A three-dimensional visualization method and system for surveying and mapping data based on the Internet of Things

Abstract

The present invention relates to the field of three-dimensional data processing technology, and in particular to a method and system for three-dimensional visualization of surveying and mapping data based on the Internet of Things. The method comprises the following steps: collecting original surveying and mapping data within a surveying and mapping area through Internet of Things sensors, wherein the original surveying and mapping data includes terrain elevation data and environmental monitoring data; preprocessing the original surveying and mapping data to obtain standard surveying and mapping data; performing multi-scale analysis on the terrain elevation data of the standard surveying and mapping data to obtain multi-scale terrain features; and performing multi-scale terrain detection on the multi-scale terrain features to obtain multi-scale terrain data. The present invention utilizes data processing technology, Internet of Things technology, and three-dimensional mapping technology to more accurately reflect the spatiotemporal relationship between terrain and environment, and construct a three-dimensional surveying and mapping visualization model, thereby improving the accuracy and effect of three-dimensional visualization of surveying and mapping data.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional data processing technology, and in particular to a three-dimensional visualization method and system for surveying and mapping data based on the Internet of Things. Background Art

[0002] The Internet of Things (IoT) was first proposed by Kevin Ashton in 1999. Its core concept is to enable interoperability between objects through technologies such as sensors and wireless communications. With the increasing prevalence of IoT devices, sensor accuracy and data transmission capacity have significantly improved. The application of IoT technology in surveying and mapping has greatly enriched surveying and mapping data. With the improvement of computer hardware performance and advancements in graphics processing technology, 3D visualization technology has also been gradually applied to surveying and mapping. Using 3D visualization technology to visualize massive amounts of surveying and mapping data can present the characteristics of surveyed areas in an intuitive 3D format. However, existing IoT-based 3D visualization technology for surveying and mapping data suffers from a lack of data currency. This is manifested in an incomplete update mechanism for surveying and mapping data, resulting in poor data timeliness. This is particularly true for the real-time, dynamic visualization of IoT data environments, which makes it difficult to achieve with existing technologies. This results in poor 3D visualization of surveying and mapping data. Summary of the Invention

[0003] Based on this, it is necessary to provide a three-dimensional visualization method and system for surveying and mapping data based on the Internet of Things to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a three-dimensional visualization method of surveying and mapping data based on the Internet of Things is provided, the method comprising the following steps:

[0005] Step S1: collecting original mapping data within the mapping area through IoT sensors, wherein the original mapping data includes terrain elevation data and environmental monitoring data; pre-processing the original mapping data to obtain standard mapping data;

[0006] Step S2: performing multi-scale analysis on the terrain elevation data of the standard surveying and mapping data to obtain multi-scale terrain features; performing terrain multi-scale detection on the multi-scale terrain features to obtain terrain multi-scale data; performing multi-factor coupling analysis on the environmental monitoring data based on the terrain multi-scale data to obtain environmental coupling data;

[0007] Step S3: performing spatiotemporal correlation pattern recognition on the multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; and constructing a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0008] Step S4: acquiring real-time surveying and mapping update data; performing surveying and mapping visualization update on the surveying and mapping three-dimensional model according to the real-time surveying and mapping update data to obtain a three-dimensional surveying and mapping visualization model; performing interactive dynamic display on the three-dimensional surveying and mapping visualization model, and outputting a three-dimensional surveying and mapping visualization report.

[0009] The present invention eliminates noise and outliers in the original surveying and mapping data through preprocessing, ensures the accuracy and consistency of terrain elevation data and environmental monitoring data, provides a high-quality data foundation for subsequent analysis, and avoids analytical deviations caused by data quality issues; converts raw data from different sources and formats into standard surveying and mapping data, realizes standardized data management, facilitates subsequent processing and analysis, improves the versatility and operability of the data, and reduces the complexity of data processing. Multi-scale analysis can capture terrain changes from macro to micro, and the generated multi-scale terrain feature data can more comprehensively reflect the complexity of the terrain, provide richer detailed information for terrain analysis, and promptly discover subtle changes and potential features in the terrain; through multi-factor coupling analysis, the terrain multi-scale data is combined with environmental monitoring data, which can reveal the interactive relationship between terrain and environment, provide a more accurate basis for environmental impact assessment and ecological research, and make it possible to understand the impact of terrain on the environment and the feedback mechanism of the environment on terrain. The spatiotemporal feature data generated by the spatiotemporal correlation model can simultaneously reflect the temporal and spatial variations of terrain and environment, providing more complete spatiotemporal information for 3D model construction. This ensures that the generated 3D model is not only spatially accurate but also reflects dynamic changes in the temporal dimension. Adaptive feature mapping can automatically adjust the mapping strategy based on the complexity of the spatiotemporal feature data. The generated mapping feature mapping data can more accurately reflect the characteristics of terrain and environment, thereby constructing a high-precision 3D model and providing a more reliable model foundation for subsequent visualization and application. The acquisition and application of real-time mapping update data enables the 3D model to promptly reflect the latest changes in the surveyed area, avoiding information obsolescence caused by data lags, improving the timeliness and practicality of the surveying data, and better meeting the needs of dynamic monitoring and real-time decision-making. Interactive dynamic display and the output of 3D surveying visualization reports provide users with an intuitive and convenient data presentation method. Users can interactively understand the detailed information of the surveyed area. At the same time, the visualization report can display complex surveying data in intuitive graphics and charts, facilitating users' rapid understanding and analysis, and improving data readability and usability. Therefore, the present invention uses data processing technology, Internet of Things technology and three-dimensional mapping technology to more accurately reflect the spatiotemporal relationship between terrain and environment, and construct a three-dimensional surveying and mapping visualization model, thereby improving the accuracy and effect of three-dimensional visualization of surveying and mapping data.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: The IoT sensor collects data at a fixed interval of 5 seconds, and the sampling frequency of the IoT sensor is 10 Hz during each collection.

[0012] Step S12: setting the positioning accuracy of the GPS sensor to ±1 cm and the measurement accuracy of the laser rangefinder to ±0.5 mm to collect terrain elevation data within the surveying area;

[0013] Step S13: setting the measurement range of the temperature sensor to -20°C to +50°C and the measurement range of the humidity sensor to 0% to 100% to collect environmental monitoring data within the surveying area;

[0014] Step S14: merging terrain elevation data and environmental monitoring data to obtain original surveying and mapping data;

[0015] Step S15: Filter and denoise the original surveying and mapping data, with the filter window size set to 3×3, to obtain surveying and mapping denoised data;

[0016] Step S16: performing normalization processing on the surveying and mapping denoising data to obtain surveying and mapping normalized data; performing standardization processing on the surveying and mapping normalized data to obtain standard surveying and mapping data.

[0017] This invention sets a fixed 5-second collection interval for IoT sensors and a sampling frequency of 10Hz, ensuring high-frequency and high-density data acquisition, thereby fully recording dynamic changes within the surveyed area and providing sufficient time series data for subsequent analysis. Setting the positioning accuracy of the GPS sensor to ±1 cm and the measurement accuracy of the laser rangefinder to ±0.5 mm allows for precise acquisition of terrain elevation data, ensuring high precision and reliability, and providing accurate terrain foundation data for 3D model construction. Setting the temperature sensor's measurement range to -20°C to +50°C and the humidity sensor's measurement range to 0% to 100% fully covers ambient temperature and humidity variations within the surveyed area, ensuring the integrity and applicability of environmental monitoring data. Terrain elevation data and environmental monitoring data are combined to form complete raw survey data, integrating terrain and environmental data. Filtering and denoising the raw survey data is performed with a 3×3 filter window size to effectively remove noise interference, improving data purity and usability. Normalization and standardization are performed on the denoised survey data to further standardize the data format, eliminate dimensional differences in data from different sensors, and ensure data consistency and comparability.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: performing nonlinear scale division processing on the terrain elevation data to obtain nonlinear scale elevation data; performing terrain natural breakpoint level detection on the nonlinear scale elevation data to obtain terrain breakpoint level data;

[0020] Step S22: performing terrain hierarchical texture recognition on the terrain breakpoint hierarchical data to generate terrain hierarchical texture data; performing terrain hierarchical structure mapping on the terrain breakpoint hierarchical data according to the terrain hierarchical texture data to obtain terrain hierarchical structure data;

[0021] Step S23: performing terrain multi-scale matching based on terrain breakpoint hierarchical data, terrain hierarchical texture data, and terrain hierarchical structure data to obtain multi-scale terrain features;

[0022] Step S24: dividing the multi-scale terrain features into adjacent elevation points to obtain terrain adjacent elevation point data; determining the height difference of the terrain adjacent elevation point data to obtain terrain adjacent height difference;

[0023] Step S25: dividing the terrain elevation data by horizontal distance to obtain terrain horizontal distance data; calculating the terrain elevation slope value by using the terrain adjacent height differences and the terrain horizontal distance data to generate the terrain elevation slope value; determining the terrain slope direction data by using the terrain adjacent height differences and the terrain horizontal distance data to generate the terrain slope direction data;

[0024] Step S26: merging the terrain elevation slope value and the terrain elevation aspect data to obtain terrain multi-scale data;

[0025] Step S27: Perform multi-factor coupling analysis on the environmental monitoring data based on the terrain multi-scale data to obtain environmental coupling data.

[0026] The present invention performs nonlinear scale division processing on terrain elevation data, which can more accurately reflect the elevation characteristics of terrain at different scales, avoid information loss or detail ambiguity caused by traditional linear division, and further performs terrain natural breakpoint level detection on nonlinear scale elevation data, which can accurately identify the natural hierarchical structure of the terrain, provide basic data that is closer to actual terrain changes for subsequent terrain feature analysis, and ensure the scientificity and accuracy of terrain level division; terrain level texture recognition is performed on terrain breakpoint level data, which can accurately extract the texture characteristics of the terrain at different levels, and can reflect the microscopic structural characteristics of the terrain in detail; terrain level structure mapping is performed on terrain breakpoint level data based on this data, which can clearly present the macroscopic level distribution of the terrain, making the hierarchical structure of the terrain more complete and detailed; terrain multi-scale matching is performed based on terrain breakpoint level data, terrain level texture data and terrain level structure data, which can comprehensively consider the breakpoints, texture and structural characteristics of the terrain, and can comprehensively reflect the comprehensive characteristics of the terrain at different scales, realizing Multi-dimensional integration of terrain features ensures the comprehensiveness and accuracy of terrain feature analysis; adjacent elevation point division of multi-scale terrain features can accurately locate the positional relationship of adjacent elevation points on the terrain. By determining the height difference of adjacent terrain elevation point data, it can accurately reflect the elevation changes between adjacent terrain points, ensuring the accuracy of the calculation results; horizontal distance division of terrain elevation data can accurately reflect the horizontal distance relationship between points on the terrain; terrain elevation slope values ​​are calculated by combining terrain adjacent height differences with terrain horizontal distance data to accurately represent the slope changes of the terrain; terrain elevation slope values ​​and terrain elevation aspect data are merged to integrate the slope and aspect characteristics of the terrain to form a complete terrain feature data set; multi-factor coupling analysis of environmental monitoring data based on multi-scale terrain data can achieve accurate analysis of the relationship between environmental factors and terrain features, providing a more scientific and comprehensive basis for environmental monitoring and analysis, and ensuring the accuracy and reliability of environmental monitoring results.

