A Method and System for Analyzing Urban Mineral Migration and Transformation

Abstract

This application relates to the field of urban mineral mobility technology and discloses an urban mineral migration and transformation analysis method and system, including: predicting the resource demand forecast of each city based on pre-acquired urban industrial data; constructing an urban mineral supply and demand tree with the city as the root node and supply and demand information as leaf nodes; the urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of obsolete products generated in year i that are recycled in year t, and the content of the j-th type of resource; matching the corresponding urban mineral mobility path for each city in the pre-constructed urban mineral knowledge graph to obtain a first mobility path set, expanding the paths to obtain a second mobility path set, and filtering to obtain urban mineral scheduling paths; this application can formulate and optimize urban mineral resource scheduling paths to improve the transportation efficiency and coordination rationality of urban mineral resources.

Description

Technical Field

[0001] This invention relates to the field of urban mineral mobility technology, and in particular to a method and system for analyzing urban mineral migration and transformation. Background Technology

[0002] With the rapid development of urbanization and industrialization, the efficient utilization and rational allocation of urban mineral resources have become crucial for ensuring resource security and promoting sustainable development. Accurately grasping the demand and supply of urban mineral resources and rationally planning their migration and transformation paths within a 10km x 10km spatial range are essential for improving resource utilization efficiency and ensuring stable urban economic development.

[0003] Current analyses of urban mineral migration and transformation mostly focus on converting urban recycled waste as much as possible. However, recycling processes for waste products are often extremely costly. Therefore, if the demand for urban resources is lower than the supply, only a portion can be recycled, while the remainder can be transported to cities with centralized recycling capabilities to improve urban resource processing efficiency. Thus, the migration, transformation, and allocation of urban minerals require further analysis. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the main objective of this invention is to provide a method and system for analyzing the migration and transformation of urban mineral resources, which can effectively solve the problems in the background technology. The specific technical solution of this invention is as follows:

[0005] A method for analyzing urban mineral migration and transformation includes:

[0006] Based on the pre-acquired urban industrial data, the resource demand is predicted using a pre-set urban mineral forecasting model to obtain the predicted resource demand for each city.

[0007] Based on the preset urban mineral information and the predicted resource demand, an urban mineral supply and demand tree is constructed with the city as the root node and the supply and demand information as the leaf nodes. The urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of the obsolete products generated in year i that are recycled in year t, the proportion of the j-th type of resource in the recycled waste products, and the content of the j-th type of resource in the products.

[0008] Combining the urban mineral supply and demand tree, a corresponding urban mineral movement path is matched for each city in the pre-constructed urban mineral knowledge graph to obtain the first movement path set;

[0009] Paths with intersections are selected from the first set of movement paths, and the paths are expanded based on the corresponding supply and demand relationships to obtain the second set of movement paths.

[0010] By using a preset path filtering model, path filtering is performed in the second set of mobile paths to obtain urban mineral scheduling paths, so as to carry out high-precision migration and transformation of urban minerals.

[0011] Specifically, the step of predicting resource demand for each city based on pre-acquired urban industrial data using a pre-set urban mineral forecasting model, includes:

[0012] Based on the production capacity data in the pre-acquired urban industrial data, the urban mineral development and utilization volume is predicted by the preset urban mineral prediction model, and the predicted amount of urban mineral resources that can be developed and utilized is obtained.

[0013] Based on the production structure data in urban industrial data, the correlation between industries is analyzed through a pre-set industrial correlation model to obtain the industrial correlation analysis results.

[0014] Based on the predicted amount of exploitable mineral resources in the cities and the results of industrial linkage analysis, the resource demand is analyzed to obtain the predicted resource demand for each city.

[0015] Specifically, based on preset urban mineral information and the predicted resource demand, a city mineral supply and demand tree is constructed with the city as the root node and supply and demand information as leaf nodes, including:

[0016] Based on the preset urban mineral information and the predicted resource demand, analyze the urban mineral supply information and resource demand information between cities;

[0017] With the city as the root node, the city's mineral supply information and resource demand information are used as leaf nodes.

[0018] Based on the geographical relationships and supply and demand relationships between cities, a connection relationship is established between the corresponding root node and leaf node to construct a city mineral supply and demand tree.

[0019] Specifically, based on the aforementioned urban mineral supply and demand tree, a corresponding urban mineral movement path is matched for each city in the pre-constructed urban mineral knowledge graph, resulting in a first set of movement paths, including:

[0020] Feature extraction is performed on leaf node information in the urban mineral supply and demand tree to construct leaf node feature vectors;

[0021] Feature extraction is performed on the pre-constructed urban mining knowledge graph to construct a graph feature vector;

[0022] By combining the leaf node feature vector and the graph feature vector, a fused feature vector is generated;

[0023] Based on the fused feature vector and the predicted resource demand, a corresponding urban mineral movement path is matched for each city in the urban mineral knowledge graph to obtain the first set of movement paths.

[0024] Specifically, based on the fused feature vector and the predicted resource demand, a corresponding urban mineral movement path is matched for each city in the urban mineral knowledge graph to obtain a first set of movement paths, including:

[0025] The fused feature vector is decomposed to obtain time constraints and space constraints respectively;

[0026] In the urban mining knowledge graph, city nodes that meet the time and space constraints are selected to obtain a candidate city set.

[0027] Based on the supply and demand information between cities, paths are constructed between cities in the candidate city set to obtain a candidate path set;

[0028] By analyzing the relationship between the urban mineral supply and the corresponding city's resource demand forecast at nodes along the path, the corresponding urban mineral movement paths are selected from the candidate path set to obtain the first movement path set.

[0029] Specifically, the first set of movement paths involves filtering out paths with intersections, and then expanding these paths based on corresponding supply and demand relationships to obtain a second set of movement paths, including:

[0030] Compare the paths in the first set of movement paths, filter out the paths that have intersection points, and obtain the first path group;

[0031] Based on the supply and demand relationship of the cities corresponding to the intersection points of the paths in the first path group, the paths are expanded with the intersection points as the center to obtain the second set of mobile paths.

[0032] Specifically, based on the supply and demand relationships of the cities corresponding to the path intersections in the first path group, the paths are expanded with the path intersections as the center to obtain a second set of movement paths, including:

[0033] Based on the supply and demand relationship of the cities corresponding to the intersection points of the paths in the first path group, search out the nodes associated with the intersection points of the paths and use them as the first node set;

[0034] Using the intersection of the paths as the center, the nodes in the first set of nodes are associated to obtain the associated paths;

[0035] The paths in the first path group are expanded based on the associated paths to obtain a second set of mobile paths.

[0036] Specifically, the step of filtering paths from the second set of mobile paths using a preset path filtering model to obtain urban mining scheduling paths for high-precision migration and transformation of urban mining resources includes:

[0037] The conversion rate of urban mineral resources for each path in the second set of mobile paths is analyzed using a preset path filtering model, and the path score for each path is calculated.

[0038] The path with the highest score is selected from the second set of moving paths and used as the urban mineral scheduling path to perform high-precision migration and transformation of urban minerals.

[0039] Specifically, the method involves analyzing the urban mineral resource conversion rate of each path in the second set of mobile paths using a preset path filtering model, and calculating the path score for each path, including:

[0040] Based on the second set of movement paths, analyze the input and output of urban mineral resources at each path node on each path.

