Urbanization resource demand prediction management system based on big data analysis

Abstract

The invention is applicable to the technical field of demand prediction, and particularly relates to a big data analysis-based urbanization resource demand prediction management system, which comprises acquisition equipment, node sorting equipment, demand writing equipment and difference updating equipment, the acquisition equipment is used for acquiring urbanization resource data of a city, segmenting the urbanization resource data into a plurality of indexes, creating branch nodes corresponding to each city, mounting leaf nodes in one-to-one correspondence with the indexes in the branch nodes, acquiring real-time data of each index, uploading the real-time data to the leaf nodes, integrating the branch nodes and the leaf nodes, and generating a binary tree; provided is a node sorting device. According to the resource demand prediction method, the demand prediction tree is constructed, the preceding city can be dynamically adjusted according to the urbanization process of the target city, the accuracy of resource demand prediction is greatly improved, in addition, the demand prediction tree can be corrected by replacing the difference item, and the accuracy of resource demand prediction is further improved.

Description

Technical Field

[0001] The present invention relates to the field of demand forecasting technology, and in particular to an urbanization resource demand forecasting and management system based on big data analysis. Background Art

[0002] Urbanization resource demand forecasting refers to the process of quantitatively or qualitatively predicting and evaluating the various resources required for a city in a certain period of time in the future based on factors such as population growth, industrial development, land use changes, and urban expansion during the urbanization process.

[0003] In existing technologies, urbanization resource demand forecasts are generally determined by analyzing the changing trends of historical data. Such static data lacks dynamic adaptability and is difficult to respond to sudden events (such as epidemics), policy adjustments, or industrial transfers. It is also impossible to dynamically adjust the forecast results based on sudden events. Identifying cities with higher urbanization provides a template for demand forecasting in cities with lower urbanization, which can dynamically adjust the forecast results and reduce trial and error costs. However, this also needs to take into account the characteristics of different cities. Different cities have significant differences in the dominant industrial structure. For example, some cities are dominated by tourism, with the service industry accounting for a high proportion; while other cities are centered on manufacturing. The two types of cities may show significant differences in land use changes, population growth, and infrastructure evolution during the urbanization process.

[0004] Therefore, “how to use leading cities with the same attributes to provide guidance for the prediction of urbanization resource demand of target cities” is the technical problem that the present invention needs to solve. Summary of the Invention

[0005] The purpose of the present invention is to provide an urbanization resource demand forecasting and management system based on big data analysis to solve the problem raised in the above background technology: "how to use leading cities with the same attributes to provide guidance for the urbanization resource demand forecasting of target cities."

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An urbanization resource demand forecasting and management system based on big data analysis, the system comprising:

[0008] The system includes: a collection device, a node sorting device, a demand writing device and a difference updating device;

[0009] The collection device is used to collect urbanization resource data of a city, divide it into several indicators, create a branch node corresponding to each city, and attach a leaf node corresponding to each indicator to the branch node, collect real-time data of each indicator, upload the real-time data to the leaf node, and integrate the branch nodes and leaf nodes to generate a binary tree;

[0010] The node sorting device is used to determine the target city for resource demand forecasting, cluster the branch nodes into target nodes and other nodes, input the real-time data into a pre-built data comparison model, sequentially calculate the similarity between the target node and other nodes, and sort the other nodes in descending order of the similarity to obtain a queue;

[0011] The demand writing device is used to configure the development process of urban urbanization, wherein each branch node corresponds to a development process, and through the development process, a preset number of predecessor nodes are selected from the front column of the queue to create a demand forecast tree, and the target node and the corresponding leaf node are written to the left side of the demand forecast tree, and the predecessor node and the corresponding leaf node are written to the right side of the demand forecast tree;

[0012] The difference update device is used to obtain hot topics in a preset platform and access them to the demand forecast tree, extract the attribute labels of the target city, compare the left and right sides of the demand forecast tree, define difference items, find cities with the same attribute labels, replace the difference items, align the left and right sides, generate a forecast report, and send the forecast report and the demand forecast tree to a preset terminal.

