CN116911055A - Digital twinning-based intelligent city planning management system - Google Patents
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Abstract
The application provides a digital twinning-based smart city planning treatment system, which relates to the technical field of smart city planning treatment and comprises the following components: the system comprises a city data collection and integration unit, a digital twin city model construction unit, a city test simulation unit, a remote city feature library construction unit, a city feature similarity judgment unit and a city planning treatment unit. The application collects and integrates various data of the current city and constructs a digital twin city model, performs experimental simulation of various data in the digital twin city model, searches for a reference and a reference place by comparing the data and characteristics of the current city and the different cities, can know the similarity degree of the current city and other cities through similarity evaluation, can draw inspiration from the successful experience of the other cities, avoids repeated mistakes, and provides a more targeted and feasible planning scheme to promote the development and management of the cities.
Description
Technical Field
The application relates to the technical field of smart city planning management, in particular to a digital twinning-based smart city planning management system.
Background
The aim of the smart city is to sense, analyze and integrate various key information of a city operation core system by fully utilizing information and communication technical means, respond to various demands of folk life, environmental protection, public safety, city service, industrial and commercial activities and the like in an intelligent manner, and create better city life for people. The core of a smart city is to make the city more intelligent, while the intelligent city is people-centric. The intelligent sensors in the city are connected through the Internet, so that the comprehensive perception of the city is realized. These sensors may be embedded in a variety of objects, including buildings, transportation facilities, public facilities, and the like. By sensing and collecting a large amount of data, the running state, environmental conditions, resource utilization and the like of the city can be known.
Urban planning management refers to the process of planning and managing cities, including urban planning, land utilization management, building and infrastructure management, urban traffic management, environmental protection, resource management and the like, and aims to realize orderly development of cities, rationality of space layout, effective utilization of resources and improvement of life quality of people. The goal of urban planning and management is to make cities adapt to various demands such as population growth, economic development, environmental protection and the like by formulating planning policies and management measures, so as to realize sustainable development.
At present, in the smart city planning treatment, the city twin data model is constructed through various data of the city, and the potential problems existing in the city are analyzed through the city twin data model, but the potential problems of the smart city are usually complex and various, multiple fields and stakeholders are involved, multidisciplinary cooperation and comprehensive decision are needed for solving the problems, and the analysis result by the twin data model is insufficient, so that the potential problems cannot be solved effectively.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
In view of this, the present application provides a digital twin-based smart city planning management system to solve the above-mentioned problems, which are not enough to effectively solve the potential problems only by means of the analysis results of the twin data model.
In order to solve the problems, the application adopts the following specific technical scheme:
a digital twinning-based smart city planning remediation system, the system comprising: the system comprises a city data collection and integration unit, a digital twin city model construction unit, a city test simulation unit, a remote city feature library construction unit, a city feature similarity judgment unit and a city planning treatment unit;
the city data collecting and integrating unit is used for collecting various data of the current city and storing the collected current city data;
the digital twin city model building unit is used for building a digital twin city model based on integrated current city data;
the city test simulation unit is used for performing test simulation of various data in the constructed digital twin city model and analyzing potential problems existing in the current city;
the remote city feature library construction unit is used for collecting various data of the remote city and constructing a remote city feature library;
the city feature similarity judging unit is used for calculating the similarity between the current city and the different-place city based on a feature point group similarity calculation method to obtain feature similarity between cities;
the city planning treatment unit is used for analyzing and comparing potential problems existing in the current city based on the feature similarity among cities, and making a planning treatment scheme.
As an embodiment herein, the digital twin city model building unit includes: the system comprises a data preprocessing module, a data integration module, a model construction module, a model simplification module, a model updating module and a current feature point extraction module;
the data preprocessing module is used for preprocessing collected city data, and comprises redundant data processing, missing data filling and abnormal data cleaning;
the data integration module is used for integrating different types of data and establishing an urban data set;
the model construction module is used for constructing a digital twin city model through a modeling tool;
the model simplifying module is used for simplifying the constructed digital twin city model based on a half folding algorithm;
the model updating module is used for acquiring updating data of the city in real time and adjusting and updating the simplified digital twin city model;
the current feature point extraction module is used for extracting the current feature points of the digital twin city model.