[0041] By combining the input and output of urban mineral resources of all path nodes on each path, the conversion rate of urban mineral resources for each path is calculated.

[0042] Based on the city's mineral resource conversion rate, the path score for each path is calculated using a preset path selection model.

[0043] An urban mineral migration and transformation analysis system, used to implement the aforementioned urban mineral migration and transformation analysis method, includes:

[0044] The demand forecasting module, based on the pre-acquired urban industrial data, uses a preset urban mineral forecasting model to predict resource demand and obtain the predicted resource demand for each city.

[0045] The urban mineral supply and demand tree construction module constructs an urban mineral supply and demand tree based on preset urban mineral information and the predicted resource demand, with the city as the root node and the supply and demand information as the leaf nodes. The urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of the obsolete products generated in year i that are recycled in year t, the proportion of the j-th type of resource in the recycled waste products, and the content of the j-th type of resource in the products.

[0046] The path matching module, in conjunction with the urban mineral supply and demand tree, matches the corresponding urban mineral movement path for each city in the pre-constructed urban mineral knowledge graph to obtain the first set of movement paths.

[0047] The path expansion module filters out paths with intersections from the first set of mobile paths, and expands the paths based on the corresponding supply and demand relationships to obtain a second set of mobile paths.

[0048] The path filtering module uses a preset path filtering model to filter paths in the second set of mobile paths to obtain urban mineral scheduling paths, so as to perform high-precision migration and transformation of urban minerals.

[0049] Compared with the prior art, this application has the following beneficial effects:

[0050] This application combines production capacity data and industry linkage analysis results to predict resource demand. By constructing an urban mineral supply and demand tree, it analyzes the supply and demand relationship of urban minerals between cities, matches urban mineral movement paths in the urban mineral knowledge graph, and expands paths with intersections. The optimal path is selected by analyzing supply and demand relationships and resource conversion rates. Based on the accurate predicted resource demand, corresponding urban mineral resource scheduling paths are formulated and the paths are expanded and optimized. This can reduce resource waste, improve the transportation efficiency and scheduling rationality of urban mineral resources, and achieve high-precision migration and transformation of urban minerals. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the process of an urban mineral migration and transformation analysis method according to Embodiment 1 of the present invention.

[0052] Figure 2 This is a schematic diagram of the urban mineral supply and demand tree in Embodiment 1 of the present invention;

[0053] Figure 3 This is a schematic diagram of the urban mineral knowledge graph in Embodiment 1 of the present invention;

[0054] Figure 4 This is a schematic diagram of the urban mineral transportation route expansion process in Embodiment 1 of the present invention;

[0055] Figure 5 This is a schematic diagram of the structure of an urban mineral migration and transformation analysis system according to Embodiment 2 of the present invention;

[0056] Figure 6 This is a schematic diagram illustrating the construction idea of ​​a stock-based model for dual prediction of total resource demand and renewable resource supply in the context of this invention. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0060] As the background of this application, the supply of recycled resources is closely related to the stock, sales volume, lifespan, and reverse logistics information of products. Reverse logistics involves several terms: obsolescence refers to the number of products that have been used and replaced by consumers since production; reuse refers to the number of obsolescence products that have been refurbished and resold in the second-hand market; idleness refers to the number of obsolescence products that are idle at home or in the office; scrapping refers to the number of obsolescence, reuse, and idle products that enter the recycling channel; and recycled resource supply refers to the number of scrapped products that are ultimately transformed into usable resources or products after resource recycling or parts reuse. According to my country's statistical system, stock data can be obtained directly from the National Statistical Yearbook; the lifespan of various products can be obtained through surveys and analyses of residents; and reverse logistics information of waste products can be obtained through surveys and analyses of various recyclers. Based on the above data, the designed accounting approach is as follows: Figure 6 As shown.

[0061] According to the law of conservation of mass, the flow and stock of matter are mutually influential. An increase in stock (retention) means that the inflow (consumption) in the overall system exceeds the outflow (scrap), and vice versa. At the same time, there is a close correlation between product scrapping, product consumption, and lifetime distribution; that is, the scrapping amount in year t is the sum of the products of the product consumption in year ti and the proportion of products scrapped in year i (i=1,2,...,t). Based on the above principles, a stock-based model can be constructed to calculate the resource potential of waste products in different spatiotemporal ranges, with the following relationship:

[0062]

[0063]

[0064] in, For the social stock of products, For product consumption, This refers to the amount of product scrap. This represents the product lifespan distribution. The social stock of products in year t. Let be the product consumption in year t. Let t represent the amount of products scrapped in year t.

[0065] This application is an invention based on the above background, and the embodiments of this application will be described in detail below.

[0066] Example 1

[0067] This embodiment provides a method for analyzing the migration and transformation of urban mineral resources, such as... Figure 1 As shown, a method for analyzing urban mineral migration and transformation includes:

[0068] S101. Based on the pre-acquired urban industrial data, predict the resource demand using a pre-set urban mineral prediction model to obtain the predicted resource demand for each city.

[0069] S102. Based on the preset urban mineral information and the predicted resource demand, construct an urban mineral supply and demand tree with the city as the root node and the supply and demand information as the leaf nodes; the urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of the obsolete products generated in year i that are recycled in year t, the proportion of the j-th type of resource in the recycled waste products, and the content of the j-th type of resource in the products;

[0070] S103. Combining the urban mineral supply and demand tree, match the corresponding urban mineral movement path for each city in the pre-constructed urban mineral knowledge graph to obtain the first movement path set.

[0071] S104. Filter out paths with intersections in the first set of movement paths, and expand the paths according to the corresponding supply and demand relationship to obtain a second set of movement paths.

[0072] S105. Using a preset path filtering model, path filtering is performed in the second set of mobile paths to obtain urban mineral scheduling paths, so as to perform high-precision migration and transformation of urban minerals.

[0073] In the context of global resource scarcity, high-resolution migration and transformation of urban mineral resources—meaning transformation within a 10km x 10km spatial range—can effectively improve resource utilization efficiency, reduce resource acquisition costs, and ensure stable urban economic development. Urban mineral resource migration and transformation is essentially resource allocation, transporting resources from one city to another to meet each city's mineral resource needs. This embodiment predicts the resource demand of each city based on urban mineral information. By analyzing the supply and demand relationship between cities, an urban mineral supply and demand tree is constructed. Based on the demand forecast and the supply and demand tree, urban mineral resource allocation paths are matched in the urban mineral knowledge graph. Paths with intersections are expanded to increase the flexibility of resource allocation. Through a path selection model, the optimal urban mineral allocation path is selected from the expanded path set. This achieves the planning of urban mineral migration and transformation paths, meeting the need for rational allocation of urban mineral resources between cities, reducing resource idleness and waste, and improving overall resource utilization efficiency.