[0027] Preferably, step S27 includes the following steps:

[0028] Step S271: performing scale-level parsing on the terrain multi-scale data and decomposing the terrain data into layers with different resolutions, where each layer corresponds to a specific terrain detail level, to obtain terrain detail layered data;

[0029] Step S272: Perform feature enhancement processing on the terrain detail layered data, enhance the slope, curvature, and terrain relief features in each scale layer to obtain multi-scale enhanced terrain features; perform terrain influencing factor detection on the multi-scale enhanced terrain features to obtain terrain influencing factor data;

[0030] Step S273: performing implicit coupling relationship detection on the environmental monitoring data according to the terrain influencing factor data to obtain implicit coupling relationship data; performing terrain and environmental multi-factor interactive fusion on the implicit coupling relationship data to obtain multi-factor interactive fusion data;

[0031] Step S274: performing environmental coupling abnormality manifestation detection on the multi-factor interactive fusion data to obtain the environmental coupling abnormality manifestation; performing environmental feature correction on the environmental coupling abnormality manifestation to obtain final environmental coupling data.

[0032] The present invention performs scale-level analysis on multi-scale terrain data and decomposes the terrain data into layers of different resolutions, each layer corresponding to a specific level of terrain detail. It can finely decompose complex terrain data according to the level of detail, so that terrain features at different resolutions can be clearly presented. It performs feature enhancement processing on the terrain detail layered data and enhances the slope, curvature, and terrain undulation features in each scale layer. It can highlight the key features of the terrain, making the slope, curvature, and terrain undulation features more obvious at different scales, and can accurately identify the potential influencing factors of terrain features on environmental monitoring data. It performs implicit coupling relationship detection on environmental monitoring data based on terrain influencing factor data. It can reveal the hidden intrinsic connection between terrain features and environmental monitoring data, identify the implicit coupling relationship between the two, and provide a key basis for further analysis; it can interactively integrate terrain and environmental multi-factors of implicit coupling relationship data, and deeply integrate terrain influencing factors with environmental monitoring data to ensure the integrity and reliability of the analysis results; it can detect abnormal manifestations of environmental coupling on multi-factor interactive fusion data, and accurately identify abnormal situations that occur during the environmental coupling process, providing a clear direction for subsequent corrections; it can perform environmental characteristic correction on abnormal manifestations of environmental coupling, and make targeted corrections to abnormal situations to ensure that the final environmental coupling data accurately reflects the actual relationship between terrain and environment.

[0033] Preferably, step S3 includes the following steps:

[0034] Step S31: performing spatiotemporal heterogeneity analysis on the multi-scale terrain features and decomposing them into multiple spatiotemporal heterogeneous units, each unit containing terrain feature information within a specific time range, thereby obtaining spatiotemporal heterogeneous unit data;

[0035] Step S32: performing spatiotemporal isomorphism mapping on the environmental coupling data, mapping the environmental coupling data to an isomorphic framework that matches the spatiotemporal heterogeneous units of the terrain feature data, to obtain environmental spatiotemporal isomorphic data;

[0036] Step S33: performing spatiotemporal correlation pattern matching on the spatiotemporal heterogeneous unit data and the environmental spatiotemporal homogeneous data to obtain spatiotemporal correlation pattern data; performing spatiotemporal feature fusion and reconstruction on the spatiotemporal correlation pattern data to generate surveying and mapping spatiotemporal feature data;

[0037] Step S34: performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data;

[0038] Step S35: constructing a three-dimensional model according to the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0039] The present invention performs spatiotemporal heterogeneity analysis on terrain feature data, decomposes the terrain feature data into multiple spatiotemporal heterogeneous units, each unit contains terrain feature information within a specific time range, and can finely decompose the terrain feature data according to the time and space dimensions, so that the changes of terrain features in different time and space ranges can be clearly presented, ensuring the spatiotemporal pertinence and accuracy of terrain feature analysis; performs spatiotemporal isomorphism mapping on environmental coupling data, maps the environmental coupling data to an isomorphic framework that matches the spatiotemporal heterogeneous units of the terrain feature data, and can accurately align the environmental coupling data with the terrain feature data in time and space, ensuring that the two are aligned in time and space. Consistency in the spatial dimension; matching spatiotemporal correlation patterns between spatiotemporally heterogeneous unit data and environmental spatiotemporally homogeneous data can identify spatiotemporal correlation patterns between terrain features and environmental coupling data, revealing the inherent connection between the two in the spatiotemporal dimension; performing spatiotemporal feature fusion reconstruction on spatiotemporal correlation pattern data can deeply fuse terrain features and environmental coupling data in the spatiotemporal dimension; adaptive feature mapping of surveying and mapping spatiotemporal feature data can adaptively extract and map key features based on the characteristics of the surveying and mapping spatiotemporal feature data, ensuring the integrity and representativeness of the feature data; and constructing a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model. This process can transform the optimized surveying and mapping feature mapping data into an intuitive three-dimensional model, realizing three-dimensional visualization of terrain and environmental features. The generated surveying and mapping three-dimensional model can accurately reflect the complex relationship between terrain and environment in the spatiotemporal dimension, providing intuitive and accurate visualization support for environmental monitoring, geographic information system applications, and related decision-making.

[0040] Preferably, step S34 includes the following steps:

[0041] Step S341: Divide the surveying and mapping spatiotemporal feature data according to different time and space scales to obtain multi-scale spatiotemporal data blocks;

[0042] Step S342: Identify the heterogeneous features in each spatiotemporal data block and extract the feature differences in the time and space dimensions to obtain spatiotemporal heterogeneous features; perform specific spatiotemporal range feature marking on the spatiotemporal heterogeneous features to obtain spatiotemporal heterogeneous labeled data;

[0043] Step S343: performing spatiotemporal dynamic change analysis on the spatiotemporal heterogeneous labeled data to obtain spatiotemporal dynamic change features; performing feature interaction detection on the spatiotemporal dynamic change features to generate change feature effect data; performing feature dependency correlation evaluation on the change feature effect data to obtain feature correlation evaluation results;

[0044] Step S344: constructing a dynamic spatiotemporal feature framework based on the feature correlation evaluation results to obtain a dynamic spatiotemporal feature framework;

[0045] Step S345: performing feature importance evaluation on the dynamic spatiotemporal feature framework to obtain feature importance evaluation data; adaptively assigning feature weights based on the feature importance evaluation results to obtain weight adaptive assignment data;

[0046] Step S346: performing feature space pairing on the weight adaptive allocation data to obtain a feature adaptive space; defining feature mapping rules on the feature adaptive space to generate feature mapping rules;

[0047] Step S347: performing feature mapping conversion on the surveying and mapping spatiotemporal feature data according to the feature mapping rules, and mapping the surveying and mapping spatiotemporal feature data to a new feature space according to the feature mapping rules to generate surveying and mapping feature mapping data.

[0048] The present invention decomposes the surveying and mapping spatiotemporal feature data into multi-scale spatiotemporal data blocks, which is convenient for fine-grained analysis of spatiotemporal features at different scales, and improves the hierarchical and targeted nature of data processing; accurately identifies heterogeneous features in spatiotemporal data blocks, highlights the differences in time and space dimensions, and provides clear identification for subsequent analysis through labeling, ensuring the accuracy and traceability of feature analysis; reveals the dynamic change law of spatiotemporal heterogeneous labeled data, identifies the interaction relationship between features, and evaluates their dependency correlation, providing a scientific basis for the construction of feature framework, ensuring the integrity and accuracy of feature relationship analysis; integrates feature dependency and dynamic change law to form a dynamic spatiotemporal feature framework. The spatial feature system provides structured support for feature importance assessment, ensuring the dynamic and adaptable nature of the feature framework; quantifies feature importance and adaptively assigns weights, optimizes feature weight allocation schemes, and improves the accuracy and effectiveness of feature mapping; reasonably allocates features to the optimized feature space based on weight allocation, and defines feature mapping rules to ensure the standardization and consistency of feature mapping; converts surveying and mapping spatiotemporal feature data to the new feature space according to the optimized mapping rules. The generated surveying and mapping feature mapping data can more accurately reflect the spatiotemporal characteristics of the terrain and environment, provide high-quality data support for 3D model construction, and improve the accuracy and reliability of 3D visualization.

[0049] Preferably, step S35 further includes the following steps:

[0050] Step S351: dividing the surveying and mapping feature mapping data into terrain units to obtain terrain unit data; marking coordinate points on the terrain unit data to generate terrain unit coordinate data; and connecting the terrain unit coordinate data to obtain a terrain region system;

[0051] Step S352: performing terrain-region temperature mapping on the terrain-region system to obtain regional temperature mapping data; overlaying the regional temperature mapping data with the terrain-temperature distribution system to generate a terrain-temperature overlay system;

[0052] Step S353: Perform terrain regional humidity overlay on the terrain regional system to obtain regional humidity overlay data; perform humidity distribution overlay on the terrain-temperature overlay system based on the regional humidity overlay data to generate a terrain-temperature and humidity overlay system;

[0053] Step S354: Associating the terrain-temperature and humidity coverage system with the surveying and mapping system to obtain surveying and mapping associated data; constructing a three-dimensional model based on the surveying and mapping associated data to obtain a surveying and mapping three-dimensional model.

[0054] The present invention divides the surveying and mapping feature mapping data into terrain units, and identifies coordinate points to generate terrain unit coordinate data. By connecting the coordinates, a terrain regional system is obtained, which can subdivide the terrain features into independent terrain units and construct a complete terrain regional system, providing an accurate geographic space framework for the subsequent fusion of multi-dimensional data. According to the terrain temperature distribution data, the terrain regional system is subjected to terrain regional temperature mapping, and the regional temperature mapping data is covered by the terrain temperature distribution system, thereby achieving accurate mapping and coverage of temperature characteristics in the terrain regional system, providing a basis for comprehensive analysis of terrain and temperature. According to the terrain humidity distribution data, the terrain regional system is subjected to terrain regional humidity superposition, and the terrain-temperature coverage system is subjected to humidity distribution coverage, thereby achieving accurate superposition and coverage of humidity characteristics in the terrain regional system, further enriching the environmental feature information of the terrain area; the terrain-temperature and humidity coverage system is associated with the surveying and mapping multi-dimensional system, and a surveying and mapping three-dimensional model is constructed based on the multi-dimensional surveying and mapping association data.

[0055] Preferably, step S4 includes the following steps:

[0056] Step S41: Acquire real-time surveying and mapping update data;

[0057] Step S42: identifying terrain change areas on the real-time surveying and mapping update data, and marking the boundaries of the terrain change areas to obtain terrain change feature boundary data;

[0058] Step S43: identifying temperature and humidity changes in the real-time surveying and mapping update data, and determining the temperature and humidity change area to obtain temperature and humidity change characteristic data;

[0059] Step S44: updating the terrain change area modeling of the surveying and mapping three-dimensional model according to the terrain change characteristic boundary data to obtain a terrain surveying and mapping update model;

[0060] Step S45: dynamically adjusting the regional color transparency of the temperature and humidity change characteristic data to obtain model color transparency adjustment data; performing color visualization rendering on the updated three-dimensional model of the terrain surveying and mapping according to the model color transparency adjustment data to obtain a three-dimensional surveying and mapping visualization model;

[0061] Step S46: interactively and dynamically display the three-dimensional surveying and mapping visualization model, and output a three-dimensional surveying and mapping visualization report.

