A city planning simulation system based on real-life three-dimensional

Abstract

The present invention discloses a city planning simulation system based on real-scene three-dimensional (3D) and relates to the technical field of city planning. The system comprises the following components: a data acquisition module, a real-scene three-dimensional (3D) and disaster simulation module, a comprehensive planning module and a comprehensive evaluation module. The present invention plans the optimal emergency evacuation route by utilizing a path planning algorithm and taking into account the area affected by the damaged infrastructure and the recovery time of the infrastructure, thereby not only ensuring the safety and feasibility of the evacuation route but also greatly improving the evacuation efficiency. At the same time, according to the emergency evacuation route planning results and the infrastructure resilience evaluation results, the type and layout of the rescue facilities are determined, the service scope and coverage of the rescue facilities are analyzed by a geographic information system, the layout of the rescue facilities is optimized, and it is ensured that the rescue resources can efficiently and accurately reach the disaster-stricken area.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, in particular to an urban planning simulation system based on real-scene three-dimensional. Background Art

[0002] With the acceleration of global urbanization, cities, as the centers of human activities, are increasing in size and complexity. However, cities also face many challenges in the process of development. Among them, the threats of natural disasters such as earthquakes, floods, and fires are particularly prominent. These disasters will not only cause huge casualties and property losses, but also have a profound impact on the city’s infrastructure and economic development. Therefore, in urban planning, how to efficiently and accurately formulate emergency management plans and evaluate infrastructure resilience to improve the city’s ability to respond to emergencies has become an urgent problem to be solved.

[0003] Traditional urban planning methods have many shortcomings in disaster simulation and decision support. First, traditional methods are often based on two-dimensional plane maps and statistical data for analysis, which makes it difficult to comprehensively and intuitively reflect the three-dimensional spatial structure of the city and the actual disaster situation. This makes it difficult to accurately predict and assess the scope and extent of the impact of disasters on the city when disasters occur. Secondly, traditional methods lack efficient and accurate simulation methods when dealing with complex disaster scenarios, making it difficult to accurately simulate and predict the disaster situation in the city after the disaster occurs. In addition, traditional planning methods also have limitations in decision support, and can often only provide a single planning scheme. They lack the ability to comprehensively consider multiple disaster types and influencing factors, resulting in planning schemes that are often not comprehensive and accurate, making it difficult to effectively respond to emergencies.

[0004] In view of the above problems, it is necessary to optimize the existing urban planning simulation system by collecting natural disaster data and urban infrastructure data and constructing a real-life three-dimensional model of the city. At the same time, it simulates the disaster situation of the city under various disaster scenarios, and provides decision support for urban planning by evaluating the resilience of urban infrastructure, planning emergency evacuation routes and rescue facility layouts. Therefore, it is of great significance to develop an urban planning simulation system based on real-life three-dimensional that comprehensively realizes the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a city planning simulation system based on real-life three-dimensional (3D) modeling. It can more intuitively display the disaster situation of the city under disaster scenarios by fitting real-life three-dimensional modeling with disaster simulation technology. At the same time, it combines geographic information system technology and multi-objective optimization algorithms, comprehensively considers multiple dimensions such as emergency evacuation efficiency, infrastructure resilience, and environmental impact, and generates more scientific and reasonable earthquake emergency planning schemes. It also improves the evaluation accuracy and feasibility of the planning schemes through a multi-dimensional evaluation system and virtual reality technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a city planning simulation system based on real-scene 3D, the system comprising the following components: a data acquisition module, a real-scene 3D and disaster simulation module, a comprehensive planning module and a comprehensive evaluation module;

[0007] The data acquisition module collects natural disaster data including earthquakes, floods and fires as well as urban infrastructure data, and performs data fusion processing;

[0008] The real-life 3D and disaster simulation module uses the collected data to construct a real-life 3D model of the city, and simulates the disaster situation of the city under various disaster scenarios in the model, including building damage, road blockage and infrastructure damage;

