Prediction methods, devices, equipment, and storage media for urban bloom zones

Abstract

The present invention discloses a prediction method, device, equipment and storage medium for urban blooming zones, and belongs to the technical field of smart city planning and construction. The present invention obtains traffic data, population data, industry data, policy data corresponding to each cold zone of the target city, and development impact data of associated areas associated with the cold zone; calculates index parameters of each cold zone according to the traffic data, population data, industry data and policy data, and calculates correction parameters according to the development impact data; calculates blooming potential index of each cold zone according to the index parameters and the correction parameters; determines the blooming prediction result of the cold zone according to the blooming potential index, thereby achieving the technical effect of improving the accuracy of urban blooming area prediction.

Description

Technical Field

[0001] The present invention relates to the field of smart city planning and construction, and in particular to a prediction method, device, equipment and storage medium for urban blooming zones. Background Art

[0002] With the process of urbanization, different areas of the city often have different development conditions. At present, due to the popularity of machine learning, the prediction method of urban blooming zones based on machine learning is often used. The prediction method of urban blooming zones based on machine learning specifically collects a large amount of urban development data to predict the future blooming zones of cities. However, this prediction method based on machine learning has insufficient transferability. The effect of data trained in one city will be reduced when applied to another city, resulting in poor prediction accuracy.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for predicting urban blooming zones, aiming to improve the accuracy of urban blooming zone prediction.

[0005] To achieve the above object, the present invention provides a method for predicting urban blooming zones, the method comprising the following steps:

[0006] Obtain the traffic data, population data, industry data, policy data corresponding to each cold zone in the target city, and the development impact data of the associated areas associated with the cold zone;

[0007] Calculate the index parameter of each cold zone according to the traffic data, the population data, the industrial data and the policy data, and calculate the correction parameter according to the development impact data;

[0008] Calculate the blooming potential index of each cold zone according to the index parameter and the correction parameter;

[0009] The bloom prediction result in the cold zone is determined according to the bloom potential index.

[0010] Optionally, before the step of obtaining traffic data, population data, industry data, policy data corresponding to each cold zone in the target city and development impact data of associated areas associated with the cold zone, the step further includes:

[0011] Obtaining economic and social indicators of each spatial unit in the target city, wherein the economic and social indicators include housing prices and rents;

[0012] Determine the regional cold and hot types corresponding to each area in the target city according to the economic and social indicators, and the urban space types include: cold area type and hot area type;

[0013] The area whose regional cooling and heating type is the cold zone type is taken as the cold zone.

[0014] Optionally, the index parameters include: traffic index, population index, industry index and policy index, and the step of calculating the index parameters of each cold zone according to the traffic data, the population data, the industry data and the policy data includes:

[0015] Calculating the traffic index according to the traffic data;

[0016] Calculate the population indicator based on the population data;

[0017] Calculate the policy indicator according to the policy data;

[0018] The industry index is calculated based on the industry data.

[0019] Optionally, the traffic data includes: traffic accessibility data, road congestion data, and distance data between a preset location and a cold zone, and the step of calculating the traffic index according to the traffic data includes:

[0020] Calculating a first sub-traffic index according to the traffic accessibility data and the road congestion data;

[0021] Calculating a second sub-traffic index according to the distance data between the preset position and the cold zone;

[0022] The traffic index is calculated according to the first sub-traffic index and the second sub-traffic index.

[0023] Optionally, the step of calculating the policy indicator according to the policy data includes:

[0024] Determine the policy impact value corresponding to each policy data according to the level of the release source of each policy data, the content of the policy data and the policy mapping relationship;

[0025] The policy indicator is calculated based on each of the policy data and the corresponding policy impact value.

[0026] Optionally, the industrial data includes: industry data and industrial chain element data, and the step of calculating the industrial index according to the industrial data includes:

[0027] The industry index is calculated based on the industry data and the industry chain element data.

[0028] Optionally, the step of calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter comprises:

[0029] Calculate the blooming potential index of each of the cold zones according to the index parameters;

[0030] The blooming potential index is updated according to the spatial distance between the cold area and the associated area of ​​the target city and the correction parameter.

