Urban and rural planning data collaborative analysis system based on multi-source data integration

Abstract

The invention relates to the technical field of data analysis, in particular to an urban and rural planning data collaborative analysis system based on multi-source data integration, and discloses an urban and rural source analysis module, an urban and rural planning data collaborative analysis module and a planning data collaborative analysis module. Through an urban and rural source analysis module, an urban and rural planning data collaborative analysis module and a planning data collaborative analysis module, a finite element analysis technology is introduced into the urban and rural planning field, a multi-physics field coupling model is constructed, and quantitative simulation of planning data related to urban and rural planning is realized. The development conditions and conflict conditions of various kinds of urban and rural planning data in different planning stages are simulated and analyzed, various potential planning problems in urban and rural planning can be found in advance, comprehensive, detailed and quantitative analysis results are provided for urban and rural planning decision makers, the decision makers can balance the advantages and disadvantages of different planning schemes from multiple angles, and the urban and rural planning efficiency is improved. And a more scientific and reasonable decision is made.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a collaborative analysis system for urban and rural planning data based on multi-source data integration. Background Technology

[0002] With the acceleration of urbanization and the deepening of the rural revitalization strategy in my country, urban and rural planning is playing an increasingly crucial role in guiding regional development, optimizing resource allocation, and ensuring the quality of life for residents. However, current urban and rural planning practices face many pressing problems that significantly hinder the scientific rigor, rationality, and sustainable development capabilities of urban and rural planning.

[0003] I. Limitations in Analytical Methods, Primarily Qualitative Judgments: Traditional urban and rural planning analysis methods largely rely on planners' experience and qualitative descriptions and judgments, lacking precise quantitative analysis tools. For example, when assessing the rationality of land development intensity, judgments are often made based on conventional indicators and rules of thumb, making it difficult to accurately measure the specific impacts of different development plans on transportation, the environment, and society. Furthermore, the analysis of the coordination between transportation planning and land use planning often remains at the theoretical level of subjective inference, failing to accurately simulate the dynamic interaction and potential conflicts between the two as factors such as population growth and urban expansion change. This primarily qualitative approach to analysis introduces significant subjectivity and uncertainty into urban and rural planning decisions, easily leading to planning errors and a series of problems during later implementation, such as traffic congestion, insufficient public service facilities, and ecological damage.

[0004] Second, the lack of dynamic simulation makes it difficult to cope with changes: Urban and rural development is a dynamic process involving continuous evolution across different planning stages and is influenced by the interaction of various factors such as population flow, economic development, and policy adjustments. However, most existing urban and rural planning analysis tools and methods lack the ability to dynamically simulate different planning stages and cannot reflect the impact of these changing factors on various elements of urban and rural planning in a timely manner. For example, when formulating a planning scheme for a new urban area, it is often difficult to accurately predict whether the area's transportation capacity will still meet demand in the next 5 or 10 years as the population gradually gathers and industries continue to develop, and whether the land use structure needs further optimization. Once actual development deviates from planning expectations, it is difficult to make rapid and targeted adjustments and optimizations, causing the planning scheme to gradually lose its adaptability and effectiveness during implementation.

[0005] Third, poor planning coordination and neglect of element relationships: Urban and rural planning is a complex system engineering project. Various planning elements (such as land, transportation, population, and infrastructure) are interconnected and mutually influential. However, in actual planning, these elements are often planned separately and independently, neglecting their synergistic relationships. For example, transportation planning fails to fully consider the changes in traffic flow brought about by land development along the route, resulting in a mismatch between transportation infrastructure construction and actual demand; or, when planning residential land use, the layout and carrying capacity of surrounding public service facilities are not considered, leading to problems such as poor convenience for residents. Due to the lack of effective collaborative analysis methods, it is difficult to fully grasp the inherent connections and potential conflicts between various planning elements, making it impossible to optimize urban and rural planning schemes as a whole, thus affecting the coordination and sustainability of urban and rural development.

[0006] In conclusion, the existing field of urban and rural planning urgently needs a technological solution that can integrate multi-source data, achieve quantitative analysis, possess dynamic simulation capabilities, and deeply explore the synergistic relationships among various planning elements, in order to improve the quality of urban and rural planning and the scientific nature of decision-making.

