Multi-terminal-oriented smart city digital twin map management method and system

Abstract

The invention discloses a multi-terminal-oriented smart city digital twin map management method and a multi-terminal-oriented smart city digital twin map management system, relates to the technical field of city map management, and solves the problem that a current city map management system cannot acquire data in time and cannot reflect traffic conditions in real time, so that city map updating lags behind. The method comprises the following steps: constructing a digital twin model of a city according to building model data and road model data; constructing a standard travel route of the travel unit from any place of departure to the destination and a corresponding standard use time interval, comparing the actual travel route of the travel unit with the actual use time, and performing corresponding operation according to a comparison result; according to the actual use duration of the travel unit, obtaining a corresponding abnormal road section in the actual travel route of the travel unit in combination with a digital twin model of the city; and updating the road data in the digital twin model of the city according to the actual travel route of the travel unit. According to the invention, intelligent management of a smart city map is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban map management, and specifically is a multi-terminal smart city digital twin map management method and system. Background Art

[0002] Urban map management method refers to the management method of 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 system (GIS), remote sensing technology, spatial data analysis, etc. to support various applications such as urban planning, traffic management, environmental monitoring, emergency response, etc. The core goal of urban map management method is to provide accurate geographic information support for urban managers, residents and various services, so as to improve the efficiency of urban operation, improve public services and promote sustainable development.

[0003] In the prior art, city map management systems often fail to obtain data in a timely manner and are unable to reflect traffic conditions in real time, resulting in delayed city map updates. To this end, the present invention proposes a multi-terminal smart city digital twin map management method and system. Summary of the invention

[0004] The purpose of the present invention is to propose a multi-terminal smart city digital twin map management method and system to solve the problems raised in the above background technology.

[0005] The technical problems to be solved by the present invention are:

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

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A multi-terminal smart city digital twin map management method includes the following steps:

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

[0010] Step S2, obtaining the travel unit category of the travel unit, and then constructing a standard travel route and a corresponding standard usage time interval of the travel unit from any departure point to the destination, comparing the actual travel route and the actual usage time of the travel unit, and performing corresponding operations according to the comparison result;

[0011] Step S3, according to the actual use time of the travel unit, combined with the digital twin model of the city, obtain the corresponding abnormal section in the actual travel route of the travel unit;

[0012] Step S4: updating the road data in the digital twin model of the city based on the actual travel routes of the travel units.

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

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

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

[0016] Step S11, converting the building model data and road model data of the city into grid data of the building model and the 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 X axis and the Y axis as the Z axis, 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;

[0018] Step S13, adding building attributes to the building model and adding road attributes to the road model;

[0019] Step S14: Integrate the building model and the corresponding building attributes, and the road model and the corresponding road attributes into a 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 a first travel unit and a second travel unit, the first travel unit is a pedestrian, and the second travel unit is a motor vehicle.

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

[0023] Step S21, obtaining the travel unit category and the departure and destination of the travel unit, planning the travel route according to the travel unit category and the departure and destination of the travel unit, planning to obtain the standard travel route of the travel unit and the corresponding standard usage time interval, and displaying the standard travel route in the digital twin model of the city;

[0024] Step S22, when the travel unit arrives at the destination, the actual travel route and actual travel time of the travel unit are obtained;

[0025] Step S23, comparing the standard usage time interval of the travel unit with the actual usage time;

[0026] When the actual usage time of the travel unit is within the standard usage time range, no operation is performed;

[0027] When the actual usage time of the travel unit is greater than the maximum endpoint value of the standard usage time interval, proceed to step S3;

[0028] When the actual usage time of the travel unit is less than the minimum endpoint value of the standard usage time interval, proceed to step S4.