[0074] In this embodiment, based on pre-acquired urban industrial data, resource demand is predicted using a pre-set urban mineral forecasting model to obtain the predicted resource demand for each city. Urban industrial data for each city is collected, including capacity data and production structure data. Capacity data includes the number of urban mineral production equipment, equipment operating time, and output per unit time in each city. Production structure data includes the scale of various industries within the city, upstream and downstream relationships between industries, and the proportion of different urban minerals consumed by each industry. Urban mineral information is input into the pre-set urban mineral forecasting model to predict capacity and analyze the impact of industrial linkages on resource demand. The urban mineral forecasting model includes machine learning models and empirical models, and outputs the predicted resource demand for each city. Combining capacity forecasting and industrial linkage analysis, and considering that urban resource demand is not static and is affected by various factors such as capacity changes and industrial restructuring, accurate prediction of resource demand can avoid situations of resource oversupply or undersupply.

[0075] Preferably, based on preset urban mineral information and the predicted resource demand, with cities as the core, the urban mineral supply and demand information between cities is organized in a tree structure. By analyzing the urban mineral resource supply and demand information between cities, with cities as root nodes and supply and demand information as leaf nodes, and combining the city's geographical location and supply and demand relationship, a connection is established between the root node and the leaf nodes to construct an urban mineral supply and demand tree. The urban mineral supply and demand tree can display the complex urban mineral supply and demand relationship between cities, quickly understand the urban mineral supply and demand situation of each city, provide a reference framework for urban mineral resource movement path planning, and reduce information confusion in the planning process.

[0076] Specifically, based on the urban mineral supply and demand tree and the pre-constructed urban mineral knowledge graph, feature extraction and feature fusion are performed respectively. In the knowledge graph, urban mineral movement paths that conform to their supply and demand relationship and demand forecast are matched for each city, forming the first movement path set. The knowledge graph includes information on cities, urban minerals, transportation, and other aspects. By fusing feature vectors, the correlation between various information can be combined, and the supply and demand relationship between cities, time, space and other factors can be comprehensively considered to improve the rationality and feasibility of resource movement paths, reduce transportation costs, and improve resource allocation efficiency.

[0077] In the first set of movement paths, some paths intersect, and the cities at these intersections contain rich supply and demand relationships. By analyzing the supply and demand relationships in these intersection cities and expanding the paths around the intersections, a second set of movement paths can be obtained. By expanding the paths, the supply and demand resources of the intersection cities can be fully utilized, the urban mineral migration paths can be optimized, the circulation efficiency of urban mineral resources can be improved, the flexibility of resource migration paths can be increased, and the overall urban mineral migration network can be optimized.

[0078] Preferably, for each path in the second set of mobile paths, the conversion rate of urban mineral resources along the path is analyzed using a preset path selection model, and the path score for each path is calculated. The path with the highest path score is selected from the second set of mobile paths as the urban mineral resource scheduling path. Urban mineral resources are then scheduled according to the urban mineral resource scheduling path to realize the migration and conversion of urban mineral resources. By selecting the urban mineral resource scheduling path with the highest resource utilization efficiency and the lowest comprehensive cost, the overall utilization efficiency of urban mineral resources can be improved, resource waste can be reduced, and the rational allocation and efficient utilization of urban mineral resources can be ensured.

[0079] This application combines production capacity data and industry linkage analysis results to predict resource demand. It analyzes the supply and demand relationship of urban mineral resources between cities by constructing an urban mineral supply and demand tree, matches urban mineral movement paths in the urban mineral knowledge graph, and expands paths with intersections. The optimal path is selected by analyzing supply and demand relationships and resource conversion rates. Based on the accurate predicted resource demand, corresponding urban mineral resource scheduling paths are formulated and the paths are expanded and optimized, which can reduce resource waste and improve the transportation efficiency and scheduling rationality of urban mineral resources.

[0080] Furthermore, the step of predicting resource demand for each city based on pre-acquired urban industrial data using a preset urban mineral forecasting model, includes:

[0081] S201. Based on the production capacity data in the pre-acquired urban industrial data, predict the amount of urban mineral development and utilization using a preset urban mineral prediction model to obtain the predicted amount of urban mineral development and utilization.

[0082] S202. Based on the production structure data in the urban industrial data, analyze the correlation between industries through a preset industrial correlation model to obtain the industrial correlation analysis results.

[0083] S203. Combining the predicted amount of exploitable mineral resources in the city with the results of industrial linkage analysis, analyze the resource demand to obtain the predicted resource demand for each city.

[0084] This embodiment analyzes the production capacity data and production structure data from urban industrial data using a pre-set urban mineral resource forecasting model to predict the exploitable amount of urban mineral resources. It then analyzes the production structure data using a pre-set industry linkage model, combining the inter-industry relationships to analyze the impact of industrial development on resource demand, obtaining industry linkage analysis results. By combining the predicted exploitable amount of urban mineral resources with the industry linkage analysis results, and considering the changes in urban mineral resource development and demand, the predicted resource demand for each city is calculated. By combining the predicted exploitable amount of urban mineral resources with the industry linkage analysis results, the accuracy of urban resource demand forecasts is improved, thereby enhancing the rationality and effectiveness of urban mineral resource allocation.

[0085] In this embodiment, firstly, based on the production capacity data in the pre-acquired urban industrial data, the urban mineral development and utilization volume is predicted using a preset urban mineral prediction model to obtain the predicted amount of urban mineral resources that can be developed and utilized. The production capacity data includes, but is not limited to, the number of urban mineral production equipment in each city over the years, equipment models (reflecting equipment production capacity), average equipment operating time, and output per unit time. The collected data is cleaned to remove duplicate, erroneous, or incomplete data, and the data is normalized to obtain pre-processed production capacity data. The urban mineral prediction model includes machine learning models, empirical models, etc. In this embodiment, the urban mineral prediction model... The model is an LSTM model, trained using a large amount of historical production capacity data to obtain a pre-trained LSTM model. The pre-processed production capacity data is then input into the pre-trained urban mineral prediction model. Through data analysis and calculation, the model outputs the predicted amount of urban mineral resources that can be developed and utilized in each city for a specific future time period (such as the next week or month). By predicting the amount of urban mineral resources to be developed and utilized, the model can analyze and obtain the changing trend of mineral supply capacity in each city. When it is found that the production capacity of a certain city cannot meet the demand in the future, the model can plan in advance to allocate corresponding urban mineral resources from other cities, so as to avoid the industrial development being hindered due to insufficient supply.

[0086] Specifically, based on the production structure data in urban mineral information, the product supply relationship and technological linkage between industries are analyzed. By analyzing the linkages between different industries, the results of industrial linkage analysis are obtained. The production structure data includes the number of enterprises, enterprise size, main product types, and cooperation relationships between industries in each industry. Using a pre-set industrial linkage model, the production structure data is analyzed. The industrial linkage model includes, but is not limited to, the input-output analysis model. The input-output analysis model determines the degree of linkage between industries by constructing an input-output table between industries and calculating the direct consumption coefficient and total consumption coefficient between each industry. For example, the amount of steel and rubber directly consumed in producing one car is calculated, as well as the amount of various urban mineral resources completely consumed in producing one car after considering all upstream links in the industrial chain. Based on the model output results, the industrial linkage analysis results are obtained.