[0007] Based on the above background, this invention proposes a collaborative analysis system for urban and rural planning data based on multi-source data integration. Through the collaborative operation of the urban and rural source analysis module, the urban and rural planning data collaboration module, and the planning data collaborative analysis module, finite element analysis technology is introduced into the field of urban and rural planning to construct a multi-physics coupling model. This aims to solve the problems of data integration difficulties, single analysis methods, lack of dynamic simulation, and poor planning coordination in existing urban and rural planning. It enables quantitative simulation of planning data related to urban and rural planning, identifies potential planning problems in advance, provides comprehensive, quantitative, and scientifically sound basis for urban and rural planning decisions, and promotes the development of urban and rural planning in a more scientific, efficient, and sustainable direction. Summary of the Invention

[0008] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a collaborative analysis system for urban and rural planning data based on multi-source data integration.

[0009] To achieve the above objectives, the present invention provides the following technical solution: The urban and rural planning data collaborative analysis system based on multi-source data integration includes an urban and rural source analysis module, an urban and rural planning data collaboration module, and a planning data collaborative analysis module. The urban-rural source analysis module is used to identify target urban and rural areas that need to be planned and to build an urban-rural source analysis model for the target urban and rural areas. The urban and rural planning data collaboration module regularly collects various types of planning data about the target urban and rural areas, inputs the planning data of each type into the urban and rural source analysis model, sets i simulation durations with equal time gradients, and the urban and rural source analysis model performs i simulations. The planning data collaborative analysis module is used to determine the urban and rural planning data collaborative analysis index, and to determine the level of urban and rural planning collaboration based on the urban and rural planning data collaborative analysis index.

[0010] Furthermore, the planning coordination analysis index corresponding to a single simulation is obtained as follows: A simulation duration is selected, and the urban-rural source analysis model is used to perform the simulation for that duration. After the simulation, various basic data from the urban-rural source analysis model are collected, and the basic data conflict index is obtained and marked as Ewdzg. Simultaneously, the planning development analysis index of various basic data is obtained. The average planning development analysis index is calculated by summing and averaging these indices. ,pass The planning synergy analysis index corresponding to this simulation was calculated. , where z1 is the first coefficient and z2 is the second coefficient.

[0011] Furthermore, the planning and development analysis index for a type of basic data is obtained as follows: select a type of basic data, obtain the planning and development analysis model corresponding to that type of basic data, use the basic data as input data for the planning and development analysis model, and output the planning and development analysis index for that type of basic data.

[0012] Furthermore, the basic data conflict index is obtained in the following way: various basic data in the urban-rural source analysis model are preprocessed and feature extracted to obtain various basic features. These basic features are then integrated into a comprehensive basic feature set to obtain the basic data conflict analysis model. The comprehensive basic feature set is used as the input data for the basic data conflict analysis model, and the basic data conflict index is output.

[0013] Furthermore, the urban and rural planning data collaborative analysis index is determined as follows: The planning collaborative analysis index corresponding to each simulation is obtained; the number of disappointments in planning development (Hsty) is determined; the average planning collaborative analysis index (Lksz) is calculated by summing and averaging all planning collaborative analysis indices. Calculate the urban and rural planning data collaborative analysis index .

[0014] Furthermore, the number of disappointments in planning and development, Hsty, is determined as follows: all planning coordination analysis indices are sorted in ascending order of their corresponding simulation durations. Two adjacent planning coordination analysis indices are compared. If the planning coordination analysis index of the previous index is greater than that of the next index, the number of disappointments in planning and development is increased by one, and this number is marked as Hsty.