As one embodiment herein, the model simplification module comprises: the system comprises a grid structure generation sub-module, a folding cost calculation sub-module, a half folding sub-module and a half folding judgment sub-module;
the grid structure generation sub-generation module is used for representing the constructed digital twin city model as a grid structure and establishing the topology of grid structure data, wherein each vertex in the grid structure represents a geographic position, the edges formed by the vertexes represent a connection relationship, and the faces formed by the edges represent a region;
the folding cost calculation sub-module is used for calculating folding cost values of all sides in the grid structure according to a half folding cost calculation formula;
the half folding sub-module is used for performing half folding processing according to folding cost values of all sides in the grid structure;
and the half folding judging submodule is used for judging whether a half which is folded according with the preset condition exists or not, if so, continuing folding, otherwise, stopping half folding and obtaining the simplified digital twin city model.
As an embodiment herein, the calculation formula of the half folding cost is:
wherein, C (u, v) represents the folding value of the side uv formed by the vertex u and the vertex v in the grid structure;
T u representing a set of triangular faces associated with a vertex u in a mesh structure;
T uv representing two triangular surfaces where the vertex u and the vertex v form an edge uv in the grid structure, wherein the two triangular surfaces are f and n respectively;
θ represents an included angle formed by the shrinkage of the triangular surface f and the triangular surface n;
d represents the degree of the deleted vertex u;
the term u-v represents the length of the edges that the vertices u and v make up in the mesh structure.
As an embodiment herein, the performing half-edge folding processing according to the folding cost values of all edges in the grid structure includes:
arranging folding cost values of all edges in the grid structure in order from small to large, and forming a to-be-folded edge set;
selecting an edge with the minimum folding cost value in the edge set to be folded for folding operation, and folding the selected edge to an adjacent edge;
and after the folding is finished, checking the shape of the formed new triangular surface by adopting an elongated triangle, if the formed new triangular surface is in the shape of the elongated triangle, not performing folding operation on the selected side and withdrawing the folding operation, otherwise, reserving the folding operation on the selected side and performing the folding operation on the next side.
As an embodiment of the present disclosure, the remote city feature library construction unit includes a remote city data collection module, a remote city data processing module, a digital twin remote city model construction module, and a remote feature point extraction module;
the remote city data collection module is used for collecting various data of the remote city;
the remote city data processing module is used for integrating and cleaning various data of the collected remote cities;
the digital twin different-place city model building module is used for building a digital twin different-place city model;
the remote feature point extraction module is used for extracting feature points of the remote city from the digital twin remote city model and obtaining a remote city feature library.
As one embodiment herein, the city feature similarity judging unit includes: the system comprises a feature point group topology similarity calculation module, a feature point group direction similarity calculation module and a city similarity calculation module;
the feature point group topology similarity calculation module is used for calculating the topology similarity of the feature point group of the current city and the feature point group of the different city in the feature library of the different city;
the characteristic point group direction similarity calculation module is used for calculating the direction similarity of the characteristic point group of the current city and the characteristic point group of the different city in the characteristic library of the different city;
the city similarity calculation module is used for calculating the feature similarity between cities according to the calculated topological similarity and distance similarity.
As one embodiment of the present disclosure, the calculating the directional similarity of the current city feature point group and the different city feature point group in the different city feature library includes:
calculating the characteristic direction of the characteristic point group of the city in different places of the current city characteristic point group by using a FREAK algorithm to obtain a gradient direction;
counting through the histogram, wherein the horizontal axis of the histogram is represented as an angle of the gradient direction, the vertical axis of the histogram is represented as the counting quantity of the gradient direction, and the peak value of the histogram is the main direction of the feature point group;
and calculating the direction similarity of the city feature point group through a feature point group direction similarity calculation formula.
As an embodiment herein, the calculation formula for calculating the feature similarity between cities according to the calculated topological similarity and the distance similarity is as follows:
wherein G represents feature similarity between cities;
sim_topo represents the topological similarity of the current city feature point group and the off-site city feature point group in the off-site city feature library;
sim_dire represents the directional similarity of the current city feature point group and the remote city feature point group in the remote city feature library.
