Rail transit modeling process exception handling method and system based on multi-source data fusion

Abstract

The embodiment of the specification provides a rail transit modeling process exception handling method and system based on multi-source data fusion, wherein the method comprises the following steps: collecting basic data of a rail transit line, wherein the basic data comprises but is not limited to line plane trend data, longitudinal section elevation data and track structure parameter data; initializing an initial framework of a single-side model of rail transit in a three-dimensional modeling environment according to the collected basic data, and determining a starting point, a direction and basic dimensions of the model; constructing various features of the single-side model on the initial framework according to design specifications and actual requirements of rail transit, performing refinement processing on the constructed single-side model, and adding materials, textures and detail features; and comparing and verifying the generated single-side model with a single-side condition of an actual rail transit line, and modifying and optimizing the model. Through the establishment of a whole-process dynamic feedback mechanism, the scheme forms a closed-loop control in three aspects of data fusion, model optimization and exception repair, and the accuracy of modeling is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present specification relate to the technical field of data modeling, in particular to a rail transit modeling process anomaly processing method based on multi-source data fusion. BACKGROUND

[0002] With the wide application of building information modeling (BIM) technology in the field of engineering construction, for complex structures such as bridges and tracks in railway engineering, the traditional manual modeling method is inefficient and lacks accuracy, and it is difficult to meet the requirements of modern engineering projects for high efficiency, accuracy and informatization.

[0003] In existing modeling methods, the anomaly diagnosis module often uses a single detection dimension, such as only detecting geometric topology or material parameters, and lacks cross-modal feature correlation analysis. Therefore, a better solution is needed. SUMMARY

[0004] Therefore, the embodiments of the present specification provide a rail transit modeling process anomaly processing method based on multi-source data fusion. One or more embodiments of the present specification also relate to a rail transit modeling process anomaly processing system based on multi-source data fusion, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects in the prior art.

[0005] According to a first aspect of the embodiments of the present specification, a rail transit modeling process anomaly processing method based on multi-source data fusion is provided, comprising:

[0006] Collecting basic data of the rail transit line, the basic data including but not limited to line plane layout data, longitudinal section elevation data, and track structure parameter data;

[0007] According to the collected basic data, initializing an initial framework of a single-side model of the rail transit in a three-dimensional modeling environment, determining the starting point, direction and basic size of the model;

[0008] According to the design specifications and actual requirements of the rail transit, constructing various features of the single-side model on the initial framework, including the geometric shape and spatial position of the track, the ballast bed, and the guardrail;

[0009] Refining the constructed single-side model, adding material, texture and detail features;

[0010] Comparing and verifying the generated single-side model with the actual single-side situation of the rail transit line, and modifying and optimizing the model according to the verification result.

[0011] In a possible implementation, in the data collection stage, the line plane layout data is obtained through global positioning system measurement and geographic information system data, and the vertical profile elevation data is obtained by means of leveling measurement or laser scanning measurement.

[0012] In a possible implementation, in the model initialization stage, a parameterized modeling technique is used in a three-dimensional modeling environment, and the position and size of the initial framework are determined by inputting the parameter values of the basic data.

[0013] In a possible implementation, in the feature construction stage, a modular design concept is used, and the track, ballast, guardrail and other features are designed as independent modules, and then combined and spliced.

[0014] In a possible implementation, in the model verification stage, the comparison verification is performed by combining the field measurement data and the simulation analysis data, and the mechanical properties and stability of the model are analyzed by using a finite element analysis software.

[0015] In a possible implementation, a parameterized modeling technique is used in a three-dimensional modeling environment, and the position and size of the initial framework are determined by inputting the parameter values of the basic data, including:

[0016] The parameters of the fusion strategy are automatically adjusted by monitoring the change rate and correlation of the data in real time.

[0017] In a possible implementation, the parameters of the fusion strategy are automatically adjusted by monitoring the change rate and correlation of the data in real time, including:

[0018] Dynamic fusion parameter adjustment factor:

[0019]

[0020] wherein, is a real-time change rate, is a cross-source correlation coefficient, is a time decay factor, is a state weight, is a historical reference value.

