Urban planning method and system based on digital twinning, terminal and storage medium

Abstract

The invention relates to a city planning method and system based on digital twinning, a terminal and a storage medium, and the method comprises the steps: obtaining multi-source data related to a city, and the multi-source data comprises geographic space data, Internet of Things perception data and business data; performing standardization processing and cleaning processing on the multi-source data to obtain standard data, the standard data including standard geographic space data, standard internet-of-things perception data and standard business data; constructing a three-dimensional grid model of the corresponding region based on the standard geographic space data; obtaining a refined digital model based on the three-dimensional grid model and the standard business data; dynamically correcting the refined digital model by using the standard internet-of-things sensing data to obtain a digital twinborn model synchronous with the actual city state; and generating a city planning strategy according to the digital twin model. The method has the effect of improving the real-time performance and accuracy of the city planning strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to a city planning method and system based on digital twinning, a terminal and a storage medium. BACKGROUND

[0002] With the continuous advancement of urban informatization construction, BIM (Building Information Modeling), GIS (Geographic Information System), CIM (City Information Modeling) and other technologies are widely used in urban planning. However, in the actual application process, these systems are often independently constructed, lacking unified data standards and interface specifications, resulting in difficulty in sharing and collaboration between data, forming a "data island" problem, which seriously restricts the fusion and utilization of urban data. In addition, existing three-dimensional modeling technologies are mainly static scene display, lacking linkage mechanism with real-time data, and cannot dynamically reflect the sudden events or temporary changes in urban operation.

[0003] For the related technologies in the above, the existing city planning method cannot efficiently integrate multi-source heterogeneous data, and it is difficult to build a model synchronized with the actual city state, resulting in lack of real-time and accuracy of planning strategy. SUMMARY

[0004] In order to improve the real-time and accuracy of city planning strategy, the present application provides a city planning method and system based on digital twinning, a terminal and a storage medium.

[0005] In the first aspect, the present application provides a city planning method based on digital twinning, which adopts the following technical solution: A city planning method based on digital twinning, comprising: Obtaining multi-source data related to the city, the multi-source data comprising geographic space data, Internet of Things sensing data and business data; Standardizing and cleaning the multi-source data to obtain standard data, the standard data comprising standard geographic space data, standard Internet of Things sensing data and standard business data; Constructing a three-dimensional grid model of the corresponding region based on the standard geographic space data; Obtaining a refined digital model based on the three-dimensional grid model and the standard business data; Using the standard Internet of Things sensing data to dynamically correct the refined digital model to obtain a digital twinning model synchronized with the actual city state; Generating a city planning strategy according to the digital twinning model.

[0006] By adopting the technical scheme, the multi-source data is standardized and cleaned, a refined digital model is constructed, the refined digital model is dynamically corrected by real-time standard Internet of Things sensing data, a digital twin model synchronized with an actual city state is generated, and city planning strategies based on the digital twin model can respond to changes in city operation states in real time, thereby improving real-time performance and accuracy of city planning strategies.

[0007] Optionally, the step of obtaining the refined digital model based on the three-dimensional grid model and the standard business data comprises: According to the standard business data, a component-level entity is obtained, which refers to a single specific component or functional unit extracted from a spatial entity. The component-level entity is associated with an Internet of Things sensor. The component-level entity is assigned a unique identification code, and the identification code structure comprises an administrative area code, a spatial hash, a timestamp, and an entity type code. The Internet of Things data of the Internet of Things sensor is determined according to the identification code. The component-level entity and the Internet of Things data are added to the three-dimensional grid model to obtain the refined digital model.

[0008] By adopting the technical scheme, the component-level entity is obtained from the standard business data, and the component-level entity is associated with the Internet of Things sensor, so that dynamic sensing of city facilities can be achieved. According to the unique identification code, the Internet of Things data corresponding to the component-level entity is determined, and the component-level entity and the Internet of Things data are added to the three-dimensional grid model to obtain the refined digital model, so that not only the form of the spatial entity can be reflected, but also the operation state and real-time data of the spatial entity can be reflected, which helps to provide data support for city planning strategies.

[0009] Optionally, a city node is obtained, and the city node comprises a spatial entity node, a personnel node, an article node, an event node, and a rule node. A semantic relationship between the city nodes is constructed, and the semantic relationship comprises a spatial topology relationship, a time sequence relationship, and a management and ownership relationship. A city knowledge graph is constructed according to the city nodes and the semantic relationship. The city knowledge graph is added to the refined digital model.

[0010] By adopting the technical scheme, the semantic relationship between city nodes is constructed, the city knowledge graph is generated according to the city nodes and the semantic relationship, and then the city knowledge graph is added to the refined digital model, so that the refined digital model can express the relationship between city elements, thereby improving the accuracy of the city planning strategy.

[0011] Optionally, the step of generating the city planning strategy according to the digital twin model comprises: performing water disaster simulation deduction based on the digital twin model; obtaining simulation data of a change of an influence range of the water disaster over time; obtaining an area affected by the water disaster according to the simulation data in a preset time period, to obtain a first high-risk area; obtaining an overflow area in the first high-risk area; executing a preset prevention strategy, the preset prevention strategy comprising: using a preset water blocking method in the overflow area to intercept the water flow; after repeating the water disaster simulation deduction for a preset number of times, obtaining an area affected by the water disaster according to the simulation data in the preset time period, to obtain a second high-risk area; determining whether the second high-risk area contains the first high-risk area; if yes, executing a preset transfer strategy; obtaining the city planning strategy based on the preset prevention strategy and the preset transfer strategy.

[0012] By adopting the technical scheme, the water disaster simulation experiment is performed based on the digital twin model, and the area affected by the water disaster is obtained at a preset time point, then the overflow area is obtained, and the preset prevention strategy is executed to block the water flow, by repeating the water disaster simulation deduction, whether the preset prevention strategy is effective can be determined according to the area affected by the water disaster, and in the case that the effect of the preset prevention strategy is not ideal, the preset transfer strategy is executed, therefore, the city planning strategy is obtained in combination with the preset prevention strategy and the preset transfer strategy.

