Rapid data acquisition method for live-action three-dimensional technology

Abstract

The invention provides a rapid data acquisition method for a live-action three-dimensional technology. The method belongs to the technical field of data acquisition, and comprises the following steps: acquiring original data through a data acquisition system based on a multi-source sensor, and planning and scheduling an original data acquisition task by utilizing a cloud computing platform and combining GIS data; in the data acquisition process, original data are preprocessed in real time through edge computing nodes, multi-source original data are fused through a data fusion algorithm, and a three-dimensional model is generated; performing automatic optimization and correction on the fused three-dimensional model by using an artificial intelligence algorithm; and illumination simulation and texture mapping technologies are combined to enhance the sense of reality and the visual effect of the model. Through planning and scheduling based on the cloud computing platform and the GIS data, the task and the time window of data acquisition can be accurately determined, so that the efficiency and the accuracy of data acquisition are improved.

Description

Technical Field

[0001] The invention provides a method for rapid data acquisition for real-scene three-dimensional technology, belonging to the technical field of data acquisition. Background Art

[0002] With the rapid development of real-life 3D technology, high-precision and high-efficiency data collection and processing have become the key to promoting the development of smart cities, cultural relics protection, digital twins and other fields. Traditional data collection methods often rely on single sensors, such as laser scanners or cameras. Such methods not only have slow data collection speeds and high costs, but also make it difficult to ensure data integrity and accuracy in complex environments (such as large light changes, severe occlusion, etc.). In addition, the data processing process usually needs to be carried out in a data center, which not only increases the delay in data transmission, but may also cause a large amount of data to be lost or damaged during transmission, affecting the accuracy and practicality of the final 3D model.

[0003] With the rapid progress of the Internet of Things, cloud computing, edge computing, and artificial intelligence technologies, new solutions have been provided for the data collection and processing of real-life 3D technology. Multi-source sensor fusion technology can make full use of the advantages of different sensors in data collection, such as the high precision of laser scanners and the rich color information of cameras, thereby significantly improving the comprehensiveness and accuracy of data. At the same time, the cloud computing platform, with its powerful data processing capabilities and flexibility, can achieve remote planning and efficient scheduling of data collection tasks, optimize resource allocation, and reduce manual intervention. Summary of the invention

[0004] The present invention provides a method for rapid data acquisition for real-scene three-dimensional technology to solve the problems mentioned in the above background technology:

[0005] The present invention proposes a method for rapid data collection for real-scene three-dimensional technology, the method comprising:

[0006] S1. Collect raw data through a data acquisition system based on multi-source sensors, and plan and schedule raw data acquisition tasks using a cloud computing platform combined with GIS data;

[0007] S2. During data collection, the raw data is pre-processed in real time through edge computing nodes, and the multi-source raw data is fused through data fusion algorithms to generate a three-dimensional model;

[0008] S3. Use artificial intelligence algorithms to automatically optimize and correct the fused 3D model; and combine lighting simulation and texture mapping technology to enhance the realism and visual effects of the model;

[0009] S4. Monitor the entire data collection and processing process through a real-time monitoring system; dynamically adjust data collection and processing strategies based on real-time feedback data.

[0010] Furthermore, the S1 includes:

[0011] S11. Clarify the purpose and requirements of data collection, select data sources from multiple GIS data sources, and pre-process the selected GIS data;

[0012] S12, integrating the pre-processed GIS data to form a unified spatial database;

[0013] S13. According to the demand analysis results, the relevant indicators of the required sensors are listed to calibrate the selected sensors and determine the sensor deployment plan;

[0014] S14. Based on GIS data and sensor deployment plan, plan the data collection route and set the time window for data collection based on multiple factors; and formulate a collaborative working strategy between multi-source sensors;

[0015] S15, assigning the planned tasks to each sensor, and setting the task priority and execution order; establishing a real-time monitoring mechanism to monitor the working status of the sensor in real time through a wireless communication network;

[0016] S16. Based on the real-time monitoring results, the emergency plan is activated and handled for any abnormal situation that occurs.

[0017] Furthermore, the S12 includes:

[0018] Convert GIS data from different sources into a unified format; and enhance key features in GIS data.

[0019] Perform data fusion operations based on the preset fusion scheme, and use the selected algorithms and tools to fuse the pre-processed GIS data;

[0020] Perform quality assessment on the fused data and iteratively optimize the fusion algorithm or parameters based on the assessment results;

[0021] Import the fused GIS data into the spatial database, perform indexing and metadata management, manage the data securely, and develop a data backup strategy.

[0022] Furthermore, the S2 includes:

[0023] S21. Select edge computing node devices according to data processing requirements; deploy edge computing nodes at the data collection site, and connect the edge computing nodes to sensors through the Internet of Things;

[0024] S22, the edge computing node receives the raw data from the sensor, verifies the data integrity and consistency, denoises the raw data through a filtering algorithm, and compresses the denoised data;

[0025] S23. Accurately match multi-source data through feature extraction and matching algorithms, and fuse data through data fusion strategies; and perform quality assessment on the fused data;

[0026] S24. Based on the fused data, a three-dimensional reconstruction algorithm is used to reconstruct the data; a preliminary three-dimensional model framework is generated according to the reconstructed data, and the preliminary model is verified.

