Two-dimensional and three-dimensional linkage display analysis method and system for tunnel monitoring points

Abstract

The invention discloses a two-dimensional and three-dimensional linkage display analysis method and system for tunnel monitoring points, and belongs to the technical field of tunnel engineering monitoring and visualization. The method comprises the following steps: constructing a tunnel integrated three-dimensional scene; establishing an accurate mapping relationship between the unique identification code of the monitoring point and the entity, the two-dimensional analysis view coordinate and the monitoring data sequence in the tunnel integrated three-dimensional scene; loading the two-dimensional analysis view and the tunnel integrated three-dimensional scene in parallel; responding to interaction selection in any view, and performing positioning and data display in the other view according to the mapping relation; performing space-time analysis on the selected point location based on the linkage state, wherein the space-time analysis comprises multi-time sequence data comparison, data change rate calculation, trend prediction and section convergence calculation; and when the monitoring data exceed the limit, synchronously carrying out visual early warning prompt in the two-dimensional view and the three-dimensional view. According to the invention, deep fusion and intelligent linkage analysis of monitoring data in space and attributes are realized, and intuition, positioning efficiency and risk early warning capability of tunnel safety monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering monitoring and visualization technology, and in particular to a two-dimensional and three-dimensional linkage display and analysis method and system based on tunnel monitoring points, which is used to improve the intelligence level of tunnel safety monitoring and data analysis. Background Technology

[0002] With the rapid development of infrastructure construction in my country, the number and scale of tunnel projects are increasing day by day. To ensure the safety of tunnels during construction and operation, the safety monitoring of tunnel structures currently mainly relies on various sensors (such as displacement gauges, stress strain gauges, convergence gauges, etc.) deployed inside the tunnel to collect real-time monitoring data such as displacement, stress, and deformation.

[0003] However, there are three main ways to display and analyze tunnel monitoring data: Two-dimensional chart display: Monitoring data is displayed in two-dimensional form such as curves and line graphs. Although this method can clearly reflect the trend of data changes over time, it cannot intuitively show the specific location of monitoring points in the tunnel space. The data is disconnected from the spatial form, making it difficult to quickly locate the specific location of anomalies.

[0004] 3D Model Display: A 3D model of the tunnel is constructed using 3D modeling technology, and monitoring points are marked on the model. While this method provides spatial location information, it lacks in-depth interaction with detailed monitoring data and has weak capabilities for analyzing the temporal changes and details of massive amounts of monitoring data. When it is necessary to view specific historical data for a particular point or conduct comparative analysis of multiple points, it is still necessary to switch to a separate 2D chart interface. This results in a disconnect between the 3D scene and the data analysis view, leading to a poor user experience and reducing the efficiency of safety warnings and decision analysis.

[0005] The few existing 2D / 3D interactive displays suffer from problems such as disconnect between monitoring data and spatial morphology, fragmented analysis views, and disconnect between analysis functions and scenes. They cannot achieve two-way, in-depth interactive feedback and are deficient in terms of linkage depth, mapping accuracy, analysis fusion, and intuitiveness of early warning. A solution that can truly achieve dynamic, intelligent, and in-depth integration of tunnel monitoring data between 2D views and 3D scenes has not yet been formed. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a two-dimensional and three-dimensional linkage display and analysis method and system for tunnel monitoring points. By establishing a two-way, dynamic and intelligent linkage mechanism between two-dimensional views and three-dimensional scenes, it enables more intuitive, efficient and in-depth fusion analysis of tunnel monitoring data, thereby improving the intelligence level of tunnel safety monitoring and the efficiency of risk early warning.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points includes the following steps: S1. Integrate the tunnel structure model and monitoring point model to establish an integrated 3D scene of the tunnel; S2. Assign a unique identifier to each monitoring point, establish a mapping relationship between the identifier and the entity point in the integrated 3D tunnel scene and the coordinate position in the 2D analysis view, and associate the identifier with the monitoring data sequence. S3. Display the two-dimensional analysis view and the integrated three-dimensional scene of the tunnel in parallel, and mark the real-time monitoring data of the monitoring points in the corresponding positions of the integrated three-dimensional scene of the tunnel; S4. Perform two-dimensional and three-dimensional linkage based on the mapping relationship established in S2; S5. In the linked state, perform spatiotemporal analysis on the selected monitoring points, including comparison of multiple time series data, calculation of data change rate, trend prediction, and cross-sectional convergence calculation; S6. Monitor point data in real time according to the early warning threshold. When the data of a certain monitoring point exceeds the limit, it will be visualized in the two-dimensional analysis view and the integrated three-dimensional scene of the tunnel, and generate linkage alarm information.