[0009] The comprehensive planning module combines the real-life 3D model with the disaster simulation results to evaluate the resilience of urban infrastructure in disaster situations, plan emergency evacuation routes, and layout rescue facilities. The urban infrastructure resilience assessment simulates the degree of damage to urban infrastructure caused by disasters, including damage to power, communication, and water supply facilities, analyzes the recovery time of infrastructure under different repair strategies, evaluates the resilience of infrastructure, and plans emergency evacuation routes based on the real-life 3D model and disaster simulation results. It analyzes the population distribution and building structure factors in the disaster-stricken area, determines the starting and ending points of emergency evacuation, and uses a path planning algorithm to consider the area affected by the damaged infrastructure and the recovery time of the infrastructure to plan emergency evacuation routes. At the same time, through virtual reality technology, planners and the public can experience the emergency evacuation route and put forward improvement suggestions. The rescue facility layout determines the types of rescue facilities, including fire-fighting facilities, medical facilities, and shelters, based on the emergency evacuation route planning results and infrastructure resilience assessment results. It also uses a geographic information system to analyze the service scope and coverage of rescue facilities, optimizes the layout of rescue facilities, and evaluates the availability and effectiveness of rescue facilities in different disaster scenarios in combination with disaster simulation results.

[0010] The comprehensive evaluation module conducts a comprehensive evaluation of the comprehensive planning scheme, provides decision-making support for urban planning, uses virtual reality technology to simulate the implementation of the comprehensive planning scheme under different disaster scenarios, and establishes a dynamic evaluation mechanism. As the city develops and the disaster situation changes, the evaluation indicators and weights are adjusted to ensure the accuracy and timeliness of the evaluation results.

[0011] Furthermore, the data acquisition module performs credibility assessment and fusion on the collected data, and its credibility assessment and fusion algorithm is: Where n is the number of data sources, D i Represents disaster data or infrastructure data collected from different channels, C i Indicates data Di The credibility of D f It is the integrated data after fusion, which is used for subsequent simulation and planning.

[0012] Furthermore, the real-scene 3D and disaster simulation module uses a point cloud fusion algorithm to construct a real-scene 3D model. Specifically, let the point cloud data set be P = {p 1 ,p 2 ,…,p n}, the fused point cloud is P f , the fusion function is f, then Among them, p i represents a single point cloud data point, g is a function for processing a single point cloud data point, P f It is the fused three-dimensional point cloud data used to construct a real-life three-dimensional model of the city.

[0013] Furthermore, the real-scene 3D and disaster simulation module simulates the disaster situation of the city under various disaster scenarios in the model. Specifically, let the disaster intensity be I and the building vulnerability coefficient be V. b , the infrastructure vulnerability coefficient is V i , the degree of damage is D, then D=I×(V b +V i ), where I represents the disaster intensity parameter including earthquake intensity and flood water level, V b Indicates the vulnerability of a building to a specific disaster, V i It indicates the vulnerability of infrastructure to specific disasters, and D indicates the extent of the disaster to the city, including damage to buildings and destruction of infrastructure.

[0014] Furthermore, the urban infrastructure resilience assessment in the comprehensive planning module analyzes the recovery time of infrastructure under different repair strategies and evaluates the resilience of infrastructure. Specifically, the performance index of infrastructure under normal conditions is set as P n , the performance index after the disaster is P d , the recovery speed is V r , the importance weight is W i , the toughness index is RI, then

[0015] Furthermore, the emergency evacuation route planning in the comprehensive planning module utilizes a path planning algorithm to plan an emergency evacuation route by considering the area affected by the damaged infrastructure and the recovery time of the infrastructure. The path planning algorithm formula is: Among them, S and E represent the starting point and end point of emergency evacuation respectively, n i represents the nodes on the path, d(n i ,n j ) represents node ni and n j The distance between i Represents node n i The influence coefficient of damaged infrastructure on evacuation time, R i is node n i The real-time risk coefficient at the location is determined according to the dynamic changes of the disaster and the uncertainty factors of the surrounding environment, and T is the evacuation time.

[0016] Furthermore, the rescue facility layout in the comprehensive planning module uses a geographic information system to analyze the service scope and coverage of the rescue facilities, optimize the layout of the rescue facilities, and maximize the coverage range. Specifically, the rescue facility location set is R = {r 1 ,r 2 ,…,r k}, the demand point set is D = {d 1 ,d 2 ,…,d l}, its coverage maximization formula Among them, r i represents the location of the rescue facility, d j represents the demand point, R r is the coverage radius of the rescue facility, w d is the weight of the demand point, dist(r i ,d j ) indicates rescue facilities i and demand point d j C represents the coverage of rescue facilities to demand points, and the coverage is maximized by optimizing the layout of rescue facilities.

[0017] Furthermore, the comprehensive evaluation module establishes a dynamic evaluation mechanism, and adjusts the evaluation indicators and weights as the city develops and the disaster situation changes. Its comprehensive evaluation index EI = w e E e +w r RI+w i E i , where E e represents the emergency evacuation efficiency index, RI is the infrastructure resilience index, E i represents the environmental impact index, w e、 w r 、w i It is a weight coefficient determined according to the focus and needs of urban planning, and is used to adjust the importance of different indicators in the comprehensive evaluation. EI is a comprehensive evaluation indicator used to evaluate the effectiveness and feasibility of comprehensive planning schemes.