As one embodiment herein, the city planning remediation unit comprises: the system comprises a remote city selection module, an analysis and comparison module and a treatment scheme planning module;
the remote city selection module is used for selecting the remote cities corresponding to the feature similarity values among the cities and taking the selected remote cities as reference cities;
the analysis and comparison module is used for analyzing successful experience and treatment measures of the reference city on potential problem treatment;
and the treatment scheme planning module is used for making and implementing a treatment scheme based on the analysis result and combining the actual situation of the current city.
The beneficial effects of the application are as follows:
1. the application collects and integrates various data of the current city and constructs a digital twin city model, and performs experimental simulation of various data in the digital twin city model, so that potential problems of the current city can be found through simulation analysis, reference and reference positions are found by comparing the data and characteristics of the current city and the different cities, similarity calculation is performed on the current city and the different cities through a characteristic point group similarity calculation method, the similarity degree of the current city and other cities can be known through similarity evaluation, inspiration can be drawn from successful experiences of the other cities, repeated mistakes are avoided, and a more targeted and feasible planning scheme is provided to promote the development and management of the cities.
2. According to the application, the urban running condition can be predicted and simulated by constructing the digital twin urban model, the experimental simulation of various data is carried out in the digital twin urban model, the urban running condition under different conditions can be simulated, the potential problems existing in the current city can be better found by simulation analysis, the problems can be recognized as early as possible, the influence degree of risks on the city and the effectiveness of coping strategies can be evaluated, and the risk early warning can be carried out.
3. The application can objectively quantify the similarity between the current city and the different-place city through the feature point group similarity calculation, and can determine the city similar to the current city, thereby being capable of referring to the experience of the similar city and carrying out customized planning and treatment scheme according to the actual condition of the current city by comparing the treatment strategy and success case of the similar city, thereby improving the pertinence and effectiveness of the planning, promoting the sustainable development of the city, reasonably configuring resources and optimizing the key field of city development according to the success experience of the similar city, further utilizing the resources to the greatest extent and improving the planning efficiency and effect.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a schematic block diagram of a digital twinning-based smart city planning remediation system in accordance with an embodiment of the present application.
In the figure:
1. a city data collecting and integrating unit; 2. a digital twin city model building unit; 201. a data preprocessing module; 202. a data integration module; 203. a model building module; 204. a model simplification module; 2041. a grid structure generation sub-module; 2042. a folding cost calculation sub-module; 2043. half folding sub-modules; 2044. a half folding judgment sub-module; 205. a model updating module; 206. the current feature point extraction module; 3. an urban test simulation unit; 4. a construction unit of a different-place city feature library; 401. a remote city data collection module; 402. a remote city data processing module; 403. a digital twin different-place city model building module; 404. the remote feature point extraction module; 5. a city feature similarity judging unit; 501. the feature point group topological similarity calculation module; 502. the feature point group direction similarity calculation module; 503. the city similarity calculation module; 6. a city planning treatment unit; 601. a remote city selection module; 602. an analysis and comparison module; 603. and a treatment scheme planning module.
Detailed Description
In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, based on the embodiments of the application, which would be apparent to one of ordinary skill in the art without undue burden are intended to be within the scope of the application.
According to an embodiment of the application, a digital twinning-based smart city planning remediation system is provided.
The application will now be further described with reference to the accompanying drawings and detailed description, as shown in fig. 1-in which a digital twin-based smart city planning remediation system according to an embodiment of the application includes: the urban data collection and integration unit 1, the digital twin urban model construction unit 2, the urban test simulation unit 3, the remote urban feature library construction unit 4, the urban feature similarity judgment unit 5 and the urban planning management unit 6;
the city data collecting and integrating unit 1 is used for collecting various data of a current city and storing the collected current city data;
it should be noted that, the collection of various types of data of the current city may include the following data in several main aspects:
population and social data: including population number, population density, population structure, ethnic distribution, education level, employment, social benefits, etc.
Economic data: including city GDP, industry structure, economic growth rate, employment rate, income level, consumption index, etc.
