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

Abstract

The embodiment of the invention provides a rail transit modeling process exception handling method and system based on multi-source data fusion, and the method comprises the steps: collecting the basic data of a rail transit line, the basic data including but not limited to line plane trend data, vertical section elevation data, and rail structure parameter data; according to the collected basic data, initializing an initial framework of a rail transit unilateral model in a three-dimensional modeling environment, and determining a starting point, a direction and a basic size of the model; according to design specifications and actual requirements of rail transit, various features of the unilateral model are constructed on the initial framework, refining processing is conducted on the constructed unilateral model, and materials, textures and detail features are added; and comparing and verifying the generated unilateral model with the unilateral condition of the actual rail transit line, and correcting and optimizing the model. According to the scheme, a full-process dynamic feedback mechanism is established, closed-loop control is formed in three levels of data fusion, model optimization and anomaly repair, and the modeling accuracy is improved.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of data modeling technology, and in particular to an anomaly handling method for rail transit modeling process based on multi-source data fusion. Background Technology

[0002] With the widespread application of Building Information Modeling (BIM) technology in the field of engineering construction, traditional manual modeling methods are inefficient and lack accuracy for complex structures such as bridges and tracks in railway engineering, making it difficult to meet the requirements of modern engineering projects for high efficiency, accuracy and informatization.

[0003] Existing modeling methods often employ a single detection dimension in their anomaly diagnosis modules, such as detecting only geometric topology or material parameters, lacking cross-modal feature correlation analysis. Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, embodiments of this specification provide an anomaly handling method for rail transit modeling based on multi-source data fusion. One or more embodiments of this specification also relate to an anomaly handling system for rail transit modeling based on multi-source data fusion, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a method for handling anomalies in the rail transit modeling process based on multi-source data fusion is provided, including: Collect basic data on rail transit lines, including but not limited to line alignment data, longitudinal profile elevation data, and track structure parameter data; Based on the collected basic data, the initial framework of the single-sided model of the rail transit is initialized in the 3D modeling environment, and the starting point, orientation and basic dimensions of the model are determined. 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; 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.

[0006] In one possible implementation, during the data collection phase, the horizontal alignment data of the route is obtained through GPS measurements and GIS data, while the longitudinal profile elevation data is obtained through leveling or laser scanning measurements.

[0007] In one possible implementation, during the model initialization phase, parametric modeling techniques are employed in a 3D modeling environment to determine the position and size of the initial frame by inputting parameter values ​​from the basic data.

[0008] In one possible implementation, during the feature construction phase, a modular design concept is adopted, in which features such as track, track bed, and guardrail are designed as independent modules, and then combined and spliced ​​together.

[0009] In one possible implementation, during 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.

[0010] In one possible implementation, parametric modeling techniques are employed in a 3D modeling environment to determine the position and dimensions 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.

[0011] In one possible implementation, the parameters of the fusion strategy are automatically adjusted by monitoring the rate of change and correlation of the data in real time, including: Dynamic fusion parameter adjustment factor:

[0012] 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.

[0013] According to a second aspect of the embodiments of this specification, an anomaly handling system for rail transit modeling based on multi-source data fusion is provided, comprising: The data collection module is configured to collect basic data of the rail transit line, including but not limited to 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 building module is configured to build various features of a 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.

[0014] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising: 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 above-mentioned anomaly handling method for rail transit modeling process based on multi-source data fusion.

[0015] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0016] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0017] This specification provides an anomaly handling method and system for rail transit modeling based on multi-source data fusion. The method includes: collecting basic data of the rail transit line, including but not limited to line alignment data, longitudinal profile elevation data, and track structure parameter data; initializing the initial framework of a single-sided rail transit model in a 3D modeling environment based on the collected basic data, determining the model's starting point, orientation, and basic dimensions; constructing various features of the single-sided model on the initial framework according to rail transit design specifications and actual needs; refining the constructed single-sided model by adding materials, textures, and detailed features; and comparing the generated single-sided model with the actual single-sided condition of the rail transit line, correcting and optimizing the model accordingly. This solution establishes a full-process dynamic feedback mechanism, forming a closed-loop control across data fusion, model optimization, and anomaly repair, thereby improving the accuracy of modeling. Attached Figure Description

[0018] Figure 1 This is a flowchart of an anomaly handling method for rail transit modeling based on multi-source data fusion, provided in one embodiment of this specification. Figure 2 This is a schematic diagram of the structure of an anomaly handling system for rail transit modeling based on multi-source data fusion, provided in one embodiment of this specification. Figure 3 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0020] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this 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” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0022] This specification provides a method for handling anomalies in the rail transit modeling process based on multi-source data fusion. This specification also relates to a system for handling anomalies in the rail transit modeling process based on multi-source data fusion, a computing device, and a computer-readable storage medium, which will be described in detail in the following embodiments.