[0015] Furthermore, the level of urban-rural planning collaboration is determined based on the urban-rural planning data collaboration analysis index. Specifically, this involves setting each urban-rural planning data collaboration analysis index. The scope corresponds to a level of urban-rural planning coordination, and the urban-rural planning data coordination analysis index. The range is (0, ], ( , ],...,( The coordination levels of urban and rural planning include Urban and Rural Planning Coordination Level 1, Urban and Rural Planning Coordination Level 2, ..., Urban and Rural Planning Coordination Level V-1, Urban and Rural Planning Coordination Level V. Among them, the coordination and consistency of Urban and Rural Planning Coordination Level 1 is higher than that of Urban and Rural Planning Coordination Level 2, and so on.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The system of this invention introduces finite element analysis technology into the field of urban and rural planning through urban and rural source analysis module, urban and rural planning data collaboration module, and planning data collaborative analysis module. It constructs a multi-physics coupling model to realize the quantitative simulation of planning data related to urban and rural planning. It simulates and analyzes the development and conflict of various types of urban and rural planning data at different planning stages, which helps to discover various potential planning problems under urban and rural planning in advance. It provides decision-makers in urban and rural planning with comprehensive, detailed and quantitative analysis results, enabling them to weigh the advantages and disadvantages of different planning schemes from multiple perspectives and make more scientific and reasonable decisions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram outlining the principle of the system of the present invention; Figure 2 A flowchart for obtaining the basic data conflict index; Figure 3 This is a flowchart illustrating the principle of the urban and rural planning data collaboration module. Detailed Implementation

[0018] Reference Figures 1 to 3 The urban and rural planning data collaborative analysis system based on multi-source data integration includes an urban and rural source analysis module, an urban and rural planning data collaboration module, and a planning data collaborative analysis module.

[0019] The urban-rural source analysis module identifies the target urban and rural areas that need to be planned and constructs an urban-rural source analysis model for the target urban and rural areas.

[0020] Construct an urban-rural source analysis model for the target urban and rural area: Obtain high-precision topographic DEM data of the target urban and rural area, import the high-precision topographic DEM data into general-purpose finite element analysis software, use tetrahedral meshes to divide the urban space, implement local densification for key areas (such as CBD) (mesh size ≤ 10m), develop a dynamic mesh adaptive algorithm (automatically adjusts unit density based on population density and land use intensity), and define dynamic parameters: Residential land: elastic modulus E = 50MPa (simulating development plasticity), transportation land: Poisson's ratio ν = 0.45 (reflecting traffic flow). Fluctuations), constraining geographical boundaries (such as administrative division lines), applying dynamic loads: population growth function: P(t)=P0·e^(rt)-traffic demand OD matrix, configuring solvers, selecting algorithms: steady-state analysis: direct sparse matrix solver, time-varying analysis: implicit Euler method (time step Δt=1 year), improving the data governance system, supplementing and integrating various basic data such as population data, traffic data, land development data, and natural resource data, developing the XStream intelligent parser to achieve dynamic adaptation of heterogeneous data, constructing a four-dimensional spatiotemporal index of "people-land-housing-industry" to solve the problem of data spatiotemporal misalignment, and finally constructing an urban-rural source analysis model for the target urban and rural areas.

[0021] The urban and rural planning data collaboration module regularly collects various types of planning data (including geospatial planning data, transportation construction planning data, infrastructure planning data, etc.) about the target urban and rural areas. It inputs the planning data of each type into the urban and rural source analysis model, sets i simulation durations with equal time gradients, and then inputs the planning data of each type into the urban and rural source analysis model. The urban and rural source analysis model performs i simulations (each simulation corresponds to a different simulation duration).

[0022] The planning coordination analysis index corresponding to a single simulation is obtained as follows: A simulation duration is selected, and the urban-rural source analysis model is used to perform the simulation for that duration. After the simulation, various basic data from the urban-rural source analysis model are collected, and the basic data conflict index is obtained and marked as Ewdzg. Simultaneously, the planning development analysis index of various basic data is obtained. The average planning development analysis index is calculated by summing and averaging these indices. ,pass The planning synergy analysis index corresponding to this simulation was calculated. Where z1 is the first coefficient and z2 is the second coefficient, with z1 having a value of 1.18 and z2 having a value of 1.04.

[0023] The planning and development analysis index for a type of basic data is obtained as follows: Select a type of basic data, obtain the planning and development analysis model corresponding to that type of basic data, use the basic data as input data for the planning and development analysis model, and output the planning and development analysis index for that type of basic data.