[0031] In addition, to achieve the above-mentioned purpose, the present invention further provides a prediction device for urban blooming zones, the prediction device for urban blooming zones comprising:

[0032] An acquisition module, used to acquire traffic data, population data, industry data, policy data corresponding to each cold zone in the target city, and development impact data of associated areas associated with the cold zone;

[0033] A calculation module, used for calculating the index parameter of each cold zone according to the traffic data, the population data, the industrial data and the policy data, and calculating the correction parameter according to the development impact data;

[0034] A correction module, used for calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter;

[0035] The prediction module is used to determine the blooming prediction result in the cold zone according to the blooming potential index.

[0036] In addition, to achieve the above-mentioned purpose, the present invention also provides a prediction device for urban bloom zones, the prediction device for urban bloom zones comprising: a memory, a processor, and a prediction program for urban bloom zones stored in the memory and executable on the processor, the prediction program for urban bloom zones being configured to implement the steps of any of the above-mentioned methods for predicting urban bloom zones.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a prediction program for urban bloom zones is stored, and when the prediction program for urban bloom zones is executed by a processor, the steps of any of the above-mentioned methods for predicting urban bloom zones are implemented.

[0038] The present invention proposes a prediction method for urban blooming zones, which can improve the accuracy of the calculated indicator parameters of cold zones by acquiring data including traffic data, population data, industrial data, policy data, etc., and calculate correction parameters based on the development impact data of related areas, which can effectively refer to the development influence capacity of related areas, thereby improving the accuracy of cold zone bloom prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the structure of a prediction device for urban blooming zones in a hardware operating environment involved in an embodiment of the present invention;

[0040] Figure 2 It is a schematic flow chart of a first embodiment of a method for predicting urban blooming zones of the present invention;

[0041] Figure 3 A schematic flow chart of a second embodiment of a method for predicting urban blooming zones according to the present invention;

[0042] Figure 4 is a flow chart of a third embodiment of a method for predicting urban blooming zones of the present invention;

[0043] Figure 5 is a schematic flow chart of a fourth embodiment of a method for predicting urban blooming zones of the present invention;

[0044] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] 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.

[0046] Reference Figure 1 , Figure 1 A schematic diagram of the structure of a prediction device for urban blooming zones in a hardware operating environment according to an embodiment of the present invention.

[0047] like Figure 1 As shown, the prediction device of the urban blooming zone may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The interactive device 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional interactive device 1003 may also be connected to the communication bus through a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0048] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation of the prediction device for urban bloom zones, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0049] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a prediction program for urban blooming zones.

[0050] exist Figure 1 In the prediction device for the urban blooming zones shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the prediction device for the urban blooming zones of the present invention can be set in the prediction device for the urban blooming zones, and the prediction device for the urban blooming zones calls the prediction program for the urban blooming zones stored in the memory 1005 through the processor 1001, and executes the prediction method for the urban blooming zones provided in the embodiment of the present invention.

[0051] The embodiment of the present invention provides a method for predicting urban blooming zones, referring to Figure 2 , Figure 2 It is a flow chart of a first embodiment of a method for predicting urban blooming zones according to the present invention.

[0052] In this embodiment, the method for predicting the urban blooming zone includes:

[0053] Step S1, obtaining traffic data, population data, industry data, policy data corresponding to each cold zone in the target city and development impact data of associated areas associated with the cold zone;

[0054] It should be noted that the cold zone here refers to the poorly developed area in the city at the current moment, and the corresponding hot zone refers to the well-developed area in the city at the current moment. Due to the different economic development levels of different cities, the data of specific judgment indicators for cold zones and hot zones in different cities may be different. Optionally, the various areas in the city can be classified according to the proportion of GDP of each spatial unit in the city to the total GDP of the city to determine whether they belong to cold zones or hot zones. Optionally, other economic and social indicators other than GDP of each spatial unit in the city can also be used to judge whether each area in the city belongs to a cold zone or a hot zone. Other economic and social indicators other than GDP here may include: house prices and rents. It should be noted that in this embodiment, the development of the cold zone is predicted. Therefore, it is necessary to obtain the traffic data, population data, industrial data, and policy data corresponding to each cold zone. In addition, since the development of the cold zone is often driven by the welfare of the adjacent areas, it is necessary to obtain the development impact data of the associated areas associated with the cold zone.