[0013] Optionally, the step of executing the preset transfer strategy comprises: in the case that the influence range continues to expand over time, predicting whether the area affected by the water disaster includes a key area based on the simulation data; if yes, obtaining an evacuation path from the key area to a safe area; pushing the evacuation path to a user mobile device of the key area; linking an emergency resource scheduling system to publish an emergency notice according to the location of the key area, the emergency notice comprising an evacuation notice and a forbidden entry notice.

[0014] By adopting the technical scheme, in the case that the influence range continuously expands over time, the area affected by the flood can be predicted based on simulation data, and in the case that the area affected by the flood includes a key area, the evacuation path is pushed to the user mobile device, and the emergency resource scheduling system issues an emergency notification, so that the public can be notified to transfer in advance before the flood arrives, to avoid casualties.

[0015] Optionally, an area associated with the key area is acquired, to obtain an associated area. Based on the simulation data, it is detected whether the associated area affected by the flood exists in the case that the influence range continuously expands. If yes, the associated area affected by the flood is marked as a target area. The target area is divided into a plurality of sub-areas. A corresponding automobile evacuation path is formulated according to each sub-area. The traffic signal lamp system is dynamically controlled to guide vehicles to be distributed to the automobile evacuation path. The passing condition of the automobile evacuation path is monitored in real time. The automobile evacuation path is updated after each interval of a preset interval time according to the passing condition.

[0016] By adopting the technical scheme, in the case that the associated area affected by the flood exists, the associated area is marked as a target area, and the target area is divided into a plurality of sub-areas, and the automobile is guided to enter the automobile evacuation path through the control of the signal lamp, so that the vehicle evacuation efficiency is effectively improved in the flood emergency scene, and the response ability and evacuation efficiency of the city in the disaster situation are enhanced.

[0017] Optionally, it is judged whether the passing condition is in a jam state after a preset passing time length. If yes, the position of a temporary resettlement point in each sub-area is determined. A base station positioning system is linked, to acquire the distance from the user mobile device to the nearest temporary resettlement point, to obtain a nearest distance. The temporary resettlement point information corresponding to the nearest distance is pushed to the user mobile device. The emergency resource scheduling system is linked to notify emergency personnel to dispatch rescue materials to the temporary resettlement point.

[0018] By adopting the technical scheme, in the case that the traffic condition is still in the congestion state after the preset traffic duration, the position of the temporary settlement point in each sub-region is determined, and the nearest temporary settlement point information is sent to the user mobile device, so that the crowd shifts to the temporary settlement point, and the emergency personnel are informed by the emergency resource scheduling system to dispatch rescue materials to the temporary settlement point, thereby reducing the probability of casualties of the city in the disaster situation.

[0019] In a second aspect, the application provides a city planning system based on digital twinning, which adopts the following technical scheme: A city planning system based on digital twinning, comprising An acquisition module for acquiring multi-source data; A memory for storing the program of the city planning method based on digital twinning; A processor, and the program in the memory can be loaded and executed by the processor and implement the city planning method based on digital twinning.

[0020] By adopting the technical scheme, the multi-source data is standardized and cleaned, a refined digital model is constructed, and the refined digital model is dynamically corrected by real-time standard Internet of Things sensing data, thereby generating a digital twinning model synchronized with the actual city state, and the city planning strategy based on the digital twinning model can respond to changes in the city operation state in real time, thereby improving the real-time and accuracy of the city planning strategy.

[0021] In a third aspect, the application provides an intelligent terminal, which adopts the following technical scheme: An intelligent terminal comprising a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to implement any of the above methods.

[0022] In a fourth aspect, the application provides a computer storage medium capable of storing a corresponding program, which has the characteristics of facilitating the improvement of the real-time and accuracy of the city planning strategy, and adopts the following technical scheme: A computer readable storage medium storing a computer program capable of being loaded and executed by a processor to implement any of the above city planning methods based on digital twinning.

[0023] In summary, the application has at least one of the following beneficial technical effects: 1. By standardizing and cleaning the multi-source data, constructing a refined digital model, and dynamically correcting the refined digital model by real-time standard Internet of Things sensing data, a digital twinning model synchronized with the actual city state is generated, and the city planning strategy based on the digital twinning model can respond to changes in the city operation state in real time, thereby improving the real-time and accuracy of the city planning strategy. 2. By obtaining the component-level entity from the standard service data and associating the component-level entity with the Internet of Things sensor, the facilities of the city can be dynamically perceived. According to the unique identification code, the Internet of Things data corresponding to the component-level entity is determined, and the component-level entity and the Internet of Things data are added to the three-dimensional grid model to obtain a refined digital model, so that not only the form of the spatial entity can be reflected, but also the running state and real-time data of the spatial entity can be reflected, which helps to provide data support for urban planning strategies; 3. In the case that the influence range continues to expand over time, the affected area of the flood can be predicted based on simulation data, and in the case that the affected area of the flood includes a key area, by pushing the evacuation path to the user mobile device and issuing an emergency notification through the emergency resource scheduling system, the public can be notified in advance to transfer before the flood arrives, so as to avoid casualties. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart of a city planning method based on digital twinning in an embodiment of the present application.

[0025] Figure 2 is a flowchart of a step of obtaining a refined digital model based on a three-dimensional grid model and standard service data in an embodiment of the present application.

[0026] Figure 3 is a flowchart of a city knowledge graph construction method in an embodiment of the present application.

[0027] Figure 4 is a flowchart of a step of generating an urban planning strategy according to a digital twinning model in an embodiment of the present application.

[0028] Figure 5 is a flowchart of a step of executing a preset transfer strategy in an embodiment of the present application.

[0029] Figure 6 is a flowchart of a dredging method in an embodiment of the present application.

[0030] Figure 7 is a flowchart of a rescue method in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. Figures 1-7 and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0032] The embodiment of the application discloses a city planning method based on digital twinning. Referring to Figure 1 The city planning method based on digital twinning comprises the following steps: Step S101: acquiring city-related multi-source data, the multi-source data comprising geographic space data, Internet of Things sensing data and business data.

[0033] The multi-source data is city-related data of different formats acquired through different data acquisition methods.