[0027] Furthermore, the S3 includes:

[0028] S31, classifying and analyzing the errors in the three-dimensional model, and automatically identifying the errors in the three-dimensional model through a non-invasive recognition algorithm;

[0029] S32. Develop corresponding elimination strategies for different types of errors;

[0030] S33. Dynamically adjust the sampling density according to different regions and features of the model, introduce external auxiliary data, and integrate it with existing data to further improve the accuracy and integrity of the model;

[0031] S34, identifying and processing repeated structures in the three-dimensional model, and reducing redundant information through algorithm optimization;

[0032] S35. Based on physical principles and scene characteristics, build a lighting model to simulate the lighting effects under different time and weather conditions;

[0033] S36. Acquire texture materials through the texture material library, and accurately map the texture materials to the model surface based on texture mapping technology.

[0034] Furthermore, the S33 includes:

[0035] Through image processing and machine learning algorithms, key feature areas in the 3D model are automatically identified, and the sampling density of each area is dynamically planned according to the importance and complexity of the feature area;

[0036] Based on the planning results, an adaptive sampling algorithm is used to dynamically adjust the sampling density during model building or optimization;

[0037] Acquire external auxiliary data (ground control points, high-precision maps), perform preprocessing, and perform data fusion operations to fuse the external auxiliary data with the existing 3D model data; after the fusion is completed, perform accuracy evaluation on the fusion results;

[0038] Use multi-source data to verify each other, discover and correct potential errors in the model, and use surface reconstruction technology to refine the model surface;

[0039] Establish a real-time feedback mechanism to monitor and evaluate each step in the model building and optimization process, and provide feedback and adjustments if problems or deficiencies are found;

[0040] Based on the real-time feedback results, an iterative optimization strategy is formulated, and the accuracy and detail expression of the model are improved through multiple iterations.

[0041] Furthermore, the corresponding elimination strategies include: for geometric errors, correction by interpolation, smoothing or resampling; for texture errors, resolution by texture repair, replacement or remapping; for semantic errors, adjustment by combining contextual information and prior knowledge.

[0042] Furthermore, the S4 includes:

[0043] S41. Comprehensively monitor, aggregate and integrate data from all links through a real-time monitoring system;

[0044] S42. Collect real-time feedback data through the monitoring system, conduct in-depth analysis and evaluation of the collected feedback data, and identify potential problems and bottlenecks;

[0045] S43. Present the analysis results to relevant personnel in a visual manner and formulate dynamic adjustment strategies based on real-time feedback data and changes in project requirements;

[0046] S44, sending the formulated strategy to each execution unit through the monitoring system, and automatically executing the strategy;

[0047] S45. Evaluate and verify the effectiveness of strategy execution based on the execution results, and adjust and optimize the strategy according to the evaluation results.

[0048] An electronic device proposed in the present invention includes a memory, a processor, and a computer program stored in and executable on the memory, wherein the processor executes the program to implement a method for rapid data acquisition for real-scene three-dimensional technology as described in any one of the above.

[0049] The present invention provides a non-temporary computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement any of the above-mentioned methods for rapid data acquisition for real-scene three-dimensional technology.

[0050] The beneficial effects of the present invention are as follows: through planning and scheduling based on cloud computing platform and GIS data, the tasks and time windows of data collection can be accurately determined, thereby improving the efficiency and accuracy of data collection; edge computing nodes perform real-time preprocessing and fusion of data, and can quickly generate preliminary 3D models, which can speed up data processing and reduce data redundancy and errors; automatic optimization and correction using artificial intelligence algorithms can significantly improve the accuracy and quality of 3D models; through lighting simulation and texture mapping technology, the realism and visual effects of the model are enhanced, making it closer to the actual scene; through a real-time monitoring system, the data collection and processing process is fully monitored, and the strategy is dynamically adjusted according to real-time feedback data The strategy can effectively respond to and solve problems in the process; the real-time monitoring system can quickly detect abnormal situations and initiate emergency plans to ensure the stability and continuity of the data collection and processing process; through the preprocessing and fusion of GIS data, a unified spatial database is formed, which not only improves the integration of data, but also enables better data security management and backup; the quality of the fused data is evaluated and optimized to ensure the accuracy and completeness of the data and improve the final effect of the 3D model; the sampling density is dynamically adjusted according to the complexity of the key feature area, which can refine the model surface and improve the detail expression of the model; the external auxiliary data is used to fuse with the existing data to improve the accuracy and details of the model and reduce potential errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a step diagram of the method described in the present invention. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0055] One embodiment of the present invention, as Figure 1As shown, a method for rapid data acquisition for real-scene three-dimensional technology, the method comprising:

[0056] S1. Collect raw data through a data acquisition system based on multi-source sensors, and plan and schedule raw data acquisition tasks using a cloud computing platform combined with GIS data;

[0057] S2. During data collection, the raw data is preprocessed in real time through edge computing nodes, and multi-source raw data is fused with high precision through data fusion algorithms to generate a three-dimensional model;

[0058] S3. Use artificial intelligence algorithms to automatically optimize and correct the fused 3D model; and combine lighting simulation and texture mapping technology to enhance the realism and visual effects of the model;

[0059] S4. The data collection and processing process is monitored throughout the entire process through a real-time monitoring system; data collection and processing strategies are dynamically adjusted based on real-time feedback data. For example, when it is found that the data quality in a certain area is poor, the collection route can be adjusted immediately or the collection density can be increased; when a bottleneck occurs during the processing, the algorithm parameters can be optimized or computing resources can be increased.