[0055] Step S2, calculating the index parameter of each cold zone according to the traffic data, the population data, the industrial data and the policy data, and calculating the correction parameter according to the development impact data;

[0056] In this embodiment, the traffic data, the population data, the industry data and the policy data are specifically the data at the current moment and the data recorded before the current moment. In this embodiment, the number of index parameters here is multiple, and specifically can include indicators at different levels. The traffic data is used to calculate indicators related to traffic, the population data is used to calculate indicators related to population, the industry data is used to calculate indicators related to industrial development, and the policy data is used to calculate indicators related to regional development. It should be noted that the policy data here often includes different types of expressions of development, because different expressions often correspond to different policy intensities. Therefore, it is necessary to establish a corresponding mapping table to map different expressions to numerical values ​​representing different intensities. The development impact data here can often have different development impact levels for different industries. The number of correction parameters here is multiple, and the correction parameters are used to adjust the blooming potential index obtained by subsequent calculations. It should be noted that the indicator parameters of each cold zone can be independently determined through traffic data, the population data, the industry data and the policy data.

[0057] Step S3, calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter;

[0058] Optionally, the blooming potential index is calculated according to the index parameter, and then the blooming potential index is updated according to the correction parameter. Preferably, the correction parameter is used as a coefficient of the index parameter, and the blooming potential index of each cold zone is calculated together with the index parameter.

[0059] Step S4, determining a bloom prediction result in a cold zone according to the bloom potential index.

[0060] Specifically, the plurality of cold zones are sorted by blooming potential index, and blooming prediction results of different cold zones are determined according to the sorting results.

[0061] In this embodiment, by acquiring data including traffic data, population data, industrial data, policy data, etc., the accuracy of the calculated indicator parameters of the cold zone can be improved, and the correction parameters are calculated based on the development impact data of the associated area, which can effectively refer to the development influence capacity of the associated area, thereby improving the accuracy of the cold zone bloom prediction results.

[0062] Further, based on the first embodiment, a second embodiment of the method for predicting urban blooming zones of the present invention is proposed. In this embodiment, referring to Figure 3 , before the step of obtaining the traffic data, population data, industry data, policy data corresponding to each cold zone in the target city and the development impact data of the associated areas associated with the cold zone, the step also includes:

[0063] Step S01, obtaining economic and social indicators of each spatial unit in the target city, wherein the economic and social indicators include housing prices and rents;

[0064] In this embodiment, the spatial unit can be a specific plot of land or a divided grid. The specific economic and social indicators need to be spatially located to a specific unit or grid using an address, so that the economic and social indicators of different spatial units in the cold zone can be obtained. The rent here specifically refers to the rent price, including residential rent price, commercial rent price, industrial rent price, etc. The house price here can include the price of commercial, residential and industrial properties. In other embodiments, the economic and social indicators can also include: total industrial output value, resident income level, consumption level, per capita consumer expenditure, etc.

[0065] Step S02, determining the regional cold and hot types corresponding to each area in the target city according to the economic and social indicators, the urban space types including: cold area type and hot area type;

[0066] It should be noted that since a region may include multiple spatial units, it is necessary to calculate the average economic and social indicators of each spatial unit in a region. Specifically, the average economic and social indicators of each region are calculated through the economic and social indicators of each spatial unit, and the regional hot and cold types are determined based on the average economic and social indicators in each region. Specifically, the target threshold of the average economic and social indicators can be set based on the development of each city. When the average economic and social indicators in the region are greater than or equal to the target threshold, the regional hot and cold type of the corresponding region is determined to be a hot zone type; when the average economic and social indicators in the region are less than the target threshold, the regional hot and cold type of the corresponding region is determined to be a cold zone type.

[0067] Step S03: taking the area whose regional cooling and heating type is the cold area type as the cold area.

[0068] Specifically, each area is marked with a cold area type or a hot area type, and the area marked with a cold area type is used as the cold area.