[0021] According to a second aspect of an embodiment of the present specification, a rail transit modeling process anomaly handling system based on multi-source data fusion is provided, including:

[0022] The data collection module is configured to collect basic data of a rail transit line, and the basic data includes but is not limited to line plane layout data, vertical profile elevation data, and track structure parameter data.

[0023] An initial modeling module is configured to initialize an initial framework of a single-side model of rail transit in a three-dimensional modeling environment according to collected basic data, and determine a starting point, a direction and basic dimensions of the model;

[0024] A feature construction module is configured to construct various features of the single-side model on the initial framework according to design specifications and actual requirements of rail transit, including geometric shapes and spatial positions of tracks, ballast beds and guardrails;

[0025] A detail processing module is configured to refine the constructed single-side model, and add materials, textures and detail features;

[0026] A model correction module is configured to compare the generated single-side model with a single-side condition of an actual rail transit line, and correct and optimize the model according to a verification result.

[0027] According to a third aspect of the embodiments of the present specification, a computing device is provided, comprising:

[0028] a memory and a processor;

[0029] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the above rail transit modeling process exception handling method based on multi-source data fusion.

[0030] According to a fourth aspect of the embodiments of the present specification, a computer readable storage medium is provided, which stores computer executable instructions, and the instructions, when executed by a processor, implement the steps of the above rail transit modeling process exception handling method based on multi-source data fusion.

[0031] According to a fifth aspect of the embodiments of the present specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above rail transit modeling process exception handling method based on multi-source data fusion.

[0032] The embodiment of the present specification provides a rail transit modeling process exception handling method and system based on multi-source data fusion, wherein the method comprises: collecting basic data of a rail transit line, the basic data comprising but not limited to line plane trend data, longitudinal section elevation data, and track structure parameter data; initializing an initial framework of a single-side model of the rail transit in a three-dimensional modeling environment according to the collected basic data, determining a starting point, a direction, and basic dimensions of the model; constructing various features of the single-side model on the initial framework according to design specifications and actual requirements of the rail transit, performing refinement processing on the constructed single-side model, and adding materials, textures, and detail features; and comparing and verifying the generated single-side model with a single-side condition of an actual rail transit line, and modifying and optimizing the model. The scheme forms a closed-loop control in three aspects of data fusion, model optimization, and exception repair through establishing a whole-process dynamic feedback mechanism, and improves the accuracy of modeling. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of a rail transit modeling process exception handling method based on multi-source data fusion provided by an embodiment of the present specification;

[0034] Figure 2 is a structural schematic diagram of a rail transit modeling process exception handling system based on multi-source data fusion provided by an embodiment of the present specification;

[0035] Figure 3 is a structural block diagram of a computing device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0036] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present specification. However, the present specification can be practiced without the specific details, other than those described herein, and it is understood that the scope of the present specification is not limited to the details below.

[0037] The terms used in one or more embodiments of the present specification are merely for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present specification. The singular forms "a", "an" and "the" used in one or more embodiments of the present specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present specification means and includes any or all possible combinations of one or more associated listed items.

[0038] It should be understood that, although the terms first, second, etc. can be employed in describing various information in one or more embodiments of the present specification, such information should not be limited to these terms. These terms are only used to differentiate one piece of information from another piece of information. For example, without departing from the scope of one or more embodiments of the present specification, first can also be referred to as second, and similarly, second can also be referred to as first. Depending on the context, the word "if' as used herein can be interpreted to mean "when" or "upon" or "in response to determining."

[0039] In the present specification, a rail transit modeling process exception handling method based on multi-source data fusion is provided, the present specification also relates to a rail transit modeling process exception handling system based on multi-source data fusion, a computing device, and a computer readable storage medium, which are described in detail one by one in the following embodiments.

[0040] Referring to Figure 1 , Figure 1 A flowchart of a rail transit modeling process exception handling method based on multi-source data fusion according to one embodiment of the present specification is shown, which specifically includes the following steps.

[0041] Step 101: Collecting basic data of the rail transit line, the basic data including but not limited to line plane layout data, vertical profile elevation data, and track structure parameter data;

[0042] In one possible implementation, in the data collection stage, the line plane layout data is obtained through global positioning system measurement and geographic information system data, and the vertical profile elevation data is obtained through leveling measurement or laser scanning measurement.