[0013] Furthermore, the acquisition device includes:

[0014] Create a module to collect urbanization resource data of cities, divide it into several indicators, create branch nodes corresponding to each city, and attach leaf nodes corresponding to the indicators to the branch nodes;

[0015] The generation module is used to collect real-time data of each indicator, upload the real-time data to the leaf nodes, integrate the branch nodes and the leaf nodes, and generate a binary tree.

[0016] Furthermore, the node sorting device includes:

[0017] An input module is used to determine the target city for resource demand forecasting, cluster the branch nodes into target nodes and other nodes, and input the real-time data into a pre-built data comparison model;

[0018] The obtaining module is used to sequentially calculate the similarity between the target node and other nodes, and sort the other nodes in descending order of the similarity to obtain a queue.

[0019] Furthermore, the demand writing device includes:

[0020] A selection module is used to configure the development process of urban urbanization, wherein each branch node corresponds to a development process, and select a preset number of leading nodes from the front of the queue through the development process to create a demand forecast tree;

[0021] The writing module is used to write the target node and the corresponding leaf node into the left side of the demand forecast tree, and write the preceding node and the corresponding leaf node into the right side of the demand forecast tree.

[0022] Furthermore, the difference update device includes:

[0023] An access module is used to obtain hot topics from a preset platform and access them into the demand forecast tree, extract attribute labels of target cities, compare the left and right sides of the demand forecast tree, and define difference items;

[0024] The sending module is used to find cities with the same attribute labels, replace the difference items, align the left and right sides, generate a forecast report, and send the forecast report and demand forecast tree to a preset terminal.

[0025] Furthermore, the creation module includes:

[0026] a configuration unit, configured to configure a weight value for each of the indicators, and divide the real-time data into a plurality of levels, wherein each level corresponds to a basic score;

[0027] An establishing unit is used to define the product of the weight value and the basic score as a comprehensive score, and establish a corresponding relationship between the comprehensive score and the city.

[0028] Furthermore, the generation module includes:

[0029] A setting unit, configured to set a time window corresponding to the real-time data and insert a label generated by the time window into the binary tree;

[0030] A construction unit is used to open the data call permission of the binary tree and build a permission management mechanism.

[0031] Furthermore, the obtaining module includes:

[0032] A calculation unit, configured to calculate the difference between the target node and the leaf nodes corresponding to the other nodes using a preset difference formula;

[0033] The difference formula is:

[0034]

[0035] Where E is the difference value, a i is the real-time data of the i-th node in the target node, bi is the real-time data of the i-th node among other nodes;

[0036] a conversion unit, configured to convert the difference value into a similarity using a similarity formula;

[0037] The similarity formula is:

[0038]

[0039] Where S is the similarity and E is the difference.

[0040] Furthermore, the selection module includes:

[0041] A recording unit, configured to record a version of the demand forecast tree and establish a mapping between the version record and the time window;

[0042] An embedding unit is used to embed a replacement mechanism into the demand forecast tree.

[0043] Furthermore, the access module includes:

[0044] The search unit is used to construct a phrase set consisting of several keywords, traverse the preset platform, find the platform hot spots containing the keywords, and define them as hot topics;

[0045] The replacement unit is used to obtain a plurality of reference cities from cities with the same attribute label, select at least one comparison indicator from each reference city, and replace the difference item.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] By determining the creation module, the resource demand forecast can be refined, the analysis results can be concretized, the analysis error can be reduced, and the dynamic adjustment of the analysis results can be facilitated. By determining the generation module, the real-time data in each indicator can be intuitively displayed, greatly improving the resource scheduling and risk warning capabilities in the urbanization process. By determining the selection module, the leading nodes of the target city in the urbanization process can be found, providing the target city with a reusable development path and strategy template, while greatly improving the accuracy of the target city's resource demand forecast. By constructing a demand forecast tree, the leading city can be dynamically adjusted according to the urbanization process of the target city, greatly improving the accuracy of the resource demand forecast. In addition, by replacing the difference items, the demand forecast tree can be corrected to further improve the forecast efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a binary tree structure provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a demand forecast tree structure provided by an embodiment of the present invention;