[0008] Furthermore, the monitoring types corresponding to the monitoring point object model in S1 include displacement, convergence, stress-strain, tilt, vibration, seepage pressure, and temperature.

[0009] Furthermore, the two-dimensional analysis view in S2 includes a monitoring data curve and a tunnel cross-section diagram.

[0010] Furthermore, the two-dimensional linkage in S4 includes: when one or more monitoring points are selected in the integrated tunnel three-dimensional scene, the corresponding monitoring point data is located and displayed in the two-dimensional analysis view; when one or more monitoring points are selected in the two-dimensional analysis view, the viewpoint is located in the integrated tunnel three-dimensional scene and the corresponding entity point is displayed, highlighting the real-time annotation data of the point.

[0011] Furthermore, in S4, the method for viewpoint positioning in the integrated 3D scene of the tunnel is as follows: control the camera of the integrated 3D scene of the tunnel to fly to a preset observation range centered on the selected monitoring point or including the point.

[0012] Furthermore, the multi-time series data comparison in S5 is as follows: based on the identification code of the selected monitoring point and the specified multiple time ranges, the corresponding monitoring data is extracted, and the monitoring data is superimposed and displayed in the same two-dimensional analysis view. At the same time, the corresponding monitoring point is highlighted in the integrated three-dimensional scene of the tunnel.

[0013] Furthermore, the data change rate calculation in S5 includes: time change rate calculation and spatial change rate calculation. Time change rate calculation refers to calculating the instantaneous change rate at each sampling moment based on the time series of monitoring data using numerical differentiation methods, or calculating the average change rate within a specified time interval, and setting a change rate threshold. When the calculated time change rate exceeds the preset change rate threshold, the curve segment of that time period is highlighted on the two-dimensional analysis chart, and the monitoring point is highlighted in the integrated tunnel 3D scene to represent the direction and intensity of the change. Spatial change rate calculation refers to calculating the change rate of multiple monitoring points of the same type in the same time period, and then rendering the results onto the surface of the tunnel structure model in the integrated tunnel 3D scene.

[0014] Furthermore, the trend prediction in S5 is as follows: based on historical monitoring data, a mathematical model is used to predict the development trend of the monitoring point within the selected time period, the prediction curve is displayed at the end of the historical data curve in the two-dimensional analysis view, and the predicted structural morphology change trend is demonstrated through animation in the tunnel integrated three-dimensional scene.

[0015] Furthermore, the cross-sectional convergence calculation in S5 is as follows: based on the layout scheme of the cross-sectional monitoring points, the monitoring points constituting the convergence cross-section are associated, and the changes in chord length, cross-sectional diameter, and cross-sectional area based on coordinates between each monitoring point are calculated. In the two-dimensional analysis view, the position of the convergence monitoring point on the cross-section is displayed, the straight line connecting the two convergence monitoring points is dynamically drawn, and the key convergence value is displayed in real time, generating a special curve showing the change of the cross-sectional convergence value over time. In the integrated three-dimensional scene of the tunnel, the cross-section being analyzed is sliced ​​and displayed, and its shape changes from the initial monitoring state to the current measured state. A convergence threshold is preset, and when the convergence threshold is exceeded, an early warning is displayed.