Infrastructure data: including data related to road networks, transportation facilities, water, electricity, gas supplies, communication facilities, medical facilities, schools, parks, etc.
Urban environment data: including air quality, water quality, noise pollution, solid waste treatment, ecological environment, etc.
Geographic and land use data: including geographic and land use related data such as land use type, land distribution, land area, topography, geological features, water distribution, etc.
Urban planning and building data: including city planning, building information, floor planning, building height, building area, etc., and building related data.
Other field data: other data about the city, such as cultural arts, travel culture, sports activities, government public services, etc., may also be collected depending on the specifics of the city.
The digital twin city model building unit 2 is used for building a digital twin city model based on integrated current city data;
as a preferred embodiment, the digital twin city model building unit 2 includes: the system comprises a data preprocessing module 201, a data integration module 202, a model construction module 203, a model simplification module 204, a model updating module 205 and a current feature point extraction module 206;
the data preprocessing module 201 is configured to perform preprocessing operations on collected city data, including processing redundant data, filling missing data, and cleaning abnormal data;
the redundant data is processed, and the repeated data is required to be subjected to duplicate removal processing;
filling of missing data, interpolation method can be used to fill the missing data according to the characteristics and distribution of the data. Common interpolation methods include mean interpolation, median interpolation, regression interpolation, etc.;
cleaning of the abnormal data, and deleting, correcting or replacing the abnormal data in the city data can be selected according to specific conditions.
The data integration module 202 is configured to integrate different types of data to create an urban data set;
it should be noted that, due to different sources of the city data, there may be different data formats and structures, and the city data is formatted uniformly by adopting the same fields and data types;
the model construction module 203 is configured to construct a digital twin city model through a modeling tool;
specifically, when a digital twin city model is constructed, the model construction can be performed through three-dimensional modeling software; three-dimensional modeling software is SketchUp, blender, 3ds Max, etc.
The model simplifying module 204 is configured to simplify the constructed digital twin city model based on a half-folding algorithm;
in particular, the basic idea of the half-folding algorithm is to reduce the complexity of the model by folding the edges in the model, thereby achieving simplification of the model. In the simplified process, proper folding edges are selected, so that the model keeps the shape and detail as much as possible, the vertexes and the surface number of the model are reduced, and the efficiency and the rendering speed of the model are improved.
As a preferred embodiment, the model simplification module 204 includes: the grid structure generation submodule 2041, the folding cost calculation submodule 2042, the half folding submodule 2043 and the half folding judgment submodule 2044;
the grid structure generation sub-module 2041 is configured to represent the constructed digital twin city model as a grid structure, and establish a topology of grid structure data, wherein each vertex in the grid structure represents a geographic position, an edge formed by the vertices represents a connection relationship, and a face formed by the edges represents a region;
it should be noted that, the constructed digital twin city model is represented as a grid structure specifically:
dividing the region of the digital twin city model into a series of small triangle grid units;
a vertex is determined at the intersection of each grid cell. Each vertex represents a geographic location, which can be determined according to an actual geographic coordinate system, so that the model corresponds to the actual geographic location;
according to the division of the grid cells, the edges of each grid cell are connected with the edges of the adjacent grid cells to form edges, and the edges represent the connection relationship between geographic positions, such as roads, rivers and the like;
the area surrounded by adjacent edges is the face. According to the connection relation of the edges, adjacent edges are combined together to form a surface which represents the area of the city.
The folding cost calculation submodule 2042 is used for calculating folding cost values of all sides in the grid structure according to a half folding cost calculation formula;
as a preferred embodiment, the calculation formula of the half folding cost is as follows:
wherein, C (u, v) represents the folding value of the side uv formed by the vertex u and the vertex v in the grid structure;
T u representing a set of triangular faces associated with a vertex u in a mesh structure;
T uv representing two triangular surfaces where the vertex u and the vertex v form an edge uv in the grid structure, wherein the two triangular surfaces are f and n respectively;
θ represents an included angle formed by the shrinkage of the triangular surface f and the triangular surface n;
d represents the degree of the deleted vertex u;
the term u-v represents the length of the edges that the vertices u and v make up in the mesh structure.