[0062] The present invention acquires real-time surveying and mapping update data, enabling timely reflection of dynamic changes in the surveying and mapping area, providing the latest data support for subsequent model updates and ensuring the timeliness and accuracy of the surveying and mapping data. Real-time surveying and mapping update data identifies terrain change areas and marks their boundaries, enabling precise location of these areas, providing clear boundary information for modeling updates of these areas and ensuring the accuracy of terrain updates. Real-time surveying and mapping update data also identifies temperature and humidity changes and determines their regions, enabling precise identification of these areas and providing data support for subsequent color transparency adjustments, ensuring the visual update of these temperature and humidity changes. Based on the terrain change characteristic boundary data, the modeling of the terrain change areas in the surveying and mapping 3D model is updated, achieving dynamic updates of terrain changes, ensuring that the surveying and mapping 3D model reflects terrain changes in real time and improving the timeliness and accuracy of the model. Dynamically adjusting the regional color transparency of the temperature and humidity change characteristic data is then used to perform color visualization rendering of the updated 3D model, resulting in a 3D surveying and mapping visualization model. This dynamic adjustment of color transparency enables intuitive visualization of temperature and humidity changes. The 3D surveying and mapping visualization model is interactively and dynamically displayed, and a 3D surveying and mapping visualization report is output. This process enhances the user's interactive experience with surveying and mapping data through interactive dynamic display, while the generated visual reports provide an intuitive and comprehensive basis for geographic information analysis and decision-making.

[0063] Preferably, step S46 includes the following steps:

[0064] Step S461: starting a 3D visualization engine through computer 3D simulation software, and loading a 3D surveying and mapping visualization model into a 3D scene display state;

[0065] Step S462: monitoring the input device mouse signal, including pressing, dragging, and releasing the left mouse button, to obtain mouse signals; monitoring the input device touch screen gesture signal, including single-finger dragging, two-finger zooming, and multi-finger rotation, to obtain touch screen gesture signals;

[0066] Step S463: marking the model visual surface of the 3D surveying and mapping visualization model according to the mouse signal and the touch screen gesture signal to obtain the model observation visual surface; determining the visual surface scaling ratio of the model observation visual surface to obtain the visual surface scaling ratio;

[0067] Step S464: interactively and dynamically display the three-dimensional surveying and mapping visualization model based on the model observation visual surface and the visual surface scaling ratio, and output a three-dimensional surveying and mapping visualization report.

[0068] The present invention activates a 3D visualization engine using computer 3D simulation software and loads a 3D surveying and mapping visualization model into a 3D scene display state, enabling rapid loading and display of the 3D surveying and mapping visualization model. This provides a foundation for real-time response for subsequent interactive operations and ensures that the model can be efficiently presented in the 3D scene. Input device mouse signals are monitored, including pressing, dragging, and releasing the left mouse button, as well as touchscreen gesture signals, including single-finger dragging, two-finger zooming, and multi-finger rotation. This process can capture user operation signals in real time, providing diverse input methods for interactive dynamic display, meeting the needs of different devices and operating habits. The 3D surveying and mapping visualization model is marked based on the mouse signals and touchscreen gesture signals to obtain the model observation visual surface, and the visual surface scaling ratio of the model observation visual surface is determined to obtain the visual surface scaling ratio. This process enables real-time interaction between user operations and the model view, and can dynamically adjust the model display state based on user input, ensuring that the user can flexibly observe various details of the model. The 3D surveying and mapping visualization model is interactively and dynamically displayed based on the model observation visual surface and the visual surface scaling ratio, and a 3D surveying and mapping visualization report is output. This process achieves interactive visualization of the model and efficient presentation of analysis results through dynamic display and report output, providing users with an intuitive and flexible surveying and mapping data visualization tool.

[0069] In this specification, a three-dimensional visualization system for surveying and mapping data based on the Internet of Things is also provided, which is used to execute the above-mentioned three-dimensional visualization method for surveying and mapping data based on the Internet of Things. The three-dimensional visualization system for surveying and mapping data based on the Internet of Things includes:

[0070] The surveying and mapping data acquisition module is used to collect original surveying and mapping data within the surveying and mapping area through IoT sensors, where the original surveying and mapping data includes terrain elevation data and environmental monitoring data; and pre-process the original surveying and mapping data to obtain standard surveying and mapping data;

[0071] The surveying and mapping feature analysis module is used to perform multi-scale analysis on the terrain elevation data of standard surveying and mapping data to obtain multi-scale terrain features; perform multi-scale terrain detection on the multi-scale terrain features to obtain multi-scale terrain data; and perform multi-factor coupling analysis on the environmental monitoring data based on the multi-scale terrain data to obtain environmental coupling data.

[0072] The surveying and mapping data feature mapping module is used to perform spatiotemporal correlation pattern recognition on multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; perform adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; and construct a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0073] The 3D surveying and mapping visualization model construction module is used to obtain real-time surveying and mapping update data; perform surveying and mapping visualization updates on the surveying and mapping 3D model according to the real-time surveying and mapping update data to obtain a 3D surveying and mapping visualization model; perform interactive dynamic display on the 3D surveying and mapping visualization model, and output a 3D surveying and mapping visualization report.

[0074] The present invention eliminates noise and outliers in the original surveying and mapping data through preprocessing of the surveying and mapping data acquisition module, ensures the accuracy and consistency of terrain elevation data and environmental monitoring data, provides a high-quality data foundation for subsequent analysis, and avoids analysis deviations caused by data quality issues; converts raw data from different sources and formats into standard surveying and mapping data, realizes standardized data management, facilitates subsequent processing and analysis, improves the versatility and operability of data, and reduces the complexity of data processing. The multi-scale analysis of the surveying and mapping feature analysis module can capture terrain changes from macro to micro, and the generated multi-scale terrain feature data can more comprehensively reflect the complexity of the terrain, provide richer detailed information for terrain analysis, and promptly discover subtle changes and potential features in the terrain; through multi-factor coupling analysis, the terrain multi-scale data is combined with environmental monitoring data, which can reveal the interactive relationship between terrain and environment, provide a more accurate basis for environmental impact assessment and ecological research, and make it possible to understand the impact of terrain on the environment and the feedback mechanism of the environment on terrain. The surveying and mapping spatiotemporal feature data generated through the spatiotemporal correlation pattern of the surveying and mapping data feature mapping module can simultaneously reflect the temporal and spatial changes of the terrain and environment, providing more complete spatiotemporal information for the construction of three-dimensional models, so that the generated surveying and mapping three-dimensional models are not only spatially accurate, but also reflect dynamic changes in the time dimension; adaptive feature mapping can automatically adjust the mapping strategy according to the complexity of the surveying and mapping spatiotemporal feature data, and the generated surveying and mapping feature mapping data can more accurately reflect the characteristics of the terrain and environment, thereby constructing a high-precision surveying and mapping three-dimensional model, providing a more reliable model foundation for subsequent visualization and application. By acquiring and applying real-time surveying and mapping update data through the three-dimensional surveying and mapping visualization model construction module, the surveying and mapping three-dimensional model can timely reflect the latest changes in the surveying and mapping area, avoiding the problem of information obsolescence caused by data lag, improving the timeliness and practicality of the surveying and mapping data, and better meeting the needs of dynamic monitoring and real-time decision-making; the interactive dynamic display and output of the three-dimensional surveying and mapping visualization report provide users with an intuitive and convenient way to present data. Users can gain an in-depth understanding of the detailed information of the surveying and mapping area through interactive operations. At the same time, the visualization report can display complex surveying and mapping data in the form of intuitive graphics and charts, which is convenient for users to quickly understand and analyze, and improves the readability and ease of use of the data. Therefore, the present invention uses data processing technology, Internet of Things technology and three-dimensional mapping technology to more accurately reflect the spatiotemporal relationship between terrain and environment, and construct a three-dimensional surveying and mapping visualization model, thereby improving the accuracy and effect of three-dimensional visualization of surveying and mapping data. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 The figure is a flowchart of a three-dimensional visualization method of surveying and mapping data based on the Internet of Things;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0077] Figure 3 for Figure 2 Detailed implementation steps of step S46 are shown in the flowchart;

[0078] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0079] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0081] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 , a three-dimensional visualization method for surveying and mapping data based on the Internet of Things, the method comprising the following steps:

[0083] Step S1: collecting original mapping data within the mapping area through IoT sensors, wherein the original mapping data includes terrain elevation data and environmental monitoring data; pre-processing the original mapping data to obtain standard mapping data;

[0084] Step S2: performing multi-scale analysis on the terrain elevation data of the standard surveying and mapping data to obtain multi-scale terrain features; performing terrain multi-scale detection on the multi-scale terrain features to obtain terrain multi-scale data; performing multi-factor coupling analysis on the environmental monitoring data based on the terrain multi-scale data to obtain environmental coupling data;

[0085] Step S3: performing spatiotemporal correlation pattern recognition on the multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; and constructing a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0086] Step S4: acquiring real-time surveying and mapping update data; performing surveying and mapping visualization update on the surveying and mapping three-dimensional model according to the real-time surveying and mapping update data to obtain a three-dimensional surveying and mapping visualization model; performing interactive dynamic display on the three-dimensional surveying and mapping visualization model, and outputting a three-dimensional surveying and mapping visualization report.

[0087] The present invention eliminates noise and outliers in the original surveying and mapping data through preprocessing, ensures the accuracy and consistency of terrain elevation data and environmental monitoring data, provides a high-quality data foundation for subsequent analysis, and avoids analytical deviations caused by data quality issues; converts raw data from different sources and formats into standard surveying and mapping data, realizes standardized data management, facilitates subsequent processing and analysis, improves the versatility and operability of the data, and reduces the complexity of data processing. Multi-scale analysis can capture terrain changes from macro to micro, and the generated multi-scale terrain feature data can more comprehensively reflect the complexity of the terrain, provide richer detailed information for terrain analysis, and promptly discover subtle changes and potential features in the terrain; through multi-factor coupling analysis, the terrain multi-scale data is combined with environmental monitoring data, which can reveal the interactive relationship between terrain and environment, provide a more accurate basis for environmental impact assessment and ecological research, and make it possible to understand the impact of terrain on the environment and the feedback mechanism of the environment on terrain. The spatiotemporal feature data generated by the spatiotemporal correlation model can simultaneously reflect the temporal and spatial variations of terrain and environment, providing more complete spatiotemporal information for 3D model construction. This ensures that the generated 3D model is not only spatially accurate but also reflects dynamic changes in the temporal dimension. Adaptive feature mapping can automatically adjust the mapping strategy based on the complexity of the spatiotemporal feature data. The generated mapping feature mapping data can more accurately reflect the characteristics of terrain and environment, thereby constructing a high-precision 3D model and providing a more reliable model foundation for subsequent visualization and application. The acquisition and application of real-time mapping update data enables the 3D model to promptly reflect the latest changes in the surveyed area, avoiding information obsolescence caused by data lags, improving the timeliness and practicality of the surveying data, and better meeting the needs of dynamic monitoring and real-time decision-making. Interactive dynamic display and the output of 3D surveying visualization reports provide users with an intuitive and convenient data presentation method. Users can interactively understand the detailed information of the surveyed area. At the same time, the visualization report can display complex surveying data in intuitive graphics and charts, facilitating users' rapid understanding and analysis, and improving data readability and usability. Therefore, the present invention uses data processing technology, Internet of Things technology and three-dimensional mapping technology to more accurately reflect the spatiotemporal relationship between terrain and environment, and construct a three-dimensional surveying and mapping visualization model, thereby improving the accuracy and effect of three-dimensional visualization of surveying and mapping data.