[0024] Each type of basic data corresponds to a planning and development analysis model. Each planning and development analysis model is built based on a neural network model. The difference between each planning and development analysis model lies in the different training data. In a specific implementation, taking population data as an example, the construction process of the planning and development analysis model corresponding to population data is disclosed as follows: a neural network model is constructed, s population data are obtained, the neural network model is trained with the population data, and a planning and development analysis index is assigned to each population data. The value range of the planning and development analysis index is (10.0~50.0). The larger the planning and development analysis index, the better the planning and development of the population data. The s population data are divided into a training set and a validation set with a ratio of 60%:40%. The neural network is iteratively trained on the training set and the validation set. After training is completed, the planning and development analysis model of the population data is constructed.

[0025] Taking traffic data as an example, the construction process of the planning and development analysis model corresponding to the public traffic data is as follows: a neural network model is constructed, s traffic data are obtained, the neural network model is trained with the traffic data, and a planning and development analysis index is assigned to each traffic data. The value range of the planning and development analysis index is (10.0~50.0). The larger the planning and development analysis index, the better the planning and development of the traffic data. The s population data are divided into a training set and a validation set with a ratio of 60%:40%. The neural network is iteratively trained on the training set and the validation set. After training is completed, the planning and development analysis model of the traffic data is constructed.

[0026] The basic data conflict index is obtained as follows: various basic data in the urban-rural source analysis model are preprocessed and feature extracted to obtain various basic features. These basic features are then integrated into a comprehensive basic feature set to obtain the basic data conflict analysis model. The comprehensive basic feature set is used as the input data for the basic data conflict analysis model, and the basic data conflict index is output as the result.

[0027] The construction process of the basic data conflict analysis model is as follows: A deep learning model is constructed, and j comprehensive basic feature sets are obtained. The deep learning model is trained using these comprehensive basic feature sets. Each comprehensive basic feature set is assigned a basic data conflict index, with the index ranging from 1.0 to 9.9. The larger the index, the greater the data conflict between different types of basic data. The j comprehensive basic feature sets are divided into a training set, a validation set, and a test set in a ratio of 70%:15%:15%. The training set, validation set, and test set are then trained. Once training is complete, the basic data conflict analysis model is constructed.

[0028] The planning data collaborative analysis module determines the urban and rural planning data collaborative analysis index, and determines the level of urban and rural planning collaboration based on the urban and rural planning data collaborative analysis index.

[0029] The urban and rural planning data collaborative analysis index is determined as follows: The planning collaborative analysis index corresponding to each simulation is obtained. All planning collaborative analysis indices are sorted in ascending order of their corresponding simulation duration. Adjacent planning collaborative analysis indices are compared; if the index of the preceding indices is greater than the index of the following indices, the number of disappointments in planning development is increased by one and marked as Hsty (if the index of the preceding indices is less than or equal to the index of the following indices, no action is taken). The average planning collaborative analysis index Lksz is calculated by summing all planning collaborative analysis indices. Calculate the urban and rural planning data collaborative analysis index .

[0030] The level of urban-rural planning collaboration is determined based on the urban-rural planning data collaboration analysis index. Specifically, this involves setting each urban-rural planning data collaboration analysis index. The scope corresponds to a level of urban-rural planning coordination, and the urban-rural planning data coordination analysis index. The range is (0, ], ( , ],...,( The coordination levels of urban and rural planning include Urban and Rural Planning Coordination Level 1, Urban and Rural Planning Coordination Level 2, ..., Urban and Rural Planning Coordination Level V-1, Urban and Rural Planning Coordination Level V. Among them, the coordination and consistency of Urban and Rural Planning Coordination Level 1 is higher than that of Urban and Rural Planning Coordination Level 2, and so on.

[0031] By introducing finite element analysis technology into the field of urban and rural planning through urban and rural source analysis modules, urban and rural planning data collaboration modules, and planning data collaboration analysis modules, a multi-physics coupling model is constructed to realize the quantitative simulation of planning data related to urban and rural planning. The simulation analysis of the development and conflict of various types of urban and rural planning data at different planning stages helps to identify various potential planning problems in urban and rural planning in advance. It provides decision-makers in urban and rural planning with comprehensive, detailed and quantitative analysis results, enabling them to weigh the advantages and disadvantages of different planning schemes from multiple perspectives and make more scientific and reasonable decisions.