[0060] The working principle of the above technical solution is: using a variety of sensors including laser radar (LiDAR), high-definition camera, inertial navigation system (INS), etc., to obtain the original data of the target area from multiple dimensions and angles; combining the prior knowledge of geographic information system (GIS), through the powerful computing power of cloud computing platform, intelligent planning and scheduling of data collection tasks. This includes determining the optimal collection path, time window, sensor configuration, etc. to improve the efficiency and accuracy of data collection; deploying edge computing nodes at the data collection site to perform preliminary processing on the collected raw data, such as denoising, compression, etc., to reduce the amount of data transmission and speed up subsequent processing; using advanced data fusion algorithms, data from different sensors are fused with high precision. Through algorithm processing, redundancy and contradictions between data are eliminated, more complete and accurate three-dimensional point cloud data is generated, and then a preliminary three-dimensional model is constructed; applying artificial intelligence technologies such as deep learning and machine learning to automatically identify and correct errors, holes, repeated structures and other problems in the three-dimensional model. This process improves the accuracy and integrity of the model through continuous learning and iteration. In order to enhance the realism and visual effects of the model, lighting simulation technology is used to simulate the changes in natural light, and texture mapping technology is used to map high-resolution texture images to the surface of the three-dimensional model to make the model more realistic. Through the real-time monitoring system, all links such as data collection, preprocessing, and model construction are monitored throughout the process to ensure the smooth progress of the entire process. According to real-time feedback data, such as data quality and processing speed, data collection and processing strategies are dynamically adjusted. For example, when it is found that the data quality in a certain area is poor, the collection route can be adjusted immediately or the collection density can be increased. When performance bottlenecks occur during the processing process, the algorithm parameters can be optimized or computing resources can be increased to ensure the stability and efficiency of the entire system.

[0061] The effects of the above technical solutions are: by integrating multiple sensors (such as LiDAR, high-definition cameras, INS, etc.), it is possible to obtain rich raw data from multiple dimensions and angles, thereby improving the comprehensiveness and accuracy of data collection; using the powerful computing power of the cloud computing platform and the prior knowledge of GIS data, intelligently plan and schedule data collection tasks to ensure the efficiency and pertinence of the collection process and reduce the collection of redundant data; deploy edge computing nodes at the data collection site to achieve real-time preprocessing of raw data, reduce data transmission delays, and speed up subsequent processing; high-precision fusion of multi-source raw data to generate accurate three-dimensional models, providing a solid foundation for subsequent model optimization and correction; automatically identify and correct errors, holes, repeated structures and other problems in the three-dimensional model, significantly improving the accuracy and integrity of the model. At the same time, combined with lighting simulation and texture mapping technology, the realism and visual effects of the model are enhanced to make it more realistic; the data collection and processing process is fully monitored to ensure the smooth progress of the entire process. Through real-time feedback data, problems can be discovered and solved in a timely manner; data collection and processing strategies can be dynamically adjusted according to real-time feedback data, such as adjusting collection routes, increasing collection density, optimizing algorithm parameters, or increasing computing resources, so as to improve the flexibility and adaptability of the system; through highly integrated and optimized data collection, processing, optimization and monitoring processes, manual intervention and repetitive work can be reduced, thereby reducing overall costs; efficient data collection and processing capabilities enable this technical solution to quickly respond to market demands and provide high-quality real-life 3D data support for various application scenarios.

[0062] In one embodiment of the present invention, the S1 includes:

[0063] S11. Clarify the purpose and requirements of data collection, select data sources with strong relevance to the target area and high data quality from multiple GIS data sources; and pre-process the selected GIS data.

[0064] S12, integrating the pre-processed GIS data to form a unified spatial database;

[0065] S13. Based on the demand analysis results, list the relevant indicators of the required sensors, including models, specifications, performance parameters, etc. Calibrate the selected sensors and determine the sensor deployment plan, including deployment location, height, angle, scanning range, etc.

[0066] S14. Based on GIS data and sensor deployment plan, plan the optimal data collection route, set the time window for data collection based on multiple factors, including weather conditions, lighting conditions, and traffic conditions; and develop a collaborative working strategy between multi-source sensors;

[0067] S15, assigning the planned tasks to each sensor, and setting the task priority and execution order; establishing a real-time monitoring mechanism to monitor the working status of the sensor in real time through a wireless communication network;

[0068] S16. Based on the real-time monitoring results, the emergency plan is activated and handled for abnormal situations (such as sensor failure, data loss, etc.).

[0069] The working principle of the above technical solution is as follows: First, clarify the specific purpose and actual needs of data collection, which is the basis of the entire data collection work; select data sources with strong relevance to the target area and high data quality from multiple GIS data sources. This step ensures that the data basis for subsequent work is reliable and accurate; clean the selected GIS data to remove duplicate, erroneous or irrelevant information. The preprocessing process improves the quality of the data and provides strong support for subsequent data fusion and collection route planning; fuse the preprocessed GIS data to form a unified spatial database. This step enables data from different sources to be used and analyzed under a unified framework; list the relevant indicators of the required sensors, including models, specifications, performance parameters, etc. according to the results of the demand analysis; calibrate the selected sensors to ensure their measurement accuracy and stability. This step is the key to ensuring the accuracy of data collection; including deployment location, height, angle, scanning range, etc., to ensure that the sensor can cover the target area and obtain valid data; plan the optimal data collection route based on GIS data and sensor deployment plan. This step takes into account a variety of factors, such as weather conditions, lighting conditions, traffic conditions, etc., to ensure the efficiency and safety of data collection; set the time window for data collection according to various factors to avoid adverse conditions and make full use of favorable opportunities; ensure the coordination between multi-source sensors to ensure the continuity and consistency of data collection; assign the planned tasks to each sensor, and set the task priority and execution order. This step ensures the orderly progress of data collection; monitor the working status of the sensor in real time through the wireless communication network. This helps to detect and handle abnormal situations in a timely manner; based on the real-time monitoring results, initiate emergency plans and handle abnormal situations (such as sensor failure, data loss, etc.). This ensures the continuity and stability of data collection.