[0050] Figure 3 A block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0051] Figure 4 A block diagram of the components of the acquisition equipment in the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0052] Figure 5 A block diagram of the composition of a node sorting device in an urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0053] Figure 6 A block diagram of the components of a demand writing device in an urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0054] Figure 7 A block diagram of the components of a difference updating device in an urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0055] Figure 8 A block diagram of the components of the creation module in the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0056] Figure 9 A block diagram of the composition of the generation module in the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0057] Figure 10 A block diagram of the components of the module in the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0058] Figure 11 A block diagram of the components of the selected modules in the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention;

[0059] Figure 12 This is a block diagram of the composition of the access module in the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] Figure 1 、 Figure 2 and Figure 3 The present invention provides a block diagram of the composition of an urbanization resource demand forecasting and management system 1 based on big data analysis, which includes: a collection device 11, a node sorting device 12, a demand writing device 13, and a difference updating device 14.

[0062] The collection device 11 is used to collect urbanization resource data of the city and divide it into several indicators, create branch nodes corresponding to each city, and mount leaf nodes corresponding to the indicators one by one to the branch nodes, collect real-time data of each indicator, and upload the real-time data to the leaf nodes, integrate the branch nodes and leaf nodes, and generate a binary tree.

[0063] Collect the city's urbanization resource data, including but not limited to population density, infrastructure data (such as roads, water and electricity load and drainage, etc.), public service coverage (such as education and medical care, etc.), industrial structure, land utilization rate and housing conditions, and divide the urbanization resource data into several indicators, such as: urbanization rate indicator, population density indicator, water and electricity load indicator and traffic flow indicator, etc.; create a branch node corresponding to each city one by one, and the branch node is also the parent node; create a leaf node corresponding to each indicator one by one, and the leaf node is also the child node, and mount the leaf node to the corresponding parent node; collect real-time data of each indicator from public data or relevant departments, and write the real-time data into the leaf node to construct a binary tree, where the binary tree is a tree-like data structure that can intuitively display the city's urbanization resource data.

[0064] The node sorting device 12 is used to determine the target city for resource demand forecasting, cluster the branch nodes into target nodes and other nodes, input the real-time data into a pre-built data comparison model, calculate the similarity between the target node and other nodes in turn, and sort the other nodes in descending order of the similarity to obtain a queue.

[0065] Identify the object for which resource demand forecasting is required, i.e., the target city; define the branch node corresponding to the target city as the target node, and define the part of the branch node other than the target node as other nodes; input real-time data into the data comparison model, and calculate the similarity between the target node and each other node one by one; the data comparison model integrates difference formulas and similarity formulas, which can perform high-concurrency, multi-dimensional comparative analysis and processing on large-scale indicator data; after the similarity calculation is completed, all other nodes are sorted in descending order of similarity to generate a queue, which intuitively reflects the similarity of each other node; in other words, the queue is a sequential set of cities that are relatively similar to the target city. Similarity means that in addition to urbanization resources, other aspects are also relatively similar, such as economic development level and regional policies.

[0066] The demand writing device 13 is used to configure the development process of urban urbanization, wherein each branch node corresponds to a development process. Through the development process, a preset number of leading nodes are selected from the front column of the queue to create a demand forecast tree. The target node and the corresponding leaf node are written to the left side of the demand forecast tree, and the leading node and the corresponding leaf node are written to the right side of the demand forecast tree.