[0023] See Figure 1 , Figure 1 A flowchart is shown of an anomaly handling method for rail transit modeling based on multi-source data fusion according to an embodiment of this specification, specifically including the following steps.

[0024] Step 101: Collect basic data of the rail transit line, including but not limited to the line alignment data, longitudinal profile elevation data, and track structure parameter data; In one possible implementation, during the data collection phase, the horizontal alignment data of the route is obtained through GPS measurements and GIS data, while the longitudinal profile elevation data is obtained through leveling or laser scanning measurements.

[0025] Among them: **Route alignment data** refers to the projected trajectory of the rail transit line on a horizontal plane, used to determine the track's geometry and orientation in two-dimensional space. **Global Positioning System (GPS)** refers to a satellite navigation system capable of acquiring surface coordinates for high-precision spatial positioning. **Geographic Information System (GIS)** refers to a platform for storing, analyzing, and visualizing geospatial data, used to integrate and manage multi-source geographic information. **Longitudinal profile elevation data** refers to elevation changes along the longitudinal direction of the track, reflecting the track's slope and vertical curve characteristics. **Leveling** refers to the traditional measurement method of obtaining elevation differences using a level instrument to establish an accurate elevation benchmark network. **Laser scanning measurement** refers to the technology of acquiring three-dimensional point cloud data using lidar, used for rapidly reconstructing high-precision models of the ground surface and structures.

[0026] 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.

[0027] 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.

[0028] Step 102: Based on the collected basic data, initialize the initial framework of the single-sided model of the rail transit in the 3D modeling environment, and determine the starting point, orientation and basic dimensions of the model; 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.

[0029] 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.

[0030] This initialization method significantly improves modeling efficiency through standardized spatial benchmarks, and precise positioning of the starting point avoids misalignment issues during subsequent model splicing. Directional parameters are linked with field survey data to ensure spatial consistency between the virtual model and the actual route. Pre-set parametric templates for basic dimensions can quickly adapt to different track systems, reducing repetitive setup time. The integrated interface of the 3D modeling environment supports real-time visual verification, facilitating early detection of design conflicts. The overall process lays a precise spatial foundation for subsequent detailed modeling, while simultaneously meeting the dual requirements of design specifications and construction guidance.

[0031] Step 103: Based on the design specifications and actual needs of rail transit, 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; In one possible implementation, during the model initialization phase, parametric modeling techniques are employed in a 3D modeling environment to determine the position and size of the initial frame by inputting parameter values ​​from the basic data.

[0032] Specifically: Parametric modeling technology refers to a digital design method that drives the geometric features of a model through variable parameters, enabling dynamic adjustment of model dimensions and positions. Basic data parameter values ​​refer to quantitative indicators reflecting the geometric characteristics of the track line, serving as input variables for modeling software to control the model generation logic. Initial frame position refers to the coordinate positioning benchmark of the model in three-dimensional space, ensuring a strict correspondence between each component and its actual spatial location. Initial frame dimensions refer to the macroscopic geometric parameters of the model's main structure, constraining the proportional range of subsequent detailed design.

[0033] As a concrete example: In a high-speed railway ballastless track modeling project, after inputting track parameters such as a curve radius of 7000m and a superelevation of 120mm into the Catia software, the system automatically generates an initial framework conforming to the CRTSⅢ type slab track. The model position is determined by binding the track centerline coordinates (X=285436.21, Y=4632781.53), and the rail top surface elevation is set to the ±0.000 datum plane. When the transition curve length parameter is adjusted from 70m to 90m, the model automatically regenerates the track slab and fastener arrangement, with the entire process taking less than 3 seconds.

[0034] This technical solution significantly improves modeling efficiency through a parameter-driven mechanism, and the direct correlation between basic data and the model ensures real-time response to design changes. Automated generation of the initial framework reduces human intervention errors, and spatial mapping of positional parameters guarantees consistency between the model and reality. Flexible adjustment of dimensional parameters supports rapid verification and comparison of multiple schemes, providing a reliable benchmark model for subsequent detailed design. The overall process significantly reduces repetitive labor intensity while meeting the precise modeling requirements of complex track structures.