[0070] The effects of the above technical solutions are as follows: by selecting data sources with strong relevance to the target area and high data quality from multiple GIS data sources and performing cleaning processing, duplicate, erroneous or irrelevant information is effectively removed, thereby improving the accuracy and reliability of subsequent data processing and modeling; the pre-processed GIS data is integrated to form a unified spatial database, which helps to eliminate redundancy and contradictions between data and provides a unified and consistent data basis for data collection and model building; the relevant indicators of the required sensors are listed according to the results of the demand analysis, and calibration is performed to ensure the measurement accuracy and stability of the sensors. At the same time, a reasonable sensor deployment plan, including deployment location, height, angle, scanning range, etc., makes data collection more comprehensive and accurate; based on GIS data and sensor deployment plans, the optimal data collection route is planned, which not only improves the efficiency of data collection, but also reduces unnecessary resource consumption. At the same time, the data collection time window is set by considering multiple factors such as weather conditions, lighting conditions, and traffic conditions to ensure the smooth progress of data collection; a collaborative work strategy between multi-source sensors is formulated to ensure the continuity and consistency of data collection and avoid the problem of missing data or repeated collection; the planned tasks are assigned to each sensor, and the task priority and execution order are set to ensure that the data collection work is carried out in an orderly manner; a real-time monitoring mechanism is established to monitor the working status of the sensor in real time through a wireless communication network, which can promptly detect and handle abnormal situations such as sensor failure and data loss, ensuring the continuity and stability of data collection; based on the real-time monitoring results, emergency plans are initiated and handled for abnormal situations, reducing data collection interruptions or quality problems caused by abnormal situations, and improving the stability and reliability of the entire data collection process.

[0071] In one embodiment of the present invention, the S12 includes:

[0072] Convert GIS data from different sources into a unified format; and enhance key features in GIS data, such as edge detection and texture enhancement, to improve the information richness and recognition of the data.

[0073] Perform data fusion operations based on the preset fusion scheme, and use the selected algorithms and tools to fuse the pre-processed GIS data;

[0074] Perform quality assessment on the fused data, including accuracy verification and consistency check, to ensure that the fusion results meet the expected requirements; perform iterative optimization on the fusion algorithm or parameters based on the assessment results;

[0075] Import the fused GIS data into the spatial database, perform indexing and metadata management, perform data security management such as encrypted storage, access control, etc., and formulate a data backup strategy.

[0076] The working principle of the above technical solution is: convert GIS data from different sources and formats into a unified format. This is the premise of data fusion, ensuring that different data sources can be seamlessly connected and interoperable; enhance the key features in GIS data, such as edge detection to highlight geographic boundaries, texture enhancement to improve the recognition of surface cover, etc. These processes are intended to improve the information richness and recognition of the data, and provide more valuable information for subsequent data analysis and modeling; based on the preset fusion scheme, the pre-processed GIS data is fused using the selected algorithms and tools. This step may involve complex operations such as spatial matching and attribute merging between multiple data sources, aiming to generate a unified data set containing more comprehensive and accurate information; perform quality assessment on the fused data, including accuracy verification and consistency check. These assessment methods are used to ensure that the fusion results meet the expected requirements, such as data accuracy, completeness and consistency; according to the assessment results, iteratively optimize the fusion algorithm or parameters. If the assessment finds that the fusion results are insufficient, the algorithm parameters can be adjusted or different fusion strategies can be tried to achieve better fusion effects; the fused GIS data is imported into the spatial database, and indexed and metadata managed. Indexes can speed up data retrieval and query, while metadata provides detailed information about data sources, content, quality, etc.; data security management is performed, such as encrypted storage to prevent data leakage, access control to ensure that only authorized users can access the data. At the same time, a data backup strategy is formulated to prevent data loss or damage.

[0077] The effects of the above technical solutions are: converting GIS data from different sources into a unified format, eliminating format differences between data, and enhancing data consistency and interoperability. This makes data exchange and sharing between different systems and platforms easier and more efficient; enhancing key features in GIS data, such as edge detection and texture enhancement, improves the information richness and recognition of data. This not only helps to improve the visual effect of the data, but also provides a more accurate and detailed information basis for subsequent data analysis and modeling; performing data fusion operations based on the preset fusion scheme, and using selected algorithms and tools to fuse the pre-processed GIS data. This process can integrate information from multiple data sources to generate a unified data set containing more comprehensive and accurate information; by evaluating the quality of the fused data, including accuracy verification and consistency checks, problems that may exist in the fusion process can be discovered and corrected in a timely manner. Iterative optimization of the fusion algorithm or parameters based on the evaluation results can further improve the fusion effect and ensure that the fusion results meet the expected requirements; importing the fused GIS data into the spatial database, and performing indexing and metadata management. This not only facilitates data retrieval and query, but also improves data management efficiency. At the same time, through metadata management, the source, content, quality and other information of the data can be recorded in detail, providing an important reference for the use and maintenance of the data; data security management, such as encrypted storage and access control, can effectively prevent data leakage and illegal access. Formulating a data backup strategy can ensure that data can be quickly restored when it is lost or damaged, ensuring the integrity and availability of the data; through data processing in the S12 stage, information from multiple data sources can be integrated to form more comprehensive and accurate geographic information. This provides strong support for decision-making in the fields of urban planning, environmental protection, disaster warning, etc. Decision makers can make more scientific and reasonable decisions based on these data, and improve the accuracy and effectiveness of decisions.