[0043] Wherein: The line plane layout data can refer to the projected trajectory of the rail transit line on the horizontal plane, which is used to determine the geometric shape and orientation of the track in two-dimensional space. The global positioning system (GPS) can refer to a satellite navigation system that can obtain ground position coordinates to achieve high-precision spatial positioning. The geographic information system (GIS) can refer to a platform for storing, analyzing and visualizing geospatial data, which is used to integrate and manage multi-source geographic information. The vertical profile elevation data can refer to the elevation change information along the vertical direction of the rail line, which can reflect the slope and vertical curve characteristics of the track. The leveling measurement can refer to the traditional measurement method of obtaining elevation difference through the level, to establish an accurate elevation reference network. The laser scanning measurement can refer to the technology of obtaining three-dimensional point cloud data through laser radar, which is used to quickly reconstruct the high-precision model of the ground and structures.

[0044] As a concrete example: In a subway modeling project, GPS-RTK technology was used to collect the horizontal alignment data of the line, with the horizontal coordinate error controlled within 5 millimeters. Existing topographic maps and pipeline data were integrated through a GIS platform. Longitudinal profile elevation data was measured using a 0.5-second-level electronic level, with key sections supplemented by vehicle-mounted laser scanning at 2 million points per second, resulting in an elevation model with an accuracy of 3 millimeters. After data verification, it was input into the BIM system, automatically compared with design specifications, and two sections with excessive slope were identified, triggering model corrections.

[0045] This method significantly improves modeling accuracy through multi-source data fusion. The combination of GPS and GIS avoids systematic errors from single data sources, while laser scanning technology captures complex structural details that traditional surveying methods struggle to cover. Leveling provides a reliable elevation benchmark, ensuring the longitudinal profile model meets operational safety requirements. An automated data verification mechanism can identify design conflicts early, reducing rework costs later. The overall technical solution balances efficiency and accuracy, providing a high-quality data foundation for the entire lifecycle management of rail transit.

[0046] Step 102: Based on the collected basic data, initialize the initial framework of the single-sided rail transit model in the 3D modeling environment, and determine the starting point, orientation, and basic dimensions of the model;

[0047] In practical applications, a 3D modeling environment refers to a software platform used to create 3D digital models, capable of integrating geospatial data and engineering parameters to achieve visual modeling. The initial framework refers to the basic structural outline of the model, used to define the modeling scope and the topological relationships between components. The starting point refers to the origin of the model's spatial coordinate system, ensuring all components are positioned based on a unified benchmark. The orientation refers to the axial definition of the model in 3D space, relating the actual route to the coordinate system. Basic dimensions refer to parameters such as the length and width of key model components, used to control the overall proportions and compliance with design specifications.

[0048] As a concrete example: In Revit modeling software, starting from the intersection point DK12+345 of the track design (X=325671.28, Y=4952132.15), the track centerline is set as the positive X-axis, and the Z-axis is vertically upward. Basic dimensions such as the rail spacing of 1435mm and the track bed width of 3.2m are input through a parametric template, and the system automatically generates an initial framework including the track and track bed. The model orientation is then adjusted to an azimuth angle of NE45° using the coordinate system rotation function, with a deviation of less than 0.1 degrees from the actual measured data.

[0049] The initialization method significantly improves the modeling efficiency by standardizing the spatial reference, and the accurate positioning of the starting point avoids the misalignment problem in subsequent model splicing. The directional parameters are linked with the field survey data to ensure the spatial consistency of the virtual model and the actual line. The pre-set parameterized template of the basic size can quickly adapt to the needs of different rail systems, reducing the repeated setting time. The integrated operation interface of the three-dimensional modeling environment supports real-time visual verification, facilitating the early detection of design conflicts. The overall process lays a precise spatial foundation for subsequent detailed modeling, while meeting the dual requirements of design specifications and construction guidance.