[0087] Preferably, industry linkage analysis can help grasp changes in resource demand from the perspective of industrial development. When a city plans to develop new industries or upgrade existing industries, the results of industry linkage analysis can be used to predict changes in the demand for related urban mineral resources in advance. For example, when a city plans to vigorously develop the electronics and information industry, industry linkage analysis can reveal that the development of this industry will significantly increase the demand for rare metals (such as indium and gallium). This allows for advance planning of the supply and scheduling of these urban mineral resources, ensuring the smooth development of the industry and avoiding delays or increased costs due to resource shortages.

[0088] Preferably, the predicted amount of exploitable urban mineral resources obtained in step S201 is combined with the industry linkage analysis results obtained in step S202. For each type of urban mineral resource in each city, based on the changes in resource demand during industrial development as shown in the industry linkage analysis results, and combined with the changes in the predicted amount of exploitable urban mineral resources, when production capacity increases, the increased resource demand can be met to a certain extent; when production capacity decreases, the corresponding urban mineral resources are planned to be allocated from other cities. By comprehensively considering changes in production capacity and changes in industry demand, the accuracy and real-time nature of the predicted resource demand for each city can be improved, enabling precise allocation of urban mineral resources and avoiding resource supply imbalances caused by inaccurate demand forecasts.

[0089] Furthermore, the construction of an urban mineral supply and demand tree based on preset urban mineral information and the predicted resource demand, with the city as the root node and supply and demand information as leaf nodes, includes:

[0090] S301. Based on the preset urban mineral information and the predicted resource demand, analyze the urban mineral supply information and resource demand information between cities.

[0091] S302. With the city as the root node, the city's mineral supply information and resource demand information are used as leaf nodes.

[0092] S303. Based on the geographical location and supply and demand relationships between cities, establish connections between the corresponding root nodes and leaf nodes to construct a city mineral supply and demand tree.

[0093] This embodiment analyzes the supply and demand of urban minerals among cities based on preset urban mineral information and the predicted resource demand, determines the supply and demand roles and specific supply and demand data of each city in urban mineral transactions, creates root nodes with cities as the core, and constructs a tree structure with supply and demand information as leaf nodes. Combining the geographical location and supply and demand relationships between cities, corresponding connections are established between the root nodes and leaf nodes to obtain the urban mineral supply and demand tree. By constructing the urban mineral supply and demand tree, the complex urban mineral supply and demand relationships between cities are intuitively displayed. By comprehensively considering geographical location and supply and demand relationships, a reference is provided for the scheduling and planning of urban mineral resources. Based on the tree structure, low-cost and high-efficiency transportation routes can be quickly selected.

[0094] In this embodiment, based on preset urban mineral information and the predicted resource demand, the supply and demand of urban mineral resources among various cities are analyzed to determine the flow trend of urban mineral resources between different cities, providing a data foundation for constructing a supply and demand tree. From the preset urban mineral information and the predicted resource demand, urban mineral supply and demand information for each city is extracted, including city name, type of urban mineral, supply quantity, supply time, demand quantity, and demand time. For example, it is obtained that city A can supply 100 tons of copper ore in the next month, and city B's demand for copper ore is 80 tons. The supply and demand information is classified according to city and type of urban mineral, with different urban mineral supply information within the same city classified as one category and demand information as another. The direction of resource supply is determined based on the supply and demand information. By analyzing the urban mineral supply and demand information between cities, the supply and demand relationship of urban mineral resources in the entire region can be quickly understood, providing a basis for resource allocation decisions and avoiding resource waste or insufficient supply caused by blind allocation.

[0095] Preferably, cities are used as root nodes, and the corresponding urban mineral supply and demand information are used as leaf nodes. A connection is established between the root node and leaf nodes, combining the geographical location and supply-demand relationships between cities, to construct an urban mineral supply-demand tree. The urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of obsolete products generated in year i that are recycled in year t, the proportion of technically feasible and economically reasonable renewable resource development and utilization of the j-th resource in recycled waste products, and the content of the j-th resource in the products. The transportation route length is analyzed based on the city's geographical location information, and combined with demand information, it is calculated whether the delivery time of urban mineral transportation can meet the demand time. For cities with supply-demand relationships and capable of timely delivery, a connection is established between the corresponding root node and leaf nodes.

[0096] like Figure 2 As shown, the urban mineral resources supplied by city A can meet the needs of city B, and city A can deliver the corresponding urban mineral resources before the demand time of city B. A directed edge is established between the supply leaf node of city A and the demand leaf node of city B, with the direction from the supplier to the demander. In the figure, the supply leaf node of city A points to the demand leaf node of city B.

[0097] Preferably, by establishing connections based on geographical location and supply and demand, the constructed urban mineral supply and demand tree can more accurately reflect the urban mineral allocation process. When allocating urban mineral resources, the tree structure can quickly match resource allocation paths that meet the conditions, reduce transportation costs, improve the efficiency and feasibility of resource allocation, avoid choosing routes with excessively high transportation costs, and prioritize resource allocation paths between cities that are close and have convenient transportation.

[0098] Furthermore, combining the aforementioned urban mineral supply and demand tree, a corresponding urban mineral movement path is matched for each city in the pre-constructed urban mineral knowledge graph, resulting in a first set of movement paths, including:

[0099] S401. Extract features from the leaf node information in the urban mineral supply and demand tree and construct leaf node feature vectors;

[0100] S402. Extract features from the pre-constructed urban mining knowledge graph and construct a graph feature vector;

[0101] S403. Combine the leaf node feature vector and the graph feature vector to generate a fused feature vector;

[0102] S404. Based on the fused feature vector and the predicted resource demand, match the corresponding urban mineral movement path for each city in the urban mineral knowledge graph to obtain the first movement path set.

[0103] This embodiment extracts relevant feature information from the urban mineral supply and demand tree and the pre-constructed urban mineral knowledge graph, constructs leaf node feature vectors and graph feature vectors, and fuses these two feature vectors to obtain a fused feature vector. Combining the resource demand forecast of each city, constraints are extracted from the fused feature vector, and paths that meet the conditions are selected in the knowledge graph to obtain the first set of movement paths. By combining the urban mineral supply and demand tree and the urban mineral knowledge graph, urban mineral movement paths that meet the demand can be quickly selected, avoiding the limitations of planning paths based on single information. The planned urban mineral movement paths are more in line with the actual resource allocation situation, improving the accuracy and reliability of the resource movement path planning process.

[0104] In this embodiment, based on the urban mineral supply and demand information in the leaf nodes of the urban mineral supply and demand tree, feature extraction is performed on the leaf node information to obtain leaf node feature vectors. First, the feature dimensions to be extracted are determined, including urban mineral type, supply or demand quantity, supply or demand time, urban mineral quality grade, etc. Each leaf node of the urban mineral supply and demand tree is traversed, and corresponding feature values ​​are extracted from the leaf node information according to the determined feature dimensions. The extracted feature values ​​are arranged in order according to the determined feature dimensions to obtain the leaf node feature vector. By constructing the leaf node feature vector, the information in the urban mineral supply and demand tree can be transformed into simple digital information.

[0105] like Figure 3 As shown, cities are used as nodes. The transportation convenience between cities is analyzed based on the transportation mode and transportation time. The corresponding transportation convenience value is calculated by weighted average. The higher the transportation convenience value, the more convenient the transportation between the cities. The transportation convenience value is used as the weight of the edge. Weighted edges are constructed between the corresponding cities to connect the cities and build a city mineral knowledge graph.