[0034] The geographic space data is data for describing the geographic features of a city, such as terrain, buildings, roads, water systems and the like. The surface and building textures can be acquired through satellite photography, unmanned aerial vehicle oblique photography and laser scanning and the like. The satellite photography can adopt a resolution of 0.5-1 m, and the unmanned aerial vehicle oblique photography can adopt a resolution of 3-5 cm.

[0035] The Internet of Things sensing data is acquired in real time by Internet of Things devices deployed at various places in a city, such as traffic cameras, air quality monitoring stations, smart electricity meters and the like, wherein the frequency of data acquisition is greater than 1 Hz.

[0036] The business data is data generated in the operation of a city, comprising BIM (Building Information Modeling), GIS (Geographic Information System) and CIM (City Information Modeling), wherein the BIM data is used for digital description of a building, such as building structure, pipeline layout, building material information and the like. The GIS data is used for describing attribute data related to geographic space, such as land attribute (land use, plot ratio), population statistics data (density distribution, age structure), infrastructure location. The CIM is a city-level digital information model, which provides a digital model of a city by integrating BIM, GIS and other business data.

[0037] Step S102: performing standardization processing and cleaning processing on the multi-source data to obtain standard data, the standard data comprising standard geographic space data, standard Internet of Things sensing data and standard business data.

[0038] The standardization processing refers to uniformly converting data of different sources and different formats into a unified format and structure recognizable by the system, comprising uniform data coding, coordinate system conversion, timestamp format standardization, unit conversion and the like.

[0039] Data cleaning is a process of identifying and handling outliers, missing values, and duplicate data in multi-source data. For example, by constructing a spatio-temporal data lake, using ETL tools to clean and align multi-source data, and establishing a unified spatio-temporal index. ETL tools are used to extract, transform, and load data, which can clean structured or unstructured data from different sources.

[0040] Standard data is data obtained after standardization and cleaning of multi-source data.

[0041] For example, the format of geographic spatial data is unified, and the semantic attributes of buildings, roads, and green spaces are defined to obtain standard geographic spatial data. CityGML 3.0 (City Geography Markup Language) can be used to implement the above-mentioned unification process.

[0042] Data collected by various Internet of Things devices is accessed through MQTT / OPC UA protocols and aligned in a unified spatio-temporal coordinate system. In this process, all data collected from Internet of Things devices is mapped to a unified coordinate system to obtain standard Internet of Things perception data. MQTT is a lightweight publish / subscribe message transmission protocol designed for Internet of Things devices in low-bandwidth and unstable network environments. OPC UA is a standardized industrial communication protocol that supports data exchange between various devices and systems.

[0043] For example, multiple traffic cameras are installed in city A, and the traffic cameras transmit data to the server through the MQTT protocol. The data of the traffic cameras in city A is mapped to the unified WGS84 coordinate system, and the timestamps are also synchronized and aligned. WGS84 is a global standard geographic coordinate system used to define locations on Earth, usually represented by latitude and longitude, and widely used in GPS and mapping services.

[0044] For example, IFC 4.0 standard is used to convert BIM, so that the component information in the BIM model (building structure information, pipeline layout information, building material information, etc.) can be geometrically aligned and attribute-matched with the geographic information in the GIS system. At the same time, GIS data is unified through CityGML format, and then fused into CIM model to form a business information collection with consistent spatial reference and unified semantic structure, obtaining standard business data.

[0045] Step S103: Based on the standard geographic spatial data, a three-dimensional grid model of the corresponding region is constructed.

[0046] The corresponding point cloud data can be obtained by laser scanning, and high-precision geographic data of the target region is generated based on the data generated by the unmanned aerial vehicle oblique photography and the point cloud data. The three-dimensional grid model is generated according to the high-precision geographic data.

[0047] For example, first, the unmanned aerial vehicle performs oblique photography from multiple angles in the a region of the urban area A to obtain two-dimensional image data, and then the laser scanning technology is used to scan the a region to obtain the point cloud data of the a region. According to the two-dimensional image data and the point cloud data, the corresponding three-dimensional grid model is generated. Finally, the three-dimensional grid model is visualized and rendered on the Web side using CesiumJS, so that the user can interactively view the three-dimensional model of the a region in the browser. CesiumJS is an open source JavaScript framework for building three-dimensional earth and maps in browsers.

[0048] Further, after the three-dimensional grid model of the corresponding region is constructed, the three-dimensional grid model is classified according to different LOD (Levels Of Detail) based on the standard business data, including LOD1, LOD2 and LOD3. The LOD1 level model mainly shows the basic skeleton structure of the city, such as terrain, road, green space and other large-scale spatial geographic information. The LOD2 level model mainly shows the facade and shape of the building, and can add more attribute information such as building material and window. The LOD3 level model provides more detailed display, such as indoor layout, indoor decoration and corridor monitoring of the building.

[0049] Step S104: obtaining a refined digital model based on the three-dimensional grid model and the standard business data.

[0050] The standard business data is fused with the three-dimensional grid model to refine the model. The specific steps can refer to the steps in the embodiments. Figure 2

[0051] For example, the road model of the urban area A is originally displayed in the three-dimensional grid model in a simple geometric form, and after the refinement, the road maintenance status and pipeline distribution information can also be displayed.

[0052] Step S105: dynamically correcting the refined digital model using standard Internet of Things sensing data to obtain a digital twin model synchronized with the actual city state.

[0053] ​By mapping the standard IoT sensing data (such as traffic flow, construction status, environmental monitoring information, etc.) to the existing refined digital model, the elements in the model such as buildings, roads, equipment status, etc. are added, deleted, updated or labeled, thereby constructing a virtual model that changes synchronously with the real city status and can reflect the real world in real time, which is the digital twin model.

[0054] For example, at a street in city A, there is an occupation construction that closes part of the lane. After the construction starts, the traffic data near the construction area is collected in real time by the Internet of Things devices such as traffic cameras, and the traffic data is uploaded to the server. After the traffic data is standardized, it is mapped to a unified coordinate system, which includes the geographical position of the construction area, the situation of the closed lane, the traffic flow of the surrounding roads, and other information. These data are integrated into the refined digital model to update the refined digital model. Specifically, temporary traffic control signs, construction equipment and other elements appear in the refined digital model to reflect the actual situation.

[0055] Step S106: generating a city planning strategy according to the digital twin model.