[0088] In the embodiment of the present invention, reference Figure 1 As shown, the three-dimensional visualization method of surveying and mapping data based on the Internet of Things includes the following steps:

[0089] Step S1: collecting original mapping data within the mapping area through IoT sensors, wherein the original mapping data includes terrain elevation data and environmental monitoring data; pre-processing the original mapping data to obtain standard mapping data;

[0090] In the embodiment of the present invention, in the surveying and mapping area, raw surveying and mapping data is collected through a pre-deployed IoT sensor network. The sensor network consists of a high-precision lidar sensor and an environmental monitoring sensor. The lidar sensor is used to obtain terrain elevation data with a measurement accuracy of ±5 cm and a sampling frequency of 10 times per second. The environmental monitoring sensor is used to collect environmental parameters, including temperature, humidity, and light intensity, with a sampling frequency of 1 time per second. The sensor transmits the collected raw data to the edge computing node through the LoRa wireless communication protocol. The data transmission adopts the AES-128 encryption algorithm to ensure the security of the data transmission. At the edge computing node, the received The original surveying and mapping data were preprocessed; first, outlier detection and elimination were performed on the environmental monitoring data; for temperature data, the reasonable range was set to -40℃ to 50℃, and data points outside this range were judged as outliers and eliminated; for humidity data, the reasonable range was set to 0% to 100%, and data points outside this range were also eliminated; for light intensity data, the reasonable range was set to 0 to 100,000 lux, and data points outside this range were eliminated; secondly, the terrain elevation data was noise-processed; the median filter algorithm was used to denoise the elevation data, and the filter window size was set to 3×3; the preprocessed data were further formatted. For terrain elevation data, its coordinate system is converted from the sensor's local coordinate system to the geographic coordinate system WGS-84, with a conversion accuracy of sub-meter level; for environmental monitoring data, time alignment is performed with the terrain elevation data based on its timestamp, and the time alignment tolerance is set to ±1 second; finally, the processed standard surveying and mapping data is stored in the back-end database. The data is stored in a structured form, and each data record contains the sensor ID, timestamp, terrain elevation data, and environmental monitoring data (including temperature and humidity); the data storage format is a key-value pair structure, where the key is the field name and the value is the corresponding data value.

[0091] Step S2: performing multi-scale analysis on the terrain elevation data of the standard surveying and mapping data to obtain multi-scale terrain features; performing terrain multi-scale detection on the multi-scale terrain features to obtain terrain multi-scale data; performing multi-factor coupling analysis on the environmental monitoring data based on the terrain multi-scale data to obtain environmental coupling data;

[0092] In an embodiment of the present invention, terrain elevation data from standard surveying and mapping data is stored in a matrix format, where each element represents the elevation value of a spatial location. Multiple scale factors are then selected, such as 1, 2, and 4, corresponding to small, medium, and large scales, respectively. For each scale factor, the elevation data is smoothed using a Gaussian filter, with the standard deviation of the Gaussian filter proportional to the scale factor. For example, when the scale factor is 1, the standard deviation is set to 1; when the scale factor is 2, the standard deviation is set to 2. Gaussian filtering generates smoothed elevation data at different scales, thereby extracting multi-scale terrain features. This multi-scale terrain feature data is further processed to detect areas of significant terrain change. Specifically, for each scale of terrain feature data, the elevation difference between adjacent pixels is calculated to generate an elevation gradient map. An elevation gradient threshold is then set, such as 10 meters per pixel, and areas in the elevation gradient map with a value greater than this threshold are marked as areas of significant change. The areas of significant change detected at different scales are merged to form final multi-scale terrain data. This multi-scale terrain data is then coupled with environmental monitoring data for analysis. Environmental monitoring data, including indicators such as the Air Quality Index (AQI) and Degree of Vegetation (NDVI), are also stored in matrix form and have the same spatial resolution as the terrain elevation data. First, the multi-scale terrain data and environmental monitoring data are normalized for their ranges. This involves subtracting the minimum value of each data element from the dataset and dividing the result by the difference between the maximum and minimum values ​​of the dataset. The coupling coefficient is then calculated by multiplying the normalized values ​​of the multi-scale terrain data with the normalized values ​​of the environmental monitoring data, taking the square root of the product, multiplying the product by 2, and dividing the result by the sum of the normalized values ​​of the two data sets. Next, the coordination index is calculated by multiplying the normalized values ​​of the multi-scale terrain data and the environmental monitoring data by a weight coefficient (assuming both weight coefficients are 0.5) and adding the results. Finally, the coupling coordination coefficient is calculated by multiplying the coupling coefficient by the coordination index and taking the square root of the result to obtain the environmental coupling data.

[0093] Step S3: performing spatiotemporal correlation pattern recognition on the multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; and constructing a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0094] In this embodiment of the present invention, spatiotemporal correlation patterns are first analyzed for terrain feature data and environmental coupling data. Both data are stored as time series and spatial grids, with each data point containing a timestamp and spatial coordinates. By aligning the timestamps, terrain feature data and environmental coupling data at the same time point are correlated to form spatiotemporal correlation data pairs. Then, based on the neighborhood relationships of the spatial grid, the spatiotemporal correlation strength between each data point and its surrounding neighborhood is calculated. For example, spatial autocorrelation analysis is used to calculate the correlation strength between each data point and its eight surrounding neighborhood points to obtain spatiotemporal correlation patterns. These correlation patterns are aggregated to generate surveying and mapping spatiotemporal feature data. Adaptive feature mapping is then performed on the surveying and mapping spatiotemporal feature data to extract key features. First, the surveying and mapping spatiotemporal feature data is normalized to scale the data range to between 0 and 1. Then, principal component analysis (PCA) is used to reduce the dimensionality of the normalized data and extract the main characteristic components. Based on the variance contribution of the characteristic components, the top few components with the highest contribution are selected as key features. These key features are remapped to the spatial grid of the original data to obtain surveying and mapping feature mapping data; a three-dimensional model is constructed based on the surveying and mapping feature mapping data. First, the surveying and mapping feature mapping data is arranged according to the coordinates of the spatial grid to form point cloud data in three-dimensional space. Then, the Delaunay triangulation model construction method is used to triangulate the point cloud data. Through triangulation, the point cloud data is connected into a continuous triangular mesh. Next, the elevation interpolation of each triangle in the triangulation is performed using linear interpolation or Kriging interpolation methods to generate a complete three-dimensional terrain surface. Finally, the three-dimensional terrain surface is combined with the feature information in the surveying and mapping feature mapping data to add texture and attributes to the three-dimensional model. The addition of texture and attributes can be enhanced by mapping the feature data to the surface of the three-dimensional model and giving the model surface different colors and textures according to the distribution and feature values ​​of the data, thereby enhancing the visualization of the model.

[0095] Step S4: acquiring real-time surveying and mapping update data; performing surveying and mapping visualization update on the surveying and mapping three-dimensional model according to the real-time surveying and mapping update data to obtain a three-dimensional surveying and mapping visualization model; performing interactive dynamic display on the three-dimensional surveying and mapping visualization model, and outputting a three-dimensional surveying and mapping visualization report.

[0096] In this embodiment of the present invention, real-time mapping update data, including the latest terrain elevation, temperature, and humidity data, is acquired through an IoT sensor network. The data import function of the Geographic Information System (GIS) software is used to compare and integrate the real-time mapping update data with existing multi-dimensional mapping-related data. The GIS software is set to a 10-minute data update interval to ensure that the 3D mapping model can promptly reflect changes in terrain and environment. The 3D mapping model is then visually updated based on the real-time mapping update data. In the 3D modeling module of the GIS software, the updated terrain elevation data is mapped to the terrain surface of the 3D model through data binding, and the temperature and humidity data are overlaid onto the model as color textures. Temperature data is represented using a cool and warm color scheme, while humidity data is visualized through transparency changes. During this process, the dynamic update function of the GIS software is used to ensure that the 3D model reflects data changes in real time. Subsequently, the 3D mapping visualization model is interactively and dynamically displayed. The interactive display function of the GIS software allows users to rotate, zoom, and navigate the model using a mouse. Detailed terrain, temperature, and humidity information can be obtained by clicking on specific areas of the model. In addition, by setting a dynamic timeline, users can view changes in surveying and mapping data at different time points; finally, a three-dimensional surveying and mapping visualization report is output. In the report generation module of the GIS software, according to the preset template, the key information of the three-dimensional model, data update records and analysis results are generated into a visualization report.

[0097] Preferably, step S1 includes the following steps:

[0098] Step S11: The IoT sensor collects data at a fixed interval of 5 seconds, and the sampling frequency of the IoT sensor is 10 Hz during each collection.

[0099] Step S12: setting the positioning accuracy of the GPS sensor to ±1 cm and the measurement accuracy of the laser rangefinder to ±0.5 mm to collect terrain elevation data within the surveying area;

[0100] Step S13: setting the measurement range of the temperature sensor to -20°C to +50°C and the measurement range of the humidity sensor to 0% to 100% to collect environmental monitoring data within the surveying area;

[0101] Step S14: merging terrain elevation data and environmental monitoring data to obtain original surveying and mapping data;

[0102] Step S15: Filter and denoise the original surveying and mapping data, with the filter window size set to 3×3, to obtain surveying and mapping denoised data;

[0103] Step S16: performing normalization processing on the surveying and mapping denoising data to obtain surveying and mapping normalized data; performing standardization processing on the surveying and mapping normalized data to obtain standard surveying and mapping data.

