A Smart City Digital Twin Map Management Method and System for Multiple Terminals

Abstract

The present invention discloses a method and system for managing a digital twin map of a smart city for multiple terminals, which relates to the technical field of urban map management, and solves the problem that the current urban map management system fails to obtain data in a timely manner and cannot reflect the traffic conditions in real time, resulting in a lag in the update of the urban map. The method includes: constructing a digital twin model of the city based on building model data and road model data; constructing a standard travel route from any departure place to a destination for a travel unit and a corresponding standard usage duration interval, comparing the actual travel route and the actual usage duration of the travel unit, and performing corresponding operations according to the comparison results; obtaining abnormal road sections corresponding to the actual travel route of the travel unit in the digital twin model of the city based on the actual usage duration of the travel unit; and updating the road data in the digital twin model of the city according to the actual travel route of the travel unit. The present invention realizes the intelligent management of the smart city map.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban map management, and specifically relates to a smart city digital twin map management method and system for multiple terminals. Background Art

[0002] The urban map management method refers to a management method for collecting, storing, processing, updating, and displaying urban map data through a series of systematic strategies and technical means. It involves the effective organization and optimization of urban geographic information to ensure the accuracy, timeliness, and accessibility of map data. This method usually combines tools such as geographic information systems (GIS), remote sensing technology, and spatial data analysis to support various applications such as urban planning, traffic management, environmental monitoring, and emergency response. The core goal of the urban map management method is to provide accurate geographic information support for urban managers, residents, and various services, thereby improving urban operation efficiency, enhancing public services, and promoting sustainable development.

[0003] In the prior art, urban map management systems often suffer from untimely data acquisition and inability to reflect real-time traffic conditions, resulting in the lag of urban map updates. Therefore, the present invention proposes a smart city digital twin map management method and system for multiple terminals. Summary of the Invention

[0004] The purpose of the present invention is to propose a smart city digital twin map management method and system for multiple terminals to solve the problems raised in the above background art.

[0005] The technical problem to be solved by the present invention is:

[0006] How to achieve the intelligent management of smart city maps.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions:

[0008] A smart city digital twin map management method for multiple terminals includes the following steps:

[0009] Step S1, obtain the building model data and road model data of the city, and construct a digital twin model of the city according to the building model data and road model data;

[0010] Step S2, obtain the category of travel units, then construct a standard travel route and the corresponding standard usage duration interval from any departure place to the destination of the travel unit, compare the actual travel route and actual usage duration of the travel unit, and perform corresponding operations according to the comparison results;

[0011] Step S3: Based on the actual usage duration of the travel unit and combined with the digital twin model of the city, obtain the abnormal sections corresponding to the actual travel route of the travel unit.

[0012] Step S4: Update the road data in the digital twin model of the city according to the actual travel route of the travel unit.

[0013] Furthermore, the building model data is the floor area, building height, external structure data of the buildings in the city, and the number of building floors.

[0014] The road model data of the city is the road width, traffic signs, and the number of road lanes in the city.

[0015] Furthermore, the said Step S1 includes the following sub-steps:

[0016] Step S11: Convert the building model data and road model data of the city into grid data of the building model and road model.

[0017] Step S12: Taking the geometric center of the city as the origin O, the due east direction as the positive direction of the X-axis, the due north direction as the positive direction of the Y-axis, and the plane perpendicular to the plane formed by the X-axis and Y-axis as the Z-axis, construct a three-dimensional coordinate system OXYZ, and construct the grid data of the building model and road model one by one within the three-dimensional coordinate system to obtain the building model and road model.

[0018] Step S13: Add building attributes to the building model and at the same time add road attributes to the road model.

[0019] Step S14: Integrate the building model with the corresponding building attributes and the road model with the corresponding road attributes and record them as the digital twin model of the city.

[0020] Furthermore, the building attributes include function type, building name, and building number, and the road attributes include the number of road lanes, road name, and road type.

[0021] Furthermore, the travel unit categories include the first travel unit and the second travel unit. The first travel unit is pedestrians, and the second travel unit is motor vehicles.