[0050] Step 103: According to the design specifications and actual needs of rail transit, build various features of the single-sided model on the initial framework, including the geometric shapes and spatial positions of the track, ballast bed, and guardrail;

[0051] In one possible implementation, in the model initialization stage, parameterized modeling technology is used in the three-dimensional modeling environment to determine the position and size of the initial framework by inputting the parameter values of the basic data.

[0052] Wherein: Parameterized modeling technology can refer to a digital design method that drives model geometric features through variable parameters, used to realize dynamic correlation adjustment of model size and position. Basic data parameter values can refer to quantitative indicators reflecting the geometric features of the rail line, which can be used as input variables of the modeling software to control the model generation logic. Initial framework position can refer to the coordinate positioning reference of the model in three-dimensional space, which can ensure that each component strictly corresponds to the actual spatial position. Initial framework size can refer to the macro-geometric parameters of the model main structure, used to constrain the scale range of subsequent detailed design.

[0053] As a specific example: in a high-speed rail ballastless track modeling project, after inputting the line parameters such as curve radius 7000m and superelevation 120mm in Catia software, the system automatically generates an initial framework that meets the CRTS III type slab track. By binding the line center coordinates (X=285436.21, Y=4632781.53), the model position is determined, and the rail top surface elevation is set to the ±0.000 reference surface. When adjusting the length parameter of the easement curve from 70m to 90m, the model automatically regenerates the ballast bed plate and fastener arrangement, with a total time of less than 3 seconds.

[0054] The technical scheme greatly improves the modeling efficiency through the parameter driving mechanism, and the direct correlation between the basic data and the model ensures that the design changes can be responded to in real time. The automatic generation of the initial framework reduces the error of manual intervention, and the spatial mapping of the position parameter ensures the consistency of the model and the actual scene. The flexible adjustment capability of the size parameter supports rapid verification of multiple scheme comparisons, providing a reliable reference model for subsequent detailed design. The overall process significantly reduces the intensity of repetitive labor, while meeting the precise modeling needs of complex rail structures.

[0055] Specifically, in the three-dimensional modeling environment, the position and size of the initial framework are determined by inputting the parameter values of the basic data, including automatically adjusting the parameters of the fusion strategy by monitoring the change rate and correlation of the data in real time.

[0056] In practical applications, the appropriate data fusion strategy is dynamically selected according to the real-time state of the data and application requirements, including weighted average method, principal component analysis method and neural network fusion method.

[0057] Specifically, the parameters of the fusion strategy are automatically adjusted by monitoring the change rate and correlation of the data in real time, including:

[0058] Dynamic fusion parameter adjustment factor:

[0059]

[0060] Wherein, is the real-time change rate, is the cross-source correlation coefficient, is the time decay factor, is the state weight, is the historical reference value.

[0061] In the above method, the coupling term of dynamic change rate and correlation is constructed: × The current data dynamic change and correlation are represented by the synergistic effect. The balance term of time decay and historical reference is introduced: the denominator is multiplied by the time decay factor and the current state weight , and the correction effect of the historical reference is superimposed.

[0062] Real-time data stream difference calculation collected by sensor network; meaning: the relative change amplitude of data characteristics in the current sampling period, reflecting the system transient characteristics; Eigenvalue decomposition of multi-source data covariance matrix; meaning: the dynamic correlation strength of different data source eigenvectors, range [0,1] ; Exponential decay model in sliding time window; meaning: the influence attenuation degree of historical data on current decision, negatively correlated with data update frequency; Rule matching result in expert system knowledge base; meaning: the priority weight of the current system running state, determined by the abnormal level and the working condition type; Statistical value of the same kind of scene in the historical database; meaning: the parameter adjustment experience value under the same environmental condition, providing decision reference.

[0063] Step 104: Refine the constructed single-sided model, add materials, textures, and detailed features;

[0064] In one possible implementation, in the feature construction stage, the modular design concept is adopted, and features such as tracks, ballast beds, and guardrails are designed as independent modules, which are then combined and spliced.