[0078] In one embodiment of the present invention, the S14 includes:

[0079] Based on the spatial analysis function of GIS data, key areas and potential risk points for data collection are identified; based on the results of demand analysis, the selection criteria for sensors are further refined.

[0080] Use GIS spatial optimization algorithms to accurately plan sensor deployment locations;

[0081] Based on the path planning function of GIS, the optimal data collection route is dynamically generated; the best path is predicted using heuristic search algorithms or machine learning models.

[0082] Set the time window for data collection based on the periodicity and seasonality of data changes, as well as the impact of specific events (such as natural disaster warnings and preparations for major events);

[0083] Develop a detailed multi-source sensor collaborative work protocol, use cloud computing or edge computing platforms to achieve real-time processing and fusion of sensor data, and design fault transfer and backup mechanisms to ensure that the entire data acquisition system can still operate stably when individual sensors fail.

[0084] The working principle of the above technical solution is: using the spatial analysis module of GIS software, such as buffer analysis (generating an area of ​​a certain width around a specific point or line) and overlay analysis (overlaying the data of two or more layers according to the spatial position to generate new spatial information), combined with multi-source GIS data such as topography, vegetation coverage, and hydrological conditions, for comprehensive analysis; by integrating GIS data of different dimensions, identify key areas for data collection, which may be areas sensitive to environmental changes, ecologically fragile areas, or areas with intensive human activities. At the same time, analyze potential risk points, such as areas prone to geological disasters and near pollution sources, to provide a scientific basis for subsequent sensor deployment and data collection; according to the results of demand analysis, clarify the specific needs of data collection, such as measurement parameters, accuracy requirements, etc. Then, combine the physical parameters of the sensor (measurement range, accuracy, resolution) and environmental adaptability (waterproof, dustproof, high temperature / low temperature resistance) and other characteristics to screen the sensor. At the same time, consider the data transmission mode (wired / wireless) and energy efficiency to ensure that the sensors can work stably in complex environments and meet the needs of long-term monitoring; use GIS spatial optimization algorithms, such as genetic algorithms or simulated annealing algorithms, combined with factors such as terrain complexity, signal coverage, and traffic accessibility to accurately plan the deployment location of sensors. Through algorithm iterative calculation, find the optimal deployment plan to ensure that the mutual exclusion and coordination relationship between sensors is properly handled, avoid data redundancy and blind spots, and achieve full coverage and efficiency of the data collection network; based on the path planning function of GIS, combined with real-time traffic information, weather forecasts, personnel / vehicle scheduling and other factors, dynamically generate the optimal data collection route. Use heuristic search algorithms or machine learning models (such as deep learning networks) to predict the best path to reduce the time cost and resource consumption during the collection process. At the same time, consider the feasibility and safety of the path to ensure the smooth progress of the data collection task; flexibly set the time window for data collection according to the periodicity and seasonal characteristics of data changes and the impact of specific events. Use time series analysis and trend prediction methods to analyze the laws and trends of data changes and determine the best time for data collection. At critical moments such as natural disaster warnings and preparations for major events, increase the frequency and density of data collection to obtain more comprehensive data support; formulate a detailed multi-source sensor collaboration protocol to clarify the data synchronization mechanism, conflict resolution strategy, and data fusion algorithm between sensors. Use the protocol to standardize the behavior of sensors and the way data is exchanged to ensure efficient collaboration between sensors; use the high-performance computing capabilities of cloud computing or edge computing platforms to achieve real-time processing and fusion of sensor data. Improve the accuracy and integrity of data through steps such as data cleaning, deduplication, and fusion. At the same time, use data fusion algorithms to integrate and analyze multi-source data to dig out deeper information and value; design failover and backup mechanisms to deal with the failure of individual sensors.When a sensor fails, the system can automatically switch to a backup sensor or adjust the collection tasks of other sensors to make up for the data loss. At the same time, sensors are regularly maintained and calibrated to ensure their long-term stable operation.