[0022] Furthermore, the said Step S2 includes the following sub-steps:

[0023] Step S21: Obtain the travel unit category of the travel unit, as well as the departure place and destination of the travel unit. Plan the travel route according to the travel unit category, departure place, and destination of the travel unit to obtain the standard travel route of the travel unit and the corresponding standard usage duration interval, and display the standard travel route in the digital twin model of the city.

[0024] Step S22: After the travel unit arrives at the destination, obtain the actual travel route and actual usage duration of the travel unit.

[0025] Step S23: Compare the standard usage duration interval of the travel unit with the actual usage duration.

[0026] When the actual usage duration of the travel unit is within the standard usage duration interval, do nothing.

[0027] When the actual usage duration of the travel unit is greater than the maximum endpoint value of the standard usage duration interval, enter Step S3.

[0028] When the actual usage duration of the travel unit is less than the minimum endpoint value of the standard usage duration interval, enter Step S4.

[0029] Furthermore, the said Step S3 includes the following sub-steps:

[0030] Step S31: When the travel unit category of the travel unit is the first travel unit, obtain the traffic lights in the first actual travel route corresponding to the first travel unit through the digital twin model of the city, and then subtract the maximum waiting duration of all traffic lights in the first actual travel route from the first actual usage duration of the first travel unit to obtain the first estimated usage duration.

[0031] Step S32: If the first estimated usage duration is less than or equal to the maximum endpoint value of the standard usage duration interval, do nothing.

[0032] If the first estimated usage duration is greater than the maximum endpoint value of the standard usage duration interval, enter the next step.

[0033] Step S33: Divide the first actual travel route of the first travel unit into first actual travel sub-routes of the same length, divide the first estimated usage duration of the first travel unit by the number of segments of the actual travel sub-routes to obtain the first sub-actual usage duration of all first actual travel sub-routes, and divide the maximum endpoint value of the standard usage duration interval by the number of segments of the first actual travel sub-routes to obtain the first sub-standard usage duration of any actual travel sub-route.

[0034] Step S34: If the first sub-actual usage duration of any first actual travel sub-route is greater than the corresponding first sub-standard usage duration, mark the corresponding first actual travel sub-route as a suspected abnormal section.

[0035] If the first sub-actual usage duration of all first actual travel sub-routes is less than or equal to the corresponding first sub-standard usage duration, do nothing; where the first actual travel route is the actual travel route of the first travel unit, and the second actual travel route is the actual travel route of the second travel unit.

[0036] Further, step S3 further includes the following sub-steps:

[0037] Step S35, when the travel unit category of the travel unit is the second travel unit, obtain the traffic lights in the second actual travel route corresponding to the second travel unit through the digital twin model of the city, and then subtract the maximum waiting time of all traffic lights in the second actual travel route from the second actual usage time of the second travel unit to obtain the second estimated usage time;

[0038] If the second estimated usage time is less than or equal to the maximum endpoint value of the standard usage time interval, no operation is performed;

[0039] If the second estimated usage time is greater than the maximum endpoint value of the standard usage time interval, proceed to the next step;

[0040] Step S36, similarly, according to steps S31 to S33, obtain the second actual travel sub-route of the second travel unit, as well as the second sub-actual usage time and the second sub-standard usage time of the second actual travel sub-route;

[0041] If the second sub-actual usage time of any second actual travel sub-route is greater than the corresponding second sub-standard usage time, mark this second actual travel sub-route as a suspected abnormal section;

[0042] If the second sub-actual usage times of all second actual travel sub-routes are less than or equal to the corresponding second sub-standard usage times, no operation is performed;

[0043] Step S37, obtain the real-time monitoring data of the suspected abnormal section and detect the real-time monitoring data. If there are abnormal behaviors on the road in the real-time monitoring data, determine that the suspected abnormal section is an abnormal section; if the real-time monitoring data is normal real-time monitoring data, no operation is performed;

[0044] Step S38, mark the abnormal section in the digital twin model of the city.

