Comprehensive management and control method and system based on digital twinning and multi-source heterogeneous data processing

Abstract

The invention provides a comprehensive management and control method and system based on digital twin and multi-source heterogeneous data processing, and the method comprises the following steps: scanning a site, obtaining the point cloud data of a surrounding environment, reconstructing the reverse modeling of the geometric shape and structure of a building according to the point cloud data, and calibrating the model obtained through the reverse modeling with the site; carrying out optimization processing on the model; a multi-source heterogeneous data fusion technology is adopted to carry out process collection and classification on heterogeneous data of various specialties of rail transit, and standardized processing of the data is realized. According to the comprehensive management and control method and system based on digital twinborn and multi-source heterogeneous data processing, accurate mapping of a digital twinborn space to a real station environment is realized, a three-dimensional model can be associated with multi-dimensional data attributes, core functions such as virtual inspection, risk management and control and scheduling decision making are realized in an auxiliary manner, and the method and the system are suitable for popularization and application. And the intelligent operation level of the station is improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and specifically, relates to a comprehensive management and control method and system based on digital twins and multi-source heterogeneous data processing. Background Art

[0002] As the scale of urban rail transit systems continues to expand and the operating mileage continues to increase, traditional station operation and maintenance methods face problems such as low efficiency, data silos, and difficulty in cross-system collaboration. Existing operation and maintenance management systems usually use separate management, manual inspections, passive alarms, etc., which makes it difficult to achieve real-time monitoring and prediction of station operation status. On the one hand, this leads to an increase in operation and maintenance costs, and on the other hand, it restricts the accuracy and timeliness of dispatching management decisions.

[0003] At present, most of the comprehensive management and control systems based on digital twins and multi-source heterogeneous data processing introduce single-professional or single-function digital and information systems for management. Different modules (such as equipment management, fault alarm, etc.) are often relatively independent; data cannot be shared, and it is difficult for systems to work together and to achieve cross-system data integration and utilization; lack of real-time data support, unable to achieve accurate positioning and fault prediction of equipment, unable to effectively improve the problems of operation and maintenance system fragmentation, data barriers, etc., resulting in a lack of necessary support for station management and decision-making support, and unable to adapt to the requirements of smart station operations.

[0004] The existing integrated management and control system based on digital twins and multi-source heterogeneous data processing has restricted communication mechanisms, interface protocols, etc. Different business function modules are relatively independent, and cannot be coordinated, resulting in a fragmented system. At the same time, data sources from different systems result in inconsistent formats, making it difficult to effectively integrate and use cross-system data. There is a lack of real-time data support for overall station situation awareness and a lack of technical support for scientific and effective intelligent decision-making, which makes it impossible to adapt to more diverse station service needs. Summary of the invention

[0005] The purpose of the present invention is to provide a comprehensive management and control method and system based on digital twins and multi-source heterogeneous data processing, aiming to solve the technical problem that the existing comprehensive management and control system based on digital twins and multi-source heterogeneous data processing cannot adapt to the intelligent operation of stations.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is: to provide a comprehensive management and control method based on digital twins and multi-source heterogeneous data processing, comprising the following steps: Scan the site to obtain point cloud data of the surrounding environment, reconstruct the building's geometry and reverse modeling of the structure based on the point cloud data, and calibrate the model obtained by reverse modeling with the site; Optimize the model; Multi-source heterogeneous data fusion technology is used to process the collection and classification of various professional heterogeneous data of rail transit, realizing standardized data processing.

[0007] Preferably, scanning the site to obtain point cloud data of the surrounding environment, reconstructing the geometric shape and reverse modeling of the structure of the building according to the point cloud data, and calibrating the model obtained by the reverse modeling with the site, includes: Obtain a large amount of point cloud data through the 3D laser scanning process, perform denoising on the point cloud data, and perform point cloud registration operations; The point cloud data is further converted into a three-dimensional model through surface fitting; the fitting formula is:

[0008] in, represents the function of fitting the surface, are the surface parameters to be solved, represents a point in the point cloud, represents the fitting error.

[0009] Compare the model with the data in the actual environment to verify the consistency; the comparison formula is:

[0010] in, represents the consistency measure between the digital twin and the actual environment, represents the calibration matrix of the adjustment parameters, represents the model points generated by reverse modeling, Actual data points representing a production environment.