[0106] Specifically, the pre-constructed urban mining knowledge graph includes rich urban mining information, such as urban geographical location, transportation network, and the location of urban mining reserve facilities. Feature extraction is performed on the knowledge graph to obtain a graph feature vector. By analyzing the structure of the urban mining knowledge graph, the feature dimensions related to urban mining movement paths are determined. The nodes and edges in the urban mining knowledge graph are traversed, and corresponding feature values ​​are extracted according to the determined feature dimensions. The extracted graph feature values ​​are arranged in the determined dimensional order to obtain the graph feature vector. By constructing the graph feature vector, valuable information for urban mining movement path planning can be extracted from the complex urban mining knowledge graph. During the path matching process, factors such as geographical relationships and transportation conditions between cities are considered to make the planned paths more consistent with reality, improving the rationality and feasibility of path planning.

[0107] Preferably, the leaf node feature vector contains supply and demand information of urban minerals, and the graph feature vector contains environmental information such as geography and transportation between cities. The leaf node feature vector and the graph feature vector are combined to obtain a fused feature vector. Feature vector fusion methods include splicing fusion, weighted fusion, etc. This embodiment adopts the direct splicing method, splicing the elements in the leaf node feature vector and the graph feature vector to obtain the fused feature vector. Through vector fusion, supply and demand information can be combined with geographical and transportation conditions to obtain a fused vector that can comprehensively describe the conditions related to the movement of urban minerals, providing richer and more accurate data for accurately matching the movement paths of urban minerals in the knowledge graph.

[0108] Specifically, by combining the fused feature vector with the resource demand forecast, a corresponding urban mineral movement path is matched for each city in the urban mineral knowledge graph, resulting in a first set of movement paths. The fused feature vector integrates information such as urban mineral supply and demand and geographical transportation. Combined with the resource demand forecast for each city, paths that meet the conditions are searched in the urban mineral knowledge graph. By analyzing the fused feature vector, the time and space constraints are extracted, and paths that meet these constraints and can satisfy the city's resource demand are selected in the knowledge graph. This yields a set of urban mineral movement paths for each city, providing specific route planning schemes for the actual allocation of urban mineral resources.

[0109] Furthermore, based on the fused feature vector and the predicted resource demand, a corresponding urban mineral movement path is matched for each city in the urban mineral knowledge graph to obtain a first set of movement paths, including:

[0110] S501. Decompose the fused feature vector to obtain the time constraint and the space constraint respectively;

[0111] S502. Select city nodes that meet the time and space constraints from the city mining knowledge graph to obtain a candidate city set.

[0112] S503. Based on the supply and demand information between cities, construct paths between cities in the candidate city set to obtain a candidate path set.

[0113] S504. By analyzing the relationship between the urban mineral supply and the corresponding city's resource demand forecast at the nodes in the path, the corresponding urban mineral movement path is selected from the candidate path set to obtain the first movement path set.

[0114] In this embodiment, the fusion feature vector integrates urban mineral information, the predicted resource demand, and data from various aspects such as geographic transportation. Decomposing the fusion feature vector allows for the extraction of constraints, revealing the specific temporal and spatial constraints on urban mineral transportation. Based on these constraints, corresponding city nodes are selected from the urban mineral knowledge graph. First, the features involved in time and spatial constraints are determined. Time-related features include urban mineral supply time, demand time, and estimated transportation time; spatial-related features include straight-line distance between cities, actual traffic distance, and transportation route type. According to the determined constraint types and corresponding feature locations, relevant elements are extracted from the fusion feature vector. The time-related features are integrated to obtain time constraint information; the spatial-related elements are integrated to obtain spatial constraint information. By decomposing the fusion feature vector to obtain constraints, the feasible range of urban mineral movement paths can be determined, avoiding the consideration of paths that do not meet time and spatial requirements during path selection, reducing invalid calculations, and improving path matching efficiency.

[0115] Preferably, based on time and spatial constraints, city nodes are filtered in the knowledge graph to select those that simultaneously meet both time and spatial constraints, forming a candidate city set. All city nodes in the city mining knowledge graph are traversed, and for each node, it is determined whether the corresponding time information meets the time constraint conditions. Among the city nodes filtered by time constraints, spatial constraints are then applied, checking whether the city node's geographical location, distance from other cities, and transportation routes meet the spatial constraints. After these two rounds of filtering based on time and spatial constraints, the remaining city nodes that simultaneously meet both time and spatial constraints form the candidate city set. By filtering city nodes that meet the constraints, cities that do not meet actual transportation conditions can be quickly eliminated, reducing unnecessary path construction and analysis, and improving the efficiency and accuracy of path planning.

[0116] Specifically, based on the supply and demand information among city nodes in the candidate city set, paths are constructed between city nodes with supply and demand relationships to obtain candidate paths. The connected paths are then integrated to obtain a candidate path set. For each city node in the candidate city set, the supply and demand relationship of urban minerals between it and other nodes is analyzed. Based on the analyzed supply and demand relationship, paths are constructed between city nodes with supply and demand relationships. The path construction steps are repeated for all city nodes with supply and demand relationships in the candidate city set. The constructed paths are then summarized to obtain a candidate path set. By constructing a candidate path set, the supply and demand relationship and geographical transportation conditions between cities can be comprehensively considered, generating multiple urban mineral movement path schemes. This provides a wealth of choices for selecting the optimal path, helps to compare paths from multiple perspectives, and thus selects the optimal urban mineral transportation route, improving the rationality and efficiency of resource allocation.

[0117] Preferably, by analyzing the relationship between the urban mineral supply at nodes along the path and the corresponding city's predicted resource demand, candidate paths are screened, eliminating paths that cannot meet demand or have unreasonable supply (such as excessive supply leading to resource waste), resulting in a first set of movement paths that conforms to actual supply demand and provides accurate route planning for the actual allocation of urban mineral resources. For each path in the candidate path set, the total urban mineral supply at all supply nodes along the path is calculated, and the calculated path supply is compared with the predicted resource demand of the path's endpoint city to determine whether the supply can meet the demand. When the supply is less than the predicted demand, the path cannot meet the demand and is excluded; when the supply is greater than the predicted demand, further analysis is conducted to determine whether there will be excessive supply leading to resource waste. Finally, paths that meet the demand and will not cause resource waste are selected to form the first set of movement paths. By selecting urban mineral movement paths, the feasibility and rationality of the planned paths in the actual allocation of urban mineral resources can be ensured, avoiding waste caused by insufficient or excessive resource supply due to unreasonable path planning, improving resource utilization efficiency, and ensuring the effective allocation and rational utilization of urban mineral resources.

[0118] Furthermore, the step of filtering out paths with intersections from the first set of movement paths, and expanding the paths based on corresponding supply and demand relationships, yields a second set of movement paths, including:

[0119] S601. Compare the paths in the first set of movement paths, filter out the paths that have intersection points, and obtain the first path group;

[0120] S602. Based on the supply and demand relationship of the cities corresponding to the intersection points of the paths in the first path group, the paths are expanded with the intersection points as the center to obtain the second set of mobile paths.