[0045] Further, step S4 includes the following sub-steps:

[0046] Step S41, obtain the first actual travel route of the first travel unit and compare the first actual travel route with the first standard travel route;

[0047] Step S42, if the first actual travel route is exactly the same as the first standard travel route, determine that the first actual travel route of the first travel unit is normal and no operation is performed;

[0048] Step S43, if there are differences between the first actual travel route and the first standard travel route, mark the different sections between the first actual travel route and the first standard travel route as the first suspected updated sections;

[0049] Step S44, count the number of times different first travel units walk on the first suspected updated sections. If the number of times a first travel unit walks on the first suspected updated sections is greater than or equal to the first number threshold, mark the first suspected updated sections as the first new sections and add them to the digital twin model of the city; if the number of times a first travel unit walks on the first suspected updated sections is less than the first number threshold, do nothing;

[0050] Step S45, obtain the second actual travel route of the second travel unit, and compare the second actual travel route with the second standard travel route;

[0051] Step S46, if the second actual travel route is exactly the same as the second standard travel route, determine that the second actual travel route of the second travel unit is normal and do nothing;

[0052] If there are differences between the second actual travel route and the second standard travel route, mark the different sections between the second actual travel route and the second standard travel route as the second suspected updated sections;

[0053] Step S47, count the number of times different second travel units drive on the second suspected updated sections. If the number of times a second travel unit drives on the second suspected updated sections is greater than or equal to the first number threshold, mark the second suspected updated sections as the second new sections and add them to the digital twin model of the city; if the number of times a second travel unit drives on the second suspected updated sections is less than the first number threshold, do nothing;

[0054] Step S48, if the first new sections and the second new sections overlap, merge the first new sections and the second new sections into common sections and mark them in the digital twin model of the city;

[0055] If the first new sections and the second new sections do not overlap, mark the first new sections and the second new sections in the digital twin model of the city at the same time.

[0056] In a second aspect, a smart city digital twin map management system for multiple terminals includes:

[0057] A data acquisition module for acquiring building model data and road model data of a city;

[0058] A model construction module for constructing a digital twin model of a city according to the building model data and the road model data;

[0059] A path planning module, which is used to obtain the category of a travel unit, then construct a standard travel route from any departure place to the destination of the travel unit and the corresponding standard usage duration interval, compare the actual travel route and actual usage duration of the travel unit, and perform corresponding operations according to the comparison result;

[0060] An anomaly analysis module, which is used to obtain the corresponding abnormal section in the actual travel route of the travel unit according to the actual usage duration of the travel unit in combination with the digital twin model of the city;

[0061] An intelligent update module, which updates the road data in the digital twin model of the city according to the actual travel route of the travel unit.

[0062] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0063] The present invention first obtains the building model data and road model data of the city, then constructs the digital twin model of the city according to the building model data and road model data, and at the same time obtains the category of the travel unit, then constructs the standard travel route from any departure place to the destination of the travel unit and the corresponding standard usage duration interval, compares the actual travel route and actual usage duration of the travel unit, performs corresponding operations according to the comparison result, obtains the corresponding abnormal section in the actual travel route of the travel unit according to the actual usage duration of the travel unit in combination with the digital twin model of the city, and finally updates the road data in the digital twin model of the city according to the actual travel route of the travel unit. The present invention realizes the intelligent management and intelligent update of the smart city map. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0065] Figure 1 It is the flowchart of the method of the present invention;

[0066] Figure 2 It is the overall system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Embodiment 1: Please refer to Figure 1As shown in the figure, the technical solution provided by the present invention is: a method for managing a digital twin map of a smart city for multiple terminals, which is used to facilitate subsequent avoidance by personnel or vehicles in the area with the help of the route data of pedestrians or vehicles that have traveled, including the following steps:

[0069] Step S1, obtain the building model data and road model data of the city, and construct a digital twin model of the city according to the building model data and road model data;

[0070] Specifically, the building model data includes the floor area of buildings in the city, the building height, the external structure data of buildings, and the number of building floors, etc.; the road model data of the city includes the road width, traffic signs, and the number of road lanes in the city, etc.;

[0071] In this embodiment, step S1 includes the following sub-steps:

[0072] Step S11, convert the building model data and road model data of the city into grid data of the building model and the road model;

[0073] In specific implementation, denoise the building model data and road model data of the city, then fill in the missing data, and perform meshing through a point cloud processing algorithm to obtain the grid data of the building model and the road model;

[0074] Step S12, take the geometric center of the city as the origin O, the due east direction as the positive direction of the X-axis, the due north direction as the positive direction of the Y-axis, and the plane perpendicular to the plane formed by the X-axis and the Y-axis as the Z-axis to construct a three-dimensional coordinate system OXYZ, and construct the grid data of the building model and the road model one by one in the three-dimensional coordinate system to obtain the building model and the road model;

[0075] Step S13, add building attributes to the building model and add road attributes to the road model at the same time;

[0076] Step S14, integrate the building model with the corresponding building attributes and the road model with the corresponding road attributes and record them as the digital twin model of the city;

[0077] It should be specifically noted that the building attributes include function type, building name, building number, etc., and the road attributes include the number of road lanes, road name, road type, etc.;

[0078] Step S2, obtain the category of the travel unit, then construct the standard travel route of the travel unit from any departure place to the destination and the corresponding standard usage duration interval, compare the actual travel route and actual usage duration of the travel unit, and perform corresponding operations according to the comparison results;

[0079] Specifically, the travel unit categories include the first travel unit and the second travel unit. The first travel unit is a pedestrian, and the second travel unit is a motor vehicle;

[0080] In this embodiment, step S2 includes the following sub-steps:

[0081] Step S21, obtain the travel unit category of the travel unit, as well as the departure place and destination of the travel unit. Plan the travel route according to the travel unit category and the departure place and destination of the travel unit. Obtain the standard travel route of the travel unit and the corresponding standard usage duration interval, and display the standard travel route in the digital twin model of the city;

[0082] Specifically, planning the travel route according to the travel unit category and the departure place and destination of the travel unit can be carried out through a deep learning model. The deep learning model can automatically optimize the travel route according to the category of the travel unit and the departure place and destination, without relying on manual intervention, improving the efficiency of route planning, reducing the need for manual calculation and adjustment. At the same time, the deep learning model can process a large amount of input data in real time and quickly generate the travel route;

[0083] Step S22, when the travel unit arrives at the destination, obtain the actual travel route and actual usage duration of the travel unit;

[0084] Step S23, compare the standard usage duration interval of the travel unit with the actual usage duration;

[0085] When the actual usage duration of the travel unit is within the standard usage duration interval, no operation is performed;

[0086] When the actual usage duration of the travel unit is greater than the maximum endpoint value of the standard usage duration interval, enter step S3;

[0087] Specifically, when the actual time spent by pedestrians and vehicles exceeds the standard duration, pedestrians or vehicles may encounter unexpected situations such as traffic accidents or traffic jams on a certain section of the travel route;

[0088] When the actual usage duration of the travel unit is less than the minimum endpoint value of the standard usage duration interval, enter step S4;

[0089] Specifically, there may be unupdated urban roads on the map. The roads may have been closed for construction before, or a new road may have been newly opened.

[0090] Step S3, according to the actual usage duration of the travel unit, combine with the digital twin model of the city to obtain the abnormal section corresponding to the actual travel route of the travel unit;

[0091] In this embodiment, step S3 includes the following sub-steps:

[0092] Step S31: When the travel unit category of the travel unit is the first travel unit, obtain the traffic lights in the first actual travel route corresponding to the first travel unit through the digital twin model of the city, and then subtract the maximum waiting time of all traffic lights in the first actual travel route from the first actual usage duration of the first travel unit to obtain the first estimated usage duration;

[0093] Step S32: If the first estimated usage duration is less than or equal to the maximum endpoint value of the standard usage duration interval, do nothing;

[0094] If the first estimated usage duration is greater than the maximum endpoint value of the standard usage duration interval, proceed to the next step;