[0016] A system for implementing a two-dimensional and three-dimensional linkage display and analysis method for any of the above-mentioned tunnel monitoring points includes a data layer, a service layer, and an application layer. The system is characterized by: the data layer having a model file library, a monitoring database, and a rule configuration library; the service layer having a model service engine, a data service engine, a linkage service engine, and an analysis service engine; and the application layer having a two-dimensional analysis module, a three-dimensional scene module, and a linkage control module. The rule configuration library contains early warning thresholds, mapping relationship rules, and visualization style schemes for the monitoring points. The application layer sends user interaction requests to the service layer. The service layer then sends the specific interaction requests to the data layer through its built-in service engines. The data layer then processes the specific data or rules through the service layer and returns them to the application layer, where they are displayed through specific modules.

[0017] The beneficial effects of this invention are as follows: This invention solves the problems of data disconnect from spatial morphology, low analysis efficiency, and lack of interactive linkage in traditional monitoring methods. It achieves deep fusion and interactive analysis of monitoring data in two and three dimensions, greatly improving the intuitiveness and efficiency of tunnel safety monitoring. By constructing an integrated three-dimensional scene of the tunnel and simultaneously displaying it in a two-dimensional analysis view, the barrier between two-dimensional data analysis and three-dimensional spatial display is broken down, achieving deep fusion of image and model consistency, giving data analysis a strong spatial orientation. Through the established mapping relationship rules, two-way linkage is achieved, making it extremely fast and accurate to discover spatial locations from data anomalies or to retrieve data from spatial locations, greatly improving work efficiency. Attached Figure Description

[0018] Figure 1 This is a general flowchart of the method described in the embodiments of the present invention.

[0019] Figure 2 This is an architecture block diagram of the system described in the embodiments of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] like Figure 1 The image shows a two-dimensional linkage display and analysis method for tunnel monitoring points provided in this application embodiment, including the following steps: S1: Construct an integrated 3D scene of the tunnel: Integrate the tunnel structure model and the monitoring point model to establish a unified 3D scene that includes spatial geometry and attribute information.

[0022] The tunnel structure model includes the BIM structural model of the tunnel segments, lining, etc.

[0023] The monitoring types corresponding to the monitoring point object model include displacement, convergence, stress and strain, tilt, vibration, seepage pressure, and temperature.

[0024] Use BIM software such as Revit to create structural models of tunnel segments and linings, then integrate the monitoring point models according to their locations in the tunnel, and generate an integrated 3D scene of the tunnel in a 3D GIS platform (such as SuperMap or Cesium).

[0025] S2: Establish mapping relationship and data association: Assign a unique identifier to each monitoring point, establish a mapping relationship between the identifier and the entity point in the integrated 3D tunnel scene and the coordinate position in the 2D analysis view, and associate the identifier with the monitoring data sequence. At the same time, the identifier is associated with the monitoring data sequence in the database.

[0026] Taking deformation monitoring as an example, a unique identifier is generated for each monitoring point (e.g., point "POINT-001"). A mapping table is established in the database to record the association between "POINT-001" and the monitoring point model in the integrated 3D tunnel scene, as well as the corresponding location of "POINT-001" on the cross-sectional view (e.g., the arch). Finally, "POINT-001" is associated with the deformation monitoring data table in the database. For all points in the "convergence monitoring" category, a warning threshold is set, and it is specified that when the threshold is reached, the point will be displayed as a "flashing red" indicator in the 3D scene. A separate "settlement rate prediction" analysis model is bound to "POINT-001".

[0027] S3: Initialization Display: Simultaneously displays the two-dimensional analysis view and the integrated three-dimensional scene of the tunnel, and marks the real-time monitoring data of the monitoring points in the corresponding positions of the integrated three-dimensional scene of the tunnel; The two-dimensional analysis view includes monitoring data curves and tunnel cross-section layout diagrams; real-time or latest data of monitoring points are labeled in the corresponding positions of the model.