The half folding submodule 2043 is used for performing half folding processing according to folding cost values of all sides in the grid structure;
as a preferred embodiment, the performing half-edge folding processing according to the folding cost values of all edges in the grid structure includes:
arranging folding cost values of all edges in the grid structure in order from small to large, and forming a to-be-folded edge set;
selecting an edge with the minimum folding cost value in the edge set to be folded for folding operation, and folding the selected edge to an adjacent edge;
and after the folding is finished, checking the shape of the formed new triangular surface by adopting an elongated triangle, if the formed new triangular surface is in the shape of the elongated triangle, not performing folding operation on the selected side and withdrawing the folding operation, otherwise, reserving the folding operation on the selected side and performing the folding operation on the next side.
It should be noted that the inspection of an elongated triangle refers to whether or not a certain internal angle of the triangle is close to 180 ° or a certain internal angle is close to 0 °. Through the inspection of the long and narrow triangle, the generation of bad shapes can be avoided when half folding treatment is carried out, and the quality and the geometric effect of the digital twin city model are improved.
The half folding judging submodule 2044 is used for judging whether a half which meets the preset condition is folded or not, if yes, folding is continued, otherwise, half folding is stopped, and the simplified digital twin city model is obtained.
The model updating module 205 is configured to acquire update data of a city in real time and adjust and update the simplified digital twin city model;
specifically, update data of the city is obtained in real time through various data sources (such as sensors, monitoring devices, satellite images, etc.). Such data may include traffic flow, weather information, population density, building changes, and the like. And processing and analyzing the acquired update data, converting the update data into a format which can be used for updating the digital twin city model, and adjusting the digital twin city model according to the update data. This may include adding new buildings, roads or other urban facilities, adjusting the state or properties of existing elements, updating topography or topography, etc.
The current feature point extraction module 206 is configured to extract a current feature point of the digital twin city model.
Specifically, the extracting of the current feature points of the digital twin city model specifically comprises the following steps:
acquiring data of a digital twin city model, wherein the data comprise geometric information such as buildings, roads, terrains and the like;
feature points in the digital twin city model are defined. The feature points may be key points or locations related to city planning, traffic flow, building shape, or other factors;
extracting feature points of the digital twin city model through edge detection; and extracting the attribute of the extracted feature points. According to the type and the target of the feature points, the attribute information such as position coordinates, height, area, shape and the like of the feature points can be extracted;
the city test simulation unit 3 is used for performing test simulation of various data in the constructed digital twin city model and analyzing potential problems existing in the current city;
specifically, a digital twin city model is used, and various experimental simulations are performed according to actual data and scenes. For example, different traffic flow conditions, building changes, population density changes, weather conditions, etc. may be simulated;
the results data of the simulation are recorded and collected. Such data may include traffic congestion conditions, building energy consumption, air quality, community utility utilization, disaster risk, etc.
And carrying out data analysis on the simulation result, exploring potential problems and trends in the simulation result, and identifying and classifying the potential problems existing in the current city. These problems may include traffic bottlenecks, energy waste, environmental pollution, community imbalance, etc.
The remote city feature library construction unit 4 is used for collecting various data of the remote city and constructing a remote city feature library;
specifically, the collection of various types of data for off-site cities may include problems and corresponding solutions to the management and planning of the individual cities, including government reports, academic research, media reports, industry white books, and the like. Note that collecting detailed information of problem descriptions and solutions for cases can categorize cases according to different topics or areas, such as traffic planning, environmental protection, social development, etc.
If some data is not available for a different period of time, the associated replacement data may be used or a data interpolation process may be performed. For example, data of adjacent time periods may be used for linear interpolation, or estimated and estimated using other relevant indices. And cooperate with government agencies, academic institutions, or professional institutions in other off-site cities to share data and experience.
As a preferred embodiment, the remote city feature library construction unit 4 includes a remote city data collection module 401, a remote city data processing module 402, a digital twin remote city model construction module 403, and a remote feature point extraction module 404;
the remote city data collection module 401 is configured to collect various data of a remote city;
the remote city data processing module 402 is configured to integrate and clean various data collected from a remote city;
the digital twin off-site city model construction module 403 is configured to construct a digital twin off-site city model;
the off-site feature point extraction module 404 is configured to extract feature points of an off-site city from a digital twin off-site city model, and obtain an off-site city feature library.
