Geographic information synchronization system and method under coordinate systems in digital twinning

Abstract

The invention discloses a geographic information synchronization system and method under coordinate systems in digital twinning, and the method comprises the steps: collecting geographic information data in a real world through a data collection module, recording a corresponding coordinate system, and converting the geographic information data collected by different data sources into a unified target geographic coordinate system through a coordinate conversion module; the information synchronization module integrates the geographic information data after coordinate conversion into a digital twin model, the error correction module monitors and corrects errors generated in the information synchronization process, and the data updating module sets the updating period and updating strategy of the geographic information data. The geographic information data in the digital twinborn model can reflect the change of the real world in time, the geographic information data from different sources are standardized, the accuracy and the real-time performance of the digital twinborn model are ensured, the method can be widely applied to the fields of smart cities, intelligent transportation, environment monitoring and the like, and the decision support capability can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular to a system and method for synchronizing geographic information of various coordinate systems in a digital twin. Background Art

[0002] With the development of digital twin technology, more and more application scenarios require accurate mapping of geographic information in the real world to the virtual environment built by Unreal Engine. However, since geographic information in the real world is usually represented by different coordinate systems (such as WGS84, GCJ-02, BD-09, etc.), the differences between these coordinate systems pose challenges to the accurate mapping of geographic information. In addition, the coordinate system used by Unreal Engine is usually a Cartesian coordinate system, which is essentially different from the earth coordinate system. Therefore, how to achieve accurate synchronization of geographic information in different coordinate systems has become a problem to be solved. Summary of the invention

[0003] In order to overcome the above-mentioned defects, the present invention provides a system and method for synchronizing geographic information of each coordinate system in a digital twin. The system and method for synchronizing geographic information of each coordinate system in a digital twin effectively solve the problem of synchronization of geographic information in different coordinate systems with the digital twin model constructed by Unreal Engine, thereby ensuring the accuracy and real-time performance of the digital twin model.

[0004] The technical solution adopted by the present invention to solve its technical problems is: a geographic information synchronization system in each coordinate system in a digital twin, including a data acquisition module, a coordinate conversion module, an information synchronization module, an error correction module, a data update module and an API interface, wherein: The data collection module is used to collect geographic information data in the real world and record its corresponding coordinate system; A coordinate conversion module is used to convert the geographic information data collected by the data collection module from different data sources into a unified target geographic coordinate system; Information synchronization module, used to seamlessly integrate geographic information data after coordinate conversion into the digital twin model; The error correction module is used to monitor the errors generated during the information synchronization process and correct the monitored errors to ensure the accuracy of the geographic information data finally synchronized to the digital twin model; The data update module is used to set the update cycle and update strategy, and then control the data acquisition module to collect data according to the set update cycle and update strategy, so that the geographic information data in the digital twin model can reflect the changes in the real world in a timely manner; API interfaces supporting multiple data formats are used for data transmission between modules.

[0005] As a further improvement of the present invention, the data acquisition module includes a data acquisition system, a coordinate system automatic identification and recording system, and a data output system. The data acquisition system collects various geographic information data emitted by various data source generating devices by wired or wireless means, and processes and stores the collected geographic information data. The coordinate system automatic identification and recording system identifies the coordinate system of the source of each geographic information data acquired by the data acquisition system, and records the coordinate system corresponding to each collected geographic information data. The data output system communicates with the coordinate conversion module, and is used to transmit each geographic information data collected by the data acquisition system and its corresponding coordinate system to the coordinate conversion module.

[0006] As a further improvement of the present invention, the coordinate conversion module includes a coordinate conversion algorithm optimization module and an elevation processing module. The coordinate conversion algorithm optimization module can optimize the calculation of the conversion algorithm between different coordinate systems, and the elevation processing module can optimize the elevation data in the geographic information data under different coordinate systems, so that the elevation data under all coordinate systems can be accurately converted into the elevation data in the same coordinate system.

[0007] As a further improvement of the present invention, the information synchronization module includes a synchronization strategy selection module and a coordinate system matching module. The synchronization strategy selection module can select and set among three modes: real-time synchronization, timed synchronization and event-triggered synchronization. The coordinate system matching module can convert geographic coordinates into an Unreal Engine coordinate system through calculation.