[0018] Compared with the prior art, this urban planning simulation system based on real-scene 3D has the following beneficial effects:

[0019] 1. The present invention plans the optimal emergency evacuation route by utilizing a path planning algorithm, taking into account the area affected by the damaged infrastructure and the recovery time of the infrastructure. This not only ensures the safety and feasibility of the evacuation route, but also greatly improves the evacuation efficiency. At the same time, based on the emergency evacuation route planning results and the infrastructure resilience assessment results, the type and layout of the rescue facilities are determined. The service scope and coverage of the rescue facilities are analyzed through the geographic information system, the layout of the rescue facilities is optimized, and it is ensured that rescue resources can reach the disaster-stricken area efficiently and accurately.

[0020] 2. The present invention conducts an in-depth assessment of the resilience of urban infrastructure in disaster situations by integrating real-life three-dimensional and disaster simulation results. It not only simulates the degree of damage to urban infrastructure caused by disasters, including damage to power, communication and water supply facilities, but also further analyzes the recovery time of infrastructure under different repair strategies, thereby comprehensively assessing the resilience of infrastructure, enabling urban planners to more accurately understand the vulnerability and recovery capacity of urban infrastructure in the face of disasters, and then formulate targeted resilience enhancement strategies.

[0021] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 It is a schematic diagram of the process structure of a city planning simulation system based on real-scene three-dimensional;

[0024] Figure 2 The figure is a flow chart of a city planning simulation system based on real-scene three-dimensional. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1

[0027] This embodiment describes in detail a specific application of a real-life three-dimensional urban planning simulation system in an earthquake disaster scenario. Through the simulation of the present invention, the city's ability to respond to earthquake disasters is improved, the safety of life and property of urban residents is protected, and the normal operation of the city is maintained.

[0028] The data collection module establishes a close cooperative relationship with the Earthquake Administration to obtain detailed historical earthquake data, including information such as magnitude, source location, affected area of ​​each earthquake, and time interval between earthquakes. It uses drones to inspect the city. The drones are equipped with high-definition cameras and a variety of professional sensors to collect all-round data on the city's infrastructure, focusing on the distribution and operation status of power facilities, the location and signal coverage of communication base stations, and the layout and aging degree of water supply pipelines. The data collected from different channels is set as D i For example, the data obtained from the Earthquake Bureau is recorded as D 1 , the data obtained by the drone inspection is recorded as D 2 The credibility of the data is C ii , determine the credibility coefficient C of each data source based on factors such as the reliability of the data source and the integrity of the data ii For the data provided by the Earthquake Administration, due to its professionalism and authority, the credibility coefficient C 1 It can be set to a higher value. Although the UAV inspection data can provide intuitive image information, it may be affected by environmental factors. The credibility coefficient C 2 , according to the data credibility evaluation and fusion formula Perform data fusion, and the integrated data D f It will serve as the accurate data basis for subsequent modules, ensuring the comprehensiveness and reliability of the data and avoiding the limitations and biases that may exist in a single data source.