[0104] In this embodiment of the present invention, an IoT sensor initiates data collection tasks at a preset fixed collection interval of 5 seconds, with a sampling frequency of 10 Hz for each collection task. The sensor collects data at a fixed frequency within the set interval and temporarily stores the collected data in a local cache. Specifically, the sensor collects data 10 times per second within each 5-second period, generating 50 data points that record the measurement values ​​at the corresponding time points. The positioning accuracy of the GPS sensor is set to ±1 cm, and the measurement accuracy of the laser rangefinder is set to ±0.5 mm. GPS sensors collect geographic coordinate information within the survey area, while laser rangefinders collect terrain elevation data. Specifically, the GPS sensor records longitude, latitude, and elevation information with an accuracy of ±1 cm during each acquisition. The laser rangefinder measures terrain elevation data with an accuracy of ±0.5 mm and stores this data in a time series format. The temperature sensor's measurement range is set to -20°C to +50°C, and the humidity sensor's measurement range is set to 0% to 100%. The temperature and humidity sensors collect environmental monitoring data within the survey area and output the data as digital signals to the data processing unit. The data processing unit receives the terrain elevation and environmental monitoring data from the sensors and uses a data fusion module to match the two data types based on timestamps and spatial coordinates, merging them to generate a complete raw mapping dataset. Specifically, the data fusion module uses time as a reference to correlate terrain elevation data with temperature and humidity data at the same point in time, forming a raw mapping dataset containing elevation, temperature, and humidity information. The data fusion tolerance is set to ±1 second to ensure accurate time alignment. The data processing unit filters and denoises the raw surveying and mapping data using a median filter algorithm with a 3×3 filter window size. The data is processed point by point to remove anomalous data points caused by sensor noise or environmental interference. Specifically, the median filter algorithm sorts the data for each data point and the data within a 3×3 area surrounding it, taking the median value as the filtered result for that point. For each point in the terrain elevation data, the value of its eight neighboring points and its own value are sorted, and the median value of this sorting is taken as the filtered elevation value, thereby generating the denoised surveying and mapping data. The data processing unit normalizes the denoised surveying and mapping data to a range of 0 to 1. Normalization involves subtracting the minimum value of the dataset from each data point's value and dividing it by the difference between the maximum and minimum values ​​of the dataset to obtain the normalized data value. Standardization involves Z-score normalization, which involves subtracting the dataset mean from each data point's value and dividing it by the dataset's standard deviation to generate the standardized surveying and mapping data.

[0105] Preferably, step S2 includes the following steps:

[0106] Step S21: performing nonlinear scale division processing on the terrain elevation data to obtain nonlinear scale elevation data; performing terrain natural breakpoint level detection on the nonlinear scale elevation data to obtain terrain breakpoint level data;

[0107] Step S22: performing terrain hierarchical texture recognition on the terrain breakpoint hierarchical data to generate terrain hierarchical texture data; performing terrain hierarchical structure mapping on the terrain breakpoint hierarchical data according to the terrain hierarchical texture data to obtain terrain hierarchical structure data;

[0108] Step S23: performing terrain multi-scale matching based on terrain breakpoint hierarchical data, terrain hierarchical texture data, and terrain hierarchical structure data to obtain multi-scale terrain features;

[0109] Step S24: dividing the multi-scale terrain features into adjacent elevation points to obtain terrain adjacent elevation point data; determining the height difference of the terrain adjacent elevation point data to obtain terrain adjacent height difference;

[0110] Step S25: dividing the terrain elevation data by horizontal distance to obtain terrain horizontal distance data; calculating the terrain elevation slope value by using the terrain adjacent height differences and the terrain horizontal distance data to generate the terrain elevation slope value; determining the terrain slope direction data by using the terrain adjacent height differences and the terrain horizontal distance data to generate the terrain slope direction data;

[0111] Step S26: merging the terrain elevation slope value and the terrain elevation aspect data to obtain terrain multi-scale data;

[0112] Step S27: Perform multi-factor coupling analysis on the environmental monitoring data based on the terrain multi-scale data to obtain environmental coupling data.

[0113] In an embodiment of the present invention, nonlinear scale division processing is performed on the terrain elevation data, and a nonlinear scale spatial filtering technology is adopted. This technology smoothes the terrain elevation data by applying nonlinear filters at different scales while retaining the edge information of the terrain; specifically, a Gaussian filter is used in combination with a nonlinear diffusion equation to perform multi-scale smoothing on the elevation data to obtain nonlinear scale elevation data at different scales; the nonlinear scale elevation data is subjected to terrain natural breakpoint hierarchy detection, and a natural breakpoint method (Jenks optimization method) is adopted. This method identifies the natural breakpoint hierarchy in the terrain elevation data by minimizing the intra-group difference and maximizing the inter-group difference; specifically, the difference between each elevation value and other elevation values ​​is calculated, and the elevation data is divided into multiple levels through iterative optimization to obtain terrain breakpoint hierarchy data; terrain hierarchy texture recognition is performed on the terrain breakpoint hierarchy data, and a terrain classification technology based on deep learning is adopted. A convolutional neural network (CNN) is used to extract and classify the terrain breakpoint hierarchy data to identify different terrains. The terrain layer texture data is generated by the texture features of the topography layer; the terrain breakpoint layer data is mapped to the terrain layer structure according to the terrain layer texture data, and the terrain layer detail algorithm with a matrix structure is used to map the terrain layer texture data to the terrain breakpoint layer data to form terrain layer structure data; the terrain breakpoint layer data is stored in a matrix, and the texture features are mapped to the corresponding terrain layer through matrix operations to obtain terrain layer structure data; based on the terrain breakpoint layer data, the terrain layer texture data and the terrain layer structure data, a multi-scale matching technology is used to find the best matching terrain scale by comparing the terrain features at different scales, and the similarity of the terrain features at different scales is specifically calculated, and the scale with the highest similarity is selected as the matching result to obtain multi-scale terrain features; the multi-scale terrain features are divided into adjacent elevation points, and a neighborhood-based division method is used to find the adjacent elevation points for each elevation point to form elevation point pairs; the height difference of each pair of adjacent elevation points is calculated to obtain the terrain adjacent height difference. The specific operations are as follows: For each pair of adjacent elevation points, the elevation difference is calculated to obtain terrain adjacent elevation difference data; the terrain elevation data is divided into regular grids using a grid-based partitioning method, and the horizontal distance of each grid is calculated; the terrain adjacent elevation difference and the terrain horizontal distance data are used to calculate the terrain elevation slope value. The slope calculation formula is used to calculate the slope value of each grid to generate the terrain elevation slope; the terrain adjacent elevation difference and the terrain horizontal distance data are used to determine the terrain aspect data. The aspect calculation formula is used to calculate the slope of each grid to generate the terrain aspect data; the terrain elevation slope value and the terrain elevation aspect data are combined to form terrain features.Data fusion technology is used to combine slope and aspect data into comprehensive terrain feature data. Specifically, the slope and aspect data are stored in the same data structure, forming multi-scale terrain data. This multi-factor coupling analysis of environmental monitoring data is then conducted using this multi-scale terrain data. A coupling coordination model is used to comprehensively analyze the multi-scale terrain data and environmental monitoring data. Specifically, the multi-scale terrain data and environmental monitoring data are standardized, and the coupling degree and coordination index are calculated to obtain the environmental coupling data.

[0114] Preferably, step S27 includes the following steps:

[0115] Step S271: performing scale-level parsing on the terrain multi-scale data and decomposing the terrain data into layers with different resolutions, where each layer corresponds to a specific terrain detail level, to obtain terrain detail layered data;

[0116] Step S272: Perform feature enhancement processing on the terrain detail layered data, enhance the slope, curvature, and terrain relief features in each scale layer to obtain multi-scale enhanced terrain features; perform terrain influencing factor detection on the multi-scale enhanced terrain features to obtain terrain influencing factor data;

[0117] Step S273: performing implicit coupling relationship detection on the environmental monitoring data according to the terrain influencing factor data to obtain implicit coupling relationship data; performing terrain and environmental multi-factor interactive fusion on the implicit coupling relationship data to obtain multi-factor interactive fusion data;

[0118] Step S274: performing environmental coupling abnormality manifestation detection on the multi-factor interactive fusion data to obtain the environmental coupling abnormality manifestation; performing environmental feature correction on the environmental coupling abnormality manifestation to obtain final environmental coupling data.

[0119] In an embodiment of the present invention, multi-scale terrain data is subjected to hierarchical scale analysis, employing a hierarchical analysis technique to decompose the terrain data into layers of varying resolution. Specifically, the terrain data is divided into different layers by setting multiple scale thresholds, such as 10 meters, 50 meters, and 100 meters, based on the required resolution of the terrain data. Each layer corresponds to a specific level of terrain detail, for example, a high-resolution layer corresponds to detailed terrain features, while a low-resolution layer corresponds to macroscopic terrain features. This generates layered terrain detail data, which is then subjected to feature enhancement processing. A feature enhancement algorithm is employed to enhance the slope, curvature, and terrain relief features at each scale level. Specifically, the slope, curvature, and terrain relief features are calculated for each level of terrain detail and enhanced using a nonlinear enhancement function (such as a logarithmic or exponential function) to highlight subtle changes in the terrain. The enhanced feature data is then referred to as the multi-scale enhanced terrain feature. Terrain influencing factors are detected using multi-scale enhanced terrain features. Correlation analysis methods are used to calculate the correlation between enhanced terrain features and terrain influencing factors (such as vegetation cover and soil type) to obtain terrain influencing factor data. Based on this terrain influencing factor data, implicit coupling relationships are detected in environmental monitoring data. Implicit coupling relationship detection techniques are used to identify implicit coupling relationships by calculating nonlinear correlations between the terrain influencing factor data and the environmental monitoring data. Specifically, methods such as mutual information or conditional entropy are used to quantify the implicit relationships between the terrain influencing factor data and the environmental monitoring data to obtain implicit coupling relationship data. The implicit coupling relationship data is then subjected to terrain and environmental multi-factor interactive fusion. Using multi-factor interactive fusion techniques, terrain influencing factor data and environmental monitoring data are fused using weighted averaging to obtain multi-factor interactive fusion data. Anomalies in environmental coupling are detected in the multi-factor interactive fusion data using anomaly detection algorithms to identify anomalies in environmental coupling. Specifically, cluster analysis or the isolation forest algorithm are used to detect outliers or patterns in the multi-factor interactive fusion data to obtain anomalies in environmental coupling. Environmental feature correction is performed on the abnormal manifestations of environmental coupling, and a correction algorithm is used to correct the abnormal manifestations. The specific operation is: based on the characteristics of the abnormal manifestations, linear regression or nonlinear correction methods are used to correct the environmental coupling data to eliminate the abnormal effects and finally obtain the corrected environmental coupling data.