[0008] As a further improvement of the present invention, the error correction module includes an error detection system, a ground control point setting system, an error correction strategy adjustment system and an error calculation correction system. The ground control point setting system is used to set reference points with known coordinates at key positions in the geographic coordinate system. The error detection system can compare the actual position of each reference point with the synchronized position to determine whether there is any deviation or error in the geographic information data during the coordinate system conversion process. The error correction strategy adjustment system can select to use iterative least squares method or Kalman filtering method for correction calculation based on the detection data of the error detection system. The error calculation correction system can perform error correction calculation on the coordinates of the synchronized geographic information data that the error detection system determines to have errors according to the algorithm selected by the error correction strategy adjustment system.

[0009] As a further improvement of the present invention, the data update module includes an update cycle setting module, an update mode setting module and a version management module. The update cycle setting module can set the data update cycle to real-time update or periodic update; the update mode setting module can set the data update mode to incremental update or full update; the version management module can store and manage historical version data.

[0010] A method for synchronizing geographic information in various coordinate systems in a digital twin, the specific steps are as follows: Step 1: Collect geographic information data from the real world and automatically identify and record the coordinate system of each data source; Step 2: Select a target coordinate system, convert each piece of collected data into a geographic coordinate system according to a common coordinate system conversion algorithm, and finally convert the coordinate systems of all data sources into the target geographic coordinate system, and then convert the target geographic coordinate system into the Unreal Engine coordinate system; Step 3: Synchronize the geographic information data after coordinate system conversion into the digital twin model; Step 4: Monitor the data errors during the synchronization process and correct the geographic information data with errors through calculation to ensure that the geographic information data finally synchronized to the digital twin model has high accuracy; Step 5: Display the geographic information data in the digital twin model through the visual interaction layer; Step 6: Set the update cycle and update mode. The above steps 1 to 5 are performed according to the set update cycle and update mode to update the data. At the same time, all historical versions in the update process are managed.

[0011] As a further improvement of the present invention, in step one, geographic information data in the real world is obtained by collecting geographic environment data monitored by environmental sensors, geographic location data detected by positioning sensors, high-resolution satellite images acquired by satellites, and real-time video streams acquired by drones.

[0012] As a further improvement of the present invention, in the step 2, the general coordinate system conversion algorithm is first optimized, and then the coordinate system conversion calculation is performed on the collected geographic information data according to the optimized scheme; Among them, the method for optimizing the general coordinate system conversion algorithm is as follows: All the collected geographic information data are divided into multiple parts, and the processor simultaneously calculates each part of the divided data on different cores; before the calculation, the repeatedly used conversion parameters are pre-calculated in the initialization stage, and the calculation results are stored in the memory; during the calculation, the calculation accuracy of the conversion algorithm is adjusted according to the actual application requirements and the current system load, and unnecessary repeated calculations are identified and eliminated in real time. At the same time, the geographic information data is calculated in parallel using hardware with parallel computing capabilities; after the calculation is completed, the appropriate data structure is selected for data storage and access; The calculation method for coordinate system conversion of the collected geographic information data is as follows: (1) Use a coordinate conversion algorithm based on a mathematical model to achieve conversion between arbitrary coordinate systems, and ultimately convert all geographic information data to the same geographic coordinate system; (2) Convert the geographic coordinate system to the Unreal Engine coordinate system (Cartesian coordinate system): First, determine a scale for scaling, then define the origin of the Unreal Engine coordinate system as a coordinate point in the destination geographic coordinate system, and then convert the longitude and latitude values ​​in the geographic coordinate system to the X and Y coordinate values ​​in the Unreal Engine coordinate system. Finally, process the elevation data in the geographic information data and convert it to the Z coordinate value in the Unreal Engine coordinate system. The Z coordinate value in the Unreal Engine coordinate system expresses the height of the buildings and terrain in the geographic information. Among them, the method for processing elevation data in geographic information data is as follows: (2.1) Convert elevation data from geographic information data from different data sources into a common coordinate system; (2.2) The elevation data in the geographic information data converted into the same coordinate system is calculated and processed using a smoothing algorithm to make the elevation surface smoother and more natural; (2.3) Use filtering techniques to remove outliers in elevation data while retaining terrain features; (2.4) Identify key features in the terrain through image processing technology and enhance these key features to make them more obvious in the digital twin model; (2.5) Use edge detection algorithms to identify terrain boundaries to ensure a more natural transition between different terrains. For areas where elevation data is missing or sparse, use interpolation methods to fill in the gaps to ensure the continuity and integrity of the entire terrain surface. (2.6) Adjust the scale of the elevation data according to the scaling ratio of the Unreal Engine coordinates to ensure that the elevation value matches the scale of the horizontal coordinate system; (2.7) Offset and correct the elevation data to make it more accurate in three-dimensional space; (2.8) Fusing elevation data from multiple data sources (combining data from different sources) to form a complete and accurate elevation model; (2.9) Design an elevation data update mechanism to handle dynamically changing elevation data and ensure that the digital twin model can reflect the latest terrain information in a timely manner.