[0029] For real-scene 3D modeling, laser scanning technology is used to perform high-precision scanning of urban buildings, roads, terrain, etc. The laser scanner scans the urban environment area by area according to the preset scanning path and accuracy requirements to obtain massive 3D point cloud data. Suppose the point cloud data set is P = {p 1 ,p 2 ,…,p n}, and use photogrammetry technology to shoot the city from multiple angles, process individual point cloud data points, and use point cloud optimization fusion formula Point cloud fusion is performed to deeply fuse the point cloud data obtained by laser scanning and the photo information obtained by photogrammetry to construct a realistic and accurate 3D model of the real city scene, which provides a real scene basis for subsequent disaster simulation. According to the characteristics of earthquake disasters, corresponding simulation parameters are set. For example, the earthquake intensity of this simulation is set to I. This parameter is reasonably set based on the historical earthquake data of the earthquake zone where the city is located and related geological research to determine the building vulnerability coefficient V b For example, for an old brick-concrete structure with a high number of floors, its vulnerability coefficient V b The vulnerability coefficient of the newly built frame structure and the seismic design standard buildings is lower. At the same time, the infrastructure vulnerability coefficient V i , evaluate different types of infrastructure separately. Taking power facilities as an example, if they are aged and lack earthquake-resistant reinforcement measures, the vulnerability coefficient V i Can be set higher. For communication base stations that have been upgraded to resist earthquakes, V i It can be set lower, according to the comprehensive disaster impact assessment formula D = I × (V b +V i ), accurately simulate the disaster situation of the city when the earthquake occurs. The simulation results can show the degree of damage to buildings (such as which buildings have cracks, collapse, etc.), the location and degree of road blockage, and the damage to infrastructure (such as power outage area, communication failure range, etc.), providing an intuitive and accurate basis for subsequent comprehensive planning, and simulating the degree of damage to urban infrastructure caused by disasters, including the damage to power, communication, water supply and other facilities. For example, through the disaster simulation results, it is known that some electric poles of power facilities in a certain area have collapsed, resulting in power outages in the area, several antennas of communication base stations have been damaged, affecting communication signal coverage, and there are multiple ruptures in water supply pipelines, causing local water outages, etc., analyzing the recovery time of infrastructure under different repair strategies, assuming that the performance index of infrastructure in normal state is P n , the performance index after the disaster is P d , the recovery speed is V r , the importance weight is W i , according to the comprehensive toughness index formula Calculate the resilience index RI of infrastructure. By calculating the resilience index of different infrastructures, its disaster resistance and recovery capabilities can be evaluated, providing a basis for the subsequent formulation of targeted infrastructure repair and strengthening plans.

[0030] Based on the real-life 3D model and disaster simulation results, we deeply analyzed the population distribution, building structure and other factors in the disaster-stricken areas. Through geographic information system technology, we combined population density data with urban geographic spatial information to accurately determine the evacuation needs of densely populated areas and different areas, and determined the starting and end points of emergency evacuation. We set the severely affected and densely populated areas as the starting point, such as large residential areas near the epicenter, and the safe refuge places around the city (such as open squares in the suburbs, large gymnasiums reinforced with earthquake resistance, etc.) as the end point. Let the starting point be S, the end point be E, and the path node set be N = {n 1 ,n 2 ,…,n m}, the distance between nodes is d(n i ,n j ), where the distance d(n i ,n j 0 can be determined comprehensively based on the actual geographical distance combined with road conditions, traffic flow and other factors. For example, for main roads with smooth traffic, the distance calculation can be relatively simple based on the geographical straight-line distance, while for narrow roads that may be damaged by earthquakes, the difficulty of passage should be considered and the distance weight should be appropriately increased to determine the area that may be affected by the damaged infrastructure and the time for the infrastructure to recover. For example, if a bridge on a main road leading to a shelter is damaged in an earthquake and its estimated repair time is t hours, then the traffic capacity of this road section will be seriously affected during this time period, and the impact coefficient C of the damaged infrastructure can be determined accordingly. i , while considering the real-time risk factor R i This requires combining factors such as the dynamic changes of disasters and the uncertainty of the surrounding environment. For example, during the aftershocks after an earthquake, some areas may experience dangerous situations such as building collapse again. At this time, the real-time risk coefficient R i The area R i is lower, according to the dynamic path optimization formula The goal of planning emergency evacuation routes is to find a path that minimizes the evacuation time T. Through algorithm calculation, after comprehensively considering the above factors, a safe and fast emergency evacuation route is planned to ensure that the affected people can evacuate from the dangerous area to a safe shelter in the shortest time. At the same time, according to the emergency evacuation route planning results and infrastructure resilience assessment results, the types of rescue facilities that need to be laid out are determined, mainly including fire-fighting facilities, medical facilities, temporary shelters, etc. The geographic information system is used to analyze the service scope and coverage of the rescue facilities. The rescue facility location set is R = {r 1 ,r 2 ,…,r k}, the demand point set is D = {d 1 ,d 2,…,d l}, the rescue facility coverage radius is R r , the demand point weight is w d , the distance function is dist(r i ,d j ), for the demand point weight w d , which is determined based on factors such as population density and disaster severity of the demand point. For example, in the area near the epicenter where the population is dense and the disaster is severe, the demand point weight w d It can be set higher, and the areas far from the epicenter and relatively less affected by the disaster, w d Can be set lower, rescue facility coverage radius R r According to the functions and actual rescue capabilities of different rescue facilities, the distance function dist(r i ,d j ) is calculated based on the actual geographical distance, taking into account the impact of factors such as road conditions on rescue traffic, and according to the coverage maximization formula Optimize the layout of rescue facilities, maximize the coverage degree C by constantly adjusting the location of rescue facilities, ensure that after an earthquake, rescue facilities can cover the areas most in need of rescue with maximum efficiency, and improve the rescue effect. Combined with virtual reality technology, planners and relevant department personnel can experience the planning scheme immersively. Through head-mounted virtual reality equipment, planners can walk in a virtual post-earthquake urban environment as if they were there, and observe emergency evacuation routes, rescue facility layouts, and the status of infrastructure from different angles. For example, in a virtual environment, you can see whether there are places on the evacuation route that need further optimization due to building collapse or road obstruction, check whether the rescue facilities are arranged in the most reasonable location, whether they can effectively cover the demand points, check the damage to the infrastructure and the expected effect after repair, etc. Based on the observation and analysis in the virtual reality experience, planners and relevant department personnel can put forward improvement suggestions, further optimize the comprehensive planning scheme, and improve the feasibility and effectiveness of the scheme.