[0035] Specifically, 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, including: automatically adjusting the parameters of the fusion strategy by monitoring the rate of change and correlation of data in real time.

[0036] In practical applications, appropriate data fusion strategies are dynamically selected based on the real-time status of the data and application requirements. These strategies include weighted average method, principal component analysis method, and neural network fusion method.

[0037] Specifically, 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.

[0038] In the above method, a coupling term between the dynamic rate of change and the correlation is constructed: × This characterizes the synergistic effect of current data dynamics and correlations. A balance term is introduced, incorporating time decay and historical benchmarks: the denominator is passed through a time decay factor. Weights of the current state The product of the two, plus the historical benchmark. Its corrective effect.

[0039] Differential calculation of real-time data streams acquired by sensor networks; meaning: the relative change in data characteristics within the current sampling period, reflecting the transient characteristics of the system; Eigenvalue decomposition of the covariance matrix of multi-source data; meaning: dynamic correlation strength of eigenvectors from different data sources, ranging from [0,1]. The exponential decay model within a sliding time window; meaning: the degree to which the influence of historical data on current decisions decays is negatively correlated with the data update frequency; Rule matching results in the expert system's knowledge base; Meaning: The priority weight of the current system's operating status, determined by both the anomaly level and the operating condition type; Statistical values ​​of similar scenarios in historical databases; meaning: empirical values ​​for parameter adjustment under the same environmental conditions, providing a reference for decision-making.

[0040] Step 104: Refine the constructed one-sided model by adding materials, textures, and detailed features; In one possible implementation, during the feature construction phase, a modular design concept is adopted, in which features such as track, track bed, and guardrail are designed as independent modules, and then combined and spliced ​​together.

[0041] Modular design refers to a design method that decomposes complex systems into independent functional units to improve component reusability and assembly efficiency. Track modules refer to standardized units containing components such as rails and fasteners, capable of matching different models and specifications according to different line requirements. Track bed modules refer to the subgrade structure units supporting the track, adaptable to various track bed types such as ballasted and ballastless. Guardrail modules refer to safety protection components at track boundaries, used for rapid deployment of safety-standard-compliant isolation facilities. Combined assembly refers to the parametric assembly process between modules, enabling rapid construction and adjustment of complex systems.

[0042] As a concrete example: 60kg / m rail modules, double-block ballastless track bed modules, and sound barrier guardrail modules are prefabricated in modeling software. The track bed module spacing is set to 650mm using interface matching technology, automatically attaching the rail modules and locking the bolt holes. The guardrail modules are positioned 3.2m off-center along the track centerline, and the system automatically generates transition section connectors to complete the overall assembly. When replacing with CRTSⅠ type rail modules, the associated track bed modules synchronously update their geometric parameters.

[0043] This design methodology significantly reduces modeling complexity through modular decomposition, and an independent module library supports the sharing of standard parts across multiple projects. Parametric interfaces ensure assembly accuracy, avoiding the cumulative error problems of traditional modeling. The flexible replacement capability of feature modules meets the needs of different design scenarios, and auxiliary modules such as protective facilities can be deployed in batches in a standardized manner. The overall process ensures both model accuracy and design efficiency, providing a structured model foundation for subsequent engineering quantity calculations and construction simulations.

[0044] Step 105: 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.

[0045] In one possible implementation, during 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.

[0046] Specifically, a full-element simulation verification environment was established: a virtual test field was built including a vehicle dynamics model (multibody system degrees of freedom > 200) and a catenary vibration model (modal order > 10). Real operational data (100,000 data points per day) was injected using a data-driven approach to verify the model's performance during emergency braking (deceleration > 1.2 m / s²). 2 Reliability under extreme conditions such as high passenger flow (load > 120%).

[0047] Furthermore, a five-dimensional evaluation system can be constructed based on the verification results: Geometric accuracy (30%) Physical property compatibility (25%) Operation and maintenance compatibility (20%) Scalability (15%) Economic indicators (accounting for 10%) generate a visual radar chart to support project decision-making, and the evaluation results are fed back to the modeling parameter library to form a closed-loop optimization.

[0048] This specification provides an anomaly handling method and system for rail transit modeling based on multi-source data fusion. The method includes: collecting basic data of the rail transit line, including but not limited to line alignment data, longitudinal profile elevation data, and track structure parameter data; initializing the initial framework of a single-sided rail transit model in a 3D modeling environment based on the collected basic data, determining the model's starting point, direction, and basic dimensions; constructing various features of the single-sided model on the initial framework according to rail transit design specifications and actual needs; refining the constructed single-sided model by adding materials, textures, and detailed features; and comparing the generated single-sided model with the actual single-sided condition of the rail transit line, correcting and optimizing the model accordingly. This solution establishes a full-process dynamic feedback mechanism, forming a closed-loop control across data fusion, model optimization, and anomaly repair, thereby improving the accuracy of modeling.