[0056] The city planning strategy refers to formulating a corresponding planning strategy based on the current situation of the city. The specific steps can refer to the embodiments of the subsequent steps.

[0057] In some embodiments, the digital twin model can be applied to the field of home-based care for the elderly, which can monitor the health of the elderly and provide early warning for emergencies.

[0058] The digital twin model can obtain data provided by component-level entities and IoT sensors, which can be applied to the field of home-based care for the elderly. For example, a camera at the entrance of a certain building on the third floor of A, a water and electricity meter, and a wireless door magnet, which obtain corresponding monitoring data, water and electricity data, and door magnetic alarm data through IoT sensors. For example, when the camera monitors that the old man has not gone out for a long time, the water and electricity meter data abnormally decreases, and the door magnet has no switching action for a long time, the digital twin model can analyze and judge that the old man may be in an abnormal state, and then trigger the sending of a warning message to the family members or community service center, realizing remote care and timely assistance for the old man.

[0059] The food types purchased by the old people are photographed and recognized by a camera, and the diet structure of the old people is analyzed by combining food recognition algorithms and health knowledge graphs. For example, when it is identified that the old people frequently purchase unbalanced food such as high-sugar, high-fat or lack of vegetables and fruits in recent period, it can be inferred by the digital twin model that the old people have the risk of unhealthy diet, and may face health risks such as abnormal blood sugar, weight gain, high blood lipids, etc. Dietary improvement suggestions and nutritional recipe recommendations can be pushed to the old people or their family members. For example, dietary improvement suggestions can be generated based on the old people's past health records, current physical indicators and dietary preferences.

[0060] By adopting the technical solution, the multi-source data is standardized and cleaned, a refined digital model is constructed, and the refined digital model is dynamically corrected by real-time standard Internet of Things sensing data, thereby generating a digital twin model synchronized with the actual city state. The city planning strategy based on the digital twin model can respond to the changes in the city operation state in real time, thereby improving the real-time and accuracy of the city planning strategy.

[0061] Reference Figure 2 The step of obtaining the refined digital model based on the three-dimensional grid model and the standard business data includes: Step S201: Obtain the component-level entity according to the standard business data. The component-level entity refers to a single specific component or functional unit extracted from a spatial entity.

[0062] The spatial entity is usually an entity that can be independently identified and managed in the city, such as a building, a bridge, etc.

[0063] The component-level entity can be associated with an Internet of Things device, such as a camera, a traffic signal, etc. in a building, which can be regarded as a component-level entity. For example, a camera in the third floor of building A can be regarded as a component-level entity; a camera in the road can be regarded as a component-level entity.

[0064] Step S202: Associate the component-level entity with the Internet of Things sensor.

[0065] For example, in a certain commercial building in city A, a plurality of Internet of Things sensors are deployed. A plurality of component-level entities are obtained from the commercial building, including air conditioners, lighting lamps, monitoring cameras, etc. on each floor. Each device corresponds to an Internet of Things sensor, such as an air conditioner temperature sensor, a lighting lamp brightness sensor, a monitoring video sensor, etc. The Internet of Things sensor is associated with the corresponding component-level entity, such as the camera, water meter and wireless door magnet at the door of the third floor of A, so that the data of the component-level entity, such as monitoring data, water meter data and door magnetic alarm, can be obtained in real time.

[0066] Step S203: Assign a unique identification code to the component-level entity, the identification code structure includes administrative code, spatial hash, timestamp, entity type code.

[0067] The identification code is used to identify and track the position, state and other attributes of the corresponding component-level entity in the digital twin model.

[0068] The administrative code is used to represent the administrative region where the component-level entity is located, such as the number of cities, districts, streets, etc.

[0069] The spatial hash is encoded according to the geographical position (such as longitude and latitude, elevation) of the component-level entity, which locates the component-level entity to the precise position.

[0070] The entity type code is the specific type of the component-level entity, such as a surveillance camera, a traffic signal, etc.

[0071] For example, a temperature and humidity sensor is deployed on a bridge in city A. It needs to generate an identification code. The code consists of the following parts: Administrative code: 010101, representing city A; Spatial hash: longitude 116.4000, latitude 39.9000, elevation 10 meters; Timestamp: 202504211045, representing April 21, 2025, 10:45; Entity type code: such as "A101", representing temperature and humidity sensor. Therefore, the identification code of the temperature and humidity sensor is 010101+116.4000, 39.9000, 10+202504211045+A101.

[0072] Step S204: Determine the Internet of Things data of the Internet of Things sensor according to the identification code.

[0073] After assigning the identification code to the component-level entity, the location of the Internet of Things sensor can be located through the identification code, and the data of the Internet of Things sensor can be obtained, and the Internet of Things data is the data collected by the Internet of Things sensor.

[0074] Step S205: Add the component-level entity and the Internet of Things data to the three-dimensional grid model to obtain a refined digital model.

[0075] First, add the component-level entity to the three-dimensional grid model, bind the component-level entity with the Internet of Things data. Extract the effective features from the Internet of Things data, and further refine the three-dimensional grid model according to the effective features to obtain a refined digital model.

[0076] For example, a certain mall in urban area A has an underground parking lot, and a plurality of cameras are installed in the underground parking lot. The cameras can perform effective feature extraction on the underground parking lot, and a small-scale modeling is performed according to the effective features to obtain an underground parking lot model. The underground parking lot model is integrated into the three-dimensional grid model, and combined with video data collected by the cameras to obtain a refined digital model.

[0077] By using the above technical solution, the component-level entity is obtained from the standard service data, and the component-level entity is associated with the Internet of Things sensor, so that dynamic perception of the facilities in the city can be realized. According to the unique identification code, the Internet of Things data corresponding to the component-level entity is determined, and the component-level entity and the Internet of Things data are added to the three-dimensional grid model to obtain a refined digital model, so that not only the form of the spatial entity can be reflected, but also the running state and real-time data of the spatial entity can be reflected, which helps to provide data support for urban planning strategies.

[0078] In the following embodiments, in order to enable the things in the refined digital model to have association capability, the present embodiment provides a city knowledge graph construction method, which refers to Figure 3 The method comprises the following steps. Step S301: Obtain a city node, which comprises a spatial entity node, a personnel node, an article node, an event node and a rule node.