[0031] At the same time, a dynamic evaluation mechanism will be established to adjust the evaluation indicators and weights in a timely manner as the city develops (such as the expansion of urban scale, increase in population, infrastructure renewal, etc.) and the earthquake situation changes (such as new discoveries in the laws of earthquake activity, the development of earthquake-resistant technology, etc.). For example, as some large-scale commercial centers and residential areas are newly built in the city, the population distribution has changed. At this time, the evaluation indicators in emergency evacuation route planning and rescue facility layout need to be adjusted accordingly, and new densely populated areas should be included in the key consideration scope. Combined with artificial intelligence technology, improvement plans can be automatically generated to provide support for the continuous optimization of urban planning.

[0032] In summary, by introducing the present invention, accurate decision-making support is provided for urban planning departments, which effectively improves the city's ability to respond to earthquake disasters, protects the lives and property of urban residents, and maintains the normal operation of the city.

[0033] Embodiment 2

[0034] This embodiment describes in detail a specific application of a real-life three-dimensional urban planning simulation system in a flood disaster scenario. Through the simulation of the present invention, a comprehensive and accurate planning scheme is provided for the city, which effectively improves the city's ability to cope with flood disasters, protects the safety of life and property of urban residents and the normal operation of the city.

[0035] The data collection module is responsible for collecting various data related to flood disasters and urban infrastructure to provide basic support for subsequent simulation and planning. On the one hand, it actively collects historical flood data, covering the frequency of previous floods, historical highest water levels, inundation range and other information, and obtains detailed hydrological data, such as river flow, flow rate change patterns, seasonal fluctuations in water levels, and urban terrain data, including terrain height, slope direction, drainage area division, etc. These data come from a wide range of sources. Through cooperation with professional departments such as the Water Conservancy Bureau, authoritative and systematic data are obtained. At the same time, Internet crawler technology is used to capture public auxiliary data from relevant meteorological and water conservancy websites. On the other hand, comprehensive collection of urban infrastructure data involves the distribution location, power supply capacity and disaster resistance of power facilities, the coverage of communication networks, base station locations and stability in disaster environments, and the pipeline layout, pipe diameter size, pump station location and its ability to cope with floods of water supply and drainage systems. Data credibility evaluation and fusion formulas are used. The data collected from different channels are integrated and processed, among which D i Represents data collected from various channels, C i is the corresponding credibility coefficient determined according to the reliability and integrity of the data source, and n is the number of data sources. Through such a fusion method, comprehensive and reliable data D is obtained. f .