[0121] This embodiment compares and analyzes the paths in the first set of mobile paths. By extracting and comparing path node information, paths with intersections are selected to form a first path group. By selecting path intersections, key paths and nodes in the urban mineral transportation network can be located. For the cities corresponding to the path intersections in the first path group, the supply and demand relationship of the cities corresponding to the path intersections is analyzed, related nodes are searched, related paths are constructed, and the original paths are expanded to obtain a second set of mobile paths. By expanding the paths through path intersections, the scattered paths can be integrated and extended, thereby optimizing the urban mineral transportation paths to meet the needs of rational allocation of urban mineral resources.

[0122] In this embodiment, the paths in the first set of mobile paths are compared and paths with intersections are selected to obtain the first path group. Paths with intersections indicate that different urban mineral transportation routes converge at certain city nodes. The cities with intersections have important geographical advantages and resource allocation potential. By selecting paths with intersections, key city nodes and path combinations can be combined to provide a basis for expanding paths based on supply and demand, thereby further optimizing the urban mineral mobile network and improving the flexibility and efficiency of resource allocation.

[0123] Preferably, the supply and demand relationship of the cities corresponding to the intersection points of the routes is analyzed. These cities occupy key positions in the urban mineral transportation network, and their supply and demand relationship affects the flow of urban mineral resources in the surrounding areas. Based on the supply and demand relationship of the intersection cities, the routes can be expanded with the intersection points as the center. This can make full use of the resource advantages and geographical advantages of the intersection cities, merge and extend the originally independent routes, and build a richer urban mineral transportation network by adding new route branches or connections. This will enable urban mineral resources to be allocated in a wider area, improving the efficiency of resource circulation and utilization.

[0124] Furthermore, based on the supply and demand relationships of the cities corresponding to the path intersections in the first path group, the paths are expanded with the path intersections as the center to obtain a second set of movement paths, including:

[0125] S701. Based on the supply and demand relationship of the cities corresponding to the path intersections in the first path group, search for the nodes associated with the path intersections and use them as the first node set.

[0126] S702. Using the intersection of the paths as the center, associate the nodes in the first set of nodes to obtain the associated paths;

[0127] S703. Expand the paths in the first path group according to the associated path to obtain a second set of mobile paths.

[0128] In this embodiment, based on the path intersections in the first path group, the supply and demand relationships of the cities corresponding to the path intersections are analyzed, and nodes with supply and demand relationships with the path intersection cities are searched out as associated nodes. The nodes associated with the path intersections are integrated into a first node set. For each city corresponding to a path intersection in the first path group, the city's urban mineral supply and demand information is analyzed. Based on the structure and data characteristics of the urban mineral knowledge graph, search rules for associated nodes are formulated. In this embodiment, a distance threshold is used (e.g., searching for city nodes within a 500-kilometer radius of the intersection city). The search is performed in the urban mineral knowledge graph according to the search rules. By traversing the nodes and edges in the graph, city nodes that meet the conditions are selected, and the selected nodes are combined into a first node set. By searching for nodes associated with path intersections, cities that can participate in the allocation of urban mineral resources can be located, avoiding blindly expanding paths. By selecting nodes based on supply and demand relationships and actual transportation conditions, it can be ensured that the cities in the first node set are feasible for resource allocation with the intersection cities, laying the foundation for the construction of effective paths, reducing the construction of ineffective paths, and improving the efficiency and effectiveness of path expansion.

[0129] Preferably, there is a supply and demand relationship between urban mineral resources between the city nodes in the first node set and the cities where the paths intersect. The city nodes are then linked together with the cities where the paths intersect, constructing associated paths. For each city node in the first node set, the geographical transportation conditions between it and the cities where the paths intersect, as well as other nodes in the set, are analyzed. This includes whether there are direct highway, railway, or waterway transportation routes, the capacity of these routes, transportation costs, and transportation time. Based on the transportation conditions between nodes and the supply and demand relationship of urban mineral resources, routes with low transportation costs, high capacity, and short transportation times are prioritized for connection. Following the corresponding path connection method, connections between nodes are established in the urban mineral knowledge graph with the intersection of the paths as the center, forming associated paths. By constructing associated paths in the urban mineral knowledge graph, resource circulation channels can be increased. Comprehensive consideration of transportation conditions and supply and demand relationships ensures that the newly constructed paths have high feasibility and practicality, increases the flexibility of urban mineral resource allocation, reduces transportation costs, and improves resource transportation efficiency.

[0130] Specifically, the paths in the first path group are expanded according to the associated paths, so that the originally relatively independent paths are connected to each other at the intersection cities. By utilizing the resource hub role of the intersection cities, urban mineral resources can flow flexibly between multiple paths, improving the comprehensive allocation capability of resources. The expanded paths are then combined into a second set of mobile paths.

[0131] Preferably, for each path in the first path group, the connection between it and associated paths is analyzed. The starting point, ending point, and intermediate nodes of the path are checked to determine which associated paths can be effectively connected to the original path, as well as the location and method of connection. For paths that can be connected, the associated path is added to the corresponding path in the first path group. The associated path can be used as a branch of the original path, extending from the intersection city. Alternatively, the associated path can be merged with the original path to form a new, longer path. At the same time, during the path expansion process, the node information, transportation mode, transportation time, and other relevant data of the path are updated. After the path expansion operation is completed for all paths in the first path group, duplicate paths in the expanded path are removed to form a second set of mobile paths. By expanding the paths in the first path group, the urban mineral transportation network can be optimized. Combined with associated paths, the efficiency of resource circulation in the network can be improved, the time and cost of resource allocation can be reduced, and the demand of different industries in the city for urban mineral resources can be met. At the same time, the second set of mobile paths provides more path selection options for path screening. The optimal path can be selected according to the actual situation, further improving the rationality and efficiency of urban mineral resource allocation.

[0132] like Figure 4 As shown, there is a path intersection between path 1 and path 2. Taking the path intersection as the center, city node M and city node N are searched as associated nodes with a preset distance. The supply and demand relationship between city node M and city node N is analyzed. There is a supply and demand relationship between city node M and city node N. Corresponding connections are established between city node M and city node N. Urban mineral transportation can be carried out between city node M and city node N, thus expanding new urban mineral transportation routes.

[0133] Furthermore, the step of filtering paths in the second set of mobile paths using a preset path filtering model to obtain urban mining scheduling paths for high-precision migration and transformation of urban mining resources includes:

[0134] S801. Using a preset path filtering model, analyze the urban mineral resource conversion rate of each path in the second mobile path set and calculate the path score for each path.

[0135] S802. Select the path with the highest path score from the second set of moving paths as the urban mineral scheduling path to perform high-precision migration and transformation of urban minerals.

[0136] This embodiment uses a preset path selection model to analyze and evaluate each path in the second set of mobile paths from multiple dimensions, such as urban mineral resource conversion rate, transportation cost, transportation time, and transportation risk. By determining the evaluation indicators and weights, calculating the scores of each indicator, and weighted summing, the path score of each path is obtained. By sorting the path scores, the path with the highest score is selected as the urban mineral resource scheduling path, and the optimal scheduling path is selected from multiple candidate paths. The selected urban mineral resource scheduling path has a high urban mineral resource conversion rate, which can reduce resource loss during transportation, improve the effective utilization of resources, and avoid resource waste.