[0079] The spatial entity node represents the specific geographic location of the spatial entity and the object represented by the spatial entity, such as a building, a road, a bridge, a green land, etc. The spatial entity node data can be extracted from the refined digital model.

[0080] The personnel node refers to the role information of the personnel in the city, such as residents, rescue personnel, medical personnel, traffic police, etc. The personnel node data can be obtained based on population statistics data and data in the public service system.

[0081] The article node represents the city resources or equipment with entity attributes, such as emergency supplies, traffic cameras, traffic signal lights, etc. The article node data can be obtained according to the identification code.

[0082] The event node is used to describe the events occurring in the city, such as floods, traffic accidents, large-scale gatherings, etc. The event node data can be obtained through the Internet of Things sensing data and the Internet of Things data.

[0083] The rule node represents the behavior specifications existing in the city, such as traffic management regulations, emergency response processes, etc. The rule node data can be obtained through policy documents, administrative regulations, legal regulations, etc. published by the government.

[0084] Step S302: Constructing semantic relationships between city nodes, including spatial topological relationships, time sequence relationships, and management attribution relationships.

[0085] The spatial topological relationship is used to describe the geographical spatial position relationship between city nodes. The geographical spatial position relationship includes proximity relationship, containment relationship, and connection relationship. For example, building A and building B are separated by a road, and building A and building B are in a proximity relationship; a park node contains several parking areas, and the park and the parking areas are in a containment relationship; in the case of an existing main road, a sub-road connected to the main road is built, and the main road and the sub-road are in a connection relationship.

[0086] The time sequence relationship is used to describe the change relationship of city nodes in the time dimension. For example, a fire occurs in a certain area of the city, and the timestamp when the fire occurs is obtained; as time goes by, rescue operations begin, and the timestamps of the fire truck leaving and arriving at the fire area are obtained; the fire is extinguished, and the timestamp when the fire is extinguished is obtained. By recording the time nodes in the process of the fire event triggering the rescue event, the time sequence relationship between the fire event and the rescue event can be reflected.

[0087] The management attribution relationship is used to describe the management responsibility, entity attribution, and area attribution relationship between city nodes. For example, the manhole cover in the road belongs to the municipal department.

[0088] The semantic relationships between city nodes can be manually input by technical personnel or obtained through a corresponding model.

[0089] Step S303: Constructing a city knowledge graph according to the city nodes and the semantic relationships.

[0090] The knowledge graph represents a relationship network composed of nodes and edges. Each node represents a spatial entity in the real world, and each edge represents the relationship between spatial entities. The nodes are city nodes, and the edges are semantic relationships between city nodes. The city nodes and the corresponding semantic relationships of the city nodes are mapped, and a knowledge graph structure is generated in a graph database, thereby forming a city knowledge graph.

[0091] Step S304: Adding the city knowledge graph to the refined digital model.

[0092] The city knowledge graph is fused with the refined digital model, enabling the refined model to have understanding ability in the semantic relationship level. For example, a user can click on building A on the Web, and can obtain relevant information of building A, such as the name of building A, the unit to which building A belongs, the type to which building A belongs, etc., through the city knowledge graph.

[0093] By adopting the technical scheme, the semantic relationship between city nodes is constructed, the city knowledge graph is generated according to the city nodes and the semantic relationship, and then the city knowledge graph is added to the refined digital model, so that the refined digital model can express the relationship between city elements, thereby improving the accuracy of the city planning strategy.

[0094] With reference to Figure 4 The step of generating the city planning strategy according to the digital twin model comprises: Step S401: water disaster simulation deduction based on the digital twin model.

[0095] The water disaster simulation deduction is a simulation of a water disaster in a city area by computer simulation technology to predict the impact of the water disaster.

[0096] For example, in the upstream area of city A, a flood disaster is simulated, a fluid mechanics simulation model is established based on the topography, drainage network and Internet of Things sensing data of city A, and the diffusion path of the flood is simulated.

[0097] Step S402: obtaining simulation data of the influence range of the water disaster changing over time.

[0098] The influence range is the area covered by the water flow during the water disaster simulation deduction.

[0099] The simulation data refers to the influence range of the water disaster at different times, including water depth, water flow velocity, water flow coverage area, etc. The simulation data is generated by the fluid mechanics simulation model.

[0100] Step S403: obtaining the area affected by the water disaster according to the simulation data in a preset time period to obtain a first high-risk area.

[0101] The preset time point is a preset constant, which is a period of time in the future after the water disaster simulation deduction starts, indicating the simulation time.

[0102] For example, a flood disaster is simulated in the upstream area of city A, and the start time is simulation time 6, and the preset time period can be set to 6 hours, so at simulation time 12, the area covered by the flood, i.e. the area affected by the water disaster, is obtained, thereby obtaining the first high-risk area.

[0103] Step S404: obtaining an overflow area in the first high-risk area.

[0104] The overflow area is a position where the flood first breaks through the original water boundary due to factors such as low terrain and concentrated water pressure, and overflow occurs, which is usually a branch point of flood diffusion.

[0105] For example, there is a river between the urban area A and the upstream area, and a low-lying area near the road X on the left bank of the river appears water overflow, and the water overflow from this point marks the area where this point is located as the overflow area.

[0106] Step S405: execute the preset prevention strategy, which includes using a preset water blocking method to block the water flow in the overflow area.

[0107] The preset prevention strategy is a strategy for preventing water overflow before the water disaster reaches the overflow area.

[0108] The preset water blocking method refers to the water blocking facilities set in advance to prevent the spread of flood in the overflow area, such as placing flood prevention boards, using mobile pump stations, sandbag embankments, etc.

[0109] Step S406: After repeating the water disaster simulation deduction for a preset number of times, obtain the second high-risk area according to the simulation data within a preset time period.

[0110] The preset time period in this step is the same as the preset time period in step S403. By obtaining the second high-risk area according to the simulation data within the same preset time period, the second high-risk area is obtained.

[0111] Step S407: Determine whether the second high-risk area contains the first high-risk area.