[0013] As a further improvement of the present invention, in step three, an API interface that supports multiple data formats is used for fast and efficient data transmission, and a concurrent processing mechanism is used for data processing during data transmission to avoid delays or blocking problems caused by large amounts of data. In step four, the correction strategy is dynamically adjusted according to actual conditions, and iterative least squares method or Kalman filtering method is selected for calculation and correction to ensure that the system can adapt to different application scenarios and changing conditions.

[0014] The beneficial effects of the present invention are as follows: the present invention standardizes geographic information data from different sources so that geographic information data in different coordinate systems can be accurately synchronized in the digital twin model constructed by Unreal Engine, thereby ensuring the accuracy and real-time performance of the digital twin model. The digital twin model can be widely used in smart cities, smart transportation, environmental monitoring and other fields, thereby effectively improving the decision-making support capability. The system has good compatibility, supports conversion between multiple coordinate systems, and can adapt to the needs of different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the present invention; Figure 2 It is a system framework diagram of the present invention; Figure 3 It is the principle diagram of the data acquisition module of the present invention; Figure 4 This is a schematic diagram of the digital twin model architecture; Figure 5 A list of various coordinate system conversion methods. DETAILED DESCRIPTION

[0016] Embodiment: A geographic information synchronization system in each coordinate system in a digital twin includes a data acquisition module 1, a coordinate conversion module 2, an information synchronization module 3, an error correction module 4, a data update module 5 and an API interface, wherein: Data collection module 1, used to collect geographic information data in the real world and record its corresponding coordinate system; The coordinate conversion module 2 is used to convert the geographic information data collected by the data collection module 1 from different data sources into a unified target geographic coordinate system; An information synchronization module 3 is used to seamlessly integrate the geographic information data after coordinate conversion into the digital twin model 6; The error correction module 4 is used to monitor the errors generated during the information synchronization process and correct the monitored errors to ensure the accuracy of the geographic information data finally synchronized to the digital twin model 6; The data update module 5 is used to set the update cycle and update strategy, and then control the data acquisition module 1 to collect data according to the set update cycle and update strategy, so that the geographic information data in the digital twin model 6 can reflect the changes in the real world in a timely manner; API interfaces supporting multiple data formats are used for data transmission between modules.

[0017] Through this system, various geographic information data can be reflected in the digital twin model 6, and the collected geographic information data in different coordinate systems can be accurately synchronized in the digital twin model 6 built by Unreal Engine, ensuring the accuracy of the digital twin model 6. This system is highly practical and can be widely used in smart cities, smart transportation, smart agriculture, environmental monitoring and other fields to improve decision-making support capabilities.

[0018] For example, when it is used for urban planning, digital twin technology can be used to simulate urban planning schemes and evaluate their feasibility. When it is used for traffic management, traffic flow can be monitored in real time, congestion can be predicted, and optimal path navigation can be provided. When it is used for agricultural irrigation, meteorological data and soil moisture can be combined to automatically adjust the irrigation amount and achieve precise irrigation. When it is used for pest and disease prevention, image recognition technology can be used to identify crop pests and diseases so that timely rescue measures can be taken. When it is used for crop growth monitoring, drones and satellite images can be used to analyze crop growth conditions.

[0019] This system has good compatibility and can support conversion between multiple coordinate systems to meet the needs of different application scenarios.

[0020] In order to support fast and efficient data transmission, this system uses an API interface for data transmission to ensure that data can be transmitted to the digital twin model 6 quickly and accurately. At the same time, the API interface can support multiple data formats to adapt to data forms from different sources.

[0021] like Figure 3As shown, the data acquisition module 1 includes a data acquisition system 11, a coordinate system automatic identification and recording system 12 and a data output system 13. The data acquisition system 11 collects various geographic information data emitted by various data source generating devices in a wired or wireless manner, and processes and stores the collected geographic information data. The coordinate system automatic identification and recording system 12 identifies the coordinate system of each source of geographic information data acquired by the data acquisition system 11, and records the coordinate system corresponding to each collected geographic information data. The data output system 13 communicates with the coordinate conversion module 2, and is used to transmit each geographic information data collected by the data acquisition system 11 and its corresponding coordinate system to the coordinate conversion module 2.

