site evaluation module and an output module. The data acquisition module is used to collect relevant information about the destination address and its surroundings; the site evaluation module is used to obtain the utilization value and predicted prospects of the destination address through the information of the data acquisition module; and the output module is used to output the evaluation results of the site evaluation module.

2. The intelligent data visualization system based on big data according to claim 1, characterized in that: The data collection module includes a site location information collection module and a history information collection module. The site location information collection module is used to obtain the specific location of the destination address in the map software; the history information collection module is used to obtain the factory information that existed in the destination address.

3. The intelligent data visualization system based on big data according to claim 2, characterized in that: The historical information collection module further includes a waste time statistics submodule and a revenue reference submodule, wherein the waste time statistics submodule is used to count the time that the destination address has not been used; The revenue reference submodule is used to calculate the cumulative revenue value of the factories existing before the destination address evaluated by the system.

4. The intelligent data visualization system based on big data according to claim 3 is characterized in that: The site evaluation module includes a commercial value evaluation module and a market value evaluation module. The commercial value evaluation module obtains the commercial value of the factory by monitoring the flow of people around the destination address; the market value evaluation module is used to evaluate the business prospects of the destination factory.

5. The intelligent data visualization system based on big data according to claim 4 is characterized in that: The output module includes a plant prospect prediction module and a plant comparison result module. The plant prospect prediction module is used to output the future business prospects of the plant; the plant comparison result module is used to output a comparison result report of each vacant plant to be rented.

6. The intelligent data visualization system based on big data according to claim 5, characterized in that: The operation method of the intelligent data visualization system mainly includes the following steps: Step S1: The visualization system obtains the image information inside and around the destination address through the camera, and obtains the location and related historical information of the destination address through the data acquisition module; Step S2: The historical information collection module system obtains the reference revenue value of the factory in the past of the destination address through the information extracted by the data acquisition module; Step S3: The commercial value assessment module analyzes the sales of takeaway restaurants in the "commercial circle" near the destination address and comprehensively assesses the commercial value of the destination address; Step S4: The market value assessment module comprehensively assesses the market value of the area where the destination address is located based on the commodity flow information of the city where the destination address is located; Step S5: The output module calculates the prospect prediction value H=Q+L of the destination address after the factory is built. If H>H1, the investment prospect of the site is determined. When 0<θ<100%, the information with a return of θ% and a risk of (100%-θ%) is output; when θ≥100%, the information that the investment prospect of the destination address is higher than the reference value by (θ%-100%) is output through the plant prospect prediction module, where H1 is the reference value stored in the database; Step S6: The system calculates the predicted income E of the destination address after the factory is built in the future = reference income value * θ; Step S7: The system obtains the factories that can be queried in other databases in the city, executes steps S1-6 and compares them, generates a comparison report and outputs the report through the factory comparison result module.

7. The intelligent data visualization system based on big data according to claim 6, characterized in that: The step S2 further comprises: Step S21: The historical information collection module obtains the closing or relocation time of all n factories registered at the address in the past in the database, which are X1, X2, ..., X from the longest to the shortest year. n , and obtain their cumulative income values ​​as Y1, Y2, ...Y n , where the cumulative revenue value is the total turnover generated by the company to which the factory belongs during all business hours of the factory, and the unit of the cumulative revenue value is RMB; Step S22: When n>3, remove the maximum and minimum cumulative revenues of n factories and calculate the cumulative average revenue of all remaining factories. The cumulative average income value at this time is recorded as the reference income value, where λ is the average annual currency inflation rate obtained by the system, 0≤λ≤0.2; when 0≤n≤3, the cumulative income of the factory closest to the present time is recorded. Denote as the reference return value, where Y p is the factory profit value closest to the present, and the value of p is 0, 1, 2 or 3.

8. The intelligent data visualization system based on big data according to claim 7, characterized in that: The step S3 further comprises: Step S31: The business value assessment module obtains all m restaurants that can be delivered at the destination address through the food delivery platform, and obtains the total monthly sales volume of D1, D2, ..., D m , then the total daily sales of all restaurants near the destination address Where G is the total number of days in the previous month, then the takeaway expenditure in the area every day is M = αH, where α is the local per capita daily food consumption expenditure; Step S32: Collect the comments section of the food delivery platform, count the number of good and bad reviews for each restaurant, and calculate the total number of good reviews for m food delivery restaurants as U1 and the total number of bad reviews as U2. The good review rate of food delivery restaurants in the region is the qualified rate of the products in the region. Step S33: Obtain a regional bird's-eye view with the destination address as the center and the farthest restaurant address distance collected by the system as the radius through map software, and mark the location points of all restaurants in the regional bird's-eye view through the restaurant positioning of the food delivery platform to obtain a discrete point map of food delivery restaurants in the region; Step S34: Use an actual area of ​​π km 2 The virtual circle is moved in the discrete point map, exhausting all positions in the discrete point map, and the position of the virtual circle is obtained under the condition of including as many discrete points as possible. The position is marked as "business circle" and the square with a side length of 2 km and the center point of the virtual circle as the center is removed from the discrete point map; Step S35: Continue to repeat step S43 until the number of discrete points contained in the obtained virtual circle is lower than the normal standard value of normal takeaway restaurants in the commercial street, and the system sets the number of all virtual circles obtained previously, that is, the number of "commercial circles" in the area, to R; Step S36: Connect the center points of the R "commercial circles" to obtain the connection line graph of all the "commercial circles" in the area and obtain the graph area of ​​the outermost connection line as S. If the destination address is not included in the connection line, the system determines that the destination address is remote; if the destination address is included in the connection line, the system calculates the commercial value of the destination address. Where γ1 is the unit conversion coefficient.

9. The intelligent data visualization system based on big data according to claim 8, characterized in that: The step S4 further comprises: Step S41: The market value assessment module obtains the total volume of goods purchased in the business district of the destination address city from the relevant data in the city website. The total volume of goods purchased in the business district last year is Z1, and the total volume of goods purchased in the business district last year is Z2. The unit of freight volume is 10,000 tons. The growth rate of the total volume of goods purchased in the business district last year is When η≤0, the system determines that the local business district has the ability to connect with goods t=1; if 0<η<10%, the system determines that the local business district has the ability to connect with goods t=2; if η≥10%, the system determines that the local business district has the ability to connect with goods t=3; Step S42: Evaluate the available area of ​​the destination address, and obtain the available volume of the destination address as V. Then the market value of the factory building at the destination address is L = γ2V t , where γ2 is the unit conversion coefficient.

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