[0095] Step S33: Divide the first actual travel route of the first travel unit into first actual travel sub-routes of the same length, divide the first estimated usage duration of the first travel unit by the number of segments of the actual travel sub-routes to obtain the first sub-actual usage duration of all first actual travel sub-routes, and divide the maximum endpoint value of the standard usage duration interval by the number of segments of the first actual travel sub-routes to obtain the first sub-standard usage duration of any actual travel sub-route;

[0096] Step S34: If the first sub-actual usage duration of any first actual travel sub-route is greater than the corresponding first sub-standard usage duration, mark the corresponding first actual travel sub-route as a suspected abnormal section;

[0097] If the first sub-actual usage duration of all first actual travel sub-routes is less than or equal to the corresponding first sub-standard usage duration, do nothing;

[0098] It should be specifically noted that in this embodiment, the first actual travel route is the actual travel route of the first travel unit, and the second actual travel route is the actual travel route of the second travel unit;

[0099] Step S35: When the travel unit category of the travel unit is the second travel unit, obtain the traffic lights in the second actual travel route corresponding to the second travel unit through the digital twin model of the city, and then subtract the maximum waiting time of all traffic lights in the second actual travel route from the second actual usage duration of the second travel unit to obtain the second estimated usage duration;

[0100] If the second estimated usage duration is less than or equal to the maximum endpoint value of the standard usage duration interval, do nothing;

[0101] If the second estimated usage duration is greater than the maximum endpoint value of the standard usage duration interval, proceed to the next step;

[0102] Step S36. Similarly, according to Steps S31 to S33, obtain the second actual travel sub-route of the second travel unit, as well as the second sub-actual usage duration and the second sub-standard usage duration of the second actual travel sub-route;

[0103] If the second sub-actual usage duration of any second actual travel sub-route is greater than the corresponding second sub-standard usage duration, then mark this second actual travel sub-route as a suspected abnormal section;

[0104] If the second sub-actual usage durations of all second actual travel sub-routes are less than or equal to the corresponding second sub-standard usage durations, then do not perform any operation;

[0105] Step S37. Obtain the real-time monitoring data of the suspected abnormal section and detect the real-time monitoring data. If there are abnormal behaviors on the road in the real-time monitoring data, then determine that the suspected abnormal section is an abnormal section; if the real-time monitoring data is normal real-time monitoring data, then do not perform any operation;

[0106] Actually, the real-time monitoring data of all sections are stored in the database. When it is determined as a suspected abnormal section, its corresponding real-time monitoring data can be obtained through the database;

[0107] It should be specifically noted that the abnormal behaviors on the road include road congestion, road accidents, road closures, etc.;

[0108] Step S38. Mark the abnormal section in the digital twin model of the city;

[0109] Actually, when the travel routes of other people include abnormal sections, the abnormal sections can be highlighted or warned through the navigation software.

[0110] Step S4. Update the road data in the digital twin model of the city according to the actual travel route of the travel unit;

[0111] In this embodiment, Step S4 includes the following sub-steps:

[0112] Step S41. Obtain the first actual travel route of the first travel unit and compare the first actual travel route with the first standard travel route;

[0113] Step S42. If the first actual travel route is exactly the same as the first standard travel route, then determine that the first actual travel route of the first travel unit is normal and do not perform any operation;

[0114] Step S

[0115] Step S44: Count the number of times different first travel units walk on the first suspected updated road section. If the number of times a first travel unit walks on the first suspected updated road section is greater than or equal to the first count threshold, mark the first suspected updated road section as the first new road section and add it to the digital twin model of the city; if the number of times a first travel unit walks on the first suspected updated road section is less than the first count threshold, do nothing.

[0116] Step S45: Obtain the second actual travel route of the second travel unit and compare the second actual travel route with the second standard travel route.

[0117] Step S46: If the second actual travel route is exactly the same as the second standard travel route, determine that the second actual travel route of the second travel unit is normal and do nothing.

[0118] If there are differences between the second actual travel route and the second standard travel route, mark the different road sections between the second actual travel route and the second standard travel route as the second suspected updated road sections.

[0119] Step S47: Count the number of times different second travel units drive on the second suspected updated road section. If the number of times a second travel unit drives on the second suspected updated road section is greater than or equal to the first count threshold, mark the second suspected updated road section as the second new road section and add it to the digital twin model of the city; if the number of times a second travel unit drives on the second suspected updated road section is less than the first count threshold, do nothing.