[0022] The various data source generating devices include sensors, satellites, drones, etc., wherein the sensors include environmental sensors capable of monitoring the temperature, humidity and light intensity in the geographical environment and GPS positioning sensors. The data sources emitted by these data source generating devices are collected and acquired by the data acquisition system 11. The data acquisition system 11 extracts, converts and loads the data from the source end to the destination end through the ETL technology, and then collects, cleans and stores the data. The processed geographic information data is classified and stored in various databases, such as NoSQL database (graph database, column storage database), MPP database (data warehouse, data mart) and Hadoop platform (HDFS, MapReduce). The coordinate system automatic identification and recording system 12 extracts the geographic information data from each database, and identifies and records the coordinate system of each source of geographic information data. Finally, the data output system 13 transmits the geographic information data marked with the corresponding coordinate system to the coordinate conversion module 2 for the coordinate conversion of the geographic information data.

[0023] The coordinate conversion module 2 includes a coordinate conversion algorithm optimization module 21 and an elevation processing module 22. The coordinate conversion algorithm optimization module 21 can optimize the conversion algorithm between different coordinate systems, and the elevation processing module 22 can optimize the elevation data in the geographic information data under different coordinate systems, so that the elevation data under all coordinate systems are accurately converted into the elevation data in the same coordinate system. When the coordinate conversion is performed on the collected geographic information data under different coordinate systems, the conversion is performed according to the standard method of coordinate system conversion, and the optimization scheme is used for calculation, thereby effectively reducing the consumption of computing resources when the coordinate conversion calculation is performed on the coordinates of each geographic information data. At the same time, the elevation data in the geographic information data under different coordinate systems is optimized, which can ensure that the height of the building and the terrain is correctly displayed in the digital twin model 6 to avoid being affected by the unevenness of the earth's surface.

[0024] The information synchronization module 3 includes a synchronization strategy selection module 31 and a coordinate system matching module 32. The synchronization strategy selection module 31 can be selected and set in three modes: real-time synchronization, scheduled synchronization and event-triggered synchronization. The coordinate system matching module 32 can convert geographic coordinates into the Unreal Engine coordinate system by calculation. All the collected geographic information data are converted into geographic information data in the same geographic coordinate system after the coordinate system conversion. These geographic information data are updated according to the synchronization strategy selected by the synchronization strategy selection module 31, so that all geographic information data can be quickly updated to the digital twin model 6. Unreal Engine uses a right-handed Cartesian coordinate system, in which the X axis points to the right, the Y axis points to the back, and the Z axis points to the top. When the geographic information data is integrated into the digital twin model 6, the coordinate system matching module 32 first converts the geographic information data in the geographic coordinate system into the right-handed Cartesian coordinate system, so that the geographic information data can be accurately displayed in the digital twin model 6.

[0025] The error correction module 4 includes an error detection system 41, a ground control point setting system 42, an error correction strategy adjustment system 43 and an error calculation correction system 44. The ground control point setting system 42 is used to set reference points of known coordinates at key positions in the geographic coordinate system. The error detection system 41 can compare the actual position of each reference point with the position after synchronization, and then judge whether there is deviation or error in the geographic information data during the coordinate system conversion process. The error correction strategy adjustment system 43 can select the iterative least square method or the Kalman filter method for correction calculation according to the detection data of the error detection system 41. The error calculation correction system 44 can perform error correction calculation on the coordinates of the synchronized geographic information data that the error detection system 41 determines to have errors according to the algorithm selected by the error correction strategy adjustment system 43. The error correction module 4 is mainly responsible for monitoring the errors that may occur during the synchronization process and correcting them to ensure that the geographic information data finally synchronized to the digital twin model 6 has high accuracy, which not only improves the accuracy of the entire system, but also enhances its application capabilities and decision support capabilities in multiple fields such as smart cities, smart transportation, and environmental monitoring.

[0026] The data update module 5 includes an update cycle setting module 51, an update mode setting module 52 and a version management module 53. The update cycle setting module 51 can set the data update cycle to real-time update or periodic update; the update mode setting module 52 can set the data update mode to incremental update or full update; the version management module 53 can store and manage historical version data.