[0067] Determine the urbanization development process of each city, where the development process should be formulated by professionals and can also be directly expressed by the urbanization rate; in the front of the queue, select other nodes with a development process higher than the target city and define them as leading nodes; further, the number of leading nodes should be predetermined by professionals. If the number of leading nodes is too large, the similarity between the leading nodes and the target nodes will be low, which will affect the reference validity of the indicator data in the leading nodes. If the number of leading nodes is too small, it may cause a large deviation in the indicators and also affect the accuracy of the guidance; create a demand forecast tree, where the demand forecast tree is a tree data structure composed of a left side and a right side. The demand forecast tree will store the corresponding indicators in the target node and the leading node in a relative mirror position; for example, as shown in the attached manual Figure 2 As shown, indicator A1 and indicator B1 are arranged in a mirror image, and the data in indicator A1 and indicator B1 are real-time data corresponding to the same indicator; the advantage of this is that the difference between the target node and the preceding node can be quickly found.

[0068] The difference updating device 14 is used to obtain hot topics in a preset platform and access them to the demand forecast tree, extract the attribute labels of the target city, compare the left and right sides of the demand forecast tree, define difference items, find cities with the same attribute labels, replace the difference items, align the left and right sides, generate a forecast report, and send the forecast report and the demand forecast tree to a preset terminal.

[0069] Obtain hot topics from preset platforms, including social media platforms, news and information platforms, and policy announcement platforms. In the interactive section of the platform, extract hot topics through topic popularity analysis; use hot topics to determine the attribute labels of the target city, where the attribute labels include: suitable for tourism, high garbage classification implementation, housing shortage, traffic congestion, and sufficient educational resources; compare the same positions on the left and right sides of the demand forecast tree to determine the indicators with the largest difference, namely the difference items; find cities with the same attribute labels as the target city, and use the found cities to correct the difference items; the specific correction process is: determine the indicators related to the attribute labels in the found cities as control indicators, and use these control indicators to replace the difference items.

[0070] The left part of the demand forecast tree is defined as a real-time item, and the right part is defined as a target item, which are written into a preset template to generate a forecast report, where the preset template is formulated by professionals; the forecast report and the demand forecast tree are sent to a preset terminal, where the preset terminal is a professional terminal.

[0071] For example, as the instructions Figure 2 As shown in the figure, the current urbanization rate of the target city is 48%, and indicator A1 is the urban population density index: 140 people / square kilometer, while the urbanization rate of the leading node is 55%, and indicator B1 is the urban population density index: 152 people / square kilometer. The prediction result at this time is: when the urbanization rate of the target city is around 55%, the urban population density per square kilometer is about 152 people. However, this prediction result is obtained on the basis that the urban development of the target city and the leading node is roughly the same. If, through analyzing hot topics on social platforms and news information platforms, it is learned that the leading node is about to build a large complex, this may lead to a significant increase in electricity consumption in the area (indicator A2). At this time, the electricity consumption index of the leading node (indicator B2) no longer meets the reference requirements. Cities with roughly the same urban development should be found to have opened large complexes and the corresponding electricity consumption index should be used as a comparison index. Indicator B2 is the difference item. The indicator of the target city is stored on the left side of the demand forecast tree, and the leading city and the difference item are stored on the right side of the demand forecast tree.

[0072] Figure 4 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The acquisition device 11 includes:

[0073] The creation module 111 is used to collect urbanization resource data of a city, divide it into several indicators, create a branch node corresponding to each city, and mount leaf nodes corresponding to the indicators in the branch node.

[0074] Collect urbanization resource data from each city. Urbanization resource data includes infrastructure construction, public service coverage, housing supply, green space ratio, traffic flow, urban expansion rate and other aspects. Structural processing is performed on the data in each aspect to obtain several quantifiable and comparable indicators. Create a branch node for each city, and under each branch node, mount multiple leaf nodes, each leaf node corresponding to an indicator.