[0011] Preferably, the process of acquiring a large amount of point cloud data through a three-dimensional laser scanning process, denoising the point cloud data, and performing a point cloud registration operation includes: The registration formula used is:

[0012] Among them, T represents the rigid body transformation matrix including the rotation matrix and the displacement vector, which is used to align the point cloud data. and represents corresponding points from two different point clouds, represents the Euclidean distance metric.

[0013] Preferably, the optimization process of the model comprises: using models of different degrees of refinement to represent the same model; wherein the level of detail is adjusted according to the distance from the model to the observer using LOD technology:

[0014] in, Indicates the LOD level, Represents the distance from the model to the observer, represents the resolution factor, Indicates the proportion of the model on the screen; Calculate the rendering complexity of the model:

[0015] in, Indicates the rendering complexity, Indicates the number of triangles in the model. Indicates the amount of rendering calculation for each triangle; Calculate the scene rendering time after LOD technology:

[0016] in, Represents the total rendering time, Indicates the number of models in the scene, Indicates The basic rendering time of each model, Indicates The LOD level of the model.

[0017] Preferably, the optimization processing of the model includes: performing low-poly baking on the fine model to generate models of different precisions, wherein when in use, the selection of the fineness of the model depends on the distance between the viewpoint and the model.

[0018] Preferably, the optimizing the model comprises: Standardize heterogeneous data; the data standardization formula is:

[0019] in, Represents the original data value, represents the mean of the data, Represents the standard deviation of the data. By standardizing the data to a uniform scale, it ensures that the data is compared and processed in the same dimension; Through the fusion algorithm, the data of different systems are mapped, associated and merged to form unified structured data; Carry out process-based collection, classification and distribution of heterogeneous data of various professions in rail transit, unify the mapping of three different coding systems of equipment coding, asset coding and component coding, and generate a unique code.

[0020] Preferably, the data of different systems are mapped, associated and merged through a fusion algorithm to finally form unified structured data, and the fusion algorithm includes: one or more of a key-value-based association matching algorithm and a semantic-based heterogeneous data matching algorithm.

[0021] Preferably, the heterogeneous data of various rail transit disciplines are collected, classified and distributed in a process-oriented manner, and the three different coding systems of equipment coding, asset coding and component coding are uniformly mapped to generate a unique code, including: Clean and format the code to extract the valid part from the code string; The three different codes of equipment code, asset code and component code are standardized; Hash the encoding to generate a unique identifier of fixed length.

[0022] Preferably, the cleaning and formatting of the code is used to extract the valid part from the code string, including: using regular expression parsing to extract the matching string:

[0023] in, is the original code, is a function that matches a regular expression. It is the normalized encoding.

[0024] The present invention also provides an integrated management and control system based on digital twins and multi-source heterogeneous data processing, which is characterized by being used to execute the integrated management and control method based on digital twins and multi-source heterogeneous data processing as described in any one of the above items, including: The model conversion module is used to scan the site, obtain point cloud data of the surrounding environment, reconstruct the geometric shape and structure of the building based on the point cloud data, and calibrate the model obtained by reverse modeling with the site; Optimization module, used to optimize the model; The fusion module is used to adopt multi-source heterogeneous data fusion technology to process and classify heterogeneous data from various professional fields of rail transit, so as to realize standardized data processing.

[0025] The beneficial effects of the comprehensive control method and system based on digital twin and multi-source heterogeneous data processing and the assembly method provided by the present invention are: compared with the prior art, the comprehensive control method and system based on digital twin and multi-source heterogeneous data processing of the present invention realizes efficient data processing and spatial topological structure construction, realizes lightweight and real-time rendering of large models, improves the smooth loading and display of large scenes and large-volume models, and ensures high-performance work in big data scenarios. The comprehensive control method based on digital twin and multi-source heterogeneous data processing performs standardized processing on data from different systems, realizes cross-system data sharing and calling, facilitates system data intercommunication, and improves data utilization efficiency. The comprehensive control method based on digital twin and multi-source heterogeneous data processing realizes the accurate mapping of the digital twin space to the real station environment, can associate the three-dimensional model with the multi-dimensional data attributes, assists in realizing the core functions such as virtual inspection, risk control and scheduling decision-making, and improves the intelligent operation level of the station. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 A flow chart of a comprehensive management and control method based on digital twins and multi-source heterogeneous data processing provided by an embodiment of the present invention Figure 1 ; Figure 2 A flow chart of a comprehensive management and control method based on digital twins and multi-source heterogeneous data processing provided by an embodiment of the present invention Figure 2 ; Figure 3 A structural block diagram of a comprehensive management and control system based on digital twins and multi-source heterogeneous data processing provided in an embodiment of the present invention.