[0085] The effects of the above technical solutions are: through the spatial analysis function of GIS data, combined with multi-dimensional information, the key areas and potential risk points of data collection can be accurately identified. This helps to concentrate limited resources on the areas that need the most attention and improve the efficiency and accuracy of data collection; based on the results of demand analysis, the selection criteria of sensors are refined to ensure that the selected sensors can accurately meet the data collection needs. This helps to reduce resource waste and data errors caused by insufficient or excessive sensor performance; using GIS's spatial optimization algorithm, combined with a variety of factors, accurately plan the deployment location of sensors. This helps to avoid data redundancy and blind spots, and ensure full coverage and efficiency of the data collection network; based on the path planning function of GIS, combined with real-time traffic information, weather forecasts and other factors, dynamically generate the optimal data collection route. This helps to reduce the time cost and resource consumption in the collection process, and improve the flexibility and efficiency of data collection; according to the periodicity of data changes, seasonal characteristics and the impact of specific events, the time window for data collection can be flexibly set. This helps ensure that data is collected during the period when data changes are most significant or the demand is most urgent, thereby improving the timeliness and value of the data; predicting the trend and law of data changes through methods such as time series analysis and trend prediction helps to collect data at the best time, thereby improving the accuracy and reliability of the data; formulating a detailed multi-source sensor collaboration protocol, clarifying the data synchronization mechanism, conflict resolution strategy and data fusion algorithm between sensors. This helps to ensure that sensors can work together efficiently and improve the overall efficiency of data collection; using cloud computing or edge computing platforms to achieve real-time processing and fusion of sensor data, improving the efficiency and accuracy of data processing. This helps to quickly obtain valuable information and provide support for decision-making; designing failover and backup mechanisms to deal with the failure of individual sensors. This helps to ensure that the entire data collection system can still operate stably when a sensor fails, reducing the risk of data loss and interruption; by comprehensively considering multiple factors to make decisions such as sensor deployment and path planning, the adaptability of the data collection system to complex environments is improved. This helps to ensure that the system can operate stably under various conditions and meet data collection needs.

[0086] In one embodiment of the present invention, the S15 includes:

[0087] Break down the overall data collection task into multiple subtasks and prioritize the subtasks;

[0088] Assign tasks to the most appropriate sensors based on the physical characteristics, measurement range, accuracy and other parameters of the sensors, as well as the specific requirements of the subtasks; and optimize the task allocation plan;

[0089] Develop detailed execution plans for each sensor and optimize wireless communication network configuration;

[0090] A real-time data collection monitoring platform is established, integrating GIS maps, sensor status display, data collection progress tracking and other functions. Through this platform, you can intuitively view the real-time working status of each sensor, data collection progress, data quality and other information.

[0091] Set anomaly detection thresholds. When the sensor's working status is abnormal (such as low battery, signal interruption, data abnormality, etc.), the system automatically triggers a warning or alarm mechanism; and notifies relevant personnel via SMS, email, etc.

[0092] Identify and classify possible anomalies, such as sensor failure, data transmission interruption, data quality issues, etc. Develop corresponding processing procedures and emergency plans for different types of anomalies.

[0093] For some abnormalities that can be automatically recovered (such as short network interruptions), an automatic troubleshooting and recovery mechanism is designed. By restarting the sensor, switching the communication path, etc., the sensor is tried to be restored to normal working state.

[0094] For abnormalities that cannot be automatically recovered, the manual intervention process is initiated. Professionals are notified to go to the site for inspection and repair to ensure that the sensor returns to normal operation as soon as possible. At the same time, the data collection plan is adjusted to ensure the continuity and integrity of data collection.

[0095] The working principle of the above technical solution is as follows: the overall data collection task is broken down into multiple specific subtasks, each of which specifies key information such as the specific area of ​​data collection, data type and collection frequency. This helps to better manage and execute each task and ensure the comprehensiveness and accuracy of the data; the subtasks are prioritized according to factors such as the urgency of the task, the importance of the data and the availability of resources. This ensures that key tasks can be executed first, thereby improving the efficiency and response speed of data collection; tasks are assigned to the most suitable sensors based on the physical characteristics of the sensors (such as measurement range, accuracy, etc.) and the specific requirements of the subtasks. This ensures that the sensors can complete the tasks accurately and efficiently; when assigning tasks, the geographical location and signal coverage of the sensors should also be considered. By optimizing the task allocation scheme, the delay and energy consumption of data transmission can be reduced, and the stability and efficiency of data transmission can be improved; a detailed execution plan is formulated for each sensor, including the task start time, end time, data collection interval and data transmission method. These plans should fully consider the influence of external factors (such as weather changes, traffic conditions, etc.) to ensure the smooth execution of tasks; the configuration of the wireless communication network is optimized to ensure stable and reliable data transmission between the sensor and the monitoring center. Adopt multi-path transmission, automatic reconnection and other mechanisms to improve the anti-interference ability and fault tolerance of data transmission and reduce the risk of data transmission interruption; establish a real-time data collection monitoring platform, integrating GIS map, sensor status display and data collection progress tracking and other functions. Through this platform, you can intuitively view the real-time working status and data collection of each sensor, and find potential problems in time; set anomaly detection threshold, and when the working status of the sensor is abnormal (such as low power, signal interruption, data abnormality, etc.), the system automatically triggers the early warning or alarm mechanism. Notify relevant personnel through SMS, email and other means so that timely measures can be taken to deal with it; identify and classify possible abnormal types, and formulate corresponding processing procedures and emergency plans. For some abnormalities that can be automatically recovered, design an automatic troubleshooting and recovery mechanism; for abnormalities that cannot be automatically recovered, start the manual intervention process to ensure that the sensor resumes normal operation as soon as possible; when the sensor fails or is abnormal, adjust the data collection plan in time to ensure the continuity and integrity of data collection. Reduce the impact of sensor failure on data collection work by reallocating tasks and adjusting the collection frequency.