[0112] By comparing the range of the second high-risk area and the range of the first high-risk area, if the second high-risk area contains the first high-risk area, it means that the effect of blocking the water flow after the preset prevention strategy is not good, so step S408 is executed. If the second high-risk area does not contain the first high-risk area, it means that the effect of blocking the water flow after the preset prevention strategy is good, and the process of this embodiment is ended.

[0113] Step S408: If yes, execute the preset transfer strategy.

[0114] On the other hand, if the second high-risk area does not contain the first high-risk area, the process of this embodiment is ended.

[0115] If yes, it means that the second high-risk area contains the first high-risk area, so the preset transfer strategy is executed, and the specific steps of the preset transfer strategy can refer to the steps in the Figure 5 embodiment.

[0116] Step S409: Obtain the urban planning strategy based on the preset prevention strategy and the preset transfer strategy.

[0117] The urban planning strategy is obtained by combining the preset prevention strategy and the preset transfer strategy in the water disaster scenario.

[0118] By adopting the technical scheme, the water disaster simulation experiment is performed based on the digital twin model, the area affected by the water disaster is obtained at a preset time point, then the overflow area is obtained, and the preset prevention strategy is executed to block the water flow, the preset prevention strategy is judged to be effective or not according to the area affected by the water disaster through repeated water disaster simulation deduction, and in the case that the effect of the preset prevention strategy is not ideal, the preset transfer strategy is executed, therefore, the urban planning strategy is obtained by combining the preset prevention strategy and the preset transfer strategy.

[0119] With reference to Figure 5 The step of executing the preset transfer strategy comprises: Step S501: In the case that the influence range continuously expands over time, whether the area affected by the water disaster includes a critical area is predicted based on simulation data.

[0120] The critical area refers to an area closely related to human activities, such as a residential area, a hospital, a subway station, etc.

[0121] In the case that the influence range continuously expands over time, an additional time period is added based on the current time for predicting the area affected by the water disaster.

[0122] In this embodiment, the additional time period can be set to 6 hours of simulation time. For example, the starting time of the flood disaster simulation is 6 hours of simulation time, the current time is 12 hours, and the additional time period is 6 hours, so at 18 hours of simulation time, the influence range of the water disaster is obtained according to the simulation data to predict whether the influence range includes a critical area.

[0123] Step S502: If yes, an evacuation path from the critical area to a safe area is obtained.

[0124] In another aspect, if the area affected by the water disaster does not include a critical area based on the simulation data, the process of this embodiment is ended.

[0125] If yes, it indicates that the area affected by the water disaster includes a critical area based on the simulation data.

[0126] The safe area refers to an area that is not affected by the water disaster in the current water disaster simulation deduction scenario.

[0127] The evacuation path is a path from the critical area to the safe area.

[0128] Exemplarily, after the flood simulation is performed, it is predicted according to simulation data that the city A will be affected by the flood, and the city C is a safe area, so it is necessary to notify the people in the city A to go to the city C in advance. The fine digital model obtains all paths from the city A to the city C to obtain candidate paths. A plurality of candidate paths are obtained from the candidate paths by using a shortest path algorithm. Real-time road traffic rates are obtained in combination with traffic perception data, and the traffic perception data includes traffic camera data, traffic signal data, positioning data of a user mobile device, and the like. The candidate paths are dynamically weighted according to the real-time road traffic rates, and an optimal path is obtained from the candidate paths. The optimal path is the evacuation path.

[0129] Step S503: Push the evacuation path to the user mobile device in the key area.

[0130] The user mobile device is a smart device associated with the user location, such as a smart phone, a tablet computer, a wearable device, and the like.

[0131] The positioning data of the user mobile device can be obtained through a base station, and the corresponding evacuation path is sent to the user mobile device in the form of a short message according to the positioning data.

[0132] Step S504: Link the emergency resource scheduling system to publish an emergency notice according to the location of the key area, and the emergency notice includes an evacuation notice and a forbidden entry notice.

[0133] The emergency resource scheduling system is a system that can unifiedly command, coordinate and allocate various emergency resources when a sudden event occurs.

[0134] Exemplarily, after the emergency notice is published through the emergency resource scheduling system, the traffic police located in the key area or the traffic police closest to the key area will receive the emergency notice. The emergency notice includes an evacuation notice and a forbidden entry notice, and the traffic police need to be at the road connection between the key area and the safe area for traffic command. On the one hand, the traffic police need to command the vehicles from the key area to the safe area to prevent road congestion, and on the other hand, the traffic police need to prohibit the vehicles from entering the key area.

[0135] By using the above technical solution, in the case that the influence range continues to expand over time, the area affected by the flood can be predicted based on simulation data, and in the case that the area affected by the flood includes a key area, the evacuation path can be pushed to the user mobile device, and the emergency notice can be published through the emergency resource scheduling system, so that the public can be notified to transfer in advance before the flood arrives, to avoid casualties.

[0136] In the following embodiments, the public may pass through the areas associated with the key area in the process of going from the key area to the safe area, and if there are many vehicles, it is easy to cause the road of the associated area to be congested. In order to improve this problem, the embodiments of the present application provide a dredging method, referring to Figure 6 The method comprises: Step S601: Obtain the areas associated with the key area to obtain the associated areas.

[0137] For example, city A is a key area, city C is a safe area, and among the paths from city A to city C, there is a path passing through city B. City A can also directly lead to city D and city E, wherein the distance from city D to city A and the distance from city E to city A are both greater than the distance from city C to city A. City B, city D and city E are the associated areas of city A, that is, the associated areas of the key area.

[0138] Step S602: Based on the simulation data, it is detected whether there is an associated area affected by the flood in the case that the influence range is continuously expanding.

[0139] When performing flood simulation deduction, the flood simulation deduction time can be set to 24 hours, the simulation data at the time point after 24 hours of flood simulation deduction is obtained, and whether there is an associated area affected by the flood is detected based on the simulation data at the time point.

[0140] For example, after performing flood simulation deduction in the upstream area of city A, it is detected whether city B, city C, city D and city E are affected by the flood at the time point of simulation time 24 hours.

[0141] Step S603: If yes, mark the associated area affected by the flood as a target area.

[0142] In another aspect, if there is no associated area affected by the flood, no operation is performed.