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

[0030] Step S31: when the travel unit category of the travel unit is the first travel unit, the traffic lights in the first actual travel route corresponding to the first travel unit are obtained through the digital twin model of the city, and then the first actual use time of the first travel unit is subtracted from the maximum waiting time of all traffic lights in the first actual travel route to obtain a first estimated use time;

[0031] Step S32: if the first estimated usage time is less than or equal to the maximum endpoint value of the standard usage time interval, no operation is performed;

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

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

[0034] Step S34, if the first sub-actual usage time of any first actual travel sub-route is greater than the corresponding first sub-standard usage time, the corresponding first actual travel sub-route is recorded as a suspected abnormal section;

[0035] If the first sub-actual usage durations of all first actual travel sub-routes are less than or equal to the corresponding first sub-standard usage durations, no operation is performed; 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.

[0036] Furthermore, the 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, the traffic lights in the second actual travel route corresponding to the second travel unit are obtained through the digital twin model of the city, and then the second actual use time of the second travel unit is subtracted from the maximum waiting time of all traffic lights in the second actual travel route to obtain the second estimated use 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 and 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, the second actual travel sub-route is recorded as a suspected abnormal section;

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

[0043] Step S37, obtaining real-time monitoring data of the suspected abnormal road section, and detecting the real-time monitoring data. If there is abnormal behavior on the road in the real-time monitoring data, the suspected abnormal road section is determined to be an abnormal road section; if the real-time monitoring data is normal real-time monitoring data, no operation is performed;

[0044] Step S38, marking the abnormal road sections in the digital twin model of the city.

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

[0046] Step S41, obtaining a first actual travel route of a first travel unit, and comparing the first actual travel route with a first standard travel route;

[0047] Step S42, if the first actual travel route is completely identical to 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;

[0048] Step S43: if the first actual travel route is different from the first standard travel route, marking the different sections between the first actual travel route and the first standard travel route as first suspected update sections;

[0049] Step S44, counting the number of times different first travel units walk on the first suspected updated road section, if the number of times the first travel unit walks on the first suspected updated road section is greater than or equal to the first number threshold, marking the first suspected updated road section as the first new road section and adding it to the digital twin model of the city; if the number of times the first travel unit walks on the first suspected updated road section is less than the first number threshold, no operation is performed;

[0050] Step S45, obtaining a second actual travel route of the second travel unit, and comparing the second actual travel route with the second standard travel route;

[0051] Step S46, if the second actual travel route is completely identical to 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;

[0052] If the second actual travel route is different from the second standard travel route, marking the different sections between the second actual travel route and the second standard travel route as the second suspected updated section;

[0053] Step S47, counting the number of times different second travel units travel on the second suspected updated road section, if the number of times the second travel unit travels on the second suspected updated road section is greater than or equal to the first number threshold, marking the second suspected updated road section as a second new road section and adding it to the digital twin model of the city; if the number of times the second travel unit travels on the second suspected updated road section is less than the first number threshold, no operation is performed;

[0054] Step S48, if the first new road section overlaps with the second new road section, the first new road section and the second new road section are merged into a common road section, and marked in the digital twin model of the city;

[0055] If the first new road section does not overlap with the second new road section, the first new road section and the second new road section are marked simultaneously in the digital twin model of the city.

[0056] The second aspect is a multi-terminal smart city digital twin map management system, including:

[0057] A data acquisition module, used to acquire building model data and road model data of the city;

[0058] A model building module, used to build a digital twin model of a city based on building model data and road model data;

[0059] The path planning module is used to obtain the travel unit category of the travel unit, and then construct the standard travel route and the corresponding standard usage time interval of the travel unit from any departure point to the destination, compare the actual travel route and the actual usage time of the travel unit, and perform corresponding operations according to the comparison results;

[0060] The abnormal analysis module is used to obtain the corresponding abnormal sections in the actual travel routes of the travel units based on the actual use time of the travel units and the digital twin model of the city;

[0061] The intelligent update module updates the road data in the city’s digital twin model based on the actual travel routes of travel units.