[0096] The effects of the above technical solutions are as follows: by breaking down the overall task into multiple subtasks and prioritizing them according to urgency, data importance and resource availability, it is possible to ensure that key tasks are executed first, thereby improving the overall efficiency of data collection; by allocating tasks according to the physical characteristics of the sensor and the specific requirements of the subtasks, it is possible to ensure that each sensor can play its maximum role and avoid waste of resources. At the same time, considering the optimization of geographical location and signal coverage, the delay and energy consumption of data transmission can be reduced, and the efficiency of resource utilization can be further improved; the detailed execution plan fully considers the influence of external factors, such as weather changes and traffic conditions, which helps to reduce data collection errors caused by external factors and ensure the accuracy and reliability of the data; by integrating GIS maps, sensor status display and data collection progress tracking functions, the working status and data collection of each sensor can be monitored in real time, and data quality problems can be discovered and handled in a timely manner; optimizing the configuration of the wireless communication network and adopting mechanisms such as multi-path transmission and automatic reconnection can improve the anti-interference ability and fault tolerance of data transmission, reduce the risk of data transmission interruption, and thus improve the stability and reliability of the system; setting up anomaly detection Thresholds can be set and early warning or alarm mechanisms can be automatically triggered, which can timely detect and handle abnormal working status of sensors, avoid problems from expanding, and ensure the stable operation of the system; for some abnormalities that can be automatically recovered, an automatic troubleshooting and recovery mechanism is designed to quickly restore the normal working status of sensors, reduce the need for manual intervention, and improve the response speed and recovery ability of the system; for abnormalities that cannot be automatically recovered, the manual intervention process is initiated and professionals are notified to go to the site for inspection and repair, which can ensure that the sensors resume normal operation as soon as possible and ensure the continuity and integrity of data collection; the establishment of a real-time data collection and monitoring platform allows managers to intuitively view the real-time working status and data collection status of each sensor, which is convenient for unified management and scheduling and improves management efficiency.

[0097] In one embodiment of the present invention, the S2 includes:

[0098] S21. Select edge computing node devices according to data processing requirements; deploy edge computing nodes at the data collection site, and connect the edge computing nodes to sensors through the Internet of Things;

[0099] S22, the edge computing node receives the raw data from the sensor, verifies the data integrity and consistency, denoises the raw data through a filtering algorithm, and compresses the denoised data;

[0100] S23, accurately match multi-source data through feature extraction and matching algorithms, and perform data fusion through data fusion strategies, including fusion order, fusion algorithm selection, fusion parameter setting, etc.; and perform quality assessment on the fused data;

[0101] S24. Based on the fused data, a three-dimensional reconstruction algorithm is used to reconstruct the data; a preliminary three-dimensional model framework is generated according to the reconstructed data, and the preliminary model is verified.

[0102] The working principle of the above technical solution is: according to the specific needs of data processing (such as computing power, storage requirements, power consumption restrictions, etc.), select appropriate edge computing node devices. These devices usually have high processing performance, low power consumption and compact design, and are suitable for deployment at the data collection site; deploy edge computing nodes at the data collection site, and connect edge computing nodes with various sensors through Internet of Things technology. This connection method ensures real-time transmission and efficient processing of data, reduces data transmission delay and bandwidth consumption; the edge computing node receives the raw data from the sensor, first checks the data integrity and consistency to ensure the accuracy and reliability of the data; denoises the raw data through the filtering algorithm to remove noise and interference signals in the data and improve the data quality. This step is crucial for subsequent data analysis and processing; compress the denoised data to reduce the amount of data transmission and reduce the network bandwidth occupation. At the same time, the selection of the compression algorithm should ensure that the data does not lose important information during the compression process; through feature extraction and matching algorithms, multi-source data from different sensors are accurately matched. These algorithms can identify common features and differences in data, providing a basis for data fusion; according to the preset fusion strategy (including fusion order, fusion algorithm selection, fusion parameter setting, etc.), multi-source data are fused. During the fusion process, factors such as data weight and correlation need to be considered to ensure the accuracy and reliability of the fusion results; the fused data is quality evaluated, including accuracy verification and consistency check. Through the evaluation results, possible problems in the fusion process can be discovered and corrected in a timely manner to ensure the accuracy and reliability of the fusion results; based on the fused data, the data is reconstructed using a 3D reconstruction algorithm. This step involves a complex calculation process, including the transformation of spatial coordinates, surface reconstruction, etc., to generate a preliminary 3D model framework; the preliminary 3D model is verified to ensure that it meets the basic geometric and spatial relationship requirements. The verification process may include geometric consistency checks of the model, verification of spatial position relationships, etc., to ensure the accuracy and reliability of the model.

[0103] The effect of the above technical solution is: by deploying edge computing nodes at the data collection site, real-time data processing is achieved. Compared with the traditional cloud computing model, edge computing reduces the delay of data transmission to the cloud, speeds up data processing, and enables the system to respond to real-time events faster; edge computing nodes verify the integrity and consistency of the raw data from the sensor, and perform denoising through filtering algorithms, effectively improving the data quality. This reduces the subsequent processing errors caused by data errors or noise interference; the denoised data is compressed, significantly reducing the amount of data transmission. This not only reduces the network bandwidth occupancy, but also improves the efficiency of data transmission, especially in bandwidth-constrained environments; through feature extraction and matching algorithms, multi-source data is accurately matched. This step ensures that the information between different data sources can be correctly corresponded and integrated, providing a basis for subsequent data fusion; data fusion is performed according to the preset fusion strategy (including fusion order, fusion algorithm selection, fusion parameter setting, etc.). This strategic fusion method can fully consider the characteristics and needs of the data and ensure the accuracy and reliability of the fusion results; based on the high-quality data after fusion, the data is reconstructed using a three-dimensional reconstruction algorithm. This process can generate accurate and detailed three-dimensional models, providing strong support for subsequent decision analysis and visualization; the preliminary three-dimensional model is verified to ensure that it meets the basic geometric and spatial relationship requirements. This step ensures the accuracy and reliability of the model and provides a solid foundation for subsequent applications; the deployment of edge computing nodes makes data processing closer to the data source, reducing resource consumption during data transmission. At the same time, the computing power of edge computing nodes also provides additional resource support for the system and improves the overall performance of the system; because edge computing nodes have independent processing and storage capabilities, when a node fails, other nodes can take over its work to ensure the continuity and stability of the system. This fault-tolerant design improves the reliability and availability of the system.