[0143] If yes, it indicates that there is an associated area affected by the flood, and therefore the associated area affected by the flood is marked as a target area.

[0144] For example, at the time point of simulation time 18 hours after performing flood simulation deduction, the flood has spread to city B, and therefore city B is marked as a target area.

[0145] Step S604: Divide the target area into a plurality of sub-areas.

[0146] In the digital twin model, the target area is divided according to the road network structure to form a plurality of sub-areas. The basis for division includes the boundary lines of the main road and the sub-main road. Among them, when dividing the sub-area, the area of the sub-area needs to be kept relatively balanced.

[0147] For example, the area of the city B is 1 million square meters, and the city B is divided into 50 sub-areas, including area b1, area b2, area b3, …, and area b50. The area of each sub-area is 20,000 square meters, and the area deviation value can be set to ±10%.

[0148] Step S605: Forming a corresponding automobile dredging path according to each sub-area.

[0149] Grouping all paths in the sub-area to obtain path groups, each path group extends to the safe area, and in the extension process, adjacent path groups do not interfere with each other in the target area.

[0150] For example, areas b1 to b10 are the outer ring areas of the city B, and vehicles from the city A are guided to the roads corresponding to areas b1 to b10, so that the vehicles form an automobile dredging path to the city C without interfering with the traffic of the inner ring area of the city B. For the inner ring area of the city B, the roads where every 5 sub-areas are located are taken as a group of automobile dredging paths, and the geographical arrangement direction of the 5 sub-areas is the direction of approaching the city C in turn. The sub-trunk roads in each group of automobile dredging paths access a main trunk road, and the main trunk road extends towards the city C. The main trunk roads in all automobile dredging paths converge into the main road to the city C.

[0151] Step S606: Dynamically controlling the traffic signal by the traffic signal system to guide the vehicles to the automobile dredging path.

[0152] The traffic signal system can control the traffic signal, for example, setting the green light duration on the corresponding lane according to the traffic flow, and the traffic flow is positively correlated with the green light duration.

[0153] For example, if the traffic flow of the main trunk road in a group of automobile dredging paths P1 is large, in the first aspect, the time of the green light in the main trunk road can be extended; in the second aspect, the traffic signal of the main trunk road can be controlled to form a “green wave zone” control strategy; in the third aspect, if the traffic flow of the main trunk road in the automobile dredging path P2 adjacent to the main trunk road is small, the traffic signal can be controlled to make the vehicles shunt to the main trunk road in the automobile dredging path P2.

[0154] Step S607: Real-time monitoring of the traffic condition of the automobile dredging path.

[0155] The traffic condition can be obtained by the traffic camera of the corresponding road, the positioning of the user mobile device, the positioning of the automobile, etc., and can reflect the traffic flow and traffic condition of the automobile dredging path in real time.

[0156] Step S608: updating the car evacuation path according to the traffic condition after every preset interval time.

[0157] The preset interval time is a preset constant, which can be adjusted according to the actual traffic condition of the car evacuation path. In the embodiment, the preset interval time can be set to 10 minutes.

[0158] For example, if the sub-trunk in the car evacuation path P1 is in a jammed condition, and after 10 minutes, the sub-trunk is still in a jammed condition, the signal light at the intersection of the sub-trunk is controlled to avoid other vehicles entering the sub-trunk, and the path corresponding to the sub-trunk is temporarily removed from the car evacuation path.

[0159] By using the above technical solution, in the case of an associated area affected by water disaster, the associated area is marked as a target area, and the target area is divided into several sub-areas. The car is guided into the car evacuation path through the control of the signal light, thereby effectively improving the vehicle evacuation efficiency in the water disaster emergency scene, and enhancing the response ability and evacuation efficiency of the city in the disaster situation.

[0160] In the following embodiment, if the traffic condition of the car evacuation path of the target area is in a jammed condition after a long time, the vehicles are stranded in the target area and cannot be evacuated to the safe area. For this situation, the application provides a rescue method, referring to Figure 7 The method comprises: Step S701: determining whether the traffic condition is in a jammed state after a preset traffic duration.

[0161] The preset traffic duration is a preset constant, which can be adjusted according to the time requirement. In the embodiment, the preset traffic duration can be set to 5 hours.

[0162] Among them, when the traffic volume in the car evacuation path is large, and the average speed is lower than the preset lower limit speed, the car evacuation path is in a jammed state. The preset lower limit speed is a preset constant, and in the embodiment, the preset lower limit speed can be set to 2 km / h.

[0163] If the traffic condition is in a jammed state after the preset traffic duration, step S702 is performed.

[0164] Step S702: if yes, determining the location of the temporary settlement point in each sub-area.

[0165] On the other hand, if the traffic condition is not in a jammed state after the preset traffic duration, the flow of the embodiment is ended.

[0166] If yes, it indicates that the traffic condition is in a congestion state after the preset traffic duration, the position of the temporary placement point in each sub-region is determined according to the digital twin model, and if there is no temporary placement point in the sub-region, the temporary placement point closest to the sub-region is taken as the temporary placement point of the sub-region.

[0167] Step S703: The base station positioning system acquires the distance from the user mobile device to the nearest temporary placement point to obtain the nearest distance.

[0168] The base station positioning system can acquire the current position of the user mobile device, and in combination with the digital twin model, acquire the position information of the user mobile device, and according to the position information, acquire the distance from the user mobile device to the nearest temporary placement point, thereby obtaining the nearest distance.

[0169] Step S704: The temporary placement point information corresponding to the nearest distance is pushed to the user mobile device.

[0170] For example, after the nearest distance is acquired, the temporary placement point information is pushed to the user mobile device in a wireless communication manner (such as a short message, an APP message, a push notification, etc.). The temporary placement point information includes the detailed address of the temporary placement point, the route guidance to the temporary placement point, and the contact number of the temporary placement point.

[0171] Step S705: The emergency resource scheduling system notifies the emergency personnel to dispatch rescue materials to the temporary placement point.

[0172] The emergency resource scheduling system sends an emergency notification corresponding to the occupation of the emergency personnel.

[0173] For example, the emergency resource scheduling system sends a corresponding emergency notification to the emergency personnel (such as a rescue team, medical personnel, etc.), and notifies the emergency response personnel to go to the target placement point in the first time to assist in the rescue material receiving and placement work.