[0120] Step S48: If the first new road section overlaps with the second new road section, merge the above road sections into a shared road section and mark it in the digital twin model of the city.

[0121] If the first new road section does not overlap with the second new road section, mark both the first new road section and the second new road section in the digital twin model of the city.

[0122] It should be specifically noted that for roads that have not been updated in time on the map and have been walked by pedestrians and vehicles many times, these roads can be incorporated into the navigation system for the subsequent use of pedestrians and vehicles, thus saving time.

[0123] In this application, if there are corresponding calculation formulas, the above calculation formulas are all dimensionless and take their numerical values for calculation. The weight coefficients, proportionality coefficients, and other coefficients in the formulas are set to obtain a result value by quantifying each parameter. Regarding the magnitudes of the weight coefficients and proportionality coefficients, as long as they do not affect the proportional relationship between the parameters and the result value, it is fine.

[0124] Embodiment 2: Please refer to Figure 2As shown below, based on another concept of the same invention, a digital twin map management method for a smart city facing multiple terminals is proposed, including:

[0125] A data acquisition module, configured to acquire building model data and road model data of a city;

[0126] A model construction module, configured to construct a digital twin model of the city according to the building model data and the road model data;

[0127] A path planning module, configured to obtain the category of a travel unit, then construct a standard travel route and a corresponding standard usage duration interval from any departure place to a destination of the travel unit, compare the actual travel route and the actual usage duration of the travel unit, and perform corresponding operations according to the comparison results;

[0128] An anomaly analysis module, configured to obtain an abnormal section corresponding to the actual travel route of the travel unit by combining the actual usage duration of the travel unit with the digital twin model of the city;

[0129] An intelligent update module, configured to update the road data in the digital twin model of the city according to the actual travel route of the travel unit.

[0130] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific embodiments shown. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A method for managing a digital twin map of a smart city for multiple terminals, characterized in that, It includes the following steps: Step S1: Obtain the building model data and road model data of the city, and construct a digital twin model of the city based on the building model data and road model data; Step S2: Obtain the category of the travel unit, then construct the standard travel route of the travel unit from any departure place to the destination and the corresponding standard usage duration interval, compare the actual travel route and the actual usage duration of the travel unit, and perform corresponding operations according to the comparison results; Step S3: According to the actual usage duration of the travel unit, combine the digital twin model of the city to obtain the abnormal section corresponding to the actual travel route of the travel unit; Step S4: Update the road data in the digital twin model of the city according to the actual travel route of the travel unit; The said Step S4 includes the following sub-steps: Step S41: Obtain the first actual travel route of the first travel unit, and compare the first actual travel route with the first standard travel route; Step S42: If the first actual travel route is exactly the same as the first standard travel route, it is determined that the first actual travel route of the first travel unit is normal, and no operation is performed; Step S43: If there are differences between the first actual travel route and the first standard travel route, mark the different sections between the first actual travel route and the first standard travel route as the first suspected update section; Step S44: Count the number of times different first travel units walk on the first suspected update section. If the number of times the first travel unit walks on the first suspected update section is greater than or equal to the first number threshold, mark the first suspected update section as the first new section and add it to the digital twin model of the city; If the number of times the first travel unit walks on the first suspected update section is less than the first number threshold, no operation is performed; Step S45: Obtain the second actual travel route of the second travel unit, and compare the second actual travel route with the second standard travel route; Step S46: If the second actual travel route is exactly the same as the second standard travel route, it is determined that the second actual travel route of the second travel unit is normal, and no operation is performed; If there are differences between the second actual travel route and the second standard travel route, mark the different sections between the second actual travel route and the second standard travel route as the second suspected update section; Step S47: Count the number of times different second travel units drive on the second suspected update section. If the number of times the second travel unit drives on the second suspected update section is greater than or equal to the first number threshold, mark the second suspected update section as the second new section and add it to the digital twin model of the city; If the number of times the second travel unit drives on the second suspected update section is less than the first number threshold, no operation is performed; Step S48: If the first new section and the second new section overlap, merge the first new section and the second new section into a common section and mark it in the digital twin model of the city; If the first new section and the second new section do not overlap, mark the first new section and the second new section in the digital twin model of the city at the same time.