[0069] In this embodiment, by obtaining the economic and social indicators of each spatial unit in the target city, the economic and social indicators include housing prices and rents, and determining the regional hot and cold types corresponding to each area in the target city according to the economic and social indicators. The urban space types include: cold area type and hot area type, so that the cold area can be accurately determined, thereby improving the accuracy of subsequent cold area data.

[0070] Further, based on the first embodiment or the second embodiment, a third embodiment of the method for predicting urban blooming zones of the present invention is proposed, referring to Figure 4 In this embodiment, the index parameters include: traffic index, population index, industry index and policy index, and the step of calculating the index parameters of each cold zone according to the traffic data, population data, industry data and policy data includes:

[0071] Step S21, calculating the traffic index according to the traffic data;

[0072] Step S22, calculating the population index according to the population data;

[0073] Step S23, calculating the policy indicator according to the policy data;

[0074] Step S24, calculating the industry index according to the industry data.

[0075] In this embodiment, different types of data are used to calculate indicators of corresponding types. It should be noted that the traffic indicators here can specifically include: per capita road area, road length, rail transit operation mileage, public transportation network density, etc. The population indicators here can include: age structure, population growth rate, etc. The policy indicators here can include tax incentive indicators, land policy indicators, talent introduction indicators, etc. The industrial indicators here include: the added value of the primary industry, the added value of the secondary industry, the added value of the tertiary industry, the industrial competitiveness indicators, and the number of industrial employees in each industry, etc. The industrial competitiveness indicators here include market share of industrial products, industrial profitability, labor productivity, etc. Optionally, in calculating the traffic indicators, the population indicators, the policy indicators, the policy indicators, and the industrial indicators, the summary indicators for judging the cold zone as a whole can be calculated by weighted calculation. In addition, in other embodiments, the indicator parameters of each cold zone can also include: the distribution density index of cultural and historical facilities in the cold zone.

[0076] In this embodiment, different data are used to calculate corresponding index parameters, thereby improving the accuracy of subsequent prediction of blooming in cold areas.

[0077] Further, based on any of the above embodiments, a fourth embodiment of the method for predicting urban blooming zones of the present invention is proposed, referring to Figure 5 In this embodiment, the traffic data includes: traffic accessibility data, road congestion data, and distance data between a preset location and a cold zone. The step of calculating the traffic index based on the traffic data includes:

[0078] Step S211, calculating a first sub-traffic index according to the traffic accessibility data and the road congestion data;

[0079] Step S212, calculating a second sub-traffic index according to the distance data between the preset position and the cold zone;

[0080] Step S213: Calculate the traffic index according to the first sub-traffic index and the second sub-traffic index.

[0081] In this embodiment, it should be noted that the first sub-traffic indicator here can specifically be traffic condition data. Specifically, the traffic accessibility data and the road congestion data can be obtained by government departments and traffic research institutions through sensor records. The traffic accessibility data here is positively correlated with the first sub-traffic indicator, the road congestion data here is negatively correlated with the first sub-traffic indicator, and the distance data between the preset position and the cold zone here is negatively correlated with the second sub-traffic indicator. The second sub-traffic indicator here is the step. The preset position here is generally pre-set and can be: a key route or a key station in traffic planning. It should be noted that there is generally service radius data for key routes and key stations. In other embodiments, the second sub-traffic indicator is determined by counting the coverage number of the preset position of the cold zone. The coverage number here refers to the number of cold zone positions covered by the service radius of the key route or key station. The second sub-traffic indicator can be used to reflect the traffic service situation of the cold zone. The larger the second sub-traffic indicator, the more key routes and key stations the cold zone is served, which can effectively reflect the traffic situation of the cold zone.

[0082] In this embodiment, the first sub-traffic index is calculated by the traffic accessibility data and the road congestion data, and the second sub-traffic index is calculated based on the distance data between the preset location and the cold zone, which can reflect the traffic status in the cold zone and thus improve the accuracy of subsequent cold zone bloom prediction.

[0083] Furthermore, the step of calculating the policy indicator according to the policy data includes:

[0084] Determine the policy impact value corresponding to each policy data according to the level of the release source of each policy data, the content of the policy data and the policy mapping relationship;

[0085] The policy indicator is calculated based on each of the policy data and the corresponding policy impact value.