[0036] The real-life 3D and disaster simulation module uses advanced technical means to build a real-life 3D model of the city, laying the foundation for accurately simulating flood disaster scenes. It uses laser scanning technology to carefully scan the city's buildings, roads, terrain, etc. to obtain massive 3D point cloud data. At the same time, it cooperates with photogrammetry technology to shoot the city from multiple angles to obtain high-resolution photos. For the collected point cloud data, the point cloud data set is set to P = {p 1 ,p 2 ,…,p n}, the fused point cloud is P f , the fusion function is f, then Among them, p i represents a single point cloud data point, g is a function for processing a single point cloud data point, P f It is the fused 3D point cloud data, which is used to construct the real-scene 3D model of the city. After the real-scene 3D model is constructed, the flood disaster simulation is carried out. According to the characteristics of the flood disaster, the corresponding key simulation parameters are set, such as flood water level, flow rate, etc., and the building vulnerability coefficient V is considered. b and infrastructure vulnerability coefficient V i The two coefficients are determined according to the structural type, construction age, water resistance of the building, the type of infrastructure, protective measures, disaster resistance performance, etc., and the comprehensive disaster impact assessment formula D = I × (V b +V i ) simulates the disaster situation of the city when the flood occurs. By substituting the corresponding coefficients and parameter values, the disaster situation of the city under the flood disaster can be accurately simulated, including the degree and scope of road flooding, the soaking and damage of buildings, infrastructure such as power outage areas, communication failure scope, and water supply and drainage system paralysis areas, etc., providing intuitive and accurate disaster scenario basis for subsequent comprehensive planning, and simulating the degree of damage to urban infrastructure by disasters, including the damage of power, communication, water supply and drainage facilities under the impact of floods. For example, floods may cause the collapse of power poles of power facilities, water inflow of substations, flooding of base stations of communication facilities, damage to lines, rupture of pipes of water supply and drainage systems, and paralysis of pump stations. The recovery time of infrastructure under different repair strategies is analyzed, and the performance index of infrastructure under normal state is set as P n , the performance index after the disaster is P d , the recovery speed is V r , the importance weight is W i Taking power facilities as an example, under normal conditions, its performance index P n It can be quantified based on its power supply capacity, stability and other factors. The performance index P d Will be reduced accordingly, the recovery speed V r According to the estimated recovery time under different repair strategies, the importance weight W i It is determined according to the importance of infrastructure to the operation of the city. For example, power facilities are the key support for the operation of the city, and their importance weight is W. i Relatively high, according to the comprehensive resilience index formula Calculate the resilience index RI of infrastructure. By evaluating the resilience index of different infrastructures, we can understand their disaster resistance and recovery capabilities, provide a basis for the subsequent formulation of targeted infrastructure repair and reinforcement plans, and ensure that the city’s infrastructure can resume normal operation as soon as possible after a flood disaster and maintain the normal life order of the city.

[0037] Based on the accurate disaster simulation results provided by the real-life 3D and disaster simulation modules, the comprehensive planning module carries out various planning work to enhance the city's ability to respond to flood disasters. For emergency evacuation route planning, the areas that may be affected by the flood and the city's geographic spatial information are fully considered, and the geographic information system technology is used to analyze factors such as population distribution and building structure in the disaster-stricken areas to determine the starting and end points of the emergency evacuation. For example, the area with high flood risk and dense population is set as the starting point, such as a residential area close to a river and with low terrain, and the area with high terrain, far away from the flood inundation area and with resettlement conditions in the city, such as high ground in the suburbs, and reinforced large buildings that can be used as temporary shelters, is set as the end point. Let the starting point be S, the end point be E, and the path node set be N = {n 1 ,n 2 ,…,n m}, the distance between nodes is d(n i ,n j ), which comprehensively considers the actual geographical distance and the traffic conditions of the road under flood conditions. For road sections that may be flooded, their distance weights need to be increased accordingly to reflect their traffic difficulty. At the same time, the areas that may be affected by the damaged infrastructure and the recovery time of the infrastructure should be considered to determine the impact coefficient of the damaged infrastructure C i For example, if a bridge has structural safety hazards due to flood impact and the estimated repair time is long, then the traffic capacity of the section where the bridge is located will be limited during this period, and its impact coefficient C i In addition, considering the dynamic characteristics of flood disasters, the real-time risk coefficient R i For example, when the flood level continues to rise, some areas close to the river and with low terrain have higher real-time risk factors. As the flood gradually recedes, the risk factors of these areas will decrease accordingly. According to the dynamic path optimization formula The goal of planning emergency evacuation routes is to find a path that minimizes the evacuation time T, thereby ensuring that the affected people can be quickly and safely evacuated from the dangerous area to the safe area when the flood strikes. In addition, according to the type of flood disaster and the characteristics of the disaster-stricken area, the types of rescue facilities that need to be laid out are determined, mainly including pit flood rescue equipment storage points, temporary medical assistance stations, disaster relief material storage points, and temporary shelters. The geographic information system is used to analyze the service scope and coverage of the rescue facilities. The rescue facility location set is R = {r 1 ,r2 ,…,r k}, the demand point set is D = {d 1 ,d 2 ,…,d l}, the rescue facility coverage radius is R r , the demand point weight is w d , the distance function is dist(r i ,d j ), for the demand point weight w d , which is determined based on factors such as population density and disaster severity of the demand point. For example, in a densely populated and severely affected area, the demand point weight w d Relatively high, while areas with less damage or sparse population, w d is relatively low, and the rescue facility coverage radius R r It is determined according to the functions and actual rescue capabilities of different rescue facilities. For example, the effective service radius of a temporary medical aid station may be determined as a certain value based on its medical resource allocation and rescue efficiency. The coverage radius of a disaster relief material storage point can be determined based on the material storage volume and distribution capacity. The coverage range maximization formula is used. Optimize the layout of rescue facilities, and maximize the coverage degree C by adjusting the location of rescue facilities to ensure that during flood disasters, rescue facilities can efficiently cover the areas most in need of rescue and provide necessary help and protection to the affected people in a timely manner. In addition, in the entire comprehensive planning process, virtual reality technology is combined to allow planners and relevant department personnel to experience the planning scheme immersively. Through virtual reality equipment, they can intuitively check whether the emergency evacuation route is reasonable, whether the layout of rescue facilities is appropriate, and the damage and repair of infrastructure in the virtual post-flood urban environment, and then put forward improvement suggestions to further optimize the comprehensive planning scheme and improve the feasibility and effectiveness of the scheme.