[0075] The generation module 112 is used to collect real-time data of each indicator, upload the real-time data to the leaf nodes, integrate the branch nodes and the leaf nodes, and generate a binary tree.

[0076] Upload the real-time data of each indicator to the corresponding leaf node, and use the branch nodes and leaf nodes to create a binary tree.

[0077] Figure 5 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The node sorting device 12 includes:

[0078] The input module 121 is used to determine the target city for resource demand forecasting, cluster the branch nodes into target nodes and other nodes, and input the real-time data into a pre-built data comparison model.

[0079] The city that needs to be forecasted for resource demand is defined as the target city, and the branch node corresponding to the target city is defined as the target node; further, the part of the branch node except the target node is other nodes.

[0080] Obtaining module 122, for sequentially calculating the similarity between the target node and other nodes, and sorting other nodes in descending order of the similarity, to obtain a queue

[0081] Using the data comparison model, the similarity between the target node and each other node is calculated, and they are sorted in descending order of similarity. The set formed by the sorted other nodes is defined as a queue.

[0082] Figure 6 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The demand writing device 13 includes:

[0083] The selection module 131 is used to configure the development process of urban urbanization, wherein each branch node corresponds to a development process, and through the development process, a preset number of leading nodes are selected from the front column of the queue to create a demand forecast tree.

[0084] Determine the urbanization development process of each city, and select a preset number of leading nodes from the front of the queue based on the development process, where the preset number is determined by professionals.

[0085] The writing module 132 is used to write the target node and the corresponding leaf node to the left side of the demand forecast tree, and write the predecessor node and the corresponding leaf node to the right side of the demand forecast tree.

[0086] Figure 7 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The difference updating device 14 includes:

[0087] The access module 141 is used to obtain hot topics in a preset platform and access them into the demand forecast tree, extract attribute labels of target cities, compare the left and right sides of the demand forecast tree, and define difference items.

[0088] Compare the left and right sides of the demand forecast tree and define the indicators corresponding to the attribute labels as difference items.

[0089] The sending module 142 is used to find cities with the same attribute labels, replace the different items, align the left and right sides, generate a forecast report, and send the forecast report and demand forecast tree to a preset terminal.

[0090] Find out the cities with the same attribute labels as the target city, and replace the difference items with the indicators corresponding to the attribute labels of the found cities; write the indicators in the replaced demand forecast tree into the preset template, generate a forecast report, and send it to the preset terminal, where the preset terminal can be the management personnel terminal.

[0091] Figure 8 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The creation module 111 includes:

[0092] The configuration unit 111 is configured to configure a weight value for each of the indicators and divide the real-time data into several levels, wherein each level corresponds to a basic score.

[0093] Professionals set a weight value for each indicator and divide the real-time data of each indicator into several levels, where the level is the fluctuation range, and each level corresponds to a basic score.

[0094] The establishing unit 112 is configured to define the product of the weight value and the basic score as a comprehensive score, and establish a corresponding relationship between the comprehensive score and the city.

[0095] The weight value is multiplied by the basic score to obtain a comprehensive score, where each city corresponds to a comprehensive score.

[0096] Figure 9 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The generation module 112 includes:

[0097] The setting unit 1121 is configured to set a time window corresponding to the real-time data and insert a label generated by the time window into the binary tree.

[0098] Since urbanization resource data does not change significantly in a short period of time, the urbanization resource data can be updated at a fixed frequency by setting a time window; by inserting labels generated by the time window into the binary tree, the generation time of each urbanization resource data can be intuitively displayed.

[0099] The construction unit 1122 is used to open the data call permission of the binary tree and build a permission management mechanism.

[0100] Since the binary tree stores urbanization resource data, some of the data may involve sensitive information. Therefore, a permission management mechanism is embedded in the binary tree. The permission management mechanism is a specific permission management method. For example, city managers can access the full data, researchers can access the desensitized data in the urbanization resource data, and corporate users can access the public data in the urbanization resource data.