[0028] For example, the synchronized display interface is divided into two areas: the left side is a 3D scene view, which loads an integrated 3D model scene of the tunnel using the Cesium engine, with the latest monitoring value displayed above each monitoring point. The right side is a 2D analysis view, displaying the deformation-time curve of "POINT-001" and a tunnel cross-section diagram to show the specific location of the monitoring point.

[0029] S4: Perform 2D / 3D linkage based on the mapping relationship established in S2: 3D to 2D linkage: When one or more monitoring points are selected in the integrated 3D scene of the tunnel, the corresponding monitoring point is located and highlighted in the 2D analysis view according to the mapping relationship, and the time series curve of the monitoring data is displayed.

[0030] 2D to 3D linkage: When one or more monitoring points are selected on the data curve or cross-sectional view in the 2D analysis view, according to the mapping relationship, the corresponding 3D entity model monitoring point is highlighted in the integrated tunnel 3D scene, and the label information of that point is displayed in a pop-up state. During positioning, the camera in the integrated tunnel 3D scene is controlled to fly to a preset observation range centered on or including the selected monitoring point.

[0031] For example, if the monitoring point model "POINT-001" is selected in the integrated 3D scene of the tunnel on the left, the corresponding 2D information for "POINT-001" is obtained by querying the mapping table. The time series curve of "POINT-001" is immediately highlighted in the curve graph on the right, and the specific monitoring point location is flashed with a red marker on the cross-sectional view. If the curve of "POINT-001" is selected in the 2D analysis view on the right, the camera in the 3D scene on the left will be driven to fly to the vicinity of the "POINT-001" point, and the object model at that point will be highlighted.

[0032] S5: In the linked state, perform spatiotemporal analysis on the selected monitoring points, including comparison of multiple time series data, calculation of data change rate, trend prediction, and cross-sectional convergence calculation. The analysis results are displayed in the two-dimensional analysis view in the form of overlaid curves, statistical charts, etc., and drive the integrated three-dimensional scene of the tunnel to intuitively display the structural change trend and spatial distribution characteristics of the point or section during the analysis period through model color gradient, deformation animation or heat map mode.

[0033] Specifically, the multi-time series data comparison is as follows: based on the identification code of the selected monitoring point and multiple specified time ranges, the corresponding monitoring data is extracted and displayed on the same two-dimensional analysis view. At the same time, the corresponding monitoring point is highlighted in the integrated three-dimensional scene of the tunnel.

[0034] The calculation of data change rate includes: time change rate calculation and spatial change rate calculation. Time change rate calculation refers to calculating the instantaneous change rate at each sampling moment based on the time series of monitoring data using numerical differentiation methods, or calculating the average change rate within a specified time interval, and setting a change rate threshold. When the calculated time change rate exceeds the preset change rate threshold, the curve segment of that time period is highlighted on the two-dimensional analysis chart, and the monitoring point is highlighted in the integrated 3D tunnel scene to represent the direction and intensity of the change. Spatial change rate calculation refers to calculating the change rate of multiple monitoring points of the same type in the same time period, and then rendering the results onto the surface of the tunnel structure model in the integrated 3D tunnel scene.

[0035] Trend prediction is as follows: Based on historical monitoring data, a mathematical model is used to predict the development trend of the monitoring points within the selected time period. The predicted curve is displayed by extending the historical data curve at the back end of the two-dimensional analysis view, and the predicted structural morphology change trend is demonstrated through animation in the integrated three-dimensional scene of the tunnel.

[0036] The cross-sectional convergence calculation is as follows: Based on the layout scheme of the cross-sectional monitoring points, the monitoring points that constitute the convergence cross-section are associated, and the changes in chord length, cross-sectional diameter, and cross-sectional area based on coordinates between each measuring point are calculated. In the two-dimensional analysis view, the position of the convergence measuring point on the cross-section is displayed, the straight line connecting the two convergence monitoring points is dynamically drawn, and the key convergence values ​​are displayed in real time, generating a special curve showing the change of the cross-sectional convergence value over time. In the integrated 3D tunnel scene, the cross-section being analyzed is sliced ​​and displayed, and its shape changes from the initial monitoring state to the current measured state. A convergence threshold is preset, and when the convergence threshold is exceeded, an early warning is displayed.