[0049] Corresponding to the above method embodiments, this specification also provides an embodiment of an anomaly handling system for rail transit modeling based on multi-source data fusion. Figure 2 This specification illustrates a schematic diagram of an anomaly handling system for rail transit modeling based on multi-source data fusion, according to one embodiment of this specification. Figure 2 As shown, the system includes: Data collection module 201 is configured to collect basic data of rail transit lines, including but not limited to line alignment data, longitudinal profile elevation data, and track structure parameter data. The initial modeling module 202 is configured to initialize the initial framework of the single-sided model of the rail transit in the 3D modeling environment based on the collected basic data, and determine the starting point, orientation and basic dimensions of the model. The feature construction module 203 is configured to construct various features of the single-sided model on the initial framework according to the design specifications and actual needs of rail transit, including the geometry and spatial position of the track, track bed, and guardrail. The detail processing module 204 is configured to refine the constructed one-sided model by adding materials, textures, and detail features. The model correction module 205 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.

[0050] This specification provides an anomaly handling method and system for rail transit modeling based on multi-source data fusion. The system includes: collecting basic data of the rail transit line, including but not limited to line alignment data, longitudinal profile elevation data, and track structure parameter data; initializing the initial framework of a single-sided rail transit model in a 3D modeling environment based on the collected basic data, determining the model's starting point, direction, and basic dimensions; constructing various features of the single-sided model on the initial framework according to rail transit design specifications and actual needs; refining the constructed single-sided model by adding materials, textures, and detailed features; and comparing the generated single-sided model with the actual single-sided condition of the rail transit line, correcting and optimizing the model. This solution establishes a full-process dynamic feedback mechanism, forming a closed-loop control at the three levels of data fusion, model optimization, and anomaly repair, thereby improving the accuracy of modeling.

[0051] The above is an illustrative scheme of an anomaly handling system for rail transit modeling based on multi-source data fusion, according to this embodiment. It should be noted that the technical solution of this anomaly handling system for rail transit modeling based on multi-source data fusion belongs to the same concept as the technical solution of the above-described anomaly handling method for rail transit modeling based on multi-source data fusion. Details not described in detail in the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the rail transit modeling process based on multi-source data fusion can be found in the description of the technical solution of the technical solution of the technical solution of the technical solution of the technical solution of the rail transit modeling process based on multi-source data fusion.

[0052] Figure 3A structural block diagram of a computing device 300 according to one embodiment of this specification is shown. 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 via a bus 330, and a database 350 is used to store data.

[0053] The computing device 300 also includes an access device 340, which enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 340 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0054] In one embodiment of this specification, the aforementioned components of the computing device 300 and Figure 3 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 3 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0055] The computing device 300 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 300 can also be a mobile or stationary server.

[0056] The processor 320 executes the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion. The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion belong to the same concept. Details not described in detail in the technical solution of the computing device can be found in the description of the technical solution of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0057] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0058] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0059] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0060] The above is an illustrative example of a computer program in this embodiment. It should be noted that the technical solution of this computer program belongs to the same concept as the technical solution of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion. Details not described in detail in the computer program's technical solution can be found in the description of the above-described method for handling anomalies in the rail transit modeling process based on multi-source data fusion.

[0061] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0062] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0063] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0064] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0065] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only 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: Collect basic data on rail transit lines, including but not limited to line alignment data, longitudinal profile elevation data, and track structure parameter data; Based on the collected basic data, the initial framework of the single-sided model of the rail transit is initialized in the 3D modeling environment, and the starting point, orientation and basic dimensions of the model are determined. Based on the design specifications and actual needs of rail transit, various features of a 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; 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.

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 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.

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

5. 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.

6. The method according to claim 3, characterized in that, In a 3D modeling environment, parametric modeling techniques are used to determine the position and dimensions 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.

7. The method according to claim 6, characterized in that, 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.

8. An anomaly handling system for rail transit modeling based on multi-source data fusion, characterized in that, include: The data collection module is configured to collect basic data of the rail transit line, including but not limited to 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.

9. 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 7.

10. A computer-readable storage medium storing computer-executable instructions that, 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 7.

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