[0101] Figure 10 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The obtaining module 122 includes:

[0102] The calculation unit 1221 is configured to calculate the difference values between the target node and the leaf nodes corresponding to other nodes using a preset difference formula.

[0103] The difference formula is:

[0104]

[0105] Where E is the difference value, a i is the i-th real-time data in the target node, b i is the i-th real-time data in other nodes;

[0106] As mentioned above, assuming a i is the real-time data of indicator A1, then b i That is the real-time data of indicator B1. The difference value between indicator A1 and indicator B1 can be calculated through the difference formula.

[0107] a conversion unit 1222, configured to convert the difference value into a similarity using a similarity formula;

[0108] The similarity formula is:

[0109]

[0110] Where S is the similarity and E is the difference.

[0111] Continue to use the difference value to calculate the similarity between indicator A1 and indicator B1; it should be noted that the similarity of the target node is the average value of the similarities of all indicators.

[0112] Figure 11 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The selection module 131 includes:

[0113] The recording unit 1311 is used to record the version of the demand forecast tree and establish a mapping between the version record and the time window.

[0114] The demand forecast tree is recorded using a versioning method; that is, each time the demand forecast tree changes (such as node adjustment, data update, or structure optimization), a corresponding version number is generated, and the complete structure of the version and the relevant branch node and leaf node parameters are saved.

[0115] The embedding unit 1312 is configured to embed a replacement mechanism into the demand prediction tree.

[0116] The replacement mechanism is as follows: the indicators related to the attribute labels in the found cities are determined as reference indicators, and the difference items are replaced by the reference indicators.

[0117] Figure 12 The following is a structural block diagram of the urbanization resource demand forecasting and management system based on big data analysis provided by an embodiment of the present invention. The access module 141 includes:

[0118] The search unit 1411 is used to construct a phrase set consisting of several keywords, traverse the preset platform, find platform hot spots containing the keywords, and define them as hot topics.

[0119] Construct a phrase set consisting of several keywords. The keywords should be formulated by professionals. Traverse social media platforms, news information platforms, policy announcement platforms, etc., and search the text content, user comments, titles, tags and other information in each platform to find out whether any of the keywords in the phrase set are included; if there are multiple contents on a certain platform that frequently appear these keywords, and the discussion and interaction volume reach the set threshold, then the phenomenon is defined as a platform hotspot; the topic corresponding to the platform hotspot is defined as a hot topic.

[0120] The replacement unit 1412 is configured to obtain a plurality of reference cities from cities with the same attribute label, select at least one comparison indicator from each reference city, and replace the difference item.

[0121] The data in the reference city with the same attribute labels as the target city are determined as the control indicators, and the difference items are replaced by this control indicator.

[0122] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Urbanization resource demand forecasting and management system based on big data analysis, characterized by: The system includes: a collection device, a node sorting device, a demand writing device and a difference updating device; The collection device is used to collect urbanization resource data of a city, divide it into several indicators, create a branch node corresponding to each city, and attach a leaf node corresponding to each indicator to the branch node, collect real-time data of each indicator, upload the real-time data to the leaf node, and integrate the branch nodes and leaf nodes to generate a binary tree; The node sorting device is used to determine the target city for resource demand forecasting, cluster the branch nodes into target nodes and other nodes, input the real-time data into a pre-built data comparison model, sequentially calculate the similarity between the target node and other nodes, and sort the other nodes in descending order of the similarity to obtain a queue; The demand writing device is used to configure the development process of urban urbanization, wherein each branch node corresponds to a development process, and through the development process, a preset number of predecessor nodes are selected from the front column of the queue to create a demand forecast tree, and the target node and the corresponding leaf node are written to the left side of the demand forecast tree, and the predecessor node and the corresponding leaf node are written to the right side of the demand forecast tree; The difference update device is used to obtain hot topics in a preset platform and access them to the demand forecast tree, extract the attribute labels of the target city, compare the left and right sides of the demand forecast tree, define difference items, find cities with the same attribute labels, replace the difference items, align the left and right sides, generate a forecast report, and send the forecast report and the demand forecast tree to a preset terminal.