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

[0063] The present invention first obtains the building model data and road model data of the city, and then constructs a digital twin model of the city according to the building model data and the road model data, and at the same time obtains the travel unit category of the travel unit, and then constructs a standard travel route and a corresponding standard usage time interval for the travel unit from any departure point to the destination, compares the actual travel route and the actual usage time 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 time of the travel unit and 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 intelligent management and intelligent updating of the smart city map. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0065] Figure 1 is a flow chart of the method of the present invention;

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

[0067] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Example 1: Please refer to Figure 1As shown, the technical solution provided by the present invention is: a multi-terminal smart city digital twin map management method, which is used to facilitate the subsequent people or vehicles passing through the area to avoid in time with the help of the route data of the people or vehicles that have walked or driven, and includes the following steps:

[0069] Step S1, obtaining building model data and road model data of a city, and constructing 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, building height, building external structure data, and number of floors of buildings in the city; the road model data includes the road width, traffic signs, and number of lanes of roads in the city;

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

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

[0073] In the specific implementation, the building model data and road model data of the city are denoised, and then the missing data is filled in, and the gridding is performed through the point cloud processing algorithm to obtain the grid data of the building model and the road model;

[0074] 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 X axis and the Y axis as the Z axis, 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, adding building attributes to the building model and adding road attributes to the road model;

[0076] Step S14, integrating the building model and the corresponding building attributes, and the road model and the corresponding road attributes into a digital twin model of the city;

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

[0078] Step S2, obtaining the travel unit category of the travel unit, and then constructing a standard travel route and a corresponding standard usage time interval of the travel unit from any departure point to the destination, comparing the actual travel route and the actual usage time of the travel unit, and performing corresponding operations according to the comparison result;

[0079] It should be specifically stated that 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, obtaining the travel unit category and the departure and destination of the travel unit, planning the travel route according to the travel unit category and the departure and destination of the travel unit, planning to obtain the standard travel route of the travel unit and the corresponding standard usage time interval, and displaying the standard travel route in the digital twin model of the city;

[0082] It should be specifically noted that the travel route can be planned according to the travel unit category and the departure and destination of the travel unit through a deep learning model. The deep learning model can automatically optimize the travel route according to the travel unit category and the departure and destination without relying on manual intervention, thereby improving the efficiency of route planning and 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 travel routes;

[0083] Step S22, when the travel unit arrives at the destination, the actual travel route and actual travel time of the travel unit are obtained;

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

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

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

[0087] It should be specified that the actual time spent by pedestrians and vehicles exceeds the standard time, and 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 time of the travel unit is less than the minimum endpoint value of the standard usage time interval, proceed to step S4;

[0089] It should be noted that the map may contain urban roads that have not been updated in a timely manner. The road may have been closed for construction, or a new road may have been opened.

[0090] Step S3, according to the actual use time of the travel unit, combined with the digital twin model of the city, obtain the corresponding abnormal section in 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, the traffic lights in the first actual travel route corresponding to the first travel unit are obtained through the digital twin model of the city, and then the first actual use time of the first travel unit is subtracted from the maximum waiting time of all traffic lights in the first actual travel route to obtain a first estimated use time;

[0093] Step S32: if the first estimated usage time is less than or equal to the maximum endpoint value of the standard usage time interval, no operation is performed;

[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, dividing the first actual travel route of the first travel unit into first actual travel sub-routes of the same length, dividing the first estimated usage time of the first travel unit by the number of segments of the actual travel sub-routes to obtain the first sub-actual usage time of all first actual travel sub-routes, and dividing the maximum endpoint value of the standard usage time interval by the number of segments of the first actual travel sub-route to obtain the first sub-standard usage time of any actual travel sub-route;

[0096] Step S34, if the first sub-actual usage time of any first actual travel sub-route is greater than the corresponding first sub-standard usage time, the corresponding first actual travel sub-route is recorded as a suspected abnormal section;

[0097] If the first sub-actual usage time of all first actual travel sub-routes is less than or equal to the corresponding first sub-standard usage time, no operation is performed;