[0104] In one embodiment of the present invention, S3 includes:

[0105] S31. Classify and analyze errors in the three-dimensional model, such as geometric errors, texture errors, semantic errors, etc., and automatically identify errors in the three-dimensional model through a non-invasive recognition algorithm;

[0106] S32. Develop corresponding elimination strategies for different types of errors; the corresponding elimination strategies include: for geometric errors, correction by interpolation, smoothing or resampling; for texture errors, resolution by texture repair, replacement or remapping; for semantic errors, adjustment by combining contextual information and prior knowledge.

[0107] S33. Dynamically adjust the sampling density according to different areas and features of the model, introduce external auxiliary data (such as ground control points, high-precision maps, etc.), and integrate them with existing data to further improve the accuracy and integrity of the model;

[0108] S34, identifying and processing repeated structures in the three-dimensional model, such as multiple scan results of the building's exterior wall, and reducing redundant information through algorithm optimization;

[0109] S35. Based on physical principles and scene characteristics, a realistic lighting model is constructed to simulate the lighting effects under different time and weather conditions;

[0110] S36. Acquire texture materials through the texture material library, and accurately map the texture materials to the model surface based on texture mapping technology (such as UV expansion, normal mapping, etc.).

[0111] The working principle of the above technical solution is as follows: the errors in the three-dimensional model are classified in detail, such as geometric errors (including size deviation, shape distortion, etc.), texture errors (texture blur, dislocation, etc.), semantic errors (object recognition errors, inaccurate attribute labeling, etc.); the three-dimensional model is non-invasively identified using advanced image processing and machine learning algorithms, that is, various types of errors are automatically identified and marked without destroying the original model structure; special elimination strategies are formulated for different types of errors. For example, for geometric errors, model correction algorithms or reference to external data may be used for calibration; for texture errors, remapping or optimization of texture mapping technology may be required; the sampling density is dynamically adjusted according to different areas and features of the model (such as detail richness, importance, etc.). Sampling points are added in key areas to improve the detail expression and accuracy of the model; external auxiliary data (such as ground control points, high-precision maps, etc.) are introduced and merged with the existing three-dimensional model data. This process corrects and supplements the model through high-precision data sources to further improve the accuracy and integrity of the model; in three-dimensional models, there are often repeated structures caused by multiple scans or improper data processing (such as multiple scans of the exterior walls of buildings). These repetitive structures are identified through algorithms and processed effectively. For the identified repetitive structures, redundant information is reduced through algorithm optimization to avoid confusion and unnecessary computational burden during model display and application. A realistic lighting model is constructed based on physical principles and scene characteristics. The model can simulate lighting effects at different times (such as day and night changes) and weather conditions (such as sunny, cloudy, rainy, etc.). The lighting model is used to simulate the lighting effects of the three-dimensional model, so that the model presents a more realistic and vivid visual effect under different lighting conditions. Rich texture materials are obtained through the texture library, including texture images of different materials, colors, and textures. Advanced texture mapping technologies such as UV unfolding and normal mapping are used to accurately map the texture materials to the surface of the three-dimensional model. This process not only enhances the visual effect of the model, but also makes the surface of the model more delicate and realistic.

[0112] The effects of the above technical solutions are as follows: by carefully classifying and automatically identifying errors in the three-dimensional model, it is possible to accurately find defects in the model and provide precise targets for subsequent corrections; formulate special elimination strategies for different types of errors to effectively reduce or eliminate errors and improve the precision and accuracy of the model; dynamically adjust the sampling density according to different areas and features of the model to ensure that key areas have higher detail expression and make the model more refined and realistic; introduce external auxiliary data to merge with existing data to further improve the accuracy and integrity of the model, especially for those areas where the original data is insufficient or difficult to obtain directly; identify and process repeated structures in the three-dimensional model, reduce redundant information through algorithm optimization, make the model structure more concise and efficient, and reduce the burden of storage and processing; the lighting model built based on physical principles and scene characteristics can simulate the lighting effects under different times and weather conditions, so that the model can be It can present realistic and vivid visual effects in different environments; it uses texture mapping technology to accurately map rich texture materials to the model surface, which not only enhances the visual effect of the model, but also makes the model surface more delicate and realistic, improving the overall sense of reality; it uses non-invasive recognition algorithms to automatically identify errors in three-dimensional models, reducing the workload of manual inspection and improving work efficiency; the technical solution for the entire S3 stage includes a series of automated processing flows, such as error classification, elimination, sampling density adjustment, repeated structure processing, etc., which reduces manual intervention and improves the level of automation; the optimized and enhanced three-dimensional models can be applied to more fields, such as urban planning, architectural design, virtual reality, game development, etc., providing high-quality three-dimensional data support for different industries; in the fields of virtual reality, games, etc., the optimized three-dimensional models can provide a more realistic and immersive user experience, enhancing user participation and satisfaction.

[0113] In one embodiment of the present invention, the S33 includes:

[0114] Through image processing and machine learning algorithms, key