[0028] In the figure: 1. Model conversion module; 2. Optimization module; 3. Fusion module. DETAILED DESCRIPTION

[0029] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0030] Please also read Figures 1 to 3, the comprehensive management and control method based on digital twins and multi-source heterogeneous data processing provided by the present invention is now described. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing includes the following steps: S1. Scan the site to obtain point cloud data of the surrounding environment, convert the point cloud data into a model, and achieve reverse modeling of the geometric shape and structure of the building based on the point cloud data to obtain a digital twin model, and calibrate the digital twin model obtained by reverse modeling with the site.

[0031] The surrounding environment includes the geometry and details of the building or site.

[0032] Step S1 preferably includes: S1.1. Obtain a large amount of point cloud data through the 3D laser scanning process, expressed as:

[0033] in, represents a point cloud dataset, Represents the first points.

[0034] The point cloud data is subjected to denoising. Preferably, noise points are removed by a filtering formula to improve the quality of the data.

[0035] The filtering formula is:

[0036] in, represents the point after filtering, Indicate point The point set in the neighborhood, Indicate point The distance to its neighboring points, Indicates the set distance threshold, which is used to determine whether it is a noise point. Represents the empty set, representing the points removed in the filter.

[0037] During 3D laser scanning, the scanning device may obtain point cloud data from multiple angles or positions under different perspectives. The result of each scan is usually partial and incomplete. Different point cloud data need to be accurately aligned through rigid body transformation operations such as rotation and translation to obtain the 3D point cloud data of the complete scene.

[0038] S1.2, use ICP (Iterative Closest Point) algorithm for point cloud registration, the formula is:

[0039] Among them, T represents the rigid body transformation matrix including the rotation matrix and the displacement vector, which is used to align the point cloud data. express The new coordinate position after the transformation matrix T. and represents corresponding points from two different point clouds, represents the Euclidean distance metric.

[0040] S1.3. The point cloud data is further converted into a three-dimensional model, i.e., a digital twin model, by surface fitting. The least squares method is used for fitting:

[0041] in, represents the function of fitting the surface, are the surface parameters to be solved, represents a point in the point cloud, represents the fitting error.

[0042] The purpose of surface fitting is to generate a smooth 3D model based on point cloud. The accuracy and consistency of point cloud data directly affect the fitting result. Point cloud registration is a prerequisite for surface fitting. The registered point cloud data is more complete and accurate, which can improve the accuracy of surface fitting and ensure the accuracy and quality of the fitting model.

[0043] S1.4. Compare the model with the data of the actual environment to verify the consistency; the comparison formula is:

[0044] in, represents the consistency measure between the digital twin and the actual environment, represents the calibration matrix of the adjustment parameters, represents the model points generated by reverse modeling, Actual data points representing a production environment.

[0045] In this step, with the help of 3D laser scanning and reverse modeling engineering, the site is scanned to obtain the geometric shape and detailed information of the building or site, and the external and internal structure of the site is captured. Point cloud data is filtered, registered, and segmented through point cloud processing to improve data quality and accuracy. The point cloud data is converted into a model to achieve reverse modeling of the geometric shape and structure of the building based on the point cloud data. Reverse modeling reconstructs the geometry of the building through surface fitting. The model obtained by reverse modeling is calibrated with the site to ensure the consistency between the digital twin environment and the on-site production environment, thereby supporting the verification of the solution in the digital twin environment.

[0046] S2. Optimize the digital twin model to improve model performance and user experience. Solve the problem of loading large models and achieve high-performance and smooth display of scenes.

[0047] Step S2 preferably includes: S2.1. Reduce the surface area of ​​the digital twin model while ensuring the visual precision of the digital twin model; S2.2. Use LOD technology (Level Of Detail) to represent the same model with models of different levels of detail. The use of LOD technology can greatly reduce the number of triangles that need to be drawn in the three-dimensional scene without changing the realism of the scene.

[0048] Merge the same components of the model. For the same components in the model, only a single component model is stored, and the rest of the same component models are added to the scene by copying, building a model reuse relationship and reducing memory usage. This can greatly save system resources while ensuring good visualization effects.