[0027] The data update module 5 is an important part of the system, responsible for ensuring that the geographic information data in the digital twin model 6 can reflect the changes in the real world in a timely manner. Among them, the real-time update strategy set by the update cycle setting module 51 is suitable for scenarios that require immediate feedback, such as traffic monitoring. When the traffic flow changes, the system needs to update the relevant data immediately; the regular update strategy is suitable for scenarios where data changes slowly, such as changes in topography, and the update cycle of such data is relatively long; The update mode setting module 52 mainly supports two modes: incremental update and full update. The incremental update mode only updates the data that has changed, which can reduce the network bandwidth usage; while the full update mode re-acquires all data when necessary (for example, when the data integrity is threatened) to ensure the integrity of the information. The version management module 53 is used to manage past versions and implement the function of supporting backtracking query to track the change trajectory of historical data.

[0028] The data update module 5 ensures that the geographic information data in the digital twin model 6 is always up to date, and can flexibly adjust the update strategy according to different application scenarios, thereby improving the practicality of the system. These functions enable the technology to be widely used in smart cities, intelligent transportation, environmental monitoring and other fields, and enhance the decision-making support capabilities of the system.

[0029] A method for synchronizing geographic information in various coordinate systems in a digital twin, the specific steps are as follows: Step 1: Collect geographic information data from the real world, and automatically identify and record the coordinate system of each data source. Commonly used geographic coordinate systems include: WGS84, GCJ-02, BD-09 and other standard coordinate systems; Step 2: Select a target coordinate system, convert the collected data into a geographic coordinate system according to a general coordinate system conversion algorithm, and finally convert the coordinate systems of all data sources into the target geographic coordinate system, and then convert the target geographic coordinate system into the Unreal Engine coordinate system. When converting the coordinate system, it is necessary to ensure that the accuracy of the converted coordinates reaches the sub-meter level to meet the needs of high-precision digital twin applications; Step 3: Synchronize the geographic information data after coordinate system conversion to the digital twin model 6. During the synchronization process, select the appropriate synchronization frequency and synchronization strategy according to the needs of the digital twin model 6, such as real-time synchronization, scheduled synchronization, event-triggered synchronization, etc. Among them, real-time synchronization is suitable for scenarios that require immediate response, such as traffic monitoring. When the traffic flow changes, it can be quickly updated to the digital twin model 6; scheduled synchronization is suitable for situations where the data update frequency is low, such as updating the terrain data every once in a while; event-triggered synchronization is synchronized only when a specific event occurs, such as monitoring abnormal climate conditions; Step 4: Monitor the data during the synchronization process for errors, and correct the geographic information data with errors through calculation to ensure that the geographic information data finally synchronized to the digital twin model 6 has high accuracy. When detecting errors, use ground control points to detect errors. By comparing the actual positions of these ground control points with the synchronized data positions, it is determined whether there are deviations or errors in the data during the synchronization process. The ground control points are reference points with known coordinates set at key locations. Error detection through ground control points can ensure the consistency and accuracy of the data, which is conducive to improving the accuracy of the comparison data. Step 5: The geographic information data in the digital twin model 6 is visualized through the interactive layer 61 (such as Figure 4 as shown) for display; Step 6: Set the update cycle and update mode. The above steps 1 to 5 are performed according to the set update cycle and update mode to update the data. At the same time, all historical versions in the update process are managed. The update cycle can be set according to application requirements, such as real-time update or periodic update. Real-time update is suitable for scenarios that require immediate response, such as traffic monitoring; periodic update is suitable for scenarios where data changes slowly, such as terrain changes; the update mode generally supports incremental update and full update. Incremental update only updates the changed part of the data to reduce network bandwidth usage; full update re-acquires all data when necessary to ensure data integrity; by managing all historical versions during the version update process, retrospective query can be supported.

[0030] In step one, geographic information data in the real world is obtained by collecting geographic environment data (such as temperature, humidity, light intensity, etc.) monitored by environmental sensors, geographic location data detected by positioning sensors, high-resolution satellite images acquired by satellites, and real-time video streams acquired by drones.