[0065] Among them, the modular design concept can refer to a design method that decomposes a complex system into independent functional units to improve component reuse rate and assembly efficiency. The track module can refer to a standardized unit containing components such as steel rails and fasteners, which can be matched with different specifications according to different line requirements. The ballast bed module can refer to a roadbed structure unit that supports the track, which can be adapted to various forms of ballast and non-ballast beds. The guardrail module can refer to a safety protection component of the track boundary, which is used to quickly deploy isolation facilities that meet safety standards. The combination and splicing can refer to the parameterized assembly process between modules, which can realize the rapid construction and adjustment of complex systems.

[0066] As a specific example: In the modeling software, precast 60kg / m steel rail modules, double-block non-ballast bed modules, and sound barrier guardrail modules. The interface matching technology sets the distance between the ballast bed modules to 650mm, automatically adsorbs the track modules and locks the bolt hole position. The guardrail module is arranged 3.2m offset from the center line of the line, and the system automatically generates transition section connectors to complete the overall splicing. When the CRTS I type track module is replaced, the associated ballast bed module is updated synchronously.

[0067] This design method significantly reduces the modeling complexity through modular decomposition, and the independent module library supports standard part sharing between multiple projects. The parameterized interface ensures the splicing accuracy and avoids the cumulative error problem of traditional modeling. The flexible replacement capability of feature modules meets the needs of different design scenarios, and the auxiliary modules such as protective facilities can be deployed in batches and standardized. The overall process not only guarantees the model accuracy but also improves the design efficiency, providing a structured model basis for subsequent engineering calculation and construction simulation.

[0068] Step 105: Compare and verify the generated single-sided model with the actual single-sided situation of the rail transit line, and modify and optimize the model according to the verification results.

[0069] In one possible implementation, in the model verification stage, the on-site measured data and simulation analysis data are combined for comparison and verification, and the finite element analysis software is used for simulation analysis to analyze the mechanical properties and stability of the model.

[0070] Specifically, the full-factor simulation verification environment: build a virtual test field containing a vehicle dynamics model (multi-body system degrees of freedom > 200) and a catenary vibration model (modal order > 10). By data-driven injection of real operation data (100,000 daily), the reliability of the model under extreme conditions such as emergency braking (deceleration > 1.2m / s 2 ), large passenger flow (load > 120%) is verified.

[0071] Further, a five-dimensional evaluation system can be constructed based on the verification results:

[0072] Geometric accuracy (30% share)

[0073] Physical property coincidence degree (25% share)

[0074] Operation compatibility (20% share)

[0075] Expansion capability (15% share)

[0076] Economic indicators (10% share) The generated visual radar chart supports project decision-making, and the evaluation results are fed back to the modeling parameter library to form a closed-loop optimization.

[0077] The embodiment of the present specification provides a rail transit modeling process exception handling method and system based on multi-source data fusion, wherein the method comprises: collecting basic data of a rail transit line, the basic data comprising but not limited to line plane layout data, longitudinal section elevation data, and track structure parameter data; initializing an initial framework of a single-side model of the rail transit in a three-dimensional modeling environment according to the collected basic data, determining a starting point, a direction and basic dimensions of the model; constructing various features of the single-side model on the initial framework according to design specifications and actual requirements of the rail transit, refining the constructed single-side model, and adding materials, textures and detailed features; comparing and verifying the generated single-side model with the single-side situation of the actual rail transit line, and modifying and optimizing the model. The present scheme forms a closed-loop control in three aspects of data fusion, model optimization and exception repair through establishing a full-process dynamic feedback mechanism, thereby improving the accuracy of modeling.

[0078] Corresponding to the above method embodiment, the present specification also provides a rail transit modeling process exception handling system based on multi-source data fusion, Figure 2 Fig. 1 shows a structure schematic diagram of a rail transit modeling process exception handling system based on multi-source data fusion provided by an embodiment of the present specification. As shown in Figure 2 , the system comprises:

[0079] A data collection module 201 configured to collect basic data of a rail transit line, the basic data comprising but not limited to line plane layout data, longitudinal section elevation data, and track structure parameter data;

[0080] The initial modeling module 202 is configured to initialize an initial framework of a single-side model of rail transit in a three-dimensional modeling environment according to the collected basic data, determine a starting point, a direction and basic dimensions of the model;

[0081] The feature construction module 203 is configured to construct various features of the single-side model on the initial framework according to design specifications and actual requirements of rail transit, including geometric shapes and spatial positions of tracks, ballast beds and guardrails;

[0082] The detail processing module 204 is configured to refine the constructed single-side model and add materials, textures and detail features;

[0083] The model correction module 205 is configured to compare and verify the generated single-side model with a single-side condition of an actual rail transit line, and correct and optimize the model according to a verification result.