[0032] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0033] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0034] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0035] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0036] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0037] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0038] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0039] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A collaborative analysis system for urban and rural planning data based on multi-source data integration, characterized in that: It includes a rural and urban source analysis module, a rural and urban planning data collaboration module, and a planning data collaboration analysis module; The urban and rural source analysis module is used to identify target urban and rural areas that need to be planned and to build an urban and rural source analysis model for the target urban and rural areas. The urban and rural planning data collaboration module regularly collects various types of planning data about the target urban and rural areas, inputs the planning data of each type into the urban and rural source analysis model, sets i simulation durations with equal time gradients, and the urban and rural source analysis model performs i simulations. The planning data collaborative analysis module is used to determine the urban and rural planning data collaborative analysis index, and to determine the level of urban and rural planning collaboration based on the urban and rural planning data collaborative analysis index.

2. The urban and rural planning data collaborative analysis system based on multi-source data integration according to claim 1, characterized in that, The planning coordination analysis index corresponding to a single simulation is obtained as follows: A simulation duration is selected, and the urban-rural source analysis model is used to perform the simulation for that duration. After the simulation, various basic data from the urban-rural source analysis model are collected, and the basic data conflict index is obtained and marked as Ewdzg. Simultaneously, the planning development analysis index of various basic data is obtained. The average planning development analysis index is calculated by summing and averaging these indices. ,pass The planning synergy analysis index corresponding to this simulation was calculated. , where z1 is the first coefficient and z2 is the second coefficient.

3. The urban and rural planning data collaborative analysis system based on multi-source data integration according to claim 2, characterized in that, The planning and development analysis index for a type of basic data is obtained as follows: Select a type of basic data, obtain the planning and development analysis model corresponding to that type of basic data, use the basic data as input data for the planning and development analysis model, and output the planning and development analysis index for that type of basic data.

4. The urban and rural planning data collaborative analysis system based on multi-source data integration according to claim 2, characterized in that, The basic data conflict index is obtained as follows: various basic data in the urban-rural source analysis model are preprocessed and feature extracted to obtain various basic features. These basic features are then integrated into a comprehensive basic feature set to obtain the basic data conflict analysis model. The comprehensive basic feature set is used as the input data for the basic data conflict analysis model, and the basic data conflict index is output as the result.

5. The urban and rural planning data collaborative analysis system based on multi-source data integration according to claim 1, characterized in that, The urban and rural planning data collaborative analysis index is determined as follows: The planning collaborative analysis index corresponding to each simulation is obtained; the number of planning development disappointments (Hsty) is determined; the average planning collaborative analysis index (Lksz) is calculated by summing and averaging all planning collaborative analysis indices. Calculate the urban and rural planning data collaborative analysis index .

6. The urban and rural planning data collaborative analysis system based on multi-source data integration according to claim 5, characterized in that, The number of disappointments in planning and development, Hsty, is determined as follows: All planning coordination analysis indices are sorted in ascending order of their corresponding simulation durations. Adjacent planning coordination analysis indices are compared. If the planning coordination analysis index of the previous index is greater than that of the next index, the number of disappointments in planning and development is increased by one, and this number is marked as Hsty.

7. The urban and rural planning data collaborative analysis system based on multi-source data integration according to claim 1, characterized in that, The level of urban-rural planning collaboration is determined based on the urban-rural planning data collaboration analysis index. Specifically, this involves setting each urban-rural planning data collaboration analysis index. The scope corresponds to a level of urban-rural planning coordination, and the urban-rural planning data coordination analysis index. The range is (0, ], ( , ], ..., ( The coordination levels of urban and rural planning include Urban and Rural Planning Coordination Level 1, Urban and Rural Planning Coordination Level 2, ..., Urban and Rural Planning Coordination Level V-1, Urban and Rural Planning Coordination Level V. Among them, the coordination and consistency of Urban and Rural Planning Coordination Level 1 is higher than that of Urban and Rural Planning Coordination Level 2, and so on.

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