[0031] In the step 2, the general coordinate system conversion algorithm is first optimized, and then the coordinate system conversion calculation is performed on the collected geographic information data according to the optimized scheme; Among them, the method for optimizing the general coordinate system conversion algorithm is as follows: All the collected geographic information data are divided into multiple parts, and the processor simultaneously calculates each part of the divided data on different cores; before calculation, the repeatedly used conversion parameters are pre-calculated in the initialization stage, and the calculation results are stored in the memory; during calculation, the calculation accuracy of the conversion algorithm is adjusted according to the actual application requirements and the current system load, and unnecessary repeated calculations are identified and eliminated in real time. At the same time, the geographic information data is calculated in parallel using hardware with parallel computing capabilities; after the calculation is completed, the appropriate data structure is selected for data storage and access, and the multi-core characteristics of the processor are used to divide all the collected geographic information data into multiple parts, and calculations are performed on different cores of the processor at the same time, thereby improving the overall processing speed; For repeatedly used transformation parameters (such as rotation matrix, translation vector, etc.), they can be pre-calculated during initialization and stored in memory to avoid recalculation during each transformation; Adjust the calculation accuracy according to the actual application requirements. For application scenarios that do not require extremely high accuracy, the numerical accuracy can be appropriately reduced to save computing resources. According to the characteristics of specific hardware platforms (such as GPU, FPGA, etc.), the parallel computing capabilities of these hardware can be used to accelerate the conversion process. Dynamically adjust the execution strategy of the conversion algorithm according to the current system load, such as using a more accurate but more computationally expensive algorithm when the system is idle, and switching to a faster but less accurate algorithm when the system is busy; Identify and eliminate unnecessary repeated calculations to ensure that each step of the calculation directly contributes to the final result. Choose appropriate data structures to store and access data to reduce the number and time of memory accesses and speed up processing; The calculation method for coordinate system conversion of the collected geographic information data is as follows: (1) Use coordinate conversion algorithms based on mathematical models to achieve conversion between arbitrary coordinate systems, and ultimately convert all geographic information data to the same geographic coordinate system, including but not limited to the following coordinate conversion algorithms: WGS84 to GCJ-02: conversion using encrypted coordinate offset obfuscation algorithm; GCJ-02 to BD-09: Use the offset algorithm provided by Baidu Maps for conversion; Conversion between arbitrary coordinate systems (ref. Figure 4 List of coordinate system transformation methods): Use general coordinate transformation matrices and geodetic principles for transformation; (2) Convert the geographic coordinate system to the Unreal Engine coordinate system (Cartesian coordinate system): First, determine a scale for scaling, then define the origin of the Unreal Engine coordinate system as a coordinate point in the destination geographic coordinate system, and then convert the longitude and latitude values ​​in the geographic coordinate system to the X and Y coordinate values ​​in the Unreal Engine coordinate system. Finally, process the elevation data in the geographic information data and convert it to the Z coordinate value in the Unreal Engine coordinate system. The Z coordinate value in the Unreal Engine coordinate system expresses the height of the buildings and terrain in the geographic information. Among them, the method for processing elevation data in geographic information data is as follows: (2.1) Convert elevation data from different geographic information data sources into a common coordinate system. For example, convert all elevation values ​​from different coordinate systems (such as WGS84, GCJ-02, etc.) into the Cartesian coordinate system used by Unreal Engine. (2.2) The elevation data in the geographic information data converted to the same coordinate system is calculated and processed using a smoothing algorithm to make the elevation surface smoother and more natural. Since the original elevation data may contain noise or discontinuity, a smoothing algorithm (such as moving average, Gaussian filtering, etc.) is used to reduce these noises to ensure that the elevation surface in the digital twin model 6 is smoother and more natural; (2.3) Use filtering technology to remove outliers in elevation data while retaining terrain features. Filtering technology can use median filtering, bilateral filtering, etc. (2.4) Identify key features in the terrain (such as peaks, valleys, rivers, etc.) through image processing technology, and enhance these key features to make them more obvious in the digital twin model 6; (2.5) Use edge detection algorithms (such as Canny edge detection) to identify terrain boundaries to ensure a more natural transition between different terrains. For areas where elevation data is missing or sparse, use interpolation methods (such as bilinear interpolation, spline interpolation, etc.) to fill in the gaps to ensure the continuity and integrity of the entire terrain surface. (2.6) Adjust the scale of the elevation data according to the scaling ratio of the Unreal Engine coordinates to ensure that the elevation value matches the scale of the horizontal coordinate system to avoid scale distortion; (2.7) Considering the influence of the curvature of the earth's surface, the elevation data is offset and corrected to make the elevation data more accurate in three-dimensional space; (2.8) Fusing elevation data from multiple data sources (e.g., satellite images, drone aerial photography, etc.) to form a complete and accurate elevation model; (2.9) Design an elevation data update mechanism to handle dynamically changing elevation data to ensure that the digital twin model 6 can reflect the latest terrain information in a timely manner. In some application scenarios, elevation data may need to be updated in real time, so it is necessary to design an efficient mechanism to handle dynamically changing elevation data.