[0120] Preferably, step S3 includes the following steps:

[0121] Step S31: performing spatiotemporal heterogeneity analysis on the multi-scale terrain features and decomposing them into multiple spatiotemporal heterogeneous units, each unit containing terrain feature information within a specific time range, thereby obtaining spatiotemporal heterogeneous unit data;

[0122] Step S32: performing spatiotemporal isomorphism mapping on the environmental coupling data, mapping the environmental coupling data to an isomorphic framework that matches the spatiotemporal heterogeneous units of the terrain feature data, to obtain environmental spatiotemporal isomorphic data;

[0123] Step S33: performing spatiotemporal correlation pattern matching on the spatiotemporal heterogeneous unit data and the environmental spatiotemporal homogeneous data to obtain spatiotemporal correlation pattern data; performing spatiotemporal feature fusion and reconstruction on the spatiotemporal correlation pattern data to generate surveying and mapping spatiotemporal feature data;

[0124] Step S34: performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data;

[0125] Step S35: constructing a three-dimensional model according to the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0126] In an embodiment of the present invention, spatiotemporal heterogeneity analysis is performed on terrain feature data to decompose the terrain feature data into multiple spatiotemporal heterogeneous units. Specifically, the data is divided into multiple time intervals and spatial regions based on the timestamps and spatial coordinates of the terrain data, and each unit contains terrain feature information within a specific time range. Specifically, the data is divided into time units by day or month, and into spatial units by natural boundaries or administrative regions of the terrain, to obtain spatiotemporal heterogeneous unit data. Spatiotemporal isomorphism mapping is performed on the environmental coupling data to map the environmental coupling data into an isomorphic framework that matches the spatiotemporal heterogeneous units of the terrain feature data. The specific operations are as follows: according to the time and space division method of terrain feature data, the environmental coupling data is divided into the same time segments and space divisions to ensure that the two are consistent in time and space; through interpolation or resampling methods, the environmental coupling data and terrain feature data are aligned in time and space to obtain environmental spatiotemporal isomorphic data; the spatiotemporal correlation pattern matching is performed on the spatiotemporal heterogeneous unit data and the environmental spatiotemporal isomorphic data, and the spatiotemporal correlation pattern between the two is identified by using correlation analysis or mutual information calculation methods to obtain spatiotemporal correlation pattern data; the spatiotemporal feature fusion and reconstruction of the spatiotemporal correlation pattern data is performed, and the terrain features and environmental features are fused by weighted average or feature fusion algorithm to generate surveying and mapping spatiotemporal feature data; the surveying and mapping spatiotemporal feature data is adaptively mapped, and the principal component analysis (PCA) or adaptive feature extraction algorithm is used to reduce the dimension of the surveying and mapping spatiotemporal feature data and extract key features; according to the variance contribution rate or importance score of the features, the main features are selected for mapping to obtain surveying and mapping feature mapping data; a three-dimensional model is constructed based on the surveying and mapping feature mapping data. First, the surveying and mapping feature mapping data is arranged according to spatial coordinates to form three-dimensional point cloud data; then, the point cloud data is meshed using Delaunay triangulation or quadtree structure to generate a three-dimensional terrain mesh; then, the mesh is texture mapped using the terrain feature data, and the terrain feature information is assigned to the three-dimensional mesh surface through texture mapping technology to generate a textured three-dimensional terrain model; finally, the three-dimensional model is optimized, such as smoothing and detail enhancement, to obtain the final surveying and mapping three-dimensional model.

[0127] Preferably, step S34 includes the following steps:

[0128] Step S341: Divide the surveying and mapping spatiotemporal feature data according to different time and space scales to obtain multi-scale spatiotemporal data blocks;

[0129] Step S342: Identify the heterogeneous features in each spatiotemporal data block and extract the feature differences in the time and space dimensions to obtain spatiotemporal heterogeneous features; perform specific spatiotemporal range feature marking on the spatiotemporal heterogeneous features to obtain spatiotemporal heterogeneous labeled data;

[0130] Step S343: performing spatiotemporal dynamic change analysis on the spatiotemporal heterogeneous labeled data to obtain spatiotemporal dynamic change features; performing feature interaction detection on the spatiotemporal dynamic change features to generate change feature effect data; performing feature dependency correlation evaluation on the change feature effect data to obtain feature correlation evaluation results;

[0131] Step S344: constructing a dynamic spatiotemporal feature framework based on the feature correlation evaluation results to obtain a dynamic spatiotemporal feature framework;

[0132] Step S345: performing feature importance evaluation on the dynamic spatiotemporal feature framework to obtain feature importance evaluation data; adaptively assigning feature weights based on the feature importance evaluation results to obtain weight adaptive assignment data;

[0133] Step S346: performing feature space pairing on the weight adaptive allocation data to obtain a feature adaptive space; defining feature mapping rules on the feature adaptive space to generate feature mapping rules;

[0134] Step S347: performing feature mapping conversion on the surveying and mapping spatiotemporal feature data according to the feature mapping rules, and mapping the surveying and mapping spatiotemporal feature data to a new feature space according to the feature mapping rules to generate surveying and mapping feature mapping data.

[0135] In an embodiment of the present invention, data is divided into multiple time intervals according to timestamps, such as by hour, day or month. Assuming that the time range of the data is from January 1, 2024 to December 31, 2024, the data is divided into 12 time intervals by month, and each interval contains one month's data; the data is divided into multiple spatial regions according to spatial coordinates, such as by grid, and the size of each grid is set to 10 meters × 10 meters or larger according to specific needs; if the study area is 1000 meters × 1000 meters, it is divided into 100 10 meters × 10 meters grids; each spatiotemporal data block contains terrain feature information within a specific time range, forming a multi-scale spatiotemporal data block; the difference in characteristic values ​​at different time points and different spatial positions in each spatiotemporal data block is calculated, such as slope changes, vegetation coverage changes, etc.The specific operations are as follows: for each spatiotemporal data block, calculate the slope change value between adjacent time points (such as adjacent hours or days); calculate the vegetation cover change value between adjacent spatial positions (such as adjacent grids); quantify these differences, for example, normalize the slope change value to the range of 0 to 1, set a threshold (such as the slope change threshold is 0.1, the vegetation cover change threshold is 0.2), mark the feature points with significant changes, and form spatiotemporal heterogeneous labeled data; use time series analysis methods to calculate the time change rate of each feature point, for example, for each slope change point, calculate its change rate in different time intervals; use spatial autocorrelation analysis to calculate the spatial distribution change of features, for example, calculate the distribution change of vegetation cover change points in different spatial regions; analyze the mutual influence between different features, for example, calculate the correlation between slope change and vegetation cover change; use mutual information or correlation analysis to evaluate the dependency between features; based on the feature correlation evaluation results, combine features with strong correlation to form feature clusters, for example, combine slope change and vegetation cover change into A feature cluster; based on the temporal and spatial distribution of the feature cluster, a dynamic spatiotemporal feature framework is constructed, which can reflect the dynamic changes of features in time and space; a method based on information entropy or variance is used to evaluate the importance of each feature and calculate the information entropy of each feature. The lower the information entropy, the more important the feature; based on the evaluation results, feature weights are adaptively assigned to obtain weight-adaptive assigned data, for example, features with larger variances are given higher weights; feature space pairing is performed on the weight-adaptive assigned data, features with similar weights are paired to form feature pairs, and slope change features with similar weights are paired with vegetation cover change features; based on the distribution and weight of the feature pairs, feature mapping rules are defined, the similarity between feature pairs is calculated, and the feature pairs are mapped to a new feature space based on the similarity; based on the feature mapping rules, feature mapping conversion is performed on the surveying and mapping spatiotemporal feature data, and for each feature point, the original feature value is converted to a new feature value based on the feature mapping rules; the converted feature value is mapped to the new feature space, specifically the new feature value is stored in a new data structure to form a new feature space.

[0136] The surveying and mapping spatiotemporal feature data is divided into different temporal and spatial scales to generate multi-scale spatiotemporal data blocks. Specifically, the data is divided into multiple time intervals based on timestamps, such as by hour, day, or month. Simultaneously, the data is divided into multiple spatial regions based on spatial coordinates, such as grids. The size of each grid can be set to 10 meters x 10 meters or larger based on specific needs. Each spatiotemporal data block contains terrain feature information within a specific time range, forming a multi-scale spatiotemporal data block. Heterogeneous features within each spatiotemporal data block are identified, and feature differences across time and space are extracted to generate spatiotemporal heterogeneous features. Specifically, the differences in feature values ​​at different time points and spatial locations within each spatiotemporal data block are calculated, such as changes in slope or vegetation cover. These differences are quantified, and feature points with significant changes are marked to generate spatiotemporal heterogeneous labeled data. Spatiotemporal dynamic change analysis is performed on the spatiotemporal heterogeneous labeled data, calculating the temporal and spatial trends of the features within each spatiotemporal data block to generate spatiotemporal dynamic change features. The specific operation involves calculating the temporal rate of change of each feature point using time series analysis. Simultaneously, spatial autocorrelation analysis is used to calculate the spatial distribution of features. Feature interaction detection is performed on spatiotemporal dynamic change features, analyzing the mutual influence between different features to generate change feature effect data. Feature dependency correlation is assessed on this change feature effect data, using mutual information or correlation analysis to evaluate the dependency between features and obtain feature correlation assessment results. Based on these feature correlation assessment results, a dynamic spatiotemporal feature framework is constructed. Specifically, features with strong correlations are grouped together to form feature clusters. Based on the temporal and spatial distribution of feature clusters, a dynamic spatiotemporal feature framework is constructed to reflect the dynamic changes of features in time and space. Feature importance is assessed within the dynamic spatiotemporal feature framework, using methods based on information entropy or variance to evaluate the importance of each feature. Based on the assessment results, feature weights are adaptively assigned to generate weight-adaptive allocation data. Specifically, the information entropy or variance of each feature is calculated, assigning higher weights to features with larger variance or lower information entropy, generating weight-adaptive allocation data. Feature-space pairing is performed on the weight-adaptive allocation data, pairing features with similar weights to form feature pairs. Based on the distribution and weights of feature pairs, feature mapping rules are defined and generated. The specific operations are: calculating the similarity between feature pairs, mapping the feature pairs to a new feature space based on the similarity, and forming feature mapping rules; performing feature mapping transformation on the surveying and mapping spatiotemporal feature data according to the feature mapping rules, mapping the original data to the new feature space, and generating surveying and mapping feature mapping data. The specific operations are: transforming each feature point according to the feature mapping rules, mapping the original feature values ​​to the new feature space, and obtaining surveying and mapping feature mapping data.

[0137] Preferably, step S35 further includes the following steps:

[0138] Step S351: dividing the surveying and mapping feature mapping data into terrain units to obtain terrain unit data; marking coordinate points on the terrain unit data to generate terrain unit coordinate data; and connecting the terrain unit coordinate data to obtain a terrain region system;

[0139] Step S352: performing terrain-region temperature mapping on the terrain-region system to obtain regional temperature mapping data; overlaying the regional temperature mapping data with the terrain-temperature distribution system to generate a terrain-temperature overlay system;

[0140] Step S353: Perform terrain regional humidity overlay on the terrain regional system to obtain regional humidity overlay data; perform humidity distribution overlay on the terrain-temperature overlay system based on the regional humidity overlay data to generate a terrain-temperature and humidity overlay system;

[0141] Step S354: Associating the terrain-temperature and humidity coverage system with the surveying and mapping system to obtain surveying and mapping associated data; constructing a three-dimensional model based on the surveying and mapping associated data to obtain a surveying and mapping three-dimensional model.

[0142] In this embodiment of the present invention, terrain distribution feature data is divided into terrain units. The "Polygon Build" tool in ArcGIS Pro is used to divide the terrain into several terrain units based on terrain slope and aspect characteristics. The "Add Geometric Attributes" function is used to identify coordinate points in the terrain unit data to generate terrain unit coordinate data. Subsequently, the "Editor" tool is used to connect the terrain unit coordinate data to form closed polygons, thereby constructing a complete terrain regional system. A terrain regional temperature mapping is performed on the terrain regional system based on the terrain temperature distribution data. Using the "Symbolize" function in ArcGIS Pro, the temperature data is color-mapped according to preset temperature ranges (low temperature zone, moderate temperature zone, and high temperature zone) to generate regional temperature mapping data. Next, the "Overlay Analysis" tool is used to overlay the regional temperature mapping data onto the terrain regional system to generate a terrain-temperature overlay system. A terrain regional humidity overlay is performed on the terrain regional system based on the terrain moisture distribution data. Using the "Raster Calculator" tool in ArcGIS Pro, the humidity data is classified according to humidity ranges (dry zone, moderate temperature zone, and humid zone) and overlaid with the terrain regional system to generate regional humidity overlay data. Subsequently, the "Overlay Analysis" function was used to overlay the regional humidity data onto the terrain-temperature overlay system, generating a terrain-temperature-humidity overlay system. This terrain-temperature-humidity overlay system was then linked to a multidimensional mapping system. Using ArcGIS Pro's "Attribute Join" function, the terrain, temperature, and humidity data were linked to generate multidimensional mapping data. Finally, using ArcGIS Pro's 3D modeling capabilities, a 3D mapping model was constructed based on the multidimensional mapping data. The 2D data was then converted to a 3D model for symbol design and view optimization.