As a preferred embodiment, the city feature similarity determining unit 5 is configured to calculate the similarity between the current city and the different city based on the feature point group similarity calculation method, so as to obtain feature similarity between cities;
the city feature similarity judging unit 5 includes: the feature point group topological similarity calculation module 501, the feature point group direction similarity calculation module 502 and the city similarity calculation module 503;
the feature point group topology similarity calculation module 501 is configured to calculate the topology similarity of the feature point group of the current city and the feature point group of the different city in the feature library of the different city;
the calculation formula of the topological similarity is as follows:
in the formula, SIM_topo represents the topological similarity of the current city feature point group and the different city feature point group in the different city feature library;
H 1 representing the ratio of the total number of topological neighbors of the current city feature point group to the total point number of the current city feature point group;
H 2 representing characteristic point groups of different citiesThe ratio of the total number of topological neighbors to the total number of feature point clusters of the different cities.
Specifically, the calculating the directional similarity of the current city feature point group and the different city feature point group in the different city feature library includes:
calculating the characteristic direction of the characteristic point group of the city in different places of the current city characteristic point group by using a FREAK algorithm to obtain a gradient direction;
counting through the histogram, wherein the horizontal axis of the histogram is represented as an angle of the gradient direction, the vertical axis of the histogram is represented as the counting quantity of the gradient direction, and the peak value of the histogram is the main direction of the feature point group;
and calculating the direction similarity of the city feature point group through a feature point group direction similarity calculation formula.
The calculation formula of the similarity of the direction of the feature point group is as follows:
in the formula, SIM_dire represents the direction similarity of the current city feature point group and the different city feature point group in the different city feature library;
θ 1 sum phi 1 Respectively representing the distribution direction of the characteristic point group of the current city and the main gradient direction of the characteristic point group;
θ 2 sum phi 2 Respectively representing the distribution direction of the characteristic point groups and the main gradient direction of the characteristic point groups of different cities.
The feature point group direction similarity calculation module 502 is configured to calculate the direction similarity of the feature point group of the current city and the feature point group of the different city in the feature library of the different city;
the city similarity calculating module 503 is configured to calculate feature similarity between cities according to the calculated topology similarity and distance similarity.
Specifically, the calculation formula for calculating the feature similarity between cities according to the calculated topological similarity and the distance similarity is as follows:
wherein G represents feature similarity between cities;
sim_topo represents the topological similarity of the current city feature point group and the off-site city feature point group in the off-site city feature library;
sim_dire represents the directional similarity of the current city feature point group and the remote city feature point group in the remote city feature library.
The city planning treatment unit 6 is configured to analyze and compare potential problems existing in the current city based on feature similarities between cities, and to formulate a planning treatment scheme.
As a preferred embodiment, the city planning remediation unit 6 includes: the system comprises a remote city selection module 601, an analysis and comparison module 602 and a treatment scheme planning module 603;
the remote city selection module 601 is configured to select a remote city corresponding to a feature similarity value between cities, and use the selected remote city as a reference city;
the analysis and comparison module 602 is configured to analyze successful experience and treatment measures of the reference city for potential problem treatment;
specifically, analyzing the successful experience and governance measures taken by the reference city in dealing with potential problems, which may involve policy, planning scheme, technical innovation, social participation, etc., comparing and analyzing the successful experience and governance measures of the reference city with the current city situation, evaluating the applicability and feasibility thereof, and determining the borrowable aspect and viable measures, extracting key elements and success factors from the governance measures of the reference city. This may include key elements in policy support, resource allocation, technical applications, social participation, etc.
The governance scheme planning module 603 is configured to formulate and implement a governance scheme based on the analysis result and in combination with the actual situation of the current city.
Specifically, a planning treatment scheme suitable for the current city is formulated according to the analysis result and the actual condition of the current city. In combination with successful experience with reference cities, feasible measures and plans are proposed.