[0084] The embodiment of the present specification provides a rail transit modeling process exception handling method and system based on multi-source data fusion, wherein the system comprises: collecting basic data of a rail transit line, the basic data including but not limited to line plane trend data, longitudinal section elevation data and track structure parameter data; initializing an initial framework of a single-side model of rail transit in a three-dimensional modeling environment according to the collected basic data, determining a starting point, a direction and basic dimensions of the model; constructing various features of the single-side model on the initial framework according to design specifications and actual requirements of rail transit, refining the constructed single-side model, and adding materials, textures and detail features; comparing and verifying the generated single-side model with a single-side condition of an actual rail transit line, and correcting and optimizing the model. The present scheme forms a closed-loop control in three aspects of data fusion, model optimization and exception repair by establishing a whole-process dynamic feedback mechanism, thereby improving the accuracy of modeling.

[0085] The above is a schematic scheme of a rail transit modeling process exception handling system based on multi-source data fusion according to the embodiment. It should be noted that the technical scheme of the rail transit modeling process exception handling system based on multi-source data fusion belongs to the same concept as the technical scheme of the rail transit modeling process exception handling method based on multi-source data fusion described above, and the details of the technical scheme of the rail transit modeling process exception handling system based on multi-source data fusion that are not described in detail can be seen from the description of the technical scheme of the rail transit modeling process exception handling method based on multi-source data fusion described above.

[0086] Figure 3A structural block diagram of a computing device 300 is shown, according to one embodiment of the present specification. The components of the computing device 300 include, but are not limited to, a memory 310 and a processor 320. The processor 320 is connected to the memory 310 through a bus 330, and a database 350 is used to store data.

[0087] The computing device 300 also includes an access device 340 that enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 340 can include one or more of any type of network interface (e.g., a network interface card (NIC)) such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC).

[0088] In one embodiment of the present specification, the above-mentioned components of the computing device 300 and other components not shown in the Figure 3 may be connected to each other, for example, through a bus. It should be understood that Figure 3 The structural block diagram of the computing device shown is only for the purpose of example, and is not a limitation on the scope of the present specification. Other components can be added or replaced as needed by those skilled in the art.

[0089] The computing device 300 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 300 can also be a mobile or stationary server.

[0090] The processor 320 is configured to execute computer-executable instructions to implement the steps of the above method for modeling process exception handling of rail transit based on multi-source data fusion.

[0091] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above method for modeling process exception handling of rail transit based on multi-source data fusion.

[0092] The above is a schematic solution of the computer-readable storage medium of an embodiment of the present specification. It should be noted that the technical solution of the storage medium and the technical solution of the above method for modeling process exception handling of rail transit based on multi-source data fusion belong to the same concept, and the details of the technical solution of the storage medium not described in detail can be referred to the description of the technical solution of the above method for modeling process exception handling of rail transit based on multi-source data fusion.

[0093] An embodiment of the present specification further provides a computer program, which, when executed in a computer, causes the computer to perform the steps of the above method for modeling process exception handling of rail transit based on multi-source data fusion.

[0094] The above is a schematic solution of the computer program of an embodiment of the present specification. It should be noted that the technical solution of the computer program and the technical solution of the above method for modeling process exception handling of rail transit based on multi-source data fusion belong to the same concept, and the details of the technical solution of the computer program not described in detail can be referred to the description of the technical solution of the above method for modeling process exception handling of rail transit based on multi-source data fusion.

[0095] The above-described embodiments of the application have several aspects, no single one of which is solely responsible for the application's desirable attributes. Without limiting the scope of the application as expressed by the claims which follow, some further embodiments make these aspects even more useful. Other embodiments can result in less desirable attributes.