[0143] As an example of the present invention, refer to Figure 2 As shown, in this example, step S4 includes:

[0144] Step S41: Acquire real-time surveying and mapping update data;

[0145] Step S42: identifying terrain change areas on the real-time surveying and mapping update data, and marking the boundaries of the terrain change areas to obtain terrain change feature boundary data;

[0146] Step S43: identifying temperature and humidity changes in the real-time surveying and mapping update data, and determining the temperature and humidity change area to obtain temperature and humidity change characteristic data;

[0147] Step S44: updating the terrain change area modeling of the surveying and mapping three-dimensional model according to the terrain change characteristic boundary data to obtain a terrain surveying and mapping update model;

[0148] Step S45: dynamically adjusting the regional color transparency of the temperature and humidity change characteristic data to obtain model color transparency adjustment data; performing color visualization rendering on the updated three-dimensional model of the terrain surveying and mapping according to the model color transparency adjustment data to obtain a three-dimensional surveying and mapping visualization model;

[0149] Step S46: interactively and dynamically display the three-dimensional surveying and mapping visualization model, and output a three-dimensional surveying and mapping visualization report.

[0150] In an embodiment of the present invention, a Python script is combined with the Pandas library to read real-time surveying and mapping update data, including multidimensional data such as terrain elevation, temperature, and humidity. The data is stored in CSV format. The OpenCV library is used to identify terrain change areas in the real-time surveying and mapping update data. The terrain change area is identified by calculating the difference image of the elevation data before and after. The contour detection function is used to mark the boundary of the change area, and terrain change characteristic boundary data is generated. The temperature and humidity changes in the real-time surveying and mapping update data are identified. The NumPy and Matplotlib libraries are used, combined with predefined color mapping (such as "coolwarm"), to generate color mapping data based on the temperature and humidity change range. At the same time, the temperature and humidity change area is determined by setting a threshold, and temperature and humidity change characteristic data is generated. Based on the terrain change characteristic boundary data, Blender software is used to perform three-dimensional modeling and update of the terrain change area. Blender supports importing boundary data and mesh editing, and updates the terrain mesh through Boolean operations to generate a terrain surveying and mapping update model. The regional color transparency of the temperature and humidity change characteristic data is dynamically adjusted. Use Matplotlib to create a custom color map, mapping the low-temperature area to blue, the moderate-temperature area to green, and the high-temperature area to red. At the same time, by adjusting the transparency parameters (e.g., 30%-50% transparency in dry areas and 70%-90% transparency in wet areas), model color transparency adjustment data is generated. Use Blender's material editor to apply the adjusted color and transparency to the terrain mapping update model to complete color visualization rendering. Perform interactive and dynamic display of the 3D mapping visualization model. Use the Three.js library combined with WebGL technology to import the model into a web page environment, enabling model rotation, scaling, and click interaction. At the same time, use Three.js's dynamic material update function to adjust the model's color and transparency based on real-time data. Finally, generate an interactive report page using HTML and CSS, which can be exported to PDF format.

[0151] As an example of the present invention, refer to Figure 3 As shown, in this example, step S46 includes:

[0152] Step S461: starting a 3D visualization engine through computer 3D simulation software, and loading a 3D surveying and mapping visualization model into a 3D scene display state;

[0153] Step S462: monitoring the input device mouse signal, including pressing, dragging, and releasing the left mouse button, to obtain mouse signals; monitoring the input device touch screen gesture signal, including single-finger dragging, two-finger zooming, and multi-finger rotation, to obtain touch screen gesture signals;

[0154] Step S463: marking the model visual surface of the 3D surveying and mapping visualization model according to the mouse signal and the touch screen gesture signal to obtain the model observation visual surface; determining the visual surface scaling ratio of the model observation visual surface to obtain the visual surface scaling ratio;

[0155] Step S464: interactively and dynamically display the three-dimensional surveying and mapping visualization model based on the model observation visual surface and the visual surface scaling ratio, and output a three-dimensional surveying and mapping visualization report.

[0156] In an embodiment of the present invention, a 3D visualization engine is started using computer 3D simulation software (such as Three.js combined with WebGL technology). A 3D surveying and mapping visualization model is loaded into a 3D scene display state. The specific operation is as follows: a 3D scene is created using the Scene object of Three.js, and the scene is rendered into the Canvas element of the browser using the WebGLRenderer. The size parameters of the renderer are set, for example, the width is the window width and the height is the window height, to ensure that the model is fully displayed. The signals of the input device are monitored. For mouse signals, JavaScript's event monitoring mechanism is used to monitor the left mouse button press, drag, and release events. For touch screen gesture signals, HTML5's touch event API is used to monitor single-finger drag, two-finger zoom, and multi-finger rotation gestures. The specific operation is as follows: mouse events are bound to mousedown, mousemove, and mouseup event handlers; touch events are bound to touchstart, touchmove, and touchend event handlers, and the coordinates and number of touch points are obtained through the event object. The 3D surveying and mapping visualization model is visually marked based on the mouse signals and touch screen gesture signals. Using the Three.js Raycaster tool, the intersection of the ray and the model is calculated based on the position of the mouse or touch point to determine the model's observed visual surface. The visual surface scaling ratio is determined for the model's observed visual surface and calculated based on the scaling ratio of the two-finger zoom gesture or the zoom value of the mouse wheel event. Based on the model's observed visual surface and the visual surface scaling ratio, the 3D mapping visualization model is interactively and dynamically displayed. The Three.js OrbitControls controller is used to implement model rotation, zooming, and translation interactions. The camera's focal length and position are dynamically adjusted based on the visual surface scaling ratio to ensure that the model maintains a reasonable display ratio during interaction. Finally, the rendering results are output to the Canvas element through the Three.js Renderer object to generate a 3D mapping visualization model.

[0157] In this specification, a three-dimensional visualization system for surveying and mapping data based on the Internet of Things is also provided, which is used to execute the above-mentioned three-dimensional visualization method for surveying and mapping data based on the Internet of Things. The three-dimensional visualization system for surveying and mapping data based on the Internet of Things includes:

[0158] The surveying and mapping data acquisition module is used to collect original surveying and mapping data within the surveying and mapping area through IoT sensors, where the original surveying and mapping data includes terrain elevation data and environmental monitoring data; and pre-process the original surveying and mapping data to obtain standard surveying and mapping data;

[0159] The surveying and mapping feature analysis module is used to perform multi-scale analysis on the terrain elevation data of standard surveying and mapping data to obtain multi-scale terrain features; perform multi-scale terrain detection on the multi-scale terrain features to obtain multi-scale terrain data; and perform multi-factor coupling analysis on the environmental monitoring data based on the multi-scale terrain data to obtain environmental coupling data.

[0160] The surveying and mapping data feature mapping module is used to perform spatiotemporal correlation pattern recognition on multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; perform adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; and construct a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

[0161] The 3D surveying and mapping visualization model construction module is used to obtain real-time surveying and mapping update data; perform surveying and mapping visualization updates on the surveying and mapping 3D model according to the real-time surveying and mapping update data to obtain a 3D surveying and mapping visualization model; perform interactive dynamic display on the 3D surveying and mapping visualization model, and output a 3D surveying and mapping visualization report.

[0162] The present invention eliminates noise and outliers in the original surveying and mapping data through preprocessing of the surveying and mapping data acquisition module, ensures the accuracy and consistency of terrain elevation data and environmental monitoring data, provides a high-quality data foundation for subsequent analysis, and avoids analysis deviations caused by data quality issues; converts raw data from different sources and formats into standard surveying and mapping data, realizes standardized data management, facilitates subsequent processing and analysis, improves the versatility and operability of data, and reduces the complexity of data processing. The multi-scale analysis of the surveying and mapping feature analysis module can capture terrain changes from macro to micro, and the generated multi-scale terrain feature data can more comprehensively reflect the complexity of the terrain, provide richer detailed information for terrain analysis, and promptly discover subtle changes and potential features in the terrain; through multi-factor coupling analysis, the terrain multi-scale data is combined with environmental monitoring data, which can reveal the interactive relationship between terrain and environment, provide a more accurate basis for environmental impact assessment and ecological research, and make it possible to understand the impact of terrain on the environment and the feedback mechanism of the environment on terrain. The surveying and mapping spatiotemporal feature data generated through the spatiotemporal correlation pattern of the surveying and mapping data feature mapping module can simultaneously reflect the temporal and spatial changes of the terrain and environment, providing more complete spatiotemporal information for the construction of three-dimensional models, so that the generated surveying and mapping three-dimensional models are not only spatially accurate, but also reflect dynamic changes in the time dimension; adaptive feature mapping can automatically adjust the mapping strategy according to the complexity of the surveying and mapping spatiotemporal feature data, and the generated surveying and mapping feature mapping data can more accurately reflect the characteristics of the terrain and environment, thereby constructing a high-precision surveying and mapping three-dimensional model, providing a more reliable model foundation for subsequent visualization and application. By acquiring and applying real-time surveying and mapping update data through the three-dimensional surveying and mapping visualization model construction module, the surveying and mapping three-dimensional model can timely reflect the latest changes in the surveying and mapping area, avoiding the problem of information obsolescence caused by data lag, improving the timeliness and practicality of the surveying and mapping data, and better meeting the needs of dynamic monitoring and real-time decision-making; the interactive dynamic display and output of the three-dimensional surveying and mapping visualization report provide users with an intuitive and convenient way to present data. Users can gain an in-depth understanding of the detailed information of the surveying and mapping area through interactive operations. At the same time, the visualization report can display complex surveying and mapping data in the form of intuitive graphics and charts, which is convenient for users to quickly understand and analyze, and improves the readability and ease of use of the data. Therefore, the present invention uses data processing technology, Internet of Things technology and three-dimensional mapping technology to more accurately reflect the spatiotemporal relationship between terrain and environment, and construct a three-dimensional surveying and mapping visualization model, thereby improving the accuracy and effect of three-dimensional visualization of surveying and mapping data.