In summary, by means of the above technical scheme, the application collects and integrates various data of the current city and constructs a digital twin city model, and performs experimental simulation of various data in the digital twin city model, so that potential problems of the current city can be found through simulation analysis, reference and reference points are found by comparing the data and characteristics of the current city and the different cities, similarity calculation is performed on the current city and the different cities through a characteristic point group similarity calculation method, the similarity degree of the current city and other cities can be known through similarity evaluation, inspiration can be drawn from successful experiences of the other cities, repeated mistakes are avoided, and a more targeted and feasible planning scheme is provided to promote the development and the treatment of the cities; according to the application, the urban running condition can be predicted and simulated by constructing the digital twin urban model, the experimental simulation of various data can be carried out in the digital twin urban model, the urban running condition under different conditions can be simulated, the potential problems existing in the current city can be better found through simulation analysis, the problems can be recognized as soon as possible, the influence degree of risks on the city and the effectiveness of coping strategies can be evaluated, and the risk early warning can be carried out; the application can objectively quantify the similarity between the current city and the different-place city through the feature point group similarity calculation, and can determine the city similar to the current city, thereby being capable of referring to the experience of the similar city and carrying out customized planning and treatment scheme according to the actual condition of the current city by comparing the treatment strategy and success case of the similar city, thereby improving the pertinence and effectiveness of the planning, promoting the sustainable development of the city, reasonably configuring resources and optimizing the key field of city development according to the success experience of the similar city, further utilizing the resources to the greatest extent and improving the planning efficiency and effect.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the application, and is not meant to limit the scope of the application, but to limit the application to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (10)
1. A digital twinning-based smart city planning remediation system, the system comprising: the system comprises a city data collection and integration unit, a digital twin city model construction unit, a city test simulation unit, a remote city feature library construction unit, a city feature similarity judgment unit and a city planning treatment unit;
the city data collecting and integrating unit is used for collecting various data of the current city and storing the collected current city data;
the digital twin city model building unit is used for building a digital twin city model based on integrated current city data;
the city test simulation unit is used for performing test simulation of various data in the constructed digital twin city model and analyzing potential problems existing in the current city;
the remote city feature library construction unit is used for collecting various data of the remote city and constructing a remote city feature library;
the city feature similarity judging unit is used for calculating the similarity between the current city and the different-place city based on a feature point group similarity calculation method to obtain feature similarity between cities;
the city planning treatment unit is used for analyzing and comparing potential problems existing in the current city based on the feature similarity among cities, and making a planning treatment scheme.
2. A digital twinning-based smart city planning remediation system in accordance with claim 1, wherein the digital twinning city model building unit comprises: the system comprises a data preprocessing module, a data integration module, a model construction module, a model simplification module, a model updating module and a current feature point extraction module;
the data preprocessing module is used for preprocessing collected city data, and comprises redundant data processing, missing data filling and abnormal data cleaning;
the data integration module is used for integrating different types of data and establishing an urban data set;
the model construction module is used for constructing a digital twin city model through a modeling tool;
the model simplifying module is used for simplifying the constructed digital twin city model based on a half folding algorithm;
the model updating module is used for acquiring updating data of the city in real time and adjusting and updating the simplified digital twin city model;
the current feature point extraction module is used for extracting the current feature points of the digital twin city model.
3. A digital twinning-based smart city planning remediation system in accordance with claim 1, wherein the model simplification module comprises: the system comprises a grid structure generation sub-module, a folding cost calculation sub-module, a half folding sub-module and a half folding judgment sub-module;
the grid structure generation sub-generation module is used for representing the constructed digital twin city model as a grid structure and establishing the topology of grid structure data, wherein each vertex in the grid structure represents a geographic position, the edges formed by the vertexes represent a connection relationship, and the faces formed by the edges represent a region;
the folding cost calculation sub-module is used for calculating folding cost values of all sides in the grid structure according to a half folding cost calculation formula;
the half folding sub-module is used for performing half folding processing according to folding cost values of all sides in the grid structure;
and the half folding judging submodule is used for judging whether a half which is folded according with the preset condition exists or not, if so, continuing folding, otherwise, stopping half folding and obtaining the simplified digital twin city model.