[0174] By using the above technical solution, in the case that the traffic condition is still in a congestion state after the preset traffic duration, the position of the temporary placement point in each sub-region is determined, the nearest temporary placement point information is sent to the user mobile device, so that the public can transfer to the temporary placement point, and the emergency personnel is notified by the emergency resource scheduling system to dispatch rescue materials to the temporary placement point, thereby reducing the probability of personnel casualties in the city under disaster conditions.

[0175] Based on the same inventive concept, the embodiments of the present application provide a city planning system based on digital twinning, comprising: The acquisition module is configured to acquire multi-source data. The memory is configured to store the program of the city planning method based on digital twinning. The processor can load and execute the program in the memory to implement the urban planning method based on the digital twin.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0177] The embodiment of the present application provides a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to implement an urban planning method based on a digital twin.

[0178] The computer storage medium includes, for example, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0179] Based on the same inventive concept, the embodiment of the present application provides an intelligent terminal, which comprises a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to implement an urban planning method based on a digital twin.

[0180] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0181] The above are preferred embodiments of the present application, and do not limit the protection scope of the present application. Any feature disclosed in the specification (including the abstract and drawings) can be replaced by other equivalent or similar features unless specifically described. That is, each feature is only an example of a series of equivalent or similar features unless specifically described.

Claims

1. A digital-twin-based urban planning method, characterized in that, The method comprises the following steps: acquiring city-related multi-source data, the multi-source data comprising geospatial data, Internet of Things (IoT) sensing data, and business data; standardizing and cleaning the multi-source data to obtain standard data, the standard data comprising standard geospatial data, standard IoT sensing data, and standard business data; constructing a three-dimensional grid model of a corresponding region based on the standard geospatial data; obtaining a refined digital model based on the three-dimensional grid model and the standard business data; using the standard IoT sensing data to dynamically correct the refined digital model to obtain a digital twin model synchronized with the actual city state; generating a city planning strategy based on the digital twin model.

2. The urban planning method based on digital twinning according to claim 1, characterized in that, The step of obtaining a refined digital model based on the three-dimensional grid model and the standard business data comprises the following steps: acquiring component-level entities from the standard business data, the component-level entities referring to single specific components or functional units extracted from spatial entities; associating the component-level entities with IoT sensors; assigning unique identification codes to the component-level entities, the identification code structure comprising an administrative area code, a spatial hash, a timestamp, and an entity type code; determining IoT data of the IoT sensors based on the identification codes; adding the component-level entities and the IoT data to the three-dimensional grid model to obtain the refined digital model.

3. The urban planning method based on digital twinning according to claim 2, characterized in that, The method further comprises the following steps: acquiring city nodes, the city nodes comprising spatial entity nodes, personnel nodes, article nodes, event nodes, and rule nodes; constructing semantic relationships between the city nodes, the semantic relationships comprising spatial topological relationships, time series relationships, and management and ownership relationships; constructing a city knowledge graph based on the city nodes and the semantic relationships; adding the city knowledge graph to the refined digital model.

4. The urban planning method based on digital twinning according to claim 1, characterized in that, The step of generating a city planning strategy based on the digital twin model comprises the following steps: conducting water disaster simulation deduction based on the digital twin model; acquiring simulation data of the influence range of a water disaster changing over time; acquiring areas affected by the water disaster based on the simulation data in a preset time period to obtain a first high-risk area; acquiring overflow areas in the first high-risk area; executing a preset prevention strategy, the preset prevention strategy comprising using a preset water blocking method to block water flow in the overflow areas; after repeating water disaster simulation deduction a preset number of times, acquiring areas affected by the water disaster based on the simulation data in the preset time period to obtain a second high-risk area; determining whether the second high-risk area contains the first high-risk area; if yes, executing a preset transfer strategy; obtaining the city planning strategy based on the preset prevention strategy and the preset transfer strategy.

5. The urban planning method based on digital twinning according to claim 4, characterized in that, The step of executing a preset transfer strategy comprises the following steps: in the case where the influence range continues to expand over time, predicting whether the area affected by the water disaster includes a critical area based on the simulation data; if yes, acquiring an evacuation path from the critical area to a safe area; pushing the evacuation path to a user mobile device of the critical area. The linkage emergency resource scheduling system publishes an emergency notice according to the position of the key area, and the emergency notice includes an evacuation notice and an entry prohibition notice.

6. The urban planning method based on digital twinning according to claim 5, characterized in that, The method further comprises: obtaining an associated area associated with the key area; based on the simulation data, predicting whether the associated area affected by the flood exists in the case of continuous expansion of the influence range; if yes, marking the associated area affected by the flood as a target area; dividing the target area into a plurality of sub-areas; formulating a corresponding automobile dredging path according to each sub-area; controlling a traffic signal lamp dynamically through a traffic signal lamp system to guide vehicles to the automobile dredging path; monitoring the traffic condition of the automobile dredging path in real time; updating the automobile dredging path according to the traffic condition after each interval of a preset interval time.

7. The urban planning method based on digital twinning according to claim 6, characterized in that, The method further comprises: judging whether the traffic condition is in a congestion state after a preset traffic duration; if yes, determining the position of a temporary settlement point in each sub-area; linking a base station positioning system to obtain the distance from the user mobile device to the nearest temporary settlement point, and obtaining the nearest distance; pushing temporary settlement point information corresponding to the nearest distance to the user mobile device; linking the emergency resource scheduling system to inform emergency personnel to dispatch rescue materials to the temporary settlement point. 8.A city planning system based on digital twinning, characterized in that, The system is used to execute the digital-twin-based urban planning method according to any one of claims 1 to 7, comprising: an acquisition module for acquiring multi-source data; a memory for storing the program of the digital-twin-based urban planning method; a processor, the program in the memory can be loaded and executed by the processor, and the digital-twin-based urban planning method is implemented.

9. A smart terminal, characterized by including a memory and a processor, the memory has a computer program which can be loaded and executed by the processor, and the method according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that, a computer program which can be loaded and executed by the processor, and the method according to any one of claims 1 to 7 is executed.

Images