[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, the traffic lights in the second actual travel route corresponding to the second travel unit are obtained through the digital twin model of the city, and then the second actual use time of the second travel unit is subtracted from the maximum waiting time of all traffic lights in the second actual travel route to obtain the second estimated use time;

[0100] 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;

[0101] If the second estimated usage time is greater than the maximum endpoint value of the standard usage time 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 and the second sub-actual usage time and the second sub-standard usage time of the second actual travel sub-route;

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

[0104] If the second sub-actual usage time of all second actual travel sub-routes is less than or equal to the corresponding second sub-standard usage time, no operation is performed;

[0105] Step S37, obtaining real-time monitoring data of the suspected abnormal road section, and detecting the real-time monitoring data. If there is abnormal behavior on the road in the real-time monitoring data, the suspected abnormal road section is determined to be an abnormal road section; if the real-time monitoring data is normal real-time monitoring data, no operation is performed;

[0106] In practice, the database stores the real-time monitoring data of all road sections. When a road section is determined to be suspected of being abnormal, the corresponding real-time monitoring data can be obtained through the database.

[0107] It should be specified that abnormal behaviors on the road include road congestion, road accidents, and road closures;

[0108] Step S38, marking the abnormal road section in the digital twin model of the city;

[0109] In practice, when other people's travel routes include abnormal sections, the abnormal sections can be highlighted or warned through navigation software.

[0110] Step S4, updating 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, obtaining a first actual travel route of a first travel unit, and comparing the first actual travel route with a first standard travel route;

[0113] Step S42, if the first actual travel route is completely identical to 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;

[0114] Step S43: if the first actual travel route is different from the first standard travel route, marking the different sections between the first actual travel route and the first standard travel route as first suspected update sections;

[0115] Step S44, counting the number of times different first travel units walk on the first suspected updated road section, if the number of times the first travel unit walks on the first suspected updated road section is greater than or equal to the first number threshold, marking the first suspected updated road section as the first new road section and adding it to the digital twin model of the city; if the number of times the first travel unit walks on the first suspected updated road section is less than the first number threshold, no operation is performed;

[0116] Step S45, obtaining a second actual travel route of the second travel unit, and comparing the second actual travel route with the second standard travel route;

[0117] Step S46, if the second actual travel route is completely identical to 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;

[0118] If the second actual travel route is different from the second standard travel route, marking the different sections between the second actual travel route and the second standard travel route as the second suspected updated section;

[0119] Step S47, counting the number of times different second travel units travel on the second suspected updated road section, if the number of times the second travel unit travels on the second suspected updated road section is greater than or equal to the first number threshold, marking the second suspected updated road section as a second new road section and adding it to the digital twin model of the city; if the number of times the second travel unit travels on the second suspected updated road section is less than the first number threshold, no operation is performed;

[0120] Step S48: if the first new road section overlaps with the second new road section, the above road sections are merged into a common road section and marked in the digital twin model of the city;

[0121] If the first new road segment does not overlap with the second new road segment, the first new road segment and the second new road segment are marked simultaneously in the digital twin model of the city;

[0122] It should be specifically noted that for roads that have not been updated in the map in a timely manner, pedestrians and vehicles have already traveled on them many times. At this time, the road can be included in the navigation system for subsequent use by pedestrians and vehicles, thereby saving time.

[0123] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0124] Example 2: Please refer to Figure 2As shown, based on another concept of the same invention, a multi-terminal smart city digital twin map management method is now proposed, including:

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

[0126] A model building module, used to build a digital twin model of a city based on building model data and road model data;

[0127] The path planning module is used to obtain the travel unit category of the travel unit, and then construct the standard travel route and the corresponding standard usage time interval of the travel unit from any departure point to the destination, compare the actual travel route and the actual usage time of the travel unit, and perform corresponding operations according to the comparison results;

[0128] The abnormal analysis module is used to obtain the corresponding abnormal sections in the actual travel routes of the travel units based on the actual use time of the travel units and the digital twin model of the city;

[0129] The intelligent update module updates the road data in the city’s digital twin model based on the actual travel routes of travel units.