[0137] In this embodiment, the urban mineral resource conversion rate of each path in the second set of mobile paths is analyzed using a preset path screening model. The urban mineral resource conversion rate reflects the degree of effective utilization of urban mineral resources during the transportation process. The conversion rate is calculated by analyzing the resource input and output of each path, and the path score is calculated in combination with other relevant factors. The higher the score, the better the path performs in terms of resource utilization and cost control, thus providing a basis for screening the optimal urban mineral scheduling path.

[0138] Preferably, after calculating the score of each path in the second set of movement paths, the path with the highest score is selected as the urban mineral resource scheduling path by comparing the scores. Urban mineral resources are then scheduled according to the urban mineral resource scheduling path to realize the migration and transformation of urban minerals. The path with the highest score indicates that, under comprehensive evaluation, it has better effects in terms of urban mineral resource conversion rate, transportation cost, transportation time and transportation risk. It can maximize the benefits of urban mineral resource migration and transformation between cities, provide the best route plan for the actual allocation of urban mineral resources, improve the efficiency and accuracy of resource allocation, ensure that urban mineral resources can flow between cities in the best way, and realize the efficient utilization and rational allocation of resources.

[0139] Furthermore, the method involves analyzing the urban mineral resource conversion rate of each path in the second set of mobile paths using a preset path filtering model, and calculating the path score for each path, including:

[0140] S901. Based on the second set of movement paths, analyze the input and output of urban mineral resources at each path node on each path;

[0141] S902. Calculate the urban mineral resource conversion rate for each path by combining the input and output of urban mineral resources for all path nodes on each path.

[0142] S903. Based on the city's mineral resource conversion rate, calculate the path score for each path using a preset path selection model.

[0143] In this embodiment, the flow of urban mineral resources along the transportation path is dynamic. Each path node involves the input and output of urban mineral resources. The input and output of urban mineral resources for each path node in the second set of movement paths are analyzed. For each path in the second set of movement paths, all path nodes included in the path are identified. Based on the connection relationship and resource supply relationship between the path node and other nodes, the input and output data of urban mineral resources for each path node are analyzed. The input includes the initial supply of the city and the replenishment of other cities during transportation. The output includes the delivery to the destination city and the consumption of other cities during transportation. By analyzing the input and output of urban mineral resources at the path nodes, the flow trajectory of urban mineral resources along the path can be fully grasped, avoiding misjudgments of path resource utilization due to ignoring changes in node resources, and making the path evaluation results more consistent with the actual transportation situation.

[0144] Specifically, by calculating the ratio of output to input, the resource utilization efficiency of the route is obtained, reflecting the degree to which urban mineral resources are effectively utilized during the transportation process. A higher conversion rate means that resources are less lost and more utilized during transportation. By calculating the urban mineral resource conversion rate, the utilization of route resources can be quantified, and routes with high and low resource utilization can be quickly identified, providing a reference indicator for route selection. This helps to optimize urban mineral resource transportation routes and improve the overall utilization efficiency of resources.

[0145] Preferably, a preset route selection model is used to calculate the route score for each route in conjunction with the urban mineral resource conversion rate. Other evaluation indicators besides the urban mineral resource conversion rate are determined, including transportation cost, transportation time, and transportation risk. Based on the actual calculation accuracy requirements and the importance of each indicator to route selection, corresponding weights are assigned to each indicator. For example, the weight of the urban mineral resource conversion rate is set at 40%, transportation cost at 30%, transportation time at 20%, and transportation risk at 10%. The route selection model performs weighted calculations, calculating the scores for each route on indicators such as transportation cost, transportation time, and transportation risk based on preset score assignment rules. The scores for urban mineral resource conversion rate, transportation cost, transportation time, and transportation risk for each route are then weighted and summed according to their respective weights to obtain the route score. Calculating route scores through the preset route selection model achieves multi-dimensional route evaluation, avoiding the one-sidedness of single-indicator evaluation. It comprehensively considers various factors affecting route selection, resulting in routes that better meet actual transportation needs and resource allocation goals, improving the overall efficiency of urban mineral resource transportation, and reducing potential risks and costs during transportation.

[0146] Example 2

[0147] In this embodiment, as Figure 5 A system for analyzing the migration and transformation of urban mineral resources is provided, for implementing the aforementioned method for analyzing the migration and transformation of urban mineral resources, comprising:

[0148] The demand forecasting module, based on the pre-acquired urban industrial data, uses a preset urban mineral forecasting model to predict resource demand and obtain the predicted resource demand for each city.

[0149] The urban mineral supply and demand tree construction module constructs an urban mineral supply and demand tree based on preset urban mineral information and the predicted resource demand, with the city as the root node and the supply and demand information as the leaf nodes. The urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of the obsolete products generated in year i that are recycled in year t, the proportion of the j-th type of resource in the recycled waste products, and the content of the j-th type of resource in the products.

[0150] The path matching module, in conjunction with the urban mineral supply and demand tree, matches the corresponding urban mineral movement path for each city in the pre-constructed urban mineral knowledge graph to obtain the first set of movement paths.

[0151] The path expansion module filters out paths with intersections from the first set of mobile paths, and expands the paths based on the corresponding supply and demand relationships to obtain a second set of mobile paths.

[0152] The path filtering module uses a preset path filtering model to filter paths in the second set of mobile paths to obtain urban mineral scheduling paths, so as to perform high-precision migration and transformation of urban minerals.

[0153] In this embodiment, the demand forecasting module analyzes and forecasts the exploitable amount of urban mineral resources based on pre-acquired urban industrial data and a preset urban mineral forecasting model. Simultaneously, it uses an industry correlation model to analyze the inter-industry relationships and combines the forecasted exploitable amount of urban mineral resources with the inter-industry relationships to analyze the resource demand forecast for each city, providing demand data for resource allocation planning. The urban mineral supply and demand tree construction module analyzes urban mineral supply and demand information between cities, using cities as root nodes and specific supply and demand information as leaf nodes. It establishes connections based on the geographical location and supply and demand relationships between cities, constructing an urban mineral supply and demand tree that reflects the urban mineral supply and demand network. This tree can demonstrate the role and interrelationships of cities in urban mineral allocation, providing a basis for urban mineral resource scheduling.

[0154] Specifically, the path matching module combines the resource demand forecast obtained from the demand forecasting module and the supply and demand tree constructed by the urban mineral supply and demand tree construction module with a pre-constructed urban mineral knowledge graph. It extracts features from the leaf node information of the supply and demand tree and the knowledge graph information to generate a fused feature vector. Based on the time and space constraints in the fused feature vector, it filters out matching urban mineral movement paths in the knowledge graph, obtaining a first set of movement paths and thus a preliminary feasible urban mineral transportation route. The path expansion module, based on the first set of movement paths obtained by the path matching module, filters out paths with intersection points and identifies the urban minerals corresponding to these intersection points. The supply and demand relationship is analyzed to find related nodes, construct related paths, and expand the paths in the first path group to obtain a second set of movement paths. The resource and geographical advantages of the cities where the paths intersect are utilized to increase the selection of urban mineral transportation paths. The path screening module analyzes each path in the second set of movement paths obtained by the path expansion module through a preset path screening model, calculates the path score of each path, and selects the path with the highest score as the urban mineral scheduling path. This provides the optimal urban mineral scheduling scheme for the migration and transformation of urban minerals, ensuring that resources can flow efficiently between cities and achieving the optimal allocation of urban mineral resources.