2. The method for managing a digital twin map of a smart city for multiple terminals according to claim 1, wherein, The building model data is the floor area, building height, external structure data of the buildings in the city, and the number of building floors; The road model data of the city is the road width, traffic signs, and the number of road lanes of the roads in the city.

3. A method for managing a digital twin map of a smart city for multiple terminals according to claim 2, characterized in that, The step S1 includes the following sub-steps: Step S11, convert the building model data and road model data of the city into grid data of the building model and the road model; Step S12, take the geometric center of the city as the origin O, the due east direction as the positive direction of the X-axis, the due north direction as the positive direction of the Y-axis, and the plane perpendicular to the plane formed by the X-axis and the Y-axis as the Z-axis to construct a three-dimensional coordinate system OXYZ. Construct the grid data of the building model and the road model one by one in the three-dimensional coordinate system to obtain the building model and the road model; Step S13, add building attributes to the building model and add road attributes to the road model at the same time; Step S14, integrate the building model with the corresponding building attributes and the road model with the corresponding road attributes and record them as the digital twin model of the city.

4. A method for managing a digital twin map of a smart city for multiple terminals according to claim 3, characterized in that, Building attributes include function type, building name, and building number, and road attributes include the number of road lanes, road name, and road type.

5. A method for managing a digital twin map of a smart city for multiple terminals according to claim 4, characterized in that, The travel unit categories include the first travel unit and the second travel unit. The first travel unit is pedestrians, and the second travel unit is motor vehicles.

6. A method for managing a digital twin map of a smart city for multiple terminals according to claim 5, characterized in that, The step S2 includes the following sub-steps: Step S21, obtain the travel unit category of the travel unit and the departure place and destination of the travel unit. Plan the travel route according to the travel unit category and the departure place and destination of the travel unit to obtain the standard travel route of the travel unit and the corresponding standard usage duration interval, and display the standard travel route in the digital twin model of the city; Step S22, when the travel unit arrives at the destination, obtain the actual travel route and actual usage duration of the travel unit; Step S23, compare the standard usage duration interval of the travel unit with the actual usage duration; When the actual usage duration of the travel unit is within the standard usage duration interval, do nothing; When the actual usage duration of the travel unit is greater than the maximum end point value of the standard usage duration interval, enter step S3; When the actual usage duration of the travel unit is less than the minimum end point value of the standard usage duration interval, enter step S4.

7. A method for managing a digital twin map of a smart city oriented to multiple terminals according to claim 6, characterized in that The step S3 includes the following sub-steps: Step S31, when the travel unit category of the travel unit is the first travel unit, obtain the traffic lights in the first actual travel route corresponding to the first travel unit through the digital twin model of the city, and then subtract the maximum waiting duration of all traffic lights in the first actual travel route from the first actual usage duration of the first travel unit to obtain the first estimated usage duration; Step S32, if the first estimated usage duration is less than or equal to the maximum end point value of the standard usage duration interval, do nothing; If the first estimated usage duration is greater than the maximum end point value of the standard usage duration interval, enter the next step; Step S33: Divide the first actual travel route of the first travel unit into first actual travel sub-routes of the same length. Divide the first estimated travel duration of the first travel unit by the number of segments of the actual travel sub-routes to obtain the first sub-actual travel durations of all the first actual travel sub-routes. Divide the maximum endpoint value of the standard travel duration interval by the number of segments of the first actual travel sub-routes to obtain the first sub-standard travel duration of any actual travel sub-route. Step S34: If the first sub-actual travel duration of any first actual travel sub-route is greater than the corresponding first sub-standard travel duration, mark the corresponding first actual travel sub-route as a suspected abnormal section. If the first sub-actual travel durations of all the first actual travel sub-routes are less than or equal to the corresponding first sub-standard travel durations, do nothing; wherein, the first actual travel route is the actual travel route of the first travel unit, and the second actual travel route is the actual travel route of the second travel unit.