[0130] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-terminal smart city digital twin map management method, characterized in that: The steps include: Step S1, obtaining building model data and road model data of a city, and constructing a digital twin model of the city according to the building model data and road model data; Step S2, obtaining the travel unit category of the travel unit, and then constructing a standard travel route and a corresponding standard usage time interval of the travel unit from any departure point to the destination, comparing the actual travel route and the actual usage time of the travel unit, and performing corresponding operations according to the comparison result; Step S3, according to the actual use time of the travel unit, combined with the digital twin model of the city, obtain the corresponding abnormal section in the actual travel route of the travel unit; Step S4: updating the road data in the digital twin model of the city based on the actual travel routes of the travel units.

2. According to a multi-terminal smart city digital twin map management method according to claim 1, it is characterized in that: The building model data includes the floor area, building height, building external structure data and number of building floors of the buildings in the city; The road model data of the city are the road width, traffic signs and the number of road lanes of the roads in the city.

3. According to a multi-terminal smart city digital twin map management method according to claim 2, it is characterized in that: The step S1 includes the following sub-steps: Step S11, converting the building model data and road model data of the city into grid data of the building model and the road model; 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 X axis and the Y axis as the Z axis, 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; Step S13, adding building attributes to the building model and adding road attributes to the road model; Step S14: Integrate the building model and the corresponding building attributes, and the road model and the corresponding road attributes into a digital twin model of the city.

4. According to a multi-terminal smart city digital twin map management method according to claim 3, it is characterized in that: Building attributes include function type, building name and building number, and road attributes include number of road lanes, road name and road type.

5. According to a multi-terminal smart city digital twin map management method according to claim 4, it is characterized in that: 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.

6. According to a multi-terminal smart city digital twin map management method according to claim 5, it is characterized in that: The step S2 includes the following sub-steps: Step S21, obtaining the travel unit category and the departure and destination of the travel unit, planning the travel route according to the travel unit category and the departure and destination of the travel unit, planning to obtain the standard travel route of the travel unit and the corresponding standard usage time interval, and displaying the standard travel route in the digital twin model of the city; Step S22, when the travel unit arrives at the destination, the actual travel route and actual travel time of the travel unit are obtained; Step S23, comparing the standard usage time interval of the travel unit with the actual usage time; When the actual usage time of the travel unit is within the standard usage time range, no operation is performed; When the actual usage time of the travel unit is greater than the maximum endpoint value of the standard usage time interval, proceed to step S3; When the actual usage time of the travel unit is less than the minimum endpoint value of the standard usage time interval, proceed to step S4.