[0038] At the same time, a dynamic evaluation mechanism is established. With the development of the city (such as the expansion of the city scale, the increase of population, the renewal of infrastructure, etc.) and the changes in flood conditions (such as the changes in the frequency of floods, the new discoveries of the laws of water level changes, etc.), the evaluation indicators and weights are adjusted in a timely manner. For example, as some large commercial centers and residential areas are newly built in the city, the population distribution has changed. At this time, the evaluation indicators in emergency evacuation route planning and rescue facility layout need to be adjusted accordingly, and new densely populated areas should be included in the key considerations. In combination with artificial intelligence technology, improvement plans are automatically generated to provide support for the continuous optimization of urban planning. Through the study and analysis of a large amount of simulation exercise data and actual urban operation data, the artificial intelligence system can automatically propose optimized planning plans based on the current actual situation of the city and future development trends, improve the city's ability to respond to flood disasters, and ensure the safety and stability of the city. In the entire evaluation process, according to the multi-indicator comprehensive evaluation formula EI=w e E e +w r RI+w i E i +w s E s Conduct a comprehensive assessment, among which the emergency evacuation efficiency index E e It can be determined based on factors such as evacuation time and number of evacuees. The infrastructure resilience index RI is calculated through the above infrastructure resilience assessment. The environmental impact index E i The degree of damage to the natural environment caused by floods (such as flooding farmland, destroying vegetation, etc.) and the impact on the environment during rescue and recovery can be considered. The social benefit index E s It can be determined based on the impact of the planning scheme on social and economic development, quality of life of residents, etc., by assigning appropriate weight coefficients to each indicator (the weight coefficient w is determined based on the city’s development priorities and the current flood disaster risks it faces). e 、w r 、w i 、w s ), comprehensively evaluate the effectiveness and feasibility of comprehensive planning schemes, and provide scientific decision-making basis for urban planning.

[0039] In summary, through the analysis of the simulation results by the present invention, the advantages and disadvantages of the planning scheme can be accurately discovered, and a detailed evaluation report and improvement suggestions can be generated, providing strong decision-making support for urban planning departments, effectively improving the overall ability of cities to cope with flood disasters, protecting the lives and property of urban residents, and maintaining the normal operation order of the city, so as to achieve scientific planning and sustainable development of cities under flood disaster scenarios.

[0040] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and range of equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A real-scene three-dimensional urban planning simulation system, characterized in that: The system includes the following components: data acquisition module, real-life 3D and disaster simulation module, comprehensive planning module and comprehensive assessment module; The data acquisition module collects natural disaster data including earthquakes, floods and fires as well as urban infrastructure data, and performs data fusion processing; The real-life 3D and disaster simulation module uses the collected data to construct a real-life 3D model of the city, and simulates the disaster situation of the city under various disaster scenarios in the model, including building damage, road blockage and infrastructure damage; The comprehensive planning module combines the real-life 3D model with the disaster simulation results to evaluate the resilience of urban infrastructure in disaster situations, plan emergency evacuation routes, and layout rescue facilities. The urban infrastructure resilience assessment simulates the degree of damage to urban infrastructure caused by disasters, including damage to power, communication, and water supply facilities, analyzes the recovery time of infrastructure under different repair strategies, evaluates the resilience of infrastructure, and plans emergency evacuation routes based on the real-life 3D model and disaster simulation results. It analyzes the population distribution and building structure factors in the disaster-stricken area, determines the starting and ending points of emergency evacuation, and uses a path planning algorithm to consider the area affected by the damaged infrastructure and the recovery time of the infrastructure to plan emergency evacuation routes. At the same time, through virtual reality technology, planners and the public can experience the emergency evacuation route and put forward improvement suggestions. The rescue facility layout determines the types of rescue facilities, including fire-fighting facilities, medical facilities, and shelters, based on the emergency evacuation route planning results and infrastructure resilience assessment results. It also uses a geographic information system to analyze the service scope and coverage of rescue facilities, optimizes the layout of rescue facilities, and evaluates the availability and effectiveness of rescue facilities in different disaster scenarios in combination with disaster simulation results. The comprehensive evaluation module conducts a comprehensive evaluation of the comprehensive planning scheme, provides decision-making support for urban planning, uses virtual reality technology to simulate the implementation of the comprehensive planning scheme under different disaster scenarios, and establishes a dynamic evaluation mechanism. As the city develops and the disaster situation changes, the evaluation indicators and weights are adjusted to ensure the accuracy and timeliness of the evaluation results.

2. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The data acquisition module performs credibility assessment and fusion on the collected data, and its credibility assessment and fusion algorithm is: Where n is the number of data sources, D i Represents disaster data or infrastructure data collected from different channels, C i Indicates data D i The credibility of D f It is the integrated data after fusion, which is used for subsequent simulation and planning.

3. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The real scene 3D and disaster simulation module uses the point cloud fusion algorithm to construct a real scene 3D model. Specifically, the point cloud data set is P = {p1, p2, ..., p n }, the fused point cloud is P f , the fusion function is f, then Among them, p i represents a single point cloud data point, g is a function for processing a single point cloud data point, P f It is the fused three-dimensional point cloud data used to construct a real-life three-dimensional model of the city.

4. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The real-life 3D and disaster simulation module simulates the disaster situation of the city under various disaster scenarios in the model. Specifically, let the disaster intensity be I and the building vulnerability coefficient be V. b , the infrastructure vulnerability coefficient is V i , the degree of damage is D, then D=I× ( V b +V i) , where I represents the disaster intensity parameter including earthquake intensity and flood water level, V b Indicates the vulnerability of a building to a specific disaster, V i It indicates the vulnerability of infrastructure to specific disasters, and D indicates the extent of the disaster to the city, including damage to buildings and destruction of infrastructure.

5. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The urban infrastructure resilience assessment in the comprehensive planning module analyzes the recovery time of infrastructure under different repair strategies and evaluates the resilience of infrastructure. Specifically, the performance index of infrastructure in a normal state is set as P n , the performance index after the disaster is P d , the recovery speed is V r , the importance weight is W i , the toughness index is RI, then 6. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The emergency evacuation route planning in the comprehensive planning module uses a path planning algorithm to plan an emergency evacuation route by considering the area affected by the damaged infrastructure and the recovery time of the infrastructure. The path planning algorithm formula is: Among them, S and E represent the starting point and end point of emergency evacuation respectively, n i represents the nodes on the path, d ( n i ,n j ) Represents node n i and n j The distance between i Represents node n i The influence coefficient of damaged infrastructure on evacuation time, R i is node n i The real-time risk coefficient at the location is determined according to the dynamic changes of the disaster and the uncertainty factors of the surrounding environment, and T is the evacuation time.

7. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The rescue facility layout in the comprehensive planning module uses the geographic information system to analyze the service scope and coverage of the rescue facilities, optimize the layout of the rescue facilities, and maximize the coverage. Specifically, the rescue facility location set is R = {r1, r2, ..., r k }, the demand point set is D = {d1, d2, …, d l }, its coverage maximization formula Among them, r i represents the location of the rescue facility, d j Represents the demand point, R r is the coverage radius of the rescue facility, w d is the weight of the demand point, dist ( r i ,d j ) Indicates rescue facilities i and demand point d j C represents the coverage of rescue facilities to demand points, and the coverage is maximized by optimizing the layout of rescue facilities.

8. The real-scene three-dimensional urban planning simulation system according to claim 1, characterized in that: The comprehensive evaluation module establishes a dynamic evaluation mechanism, and adjusts the evaluation indicators and weights as the city develops and the disaster situation changes. Its comprehensive evaluation index EI = w e E e +w r RI+w i E i , where E e represents the emergency evacuation efficiency index, RI is the infrastructure resilience index, E i represents the environmental impact index, w e、 w r 、w i It is a weight coefficient determined according to the focus and needs of urban planning, and is used to adjust the importance of different indicators in the comprehensive evaluation. EI is a comprehensive evaluation indicator used to evaluate the effectiveness and feasibility of comprehensive planning schemes.

Images