[0155] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing the migration and transformation of urban mineral resources, characterized in that, include: Based on the pre-acquired urban industrial data, the resource demand is predicted using a pre-set urban mineral forecasting model to obtain the predicted resource demand for each city. Based on the preset urban mineral information and the predicted resource demand, an urban mineral supply and demand tree is constructed with the city as the root node and the supply and demand information as the leaf nodes. The urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of the obsolete products generated in year i that are recycled in year t, the proportion of the j-th type of resource in the recycled waste products, and the content of the j-th type of resource in the products. Feature extraction is performed on leaf node information in the urban mineral supply and demand tree to construct leaf node feature vectors; Feature extraction is performed on the pre-constructed urban mining knowledge graph to construct a graph feature vector; By combining the leaf node feature vector and the graph feature vector, a fused feature vector is generated; The fused feature vector is decomposed to obtain time constraints and space constraints respectively; In the urban mining knowledge graph, city nodes that meet the time and space constraints are selected to obtain a candidate city set. Based on the supply and demand information between cities, paths are constructed between cities in the candidate city set to obtain a candidate path set; By analyzing the relationship between the urban mineral supply and the corresponding city's resource demand forecast at the nodes in the path, the corresponding urban mineral movement paths are selected from the candidate path set to obtain the first movement path set. Paths with intersections are selected from the first set of movement paths, and the paths are expanded based on the corresponding supply and demand relationships to obtain the second set of movement paths. By using a preset path filtering model, path filtering is performed in the second set of mobile paths to obtain urban mineral scheduling paths, so as to carry out high-precision migration and transformation of urban minerals.

2. The method for analyzing urban mineral migration and transformation according to claim 1, characterized in that, The step of predicting resource demand for each city based on pre-acquired urban industrial data and a pre-set urban mineral forecasting model includes: Based on the production capacity data in the pre-acquired urban industrial data, the urban mineral development and utilization volume is predicted by the preset urban mineral prediction model, and the predicted amount of urban mineral resources that can be developed and utilized is obtained. Based on the production structure data in urban industrial data, the correlation between industries is analyzed through a pre-set industrial correlation model to obtain the industrial correlation analysis results. Based on the predicted amount of exploitable mineral resources in the cities and the results of industrial linkage analysis, the resource demand is analyzed to obtain the predicted resource demand for each city.

3. The method for analyzing urban mineral migration and transformation according to claim 1, characterized in that, The method, based on preset urban mineral information and the predicted resource demand, constructs an urban mineral supply and demand tree with cities as root nodes and supply and demand information as leaf nodes, including: Based on the preset urban mineral information and the predicted resource demand, analyze the urban mineral supply information and resource demand information between cities; With the city as the root node, the city's mineral supply information and resource demand information are used as leaf nodes. Based on the geographical relationships and supply and demand relationships between cities, a connection relationship is established between the corresponding root node and leaf node to construct a city mineral supply and demand tree.

4. The method for analyzing urban mineral migration and transformation according to claim 1, characterized in that, The first set of movement paths involves selecting paths with intersections from the first set of movement paths, and then expanding these paths based on corresponding supply and demand relationships to obtain a second set of movement paths, including: Compare the paths in the first set of movement paths, filter out the paths that have intersection points, and obtain the first path group; Based on the supply and demand relationship of the cities corresponding to the intersection points of the paths in the first path group, the paths are expanded with the intersection points as the center to obtain the second set of mobile paths.

5. The method for analyzing urban mineral migration and transformation according to claim 4, characterized in that, The second set of mobile paths is obtained by expanding the paths based on the supply and demand relationships of the cities corresponding to the intersection points of the paths in the first path group, with the intersection points as the center. This includes: Based on the supply and demand relationship of the cities corresponding to the intersection points of the paths in the first path group, search out the nodes associated with the intersection points of the paths and use them as the first node set; Using the intersection of the paths as the center, the nodes in the first set of nodes are associated to obtain the associated paths; The paths in the first path group are expanded based on the associated paths to obtain a second set of mobile paths.

6. The method for analyzing urban mineral migration and transformation according to claim 1, characterized in that, The step of filtering paths from the second set of mobile paths using a preset path filtering model to obtain urban mineral resource scheduling paths for high-precision migration and transformation of urban mineral resources includes: The conversion rate of urban mineral resources for each path in the second set of mobile paths is analyzed using a preset path filtering model, and the path score for each path is calculated. The path with the highest score is selected from the second set of moving paths and used as the urban mineral scheduling path to perform high-precision migration and transformation of urban minerals.

7. The method for analyzing urban mineral migration and transformation according to claim 6, characterized in that, The method involves analyzing the urban mineral resource conversion rate of each path in the second set of mobile paths using a preset path filtering model, and calculating the path score for each path, including: Based on the second set of movement paths, analyze the input and output of urban mineral resources at each path node on each path. By combining the input and output of urban mineral resources of all path nodes on each path, the conversion rate of urban mineral resources for each path is calculated. Based on the city's mineral resource conversion rate, the path score for each path is calculated using a preset path selection model.

8. A system for analyzing the migration and transformation of urban mineral resources, characterized in that, A method for implementing an urban mineral migration and transformation analysis method as described in any one of claims 1 to 7, comprising: The demand forecasting module, based on the pre-acquired urban industrial data, uses a preset urban mineral forecasting model to predict resource demand and obtain the predicted resource demand for each city. The urban mineral supply and demand tree construction module constructs an urban mineral supply and demand tree based on preset urban mineral information and the predicted resource demand, with the city as the root node and the supply and demand information as the leaf nodes. The urban mineral information includes the supply of the j-th type of renewable resource in year t, the quantity of waste products entering the recycling system in year t, the proportion of the obsolete products generated in year i that are recycled in year t, the proportion of the j-th type of resource in the recycled waste products, and the content of the j-th type of resource in the products. The path matching module extracts features from leaf node information in the urban mineral supply and demand tree to construct leaf node feature vectors; extracts features from a pre-constructed urban mineral knowledge graph to construct graph feature vectors; combines the leaf node feature vectors and graph feature vectors to generate a fused feature vector; decomposes the fused feature vector to obtain time constraints and spatial constraints; filters urban nodes that meet the time and spatial constraints in the urban mineral knowledge graph to obtain a candidate city set; constructs paths between cities in the candidate city set based on supply and demand information between cities to obtain a candidate path set; and selects corresponding urban mineral movement paths from the candidate path set by analyzing the relationship between the urban mineral supply and the corresponding city's predicted resource demand at the nodes in the path, to obtain a first movement path set. The path expansion module filters out paths with intersections from the first set of mobile paths, and expands the paths based on the corresponding supply and demand relationships to obtain a second set of mobile paths. The path filtering module uses a preset path filtering model to filter paths in the second set of mobile paths to obtain urban mineral scheduling paths, so as to perform high-precision migration and transformation of urban minerals.

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