4. A digital twinning-based smart city planning remediation system in accordance with claim 3 wherein the half folding cost is calculated by the formula:
wherein, C (u, v) represents the folding value of the side uv formed by the vertex u and the vertex v in the grid structure;
T u representing a set of triangular faces associated with a vertex u in a mesh structure;
T uv representing two triangular surfaces where the vertex u and the vertex v form an edge uv in the grid structure, wherein the two triangular surfaces are f and n respectively;
θ represents an included angle formed by the shrinkage of the triangular surface f and the triangular surface n;
d represents the degree of the deleted vertex u;
the term u-v represents the length of the edges that the vertices u and v make up in the mesh structure.
5. A digital twinning-based smart city planning remediation system in accordance with claim 3 wherein the performing a half-folding process based on folding cost values for all sides of the grid structure comprises:
arranging folding cost values of all edges in the grid structure in order from small to large, and forming a to-be-folded edge set;
selecting an edge with the minimum folding cost value in the edge set to be folded for folding operation, and folding the selected edge to an adjacent edge;
and after the folding is finished, checking the shape of the formed new triangular surface by adopting an elongated triangle, if the formed new triangular surface is in the shape of the elongated triangle, not performing folding operation on the selected side and withdrawing the folding operation, otherwise, reserving the folding operation on the selected side and performing the folding operation on the next side.
6. A digital twinning-based smart city planning remediation system in accordance with claim 1, wherein the off-site city feature library building unit comprises: the system comprises a remote city data collection module, a remote city data processing module, a digital twin remote city model construction module and a remote characteristic point extraction module;
the remote city data collection module is used for collecting various data of the remote city;
the remote city data processing module is used for integrating and cleaning various data of the collected remote cities;
the digital twin different-place city model building module is used for building a digital twin different-place city model;
the remote feature point extraction module is used for extracting feature points of the remote city from the digital twin remote city model and obtaining a remote city feature library.
7. A digital twinning-based smart city planning remediation system in accordance with claim 1, wherein the city feature similarity determination unit comprises: the system comprises a feature point group topology similarity calculation module, a feature point group direction similarity calculation module and a city similarity calculation module;
the feature point group topology similarity calculation module is used for calculating the topology similarity of the feature point group of the current city and the feature point group of the different city in the feature library of the different city;
the characteristic point group direction similarity calculation module is used for calculating the direction similarity of the characteristic point group of the current city and the characteristic point group of the different city in the characteristic library of the different city;
the city similarity calculation module is used for calculating the feature similarity between cities according to the calculated topological similarity and distance similarity.
8. The digital twinning-based intelligent city planning harness of claim 7, wherein the calculating the directional similarity of the current city feature point cluster and the off-site city feature clusters in the off-site city feature library comprises:
calculating the characteristic direction of the characteristic point group of the city in different places of the current city characteristic point group by using a FREAK algorithm to obtain a gradient direction;
counting through the histogram, wherein the horizontal axis of the histogram is represented as an angle of the gradient direction, the vertical axis of the histogram is represented as the counting quantity of the gradient direction, and the peak value of the histogram is the main direction of the feature point group;
and calculating the direction similarity of the city feature point group through a feature point group direction similarity calculation formula.
9. The digital twinning-based intelligent city planning harness of claim 8, wherein the calculation formula for calculating the feature similarity between cities according to the calculated topology similarity and distance similarity is:
wherein G represents feature similarity between cities;
SIMtopo represents the topological similarity of the current city feature point group and the different city feature point group in the different city feature library;
SIMdire represents the directional similarity of the current city feature point group and the different city feature point group in the different city feature library.
10. A digital twinning-based smart city planning remediation system in accordance with claim 1, wherein the city planning remediation unit comprises: the system comprises a remote city selection module, an analysis and comparison module and a treatment scheme planning module;
the remote city selection module is used for selecting the remote cities corresponding to the feature similarity values among the cities and taking the selected remote cities as reference cities;
the analysis and comparison module is used for analyzing successful experience and treatment measures of the reference city on potential problem treatment;
and the treatment scheme planning module is used for making and implementing a treatment scheme based on the analysis result and combining the actual situation of the current city.
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