[0086] In this embodiment, optionally, the policy mapping relationship can be specifically a policy mapping relationship table, which is specifically obtained by obtaining policies and policy effects issued at different levels, and constructing a policy mapping relationship table, in which different policies correspond to different policy impact values. By obtaining the level of the policy release source, the content of the policy data and searching the corresponding policy impact value in the policy mapping relationship table. It should be noted that in this embodiment, determining the policy impact value is actually converting non-standardized policy statements into standardized values, which can effectively extract non-data information. In addition, the policy data here is specific digital data, such as: the extent of tax incentives, the amount of policy subsidies, and the incentive funds of talent introduction policies. Compared with only collecting and using policy data, calculating the policy indicators according to each of the policy data and the corresponding policy impact values ​​can efficiently reflect the actual implementation of the policy and the degree of support for the development of cold areas, thereby improving the accuracy of predicting the blooming of cold areas.

[0087] Furthermore, the industrial data includes: industry data and industrial chain element data, and the step of calculating the industrial index according to the industrial data includes:

[0088] The industry index is calculated based on the industry data and the industry chain element data.

[0089] Specifically, the main industry types of the cold zone are obtained, and the target industry is determined based on the main industry types and the industrial chain relationship, and the corresponding industrial chain element data is determined based on the target industry. This is because the corresponding industrial chain elements of different main industry types are different, and the associated target industries are also different. Therefore, in this embodiment, it is necessary to determine the main industry types of each cold zone. Then, the industry indicators are calculated based on the industry data of the main industry types and the industrial chain element data. For example: semiconductor industry, based on the industrial chain relationship, the target industries include: electronic design automation software industry, intellectual property industry, semiconductor materials industry, integrated circuit design, wafer manufacturing, packaging and testing, etc. According to the industrial chain element data.

[0090] Further, based on any of the above embodiments, a fifth embodiment of the method for predicting urban blooming zones of the present invention is proposed. In this embodiment, the step of calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter includes:

[0091] Calculate the blooming potential index of each of the cold zones according to the index parameters;

[0092] The blooming potential index is updated according to the spatial distance between the cold area and the associated area of ​​the target city and the correction parameter.

[0093] In this embodiment, the blooming potential index of each of the cold zones is calculated according to the index parameters.

[0094] Since the cold zone and the associated area of ​​the target city are often of the same cold zone type, after obtaining the blooming potential index of each cold zone, the spatial distance between any two cold zones is calculated, and the spatial distance is negatively correlated with the coefficient of the correction parameter, that is, the larger the spatial distance, the smaller the influence of the corresponding correction parameter on the blooming potential index, and the smaller the spatial distance, the greater the influence of the corresponding correction parameter on the blooming potential index. Optionally, the development impact data of the associated area associated with the cold zone may include the development impact data of the hot zone, and the correction parameter is calculated jointly by the development impact data of the hot zone and the development impact data of the cold zone. And the associated area may also be of hot zone type.

[0095] In this embodiment, the blooming potential index of each of the cold zones is calculated using the index parameters; and then the blooming potential index is updated based on the spatial distance between the cold zone and the associated area of ​​the target city and the correction parameters, thereby improving the accuracy of calculating the blooming potential index of each of the cold zones.

[0096] In addition, an embodiment of the present invention further provides a prediction device for urban blooming zones, the prediction device for urban blooming zones comprising:

[0097] An acquisition module, used to acquire traffic data, population data, industry data, policy data corresponding to each cold zone in the target city, and development impact data of associated areas associated with the cold zone;

[0098] A calculation module, used for calculating the index parameter of each cold zone according to the traffic data, the population data, the industrial data and the policy data, and calculating the correction parameter according to the development impact data;

[0099] A correction module, used for calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter;

[0100] The prediction module is used to determine the blooming prediction result in the cold zone according to the blooming potential index.

[0101] In addition, an embodiment of the present invention also proposes a prediction device for urban bloom zones, which includes: a memory, a processor, and a prediction program for urban bloom zones stored in the memory and executable on the processor, wherein the prediction program for urban bloom zones is configured to implement the steps of any of the above-mentioned embodiments of the method for predicting urban bloom zones.