8. A method for managing a digital twin map of a smart city for multiple terminals according to claim 7, characterized in that, The said Step S3 further includes the following sub-steps: Step S35: When the travel unit category of the travel unit is the second travel unit, obtain the traffic lights in the second actual travel route corresponding to the second travel unit through the digital twin model of the city, and then subtract the maximum waiting duration of all the traffic lights in the second actual travel route from the second actual travel duration of the second travel unit to obtain the second estimated travel duration. If the second estimated travel duration is less than or equal to the maximum endpoint value of the standard travel duration interval, do nothing. If the second estimated travel duration is greater than the maximum endpoint value of the standard travel duration interval, proceed to the next step. Step S36: Similarly, according to Steps S31 to S33, obtain the second actual travel sub-routes of the second travel unit and the second sub-actual travel durations and second sub-standard travel durations of the second actual travel sub-routes. If the second sub-actual travel duration of any second actual travel sub-route is greater than the corresponding second sub-standard travel duration, mark the second actual travel sub-route as a suspected abnormal section. If the second sub-actual travel durations of all the second actual travel sub-routes are less than or equal to the corresponding second sub-standard travel durations, do nothing. Step S37: Obtain the real-time monitoring data of the suspected abnormal section and detect the real-time monitoring data. If there are abnormal behaviors on the road in the real-time monitoring data, determine that the suspected abnormal section is an abnormal section; if the real-time monitoring data is normal real-time monitoring data, do nothing. Step S38: Mark the abnormal section in the digital twin model of the city.

9. A smart city digital twin map management system for multiple terminals, characterized in that, A method for managing a digital twin map of a smart city for multiple terminals according to any one of claims 1-8, comprising: A data acquisition module for acquiring building model data and road model data of the city. A model construction module for constructing a digital twin model of the city according to the building model data and the road model data. A path planning module, which is used to obtain the category of a travel unit, then construct a standard travel route from any departure place to a destination for the travel unit and the corresponding standard usage duration interval, compare the actual travel route and the actual usage duration of the travel unit, and perform corresponding operations according to the comparison results; An anomaly analysis module, which is used to obtain the abnormal sections corresponding to the actual travel route of the travel unit in combination with the digital twin model of the city according to the actual usage duration of the travel unit; An intelligent update module, which updates the road data in the digital twin model of the city according to the actual travel route of the travel unit. The working process is as follows: Obtain the first actual travel route of the first travel unit, and compare the first actual travel route with the first standard travel route; If the first actual travel route is exactly the same as the first standard travel route, it is determined that the first actual travel route of the first travel unit is normal and no operation is performed; if there are differences between the first actual travel route and the first standard travel route, the different sections between the first actual travel route and the first standard travel route are marked as the first suspected update sections; Count the number of times different first travel units walk on the first suspected update section. If the number of times the first travel unit walks on the first suspected update section is greater than or equal to the first number threshold, the first suspected update section is marked as the first new section and added to the digital twin model of the city; if the number of times the first travel unit walks on the first suspected update section is less than the first number threshold, no operation is performed; Obtain the second actual travel route of the second travel unit, and compare the second actual travel route with the second standard travel route; If the second actual travel route is exactly the same as the second standard travel route, it is determined that the second actual travel route of the second travel unit is normal and no operation is performed; if there are differences between the second actual travel route and the second standard travel route, the different sections between the second actual travel route and the second standard travel route are marked as the second suspected update sections; Count the number of times different second travel units drive on the second suspected update section. If the number of times the second travel unit drives on the second suspected update section is greater than or equal to the first number threshold, the second suspected update section is marked as the second new section and added to the digital twin model of the city; if the number of times the second travel unit drives on the second suspected update section is less than the first number threshold, no operation is performed; If the first new section overlaps with the second new section, the first new section and the second new section are merged into a shared section and marked in the digital twin model of the city; If the first new section does not overlap with the second new section, the first new section and the second new section are both marked in the digital twin model of the city.

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