7. The multi-terminal smart city digital twin map management method according to claim 6 is 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, the traffic lights in the first actual travel route corresponding to the first travel unit are obtained through the digital twin model of the city, and then the first actual use time of the first travel unit is subtracted from the maximum waiting time of all traffic lights in the first actual travel route to obtain a first estimated use time; Step S32: if the first estimated usage time is less than or equal to the maximum endpoint value of the standard usage time interval, no operation is performed; If the first estimated usage duration is greater than the maximum endpoint value of the standard usage duration interval, proceed to the next step; Step S33, dividing the first actual travel route of the first travel unit into first actual travel sub-routes of the same length, dividing the first estimated usage time of the first travel unit by the number of segments of the actual travel sub-routes to obtain the first sub-actual usage time of all first actual travel sub-routes, and dividing the maximum endpoint value of the standard usage time interval by the number of segments of the first actual travel sub-route to obtain the first sub-standard usage time of any actual travel sub-route; Step S34, if the first sub-actual usage time of any first actual travel sub-route is greater than the corresponding first sub-standard usage time, the corresponding first actual travel sub-route is recorded as a suspected abnormal section; If the first sub-actual usage durations of all first actual travel sub-routes are less than or equal to the corresponding first sub-standard usage durations, no operation is performed; 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. The multi-terminal smart city digital twin map management method according to claim 7 is characterized in that: The step S3 further comprises the following sub-steps: Step S35: when the travel unit category of the travel unit is the second travel unit, the traffic lights in the second actual travel route corresponding to the second travel unit are obtained through the digital twin model of the city, and then the second actual use time of the second travel unit is subtracted from the maximum waiting time of all traffic lights in the second actual travel route to obtain the second estimated use time; 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; If the second estimated usage time is greater than the maximum endpoint value of the standard usage time interval, proceed to the next step; Step S36: similarly, according to steps S31 to S33, obtain the second actual travel sub-route of the second travel unit and the second sub-actual usage time and the second sub-standard usage time of the second actual travel sub-route; If the second sub-actual usage time of any second actual travel sub-route is greater than the corresponding second sub-standard usage time, the second actual travel sub-route is recorded as a suspected abnormal section; If the second sub-actual usage time of all second actual travel sub-routes is less than or equal to the corresponding second sub-standard usage time, no operation is performed; Step S37, obtaining real-time monitoring data of the suspected abnormal road section, and detecting the real-time monitoring data. If there is abnormal behavior on the road in the real-time monitoring data, the suspected abnormal road section is determined to be an abnormal road section; if the real-time monitoring data is normal real-time monitoring data, no operation is performed; Step S38, marking the abnormal road sections in the digital twin model of the city.

9. The multi-terminal smart city digital twin map management method according to claim 8 is characterized in that: The step S4 includes the following sub-steps: Step S41, obtaining a first actual travel route of a first travel unit, and comparing the first actual travel route with a first standard travel route; Step S42, if the first actual travel route is completely identical to 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 the first actual travel route is different from the first standard travel route, marking the different sections between the first actual travel route and the first standard travel route as first suspected update sections; Step S44, counting the number of times different first travel units walk on the first suspected updated road section, if the number of times the first travel unit walks on the first suspected updated road section is greater than or equal to the first count threshold, marking the first suspected updated road section as the first new road section and adding it to the digital twin model of the city; If the number of times the first travel unit walks the first suspected updated road section is less than the first number threshold, no operation is performed; Step S45, obtaining a second actual travel route of the second travel unit, and comparing the second actual travel route with the second standard travel route; Step S46, if the second actual travel route is completely identical to 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 the second actual travel route is different from the second standard travel route, marking the different sections between the second actual travel route and the second standard travel route as the second suspected updated section; Step S47, counting the number of times different second travel units travel on the second suspected updated road section, if the number of times the second travel unit travels on the second suspected updated road section is greater than or equal to the first number threshold, marking the second suspected updated road section as a second new road section and adding it to the digital twin model of the city; If the number of times the second travel unit travels the second suspected updated road section is less than the first number threshold, no operation is performed; Step S48, if the first new road section overlaps with the second new road section, the first new road section and the second new road section are merged into a common road section, and marked in the digital twin model of the city; If the first new road section does not overlap with the second new road section, the first new road section and the second new road section are marked simultaneously in the digital twin model of the city.

10. A multi-terminal smart city digital twin map management system, characterized in that: In combination with a multi-terminal smart city digital twin map management method according to any one of claims 1 to 9, the method comprises: A data acquisition module, used to acquire building model data and road model data of the city; A model building module, used to build a digital twin model of a city based on building model data and road model data; The path planning module is used to obtain the travel unit category of the travel unit, and then construct the standard travel route and the corresponding standard usage time interval of the travel unit from any departure point to the destination, compare the actual travel route and the actual usage time of the travel unit, and perform corresponding operations according to the comparison results; The abnormal analysis module is used to obtain the corresponding abnormal sections in the actual travel routes of the travel units based on the actual use time of the travel units and the digital twin model of the city; The intelligent update module updates the road data in the city’s digital twin model based on the actual travel routes of travel units.

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