[0049] LOD technology adjusts the level of detail based on the distance from the model to the observer:

[0050] in, Indicates the LOD level, Represents the distance from the model to the observer, represents the resolution factor, Indicates the proportion of the model on the screen.

[0051] Calculate the rendering complexity of the model:

[0052] in, Indicates the rendering complexity, Indicates the number of triangles in the model. Indicates the amount of rendering calculations for each triangle.

[0053] Finally, calculate the scene rendering time after LOD technology:

[0054] in, Represents the total rendering time, Indicates the number of models in the scene, Indicates The basic rendering time of each model, Indicates The LOD level of the model.

[0055] Example 1: Taking a single station model operation as an example, the scene includes: station main body model, equipment model and component model. The specific model information is: Station model: high-precision model (100,000 triangles, LOD=1), medium-precision model (50,000 triangles, LOD=2), low-precision model (10,000 triangles, LOD=3). It takes 10 seconds to render the high-precision model. The corresponding calculation complexity parameters of the triangles In the actual rendering process, the F parameter is often estimated through experiments or experience.

[0056] Device model: high-precision model (50,000 triangles, LOD=1), medium-precision model (25,000 triangles, LOD=2), low-precision model (5,000 triangles, LOD=3). Rendering with the high-precision model takes 5 seconds, corresponding to the calculation complexity parameters of the triangles. .

[0057] Component models: high-precision model (200,000 triangles, LOD=1), medium-precision model (100,000 triangles, LOD=2), low-precision model (20,000 triangles, LOD=3). The high-precision model takes 3 seconds to render, corresponding to the calculation complexity parameters of the triangles. .

[0058] All high-precision models are used for rendering. According to the above scene rendering time formula, the total rendering time is:

[0059] Based on the distance between the view angle and each building, the corresponding level of detail is selected using the LOD formula: The station model is 100 meters away and uses a high-precision model, corresponding to LOD=1; The device model is 200 meters away, using a medium-precision model, corresponding to LOD=2; The component model is 300 meters away and uses a low-precision model, corresponding to LOD=3.

[0060] The calculation complexity of each model is calculated according to the rendering complexity formula of the calculation model:

[0061]

[0062]

[0063] By reasonably selecting the total rendering time after optimizing the models at different LOD levels, the total rendering time of the scene is calculated according to the formula:

[0064] LOD technology reduces the rendering complexity of distant objects, reducing the 18 seconds when using high-precision models to 13.5 seconds, significantly improving the rendering performance of three-dimensional scenes. It reduces system resource usage and improves the efficiency of real-time rendering while maintaining visual quality.

[0065] S2.3. Perform low-poly baking on the fine model to generate models of different precisions. When used on the platform, models of different precisions are displayed according to the distance between the viewpoint and the model. That is, the low-poly model is called when it is far away, and the high-poly model is called when it is close.

[0066] S3. Use multi-source heterogeneous data fusion technology to process and classify heterogeneous data of various rail transit disciplines to achieve standardized data processing, thereby solving the problem of efficient and rapid application of data on the smart operation and maintenance platform.

[0067] Step S3 preferably includes: S3.1. Standardize heterogeneous data; that is, convert data in different formats, such as device status data, sensor data, etc., into a unified representation. The data standardization formula is:

[0068] in, Represents the original data value, represents the mean of the data, Represents the standard deviation of the data. By standardizing the data to a uniform scale, it ensures that the data is compared and processed on the same dimension.

[0069] S3.2, Multi-source data fusion processing. That is, through the fusion algorithm, the data of different systems are mapped, associated and merged to finally form unified structured data.

[0070] The fusion algorithms include: Key-value-based association matching algorithm. If the same primary key (such as equipment number, station ID) exists in two sets of data sources, data is merged through key-value matching:

[0071] in, is the fused dataset, , are two data sets to be fused. Represents a primary key-based association operation.

[0072] Semantic-based heterogeneous data matching algorithm. For heterogeneous data sources without clear key values, a semantic matching algorithm is used to determine the correspondence between different data sources by comparing the semantic similarity of fields or data:

[0073] in, Represents the fields in data sources A and B. and The similarity between The similarity of the attributes, Represents the total number of attributes in the data source.

[0074] In this step, heterogeneous data standardization processing converts data in different formats such as SQL, JSON and XML into a unified table structure, associates the device status with sensor data through the device ID (primary key), and uses a semantic matching algorithm to semantically match the device description with data such as the device status to achieve multi-source data fusion processing.