[0163] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0164] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional visualization method for surveying and mapping data based on the Internet of Things, characterized in that: The following steps are involved: Step S1: collecting original mapping data within the mapping area through IoT sensors, wherein the original mapping data includes terrain elevation data and environmental monitoring data; pre-processing the original mapping data to obtain standard mapping data; Step S2: Perform multi-scale analysis on the terrain elevation data of the standard surveying and mapping data to obtain multi-scale terrain features; Perform multi-scale terrain detection on multi-scale terrain features to obtain multi-scale terrain data; perform multi-factor coupling analysis on environmental monitoring data based on the multi-scale terrain data to obtain environmental coupling data; Step S3: performing spatiotemporal correlation pattern recognition on the multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; constructing a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model; the adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain the surveying and mapping feature mapping data is specifically: Divide the surveying and mapping spatiotemporal feature data according to different time and space scales to obtain multi-scale spatiotemporal data blocks; Identify the heterogeneous features in each spatiotemporal data block and extract the feature differences between adjacent time points and adjacent spatial locations to obtain spatiotemporal heterogeneous features; Marking the spatiotemporal heterogeneity features with specific spatiotemporal range features to obtain spatiotemporal heterogeneous labeled data; Performing spatiotemporal dynamic change analysis on spatiotemporal heterogeneous labeled data to obtain spatiotemporal dynamic change features; the spatiotemporal dynamic change analysis specifically calculates the change rate in different time intervals and the distribution change in different spatial regions; performing feature interaction detection on the spatiotemporal dynamic change features to generate change feature effect data; performing feature dependency correlation evaluation on the change feature effect data to obtain feature correlation evaluation results; A dynamic spatiotemporal feature framework is constructed based on the feature correlation evaluation results to obtain a dynamic spatiotemporal feature framework; Perform feature importance evaluation on the dynamic spatiotemporal feature framework to obtain feature importance evaluation data; According to the feature importance evaluation results, feature weights are adaptively assigned to obtain weight adaptive allocation data; Perform feature space pairing on the weight adaptive allocation data to obtain a feature adaptive space; Defining feature mapping rules for feature adaptive space and generating feature mapping rules; Performing feature mapping conversion on the surveying and mapping spatiotemporal feature data according to the feature mapping rules, and mapping the surveying and mapping spatiotemporal feature data to a new feature space according to the feature mapping rules to generate surveying and mapping feature mapping data; Step S4: acquiring real-time surveying and mapping update data; performing surveying and mapping visualization update on the surveying and mapping three-dimensional model according to the real-time surveying and mapping update data to obtain a three-dimensional surveying and mapping visualization model; performing interactive dynamic display on the three-dimensional surveying and mapping visualization model, and outputting a three-dimensional surveying and mapping visualization report.

2. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 1, characterized in that: The IoT sensor includes a GPS sensor, a laser rangefinder, a temperature sensor, and a humidity sensor. Step S1 includes the following steps: Step S11: The IoT sensor collects data at a fixed interval of 5 seconds, and the sampling frequency of the IoT sensor is 10 Hz during each collection. Step S12: setting the positioning accuracy of the GPS sensor to ±1 cm and the measurement accuracy of the laser rangefinder to ±0.5 mm to collect terrain elevation data within the surveying area; Step S13: setting the measurement range of the temperature sensor to -20°C to +50°C and the measurement range of the humidity sensor to 0% to 100% to collect environmental monitoring data within the surveying area; Step S14: merging terrain elevation data and environmental monitoring data to obtain original surveying and mapping data; Step S15: Filter and denoise the original surveying and mapping data, with the filter window size set to 3×3, to obtain surveying and mapping denoised data; Step S16: performing normalization processing on the surveying and mapping denoising data to obtain surveying and mapping normalized data; performing standardization processing on the surveying and mapping normalized data to obtain standard surveying and mapping data.

3. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing nonlinear scale division processing on the terrain elevation data to obtain nonlinear scale elevation data; performing terrain natural breakpoint level detection on the nonlinear scale elevation data to obtain terrain breakpoint level data; Step S22: performing terrain hierarchical texture recognition on the terrain breakpoint hierarchical data to generate terrain hierarchical texture data; performing terrain hierarchical structure mapping on the terrain breakpoint hierarchical data according to the terrain hierarchical texture data to obtain terrain hierarchical structure data; Step S23: performing terrain multi-scale matching based on terrain breakpoint hierarchical data, terrain hierarchical texture data, and terrain hierarchical structure data to obtain multi-scale terrain features; Step S24: dividing the multi-scale terrain features into adjacent elevation points to obtain terrain adjacent elevation point data; determining the height difference of the terrain adjacent elevation point data to obtain terrain adjacent height difference; Step S25: dividing the terrain elevation data by horizontal distance to obtain terrain horizontal distance data; calculating the terrain elevation slope value by using the terrain adjacent height differences and the terrain horizontal distance data to generate the terrain elevation slope value; determining the terrain slope direction data by using the terrain adjacent height differences and the terrain horizontal distance data to generate the terrain slope direction data; Step S26: merging the terrain elevation slope value and the terrain elevation aspect data to obtain terrain multi-scale data; Step S27: Perform multi-factor coupling analysis on the environmental monitoring data based on the terrain multi-scale data to obtain environmental coupling data.

4. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 3, characterized in that: Step S27 further includes the following steps: Step S271: performing scale-level parsing on the terrain multi-scale data and decomposing the terrain data into layers with different resolutions, where each layer corresponds to a specific terrain detail level, to obtain terrain detail layered data; Step S272: Perform feature enhancement processing on the terrain detail layered data, enhance the slope, curvature, and terrain relief features in each scale layer to obtain multi-scale enhanced terrain features; perform terrain influencing factor detection on the multi-scale enhanced terrain features to obtain terrain influencing factor data; Step S273: performing implicit coupling relationship detection on the environmental monitoring data according to the terrain influencing factor data to obtain implicit coupling relationship data; performing terrain and environmental multi-factor interactive fusion on the implicit coupling relationship data to obtain multi-factor interactive fusion data; Step S274: performing environmental coupling abnormality manifestation detection on the multi-factor interactive fusion data to obtain the environmental coupling abnormality manifestation; performing environmental feature correction on the environmental coupling abnormality manifestation to obtain final environmental coupling data.

5. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing spatiotemporal heterogeneity analysis on the multi-scale terrain features and decomposing them into multiple spatiotemporal heterogeneous units, each unit containing terrain feature information within a specific time range, thereby obtaining spatiotemporal heterogeneous unit data; Step S32: performing spatiotemporal isomorphism mapping on the environmental coupling data, mapping the environmental coupling data to an isomorphic framework that matches the spatiotemporal heterogeneous units of the terrain feature data, to obtain environmental spatiotemporal isomorphic data; Step S33: performing spatiotemporal correlation pattern matching on the spatiotemporal heterogeneous unit data and the environmental spatiotemporal homogeneous data to obtain spatiotemporal correlation pattern data; performing spatiotemporal feature fusion and reconstruction on the spatiotemporal correlation pattern data to generate surveying and mapping spatiotemporal feature data; Step S34: performing adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; Step S35: constructing a three-dimensional model according to the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model.

6. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 5, characterized in that: Step S35 includes the following steps: Step S351: dividing the surveying and mapping feature mapping data into terrain units to obtain terrain unit data; marking coordinate points on the terrain unit data to generate terrain unit coordinate data; and connecting the terrain unit coordinate data to obtain a terrain region system; Step S352: performing terrain-region temperature mapping on the terrain-region system to obtain regional temperature mapping data; overlaying the regional temperature mapping data with the terrain-temperature distribution system to generate a terrain-temperature overlay system; Step S353: Perform terrain regional humidity overlay on the terrain regional system to obtain regional humidity overlay data; perform humidity distribution overlay on the terrain-temperature overlay system based on the regional humidity overlay data to generate a terrain-temperature and humidity overlay system; Step S354: Associating the terrain-temperature and humidity coverage system with the surveying and mapping system to obtain surveying and mapping associated data; constructing a three-dimensional model based on the surveying and mapping associated data to obtain a surveying and mapping three-dimensional model.

7. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Acquire real-time surveying and mapping update data; Step S42: identifying terrain change areas on the real-time surveying and mapping update data, and marking the boundaries of the terrain change areas to obtain terrain change feature boundary data; Step S43: identifying temperature and humidity changes in the real-time surveying and mapping update data, and determining the temperature and humidity change area to obtain temperature and humidity change characteristic data; Step S44: updating the terrain change area modeling of the surveying and mapping three-dimensional model according to the terrain change characteristic boundary data to obtain a terrain surveying and mapping update model; Step S45: dynamically adjusting the regional color transparency of the temperature and humidity change characteristic data to obtain model color transparency adjustment data; performing color visualization rendering on the updated three-dimensional model of the terrain surveying and mapping according to the model color transparency adjustment data to obtain a three-dimensional surveying and mapping visualization model; Step S46: interactively and dynamically display the three-dimensional surveying and mapping visualization model, and output a three-dimensional surveying and mapping visualization report.

8. The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 7, characterized in that: Step S46 includes the following steps: Step S461: starting a 3D visualization engine through computer 3D simulation software, and loading a 3D surveying and mapping visualization model into a 3D scene display state; Step S462: monitoring the input device mouse signal, including pressing, dragging, and releasing the left mouse button, to obtain mouse signals; monitoring the input device touch screen gesture signal, including single-finger dragging, two-finger zooming, and multi-finger rotation, to obtain touch screen gesture signals; Step S463: marking the model visual surface of the 3D surveying and mapping visualization model according to the mouse signal and the touch screen gesture signal to obtain the model observation visual surface; determining the visual surface scaling ratio of the model observation visual surface to obtain the visual surface scaling ratio; Step S464: interactively and dynamically display the three-dimensional surveying and mapping visualization model based on the model observation visual surface and the visual surface scaling ratio, and output a three-dimensional surveying and mapping visualization report.

9. A three-dimensional visualization system for surveying and mapping data based on the Internet of Things, characterized in that: The method for three-dimensional visualization of surveying and mapping data based on the Internet of Things according to claim 1 is used to execute the method, and the three-dimensional visualization system for surveying and mapping data based on the Internet of Things comprises: The surveying and mapping data acquisition module is used to collect original surveying and mapping data within the surveying and mapping area through IoT sensors, where the original surveying and mapping data includes terrain elevation data and environmental monitoring data; and pre-process the original surveying and mapping data to obtain standard surveying and mapping data; The surveying and mapping feature analysis module is used to perform multi-scale analysis on the terrain elevation data of standard surveying and mapping data to obtain multi-scale terrain features; perform multi-scale terrain detection on the multi-scale terrain features to obtain multi-scale terrain data; and perform multi-factor coupling analysis on the environmental monitoring data based on the multi-scale terrain data to obtain environmental coupling data. The surveying and mapping data feature mapping module is used to perform spatiotemporal correlation pattern recognition on multi-scale terrain features and environmental coupling data to generate surveying and mapping spatiotemporal feature data; perform adaptive feature mapping on the surveying and mapping spatiotemporal feature data to obtain surveying and mapping feature mapping data; and construct a three-dimensional model based on the surveying and mapping feature mapping data to obtain a surveying and mapping three-dimensional model. The 3D surveying and mapping visualization model construction module is used to obtain real-time surveying and mapping update data; perform surveying and mapping visualization updates on the surveying and mapping 3D model according to the real-time surveying and mapping update data to obtain a 3D surveying and mapping visualization model; perform interactive dynamic display on the 3D surveying and mapping visualization model, and output a 3D surveying and mapping visualization report.

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