[0032] In the step three, an API interface that supports multiple data formats is used for fast and efficient data transmission, and a concurrent processing mechanism is used for data processing during data transmission to avoid delays or blocking problems caused by large amounts of data. In the step four, the correction strategy is dynamically adjusted according to actual conditions, and the iterative least squares method (using this method to correct data can reduce the impact of cumulative errors) or the Kalman filter method (this is a commonly used estimation method that can effectively process noise data and improve correction efficiency) is used for calculation and correction to ensure that the system can adapt to different application scenarios and changing conditions. Whether to use the iterative least squares method or the Kalman filter method can be selected according to actual needs or the two can be combined to dynamically adjust the correction strategy according to actual conditions to ensure that the system has strong adaptability.

Claims

1. A geographic information synchronization system in each coordinate system in a digital twin, characterized by: It comprises a data acquisition module (1), a coordinate conversion module (2), an information synchronization module (3), an error correction module (4), a data update module (5) and an API interface, wherein: A data collection module (1) is used to collect geographic information data in the real world and record its corresponding coordinate system; A coordinate conversion module (2) is used to convert the geographic information data collected by the data collection module (1) from different data sources into a unified target geographic coordinate system; An information synchronization module (3) for seamlessly integrating the coordinate-converted geographic information data into the digital twin model (6); An error correction module (4) is used to monitor errors generated during the information synchronization process and correct the monitored errors to ensure the accuracy of the geographic information data finally synchronized to the digital twin model (6); A data updating module (5) is used to set an updating cycle and an updating strategy, thereby controlling the data acquisition module (1) to collect data according to the set updating cycle and updating strategy, so that the geographic information data in the digital twin model (6) can timely reflect the changes in the real world; API interfaces supporting multiple data formats are used for data transmission between modules.

2. The geographic information synchronization system in each coordinate system in the digital twin according to claim 1, characterized in that: The data acquisition module (1) comprises a data acquisition system (11), a coordinate system automatic identification and recording system (12) and a data output system (13). The data acquisition system (11) collects various geographic information data emitted by various data source generating devices by wired or wireless means, and processes and stores the collected geographic information data. The coordinate system automatic identification and recording system (12) identifies the coordinate system of each source of geographic information data acquired by the data acquisition system (11), and records the coordinate system corresponding to each collected geographic information data. The data output system (13) communicates with the coordinate conversion module (2) and is used to transmit each geographic information data acquired by the data acquisition system (11) and its corresponding coordinate system to the coordinate conversion module (2).

3. The geographic information synchronization system in each coordinate system in the digital twin according to claim 1, characterized in that: The coordinate conversion module (2) comprises a coordinate conversion algorithm optimization module (21) and an elevation processing module (22). The coordinate conversion algorithm optimization module (21) can optimize the conversion algorithm between different coordinate systems, and the elevation processing module (22) can optimize the elevation data in the geographic information data under different coordinate systems, so that the elevation data under all coordinate systems can be accurately converted into the elevation data in the same coordinate system.

4. The geographic information synchronization system in each coordinate system in the digital twin according to claim 1, characterized in that: The information synchronization module (3) comprises a synchronization strategy selection module (31) and a coordinate system matching module (32); the synchronization strategy selection module (31) is capable of selecting and setting in three modes: real-time synchronization, timed synchronization and event-triggered synchronization; and the coordinate system matching module (32) is capable of converting geographic coordinates into an Unreal Engine coordinate system through calculation.

5. The geographic information synchronization system in each coordinate system in the digital twin according to claim 1, characterized in that: The error correction module (4) comprises an error detection system (41), a ground control point setting system (42), an error correction strategy adjustment system (43) and an error calculation correction system (44). The ground control point setting system (42) is used to set reference points with known coordinates at key positions in the geographic coordinate system. The error detection system (41) can compare the actual position of each reference point with the position after synchronization, thereby judging whether there is deviation or error in the geographic information data during the coordinate system conversion process. The error correction strategy adjustment system (43) can select to use iterative least square method or Kalman filtering method to perform correction calculation according to the detection data of the error detection system (41). The error calculation correction system (44) can perform error correction calculation on the coordinates of the synchronized geographic information data that the error detection system (41) determines to have errors according to the algorithm selected by the error correction strategy adjustment system (43).