[0075] S3.3. Carry out process-based collection, classification and distribution of heterogeneous data of various rail transit disciplines, unify the mapping of three different coding systems, namely equipment coding, asset coding and component coding, and generate a unique code. This will solve the pain point of "one object with multiple codes" in asset management and achieve accurate mapping of digital space and physical space models.

[0076] This step may include: S3.3.1. Code standardization. The code needs to be cleaned and formatted to extract the valid part from the code string. Use regular expression parsing to extract the matching string:

[0077] in, is the original code, is a function that matches a regular expression. It is a normalized code. For example, if the device code is "DEV-12345", the asset code is "ASSET-54321", and the component code is "COMP-67890", the digital part of the device code is obtained as the device ID through regular matching normalization processing, that is, device id="12345", asset id="54321", and component id="67890".

[0078] This step can effectively solve the problem of inconsistent formats of equipment codes, asset codes and component codes extracted from different systems or databases.

[0079] S3.3.2, the three different codes of equipment code, asset code and component code are standardized. In this step, a unique code is generated by string concatenation.

[0080]

[0081] in, , , Represents equipment code, asset code and component code respectively. Represents a string concatenation operation.

[0082] For example, after normalization, , , The result after splicing is .

[0083] S3.3.3. Perform hash mapping on the code. That is, further use the hash algorithm to generate a unique identifier of fixed length:

[0084] in, Represents the concatenated string. represents a hash function, Represents a fixed-length code generated by a hash algorithm.

[0085] The setting of this step ensures that the generated code is unique and of fixed length, which is convenient for storage and retrieval. It can effectively solve the problem of long code length in actual processing, which leads to slow retrieval and storage efficiency.

[0086] The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing provided by the present invention, compared with the prior art, realizes efficient data processing and spatial topological structure construction, realizes lightweight and real-time rendering of large models, improves the smooth loading and display of large scenes and large-volume models, and ensures high-performance work in big data scenarios. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing performs standardized processing on data from different systems, realizes cross-system data sharing and calling, facilitates system data intercommunication, and improves data utilization efficiency. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing realizes the accurate mapping of the digital twin space to the real station environment, can associate the three-dimensional model with the multi-dimensional data attributes, assists in realizing core functions such as virtual inspection, risk control and scheduling decision-making, and improves the intelligent operation level of the station.

[0087] The present invention also provides a comprehensive management and control system based on digital twins and multi-source heterogeneous data processing, please refer to Figures 1 to 3, used to execute the comprehensive management and control method based on digital twins and multi-source heterogeneous data processing as described in any of the above items, including: a model conversion module 1, an optimization module 2 and a fusion module 3, the model conversion module 1 is used to scan the site, obtain point cloud data of the surrounding environment, reconstruct the geometric shape and structure of the building according to the point cloud data, and calibrate the model obtained by the reverse modeling with the site; the optimization module 2 is used to optimize the model; the fusion module 3 is used to adopt multi-source heterogeneous data fusion technology to process and classify the heterogeneous data of various professions in rail transit, so as to realize standardized data processing.

[0088] The present invention provides a comprehensive management and control system based on digital twins and multi-source heterogeneous data processing, constructs a centrally controlled, controllable and manageable three-dimensional intelligent operation and maintenance management platform, takes the urban rail model as a carrier, solves the problems of dispersion and fragmentation of various professional information systems in the urban rail industry and the inability to share dynamic information, provides functional services in multiple fields such as assets, space, intelligence, safety, energy saving, etc., and realizes the reliability of fault prediction and the scalability of the system.

[0089] The present invention provides a comprehensive management and control method and system based on digital twin and multi-source heterogeneous data processing. Through the application of digital twin technology and multi-source heterogeneous data fusion technology, professional subsystems are unified and integrated, and functional modules that meet various professional needs are developed to provide a unified platform for subway operation managers, technicians, and production personnel, forming an intelligent improvement of traditional business needs. Functional modules include but are not limited to (fault alarm module, virtual inspection module, passenger flow monitoring module, risk emergency response module, etc.). With the help of data carriers with multiple scenarios and multiple applications for rail transit operation and maintenance, model mapping, equipment and facility operation data association and other processes are completed to realize enterprise-level intelligent operation and maintenance management.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive management and control method based on digital twins and multi-source heterogeneous data processing, characterized in that: The following steps are involved: Scan the site to obtain point cloud data of the surrounding environment, reconstruct the building's geometry and reverse modeling of the structure based on the point cloud data, and calibrate the model obtained by reverse modeling with the site; Optimize the model; Multi-source heterogeneous data fusion technology is used to process the collection and classification of various professional heterogeneous data of rail transit, realizing standardized data processing.

2. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 1, characterized in that: The method includes scanning the site to obtain point cloud data of the surrounding environment, reconstructing the geometric shape and structure of the building according to the point cloud data, and calibrating the model obtained by the reverse modeling with the site, including: Obtain a large amount of point cloud data through the 3D laser scanning process, perform denoising on the point cloud data, and perform point cloud registration operations; The point cloud data is further converted into a three-dimensional model through surface fitting; the fitting formula is: ； in, represents the function of fitting the surface, are the surface parameters to be solved, represents a point in the point cloud, represents the fitting error; Compare the model with the data in the actual environment to verify the consistency; the comparison formula is: ； in, represents the consistency measure between the digital twin and the actual environment, represents the calibration matrix of the adjustment parameters, represents the model points generated by reverse modeling, Actual data points representing a production environment.

3. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 2, characterized in that: The method of obtaining a large amount of point cloud data through the three-dimensional laser scanning process, denoising the point cloud data, and performing point cloud registration operations includes: The registration formula used is: ； Where T represents the rigid body transformation matrix including the rotation matrix and the displacement vector. and represents corresponding points from two different point clouds, represents the Euclidean distance metric, express The new coordinate position after the transformation matrix T.

4. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 2, characterized in that: The optimization process of the model includes: using models of different degrees of refinement to represent the same model; wherein the level of detail is adjusted according to the distance from the model to the observer using LOD technology: ； in, Indicates the LOD level, Represents the distance from the model to the observer, represents the resolution factor, Indicates the proportion of the model on the screen; Calculate the rendering complexity of the model: ； in, Indicates the rendering complexity, Indicates the number of triangles in the model. Indicates the amount of rendering calculation for each triangle; Calculate the scene rendering time after LOD technology: ； in, Represents the total rendering time, Indicates the number of models in the scene, Indicates The basic rendering time of each model, Indicates The LOD level of the model.

5. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 2 or 4, characterized in that: The optimization process of the model includes: performing low-poly baking on the fine model to generate models of different precisions, wherein when in use, the selection of the fineness of the model depends on the distance between the viewpoint and the model.

6. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 1, characterized in that: The optimization process of the model includes: Standardize heterogeneous data; the data standardization formula is: ； in, Represents the original data value, represents the mean of the data, Represents the standard deviation of the data, which ensures that the data are compared and processed in the same dimension by standardizing the data to a uniform scale; Through the fusion algorithm, the data of different systems are mapped, associated and merged to form unified structured data; Carry out process-based collection, classification and distribution of heterogeneous data of various professions in rail transit, unify the mapping of three different coding systems of equipment coding, asset coding and component coding, and generate a unique code.

7. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 6, characterized in that: The data of different systems are mapped, associated and merged through the fusion algorithm to finally form unified structured data. The fusion algorithm includes: one or more of a key-value-based association matching algorithm and a semantic-based heterogeneous data matching algorithm.

8. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 6, characterized in that: The process of collecting, classifying and distributing heterogeneous data of various rail transit disciplines is described, and the three different coding systems of equipment coding, asset coding and component coding are uniformly mapped to generate a unique code, including: Clean and format the code to extract the valid part from the code string; The three different codes of equipment code, asset code and component code are standardized; Hash the encoding to generate a unique identifier of fixed length.

9. The comprehensive management and control method based on digital twins and multi-source heterogeneous data processing according to claim 8, characterized in that: The encoding cleaning and formatting is used to extract the valid part from the encoded string, including: using regular expression parsing to extract the matching string: ； in, is the original code, is a function that matches a regular expression. It is the normalized encoding.

10. Comprehensive management and control system based on digital twins and multi-source heterogeneous data processing, characterized by: A comprehensive management and control method based on digital twins and multi-source heterogeneous data processing as described in any one of claims 1 to 9, comprising: The model conversion module is used to scan the site, obtain point cloud data of the surrounding environment, reconstruct the geometric shape and structure of the building based on the point cloud data, and calibrate the model obtained by reverse modeling with the site; Optimization module, used to optimize the model; The fusion module is used to adopt multi-source heterogeneous data fusion technology to process and classify heterogeneous data from various professional fields of rail transit, so as to realize standardized data processing.

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