2. The urbanization resource demand forecasting and management system based on big data analysis according to claim 1 is characterized in that: The acquisition equipment includes: Create a module to collect urbanization resource data of cities, divide it into several indicators, create branch nodes corresponding to each city, and attach leaf nodes corresponding to the indicators to the branch nodes; The generation module is used to collect real-time data of each indicator, upload the real-time data to the leaf nodes, integrate the branch nodes and the leaf nodes, and generate a binary tree.

3. The urbanization resource demand forecasting and management system based on big data analysis according to claim 1 is characterized in that: The node sorting device includes: An input module is used to determine the target city for resource demand forecasting, cluster the branch nodes into target nodes and other nodes, and input the real-time data into a pre-built data comparison model; The obtaining module is used to sequentially calculate the similarity between the target node and other nodes, and sort the other nodes in descending order of the similarity to obtain a queue.

4. The urbanization resource demand forecasting and management system based on big data analysis according to claim 2 is characterized in that: The demand writing device includes: A selection module is used to configure the development process of urban urbanization, wherein each branch node corresponds to a development process, and select a preset number of leading nodes from the front of the queue through the development process to create a demand forecast tree; The writing module is used to write the target node and the corresponding leaf node into the left side of the demand forecast tree, and write the preceding node and the corresponding leaf node into the right side of the demand forecast tree.

5. The urbanization resource demand forecasting and management system based on big data analysis according to claim 1 is characterized in that: The difference updating device comprises: An access module is used to obtain hot topics from a preset platform and access them into the demand forecast tree, extract attribute labels of target cities, compare the left and right sides of the demand forecast tree, and define difference items; The sending module is used to find cities with the same attribute labels, replace the difference items, align the left and right sides, generate a forecast report, and send the forecast report and demand forecast tree to a preset terminal.

6. The urbanization resource demand forecasting and management system based on big data analysis according to claim 4 is characterized in that: The creation module includes: a configuration unit, configured to configure a weight value for each of the indicators, and divide the real-time data into a plurality of levels, wherein each level corresponds to a basic score; An establishing unit is used to define the product of the weight value and the basic score as a comprehensive score, and establish a corresponding relationship between the comprehensive score and the city.

7. The urbanization resource demand forecasting and management system based on big data analysis according to claim 2 is characterized in that: The generation module includes: A setting unit, configured to set a time window corresponding to the real-time data and insert a label generated by the time window into the binary tree; A construction unit is used to open the data call permission of the binary tree and build a permission management mechanism.

8. The urbanization resource demand forecasting and management system based on big data analysis according to claim 3 is characterized in that: The obtaining module includes: A calculation unit, configured to calculate the difference between the target node and the leaf nodes corresponding to the other nodes using a preset difference formula; The difference formula is: Where E is the difference value, a i is the real-time data of the i-th node in the target node, b i is the real-time data of the i-th node among other nodes; a conversion unit, configured to convert the difference value into a similarity using a similarity formula; The similarity formula is: Where S is the similarity and E is the difference.

9. The urbanization resource demand forecasting and management system based on big data analysis according to claim 4 is characterized in that: The selection module includes: A recording unit, configured to record a version of the demand forecast tree and establish a mapping between the version record and the time window; An embedding unit is used to embed a replacement mechanism into the demand forecast tree.

10. The urbanization resource demand forecasting and management system based on big data analysis according to claim 5, characterized in that: The access module includes: The search unit is used to construct a phrase set consisting of several keywords, traverse the preset platform, find the platform hot spots containing the keywords, and define them as hot topics; The replacement unit is used to obtain a plurality of reference cities from cities with the same attribute label, select at least one comparison indicator from each reference city, and replace the difference item.

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