[0096] The computer readable medium can include any entity or apparatus capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0097] It should be noted that for the foregoing method embodiments, the acts described therein can be performed in a different order from the order described, and that some acts can be performed in parallel or concurrently. In addition, some of the acts described above can not be performed in all embodiments. Furthermore, the acts described above can be performed by different parties in some embodiments. Furthermore, each of the acts described above can be performed by specialized hardware components or modules or can be embodied in a software-specific or a generalized computing system or module. Similarly, general-purpose computing systems and modules can be configured to constitute one or more specialized components or modules described above.

[0098] In the above embodiments, the description of each embodiment is focused on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0099] The above-disclosed specification of the present application is only used to help explain the present application. The alternative embodiments do not describe all the details and do not limit the application to the specific embodiments described. Obviously, according to the content of the embodiments of the present application, many modifications and changes can be made. The present application selects and describes these embodiments in order to better explain the principles and practical application of the embodiments of the present application, so that those skilled in the art can well understand and use the present application. The present application is limited by the claims and their full scope and equivalents.

Claims

1. A method for handling anomalies in rail transit modeling based on multi-source data fusion, characterized in that, include: During the data collection phase, basic data of the rail transit line is collected, including the line alignment data, longitudinal profile elevation data, and track structure parameter data. During the model initialization phase, based on the collected basic data, the initial framework of the single-sided rail transit model is initialized in the 3D modeling environment, and the starting point, orientation, and basic dimensions of the model are determined. In the feature construction phase, based on the design specifications and actual needs of rail transit, various features of the single-sided model are constructed on the initial framework, including the geometry and spatial position of the track, track bed, and guardrail. The constructed one-sided model is then refined by adding materials, textures, and detailed features; During the model verification phase, the generated one-sided model is compared and verified with the one-sided situation of the actual rail transit line, and the model is corrected and optimized based on the verification results. In the model initialization phase, parametric modeling technology is used in the 3D modeling environment to determine the position and size of the initial frame by inputting parameter values ​​of basic data; In the 3D modeling environment, parametric modeling technology is used to determine the position and size of the initial frame by inputting parameter values ​​from the basic data, including: By monitoring the rate of change and correlation of data in real time, the parameters of the fusion strategy are automatically adjusted; By monitoring the rate of change and correlation of data in real time, the parameters of the fusion strategy are automatically adjusted, including: Dynamic fusion parameter adjustment factor: in, For real-time rate of change, For cross-source correlation coefficients, The time decay factor, For state weights, This is a historical benchmark value.

2. The method according to claim 1, characterized in that, During the data collection phase, the horizontal alignment data of the route is obtained through GPS measurement and geographic information system data, while the longitudinal profile elevation data is obtained through leveling or laser scanning measurement.

3. The method according to claim 1, characterized in that, In the feature construction stage, a modular design concept is adopted, in which the track, track bed, and guardrail features are designed as independent modules, and then combined and spliced.

4. The method according to claim 1, characterized in that, In the model verification stage, a combination of on-site measured data and simulation analysis data is used for comparative verification. The simulation analysis uses finite element analysis software to analyze the mechanical properties and stability of the model.

5. An anomaly handling system for rail transit modeling based on multi-source data fusion, used to implement the anomaly handling method for rail transit modeling based on multi-source data fusion as described in any one of claims 1 to 4, characterized in that, include: The data collection module is configured to collect basic data of the rail transit line, including the line alignment data, longitudinal profile elevation data, and track structure parameter data. The initial modeling module is configured to initialize the initial framework of a single-sided rail transit model in a 3D modeling environment based on the collected basic data, and to determine the model's starting point, orientation, and basic dimensions. The feature construction module is configured to construct various features of the single-sided model on the initial framework, including the geometry and spatial position of the track, track bed, and guardrail, based on the design specifications and actual needs of rail transit. The detail processing module is configured to refine the built single-sided model by adding materials, textures, and detail features. The model correction module is configured to compare and verify the generated one-sided model with the one-sided situation of the actual rail transit line, and correct and optimize the model based on the verification results.

6. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the anomaly handling method for rail transit modeling process based on multi-source data fusion as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the anomaly handling method for rail transit modeling process based on multi-source data fusion as described in any one of claims 1 to 4.

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