[0102] In addition, an embodiment of the present invention further proposes a storage medium, on which a prediction program for urban bloom zones is stored. When the prediction program for urban bloom zones is executed by a processor, the steps of any of the above-mentioned embodiments of the method for predicting urban bloom zones are implemented.

[0103] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system 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 system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0104] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0106] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for predicting urban blooming zones, characterized in that: The method for predicting urban bloom zones comprises the following steps: Obtain the traffic data, population data, industry data, policy data corresponding to each cold zone in the target city, and the development impact data of the associated areas associated with the cold zone; Calculate the index parameter of each cold zone according to the traffic data, the population data, the industrial data and the policy data, and calculate the correction parameter according to the development impact data; Calculate the blooming potential index of each cold zone according to the index parameter and the correction parameter; The bloom prediction result in the cold zone is determined according to the bloom potential index.

2. The method for predicting urban blooming zones according to claim 1, characterized in that: The step of obtaining the traffic data, population data, industry data, policy data corresponding to each cold zone in the target city and the development impact data of the associated areas associated with the cold zone also includes: Obtaining economic and social indicators of each spatial unit in the target city within a preset time interval, wherein the economic and social indicators include housing prices and rents; Determine the regional cold and hot types corresponding to each area in the target city according to the economic and social indicators, and the urban space types include: cold area type and hot area type; The area whose regional cooling and heating type is the cold zone type is taken as the cold zone.

3. The method for predicting urban blooming zones according to claim 1, characterized in that: The index parameters include: traffic index, population index, industry index and policy index, and the step of calculating the index parameters of each cold zone according to the traffic data, population data, industry data and policy data includes: Calculating the traffic index according to the traffic data; Calculate the population indicator based on the population data; Calculate the policy indicator according to the policy data; The industry index is calculated based on the industry data.

4. The method for predicting urban blooming zones according to claim 3, characterized in that: The traffic data includes: traffic accessibility data, road congestion data, and distance data between a preset location and a cold zone. The step of calculating the traffic index based on the traffic data includes: Calculating a first sub-traffic index according to the traffic accessibility data and the road congestion data; Calculating a second sub-traffic index according to the distance data between the preset position and the cold zone; The traffic index is calculated according to the first sub-traffic index and the second sub-traffic index.

5. The method for predicting urban bloom zones according to claim 3, characterized in that: The step of calculating the policy indicator according to the policy data comprises: Determine the policy impact value corresponding to each policy data according to the level of the release source of each policy data, the content of the policy data and the policy mapping relationship; The policy indicator is calculated based on each of the policy data and the corresponding policy impact value.

6. The method for predicting urban blooming zones according to claim 3, characterized in that: The industrial data includes: industry data and industrial chain element data. The step of calculating the industrial index according to the industrial data includes: The industry index is calculated based on the industry data and the industry chain element data.

7. The method for predicting urban blooming zones according to any one of claims 1 to 6, characterized in that: The step of calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter comprises: Calculate the blooming potential index of each of the cold zones according to the index parameters; The blooming potential index is updated according to the spatial distance between the cold area and the associated area of ​​the target city and the correction parameter.

8. A prediction device for urban blooming zones, characterized in that: The prediction device of the urban blooming zone includes: An acquisition module, used to acquire traffic data, population data, industry data, policy data corresponding to each cold zone in the target city, and development impact data of associated areas associated with the cold zone; A calculation module, used for calculating the index parameter of each cold zone according to the traffic data, the population data, the industrial data and the policy data, and calculating the correction parameter according to the development impact data; A correction module, used for calculating the blooming potential index of each cold zone according to the index parameter and the correction parameter; The prediction module is used to determine the blooming prediction result in the cold zone according to the blooming potential index.

9. A prediction device for urban blooming zones, characterized in that: The urban bloom zone prediction device includes: a memory, a processor, and an urban bloom zone prediction program stored in the memory and executable on the processor, wherein the urban bloom zone prediction program is configured to implement the steps of the urban bloom zone prediction method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a prediction program for urban bloom zones, and when the prediction program for urban bloom zones is executed by a processor, the steps of the method for predicting urban bloom zones according to any one of claims 1 to 7 are implemented.

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