[0037] For example, to obtain a comparison of "POINT-001" data between "this week" and "last week", the data for the corresponding period is retrieved from the database, a comparison calculation is performed, and a difference curve and a rate of change report are generated. The comparison curve is then overlaid on the original data curve. Simultaneously, based on the calculated deformation difference, structural deformation data from "last week" to "this week" is generated, forming a corresponding tunnel deformation gradient animation in the integrated 3D tunnel scene, visually demonstrating the deformation development process.

[0038] S6: Intelligent Early Warning and Linkage Notification: Based on early warning thresholds and rules, the system judges monitoring data in real time. When the data at a certain point exceeds the limit or meets complex early warning conditions, the early warning point is marked on the curve graph in the 2D view, and the corresponding monitoring point model in the 3D scene changes color and / or flashes, generating linkage alarm information. This enables the visualization of safety risks in 2D and 3D space.

[0039] For example, the system reads the latest data for "POINT-001" in real time and calls the preset threshold for that point for judgment. If the deformation of "POINT-001" exceeds the threshold, the point is marked as a red inverted triangle on the 2D curve graph according to the configured warning style. At the same time, in the integrated 3D tunnel scene, the model of that point starts to flash red and a warning box containing the point ID and the value exceeding the limit pops up, thus realizing cross-view joint alarm.

[0040] like Figure 2 As shown in the embodiment of this application, a tunnel monitoring point two-dimensional linkage display and analysis system for implementing the above method is provided, comprising: Data layer: The model file library stores BIM models, 2D cross-sections, and other files; the monitoring database consists of MySQL and IOTDB databases that store point metadata and monitoring data; the rule configuration library stores early warning thresholds and rules, analysis models, visualization parameters, etc.

[0041] Service Layer: Developed using the Java Spring Boot framework. The model service engine uses GeoServer to publish 3D scene services; the data service engine provides a RESTful API for the front-end to query data; the linkage service engine maintains mapping relationships and handles linkage logic; the analysis service engine is implemented in Python or Java, encapsulating various analysis algorithms such as data comparison, trend prediction, and statistical calculation, and provides an asynchronous analysis API.

[0042] Application Layer: A React.js frontend application. The 3D visualization module uses Three.js; the 2D analysis module uses ECharts to draw curves and Canvas to draw tunnel line diagrams; the linkage control module acts as a bus to coordinate data and command exchange.

[0043] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the technical principles of this application, such as the selection of 3D models and databases, the establishment of mapping rules, and adjustments to specific system development methods and visualization schemes, should fall within the protection scope of this application.

Claims

1. A method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points, characterized in that, Includes the following steps: S1. Integrate the tunnel structure model and monitoring point model to establish an integrated 3D scene of the tunnel; S2. Assign a unique identifier to each monitoring point, establish a mapping relationship between the identifier and the entity point in the integrated 3D tunnel scene and the coordinate position in the 2D analysis view, and associate the identifier with the monitoring data sequence. S3. Display the two-dimensional analysis view and the integrated three-dimensional scene of the tunnel in parallel, and mark the real-time monitoring data of the monitoring points in the corresponding positions of the integrated three-dimensional scene of the tunnel; S4. Perform two-dimensional and three-dimensional linkage based on the mapping relationship established in S2; S5. In the linked state, perform spatiotemporal analysis on the selected monitoring points, including comparison of multiple time series data, calculation of data change rate, trend prediction, and cross-sectional convergence calculation; S6. Monitor point data in real time according to the early warning threshold. When the data of a certain monitoring point exceeds the limit, it will be visualized in the two-dimensional analysis view and the integrated three-dimensional scene of the tunnel, and generate linkage alarm information.

2. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that: The monitoring types corresponding to the monitoring point model in S1 include displacement, convergence, stress-strain, tilt, vibration, seepage pressure, and temperature.

3. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that: The two-dimensional analysis view in S2 includes monitoring data curves and tunnel cross-section diagrams.

4. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that, The two-dimensional and three-dimensional linkage in S4 includes: when one or more monitoring points are selected in the integrated tunnel three-dimensional scene, the corresponding monitoring point data is located and displayed in the two-dimensional analysis view; when one or more monitoring points are selected in the two-dimensional analysis view, the viewpoint is located in the integrated tunnel three-dimensional scene and the corresponding entity point is displayed, highlighting the real-time annotation data of the point.

5. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 4, characterized in that, The method for viewpoint positioning in the integrated 3D scene of the tunnel in S4 is as follows: control the camera of the integrated 3D scene of the tunnel to fly to a preset observation range centered on the selected monitoring point or including the point.

6. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that, The multi-time series data comparison in S5 is as follows: based on the identification code of the selected monitoring point and the specified multiple time ranges, the corresponding monitoring data is extracted, and the monitoring data is superimposed and displayed in the same two-dimensional analysis view. At the same time, the corresponding monitoring point is highlighted in the integrated three-dimensional scene of the tunnel.

7. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that, The data change rate calculation in S5 includes: time change rate calculation and spatial change rate calculation. Time change rate calculation refers to calculating the instantaneous change rate at each sampling moment based on the time series of monitoring data using numerical differentiation methods, or calculating the average change rate within a specified time interval, and setting a change rate threshold. When the calculated time change rate exceeds the preset change rate threshold, the curve segment of that time period is highlighted on the two-dimensional analysis chart, and the monitoring point is highlighted in the integrated tunnel 3D scene to represent the direction and intensity of the change. Spatial change rate calculation refers to calculating the change rate of multiple monitoring points of the same type in the same time period, and then rendering the results onto the surface of the tunnel structure model in the integrated tunnel 3D scene.

8. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that, The trend prediction in S5 is as follows: based on historical monitoring data, a mathematical model is used to predict the development trend of the monitoring point within the selected time period. The predicted curve is displayed by extending the historical data curve at the back end of the two-dimensional analysis view, and the predicted structural morphology change trend is demonstrated by animation in the integrated three-dimensional scene of the tunnel.

9. The method for two-dimensional and three-dimensional linkage display and analysis of tunnel monitoring points according to claim 1, characterized in that, The cross-sectional convergence calculation in S5 is as follows: based on the layout scheme of the cross-sectional monitoring points, the monitoring points constituting the convergence cross-section are associated, and the changes in chord length, cross-sectional diameter, and cross-sectional area based on coordinates between each monitoring point are calculated. In the two-dimensional analysis view, the position of the convergence monitoring point on the cross-section is displayed, the straight line connecting the two convergence monitoring points is dynamically drawn, and the key convergence value is displayed in real time, generating a special curve showing the change of the cross-sectional convergence value over time. In the integrated three-dimensional scene of the tunnel, the cross-section being analyzed is sliced ​​and displayed, and its shape changes from the initial monitoring state to the current measured state. A convergence threshold is preset, and when the convergence threshold is exceeded, an early warning is displayed.

10. A system for implementing the two-dimensional and three-dimensional linkage display and analysis method for tunnel monitoring points according to any one of claims 1-9, comprising a data layer, a service layer, and an application layer, characterized in that: The data layer includes a model file library, a monitoring database, and a rule configuration library; the service layer includes a model service engine, a data service engine, a linkage service engine, and an analysis service engine; the application layer includes a two-dimensional analysis module, a three-dimensional scene module, and a linkage control module. The rule configuration library includes early warning thresholds for monitoring points, mapping relationship rules, and visualization style schemes. The application layer sends user interaction requests to the service layer. The service layer then sends the specific interaction requests to the data layer through its built-in service engines. The data layer then processes the specific data or rules through the service layer and returns them to the application layer, where they are displayed through specific modules.

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