6. The geographic information synchronization system in each coordinate system in the digital twin according to claim 1, characterized in that: The data update module (5) comprises an update cycle setting module (51), an update mode setting module (52) and a version management module (53); the update cycle setting module (51) is capable of setting the data update cycle to real-time update or periodic update; the update mode setting module (52) is capable of setting the data update mode to incremental update or full update; and the version management module (53) is capable of storing and managing historical version data.

7. A method for synchronizing geographic information in each coordinate system of a digital twin using the geographic information synchronization system in each coordinate system of the digital twin according to any one of claims 1 to 6, the specific steps being as follows: Step 1: Collect geographic information data from the real world and automatically identify and record the coordinate system of each data source; Step 2: Select a target coordinate system, convert each piece of collected data into a geographic coordinate system according to a common coordinate system conversion algorithm, and finally convert the coordinate systems of all data sources into the target geographic coordinate system, and then convert the target geographic coordinate system into the Unreal Engine coordinate system; Step 3: Synchronize the geographic information data after coordinate system conversion into the digital twin model (6); Step 4: Monitor the data errors during synchronization and correct the geographic information data with errors through calculation; Step 5: Displaying the geographic information data in the digital twin model (6) through the visualization interaction layer (61); Step 6: Set the update cycle and update mode. The above steps 1 to 5 are performed according to the set update cycle and update mode to update the data. At the same time, all historical versions in the update process are managed.

8. The method for synchronizing geographic information in each coordinate system in a digital twin according to claim 7, characterized in that: In the step 1, geographic information data in the real world is obtained by collecting geographic environment data monitored by environmental sensors, geographic location data detected by positioning sensors, high-resolution satellite images acquired by satellites, and real-time video streams acquired by drones.

9. The method for synchronizing geographic information in each coordinate system in a digital twin according to claim 7, characterized in that: In the step 2, the general coordinate system conversion algorithm is first optimized, and then the coordinate system conversion calculation is performed on the collected geographic information data according to the optimized scheme; Among them, the method for optimizing the general coordinate system conversion algorithm is as follows: All the collected geographic information data are divided into multiple parts, and the processor simultaneously calculates each part of the divided data on different cores; before the calculation, the repeatedly used conversion parameters are pre-calculated in the initialization stage, and the calculation results are stored in the memory; during the calculation, the calculation accuracy of the conversion algorithm is adjusted according to the actual application requirements and the current system load, and unnecessary repeated calculations are identified and eliminated in real time. At the same time, the geographic information data is calculated in parallel using hardware with parallel computing capabilities; after the calculation is completed, the appropriate data structure is selected for data storage and access; The calculation method for coordinate system conversion of the collected geographic information data is as follows: (1) Use a coordinate conversion algorithm based on a mathematical model to achieve conversion between arbitrary coordinate systems, and ultimately convert all geographic information data to the same geographic coordinate system; (2) Convert the geographic coordinate system to the Unreal Engine coordinate system: First, determine a scale for scaling, then define the origin of the Unreal Engine coordinate system as a coordinate point in the destination geographic coordinate system, then convert the longitude and latitude values ​​in the geographic coordinate system to the X and Y coordinate values ​​in the Unreal Engine coordinate system, and finally, process the elevation data in the geographic information data and convert it to the Z coordinate value in the Unreal Engine coordinate system. The Z coordinate value in the Unreal Engine coordinate system expresses the height of the buildings and terrain in the geographic information; Among them, the method for processing elevation data in geographic information data is as follows: (2.1) Convert elevation data from different geographic information data sources into a common coordinate system; (2.2) The elevation data in the geographic information data converted into the same coordinate system is calculated using a smoothing algorithm; (2.3) Use filtering techniques to remove outliers in elevation data while retaining terrain features; (2.4) Identify key features in the terrain through image processing technology and enhance these key features; (2.5) Use edge detection algorithms to identify terrain boundaries. For areas where elevation data is missing or sparse, use interpolation methods to fill in the gaps. (2.6) Adjust the scale of the elevation data according to the scaling ratio of the Unreal Engine coordinates; (2.7) Offset and correct elevation data; (2.8) Fuse elevation data from multiple data sources to form a complete and accurate elevation model; (2.9) Design an elevation data update mechanism to handle dynamically changing elevation data.

10. The method for synchronizing geographic information in each coordinate system in a digital twin according to claim 7, characterized in that: In the step three, an API interface supporting multiple data formats is used for fast and efficient data transmission, and a concurrent processing mechanism is used for data processing during the data transmission process. In the step four, the correction strategy is dynamically adjusted according to the actual situation, and the iterative least squares method or Kalman filtering method is selected for calculation and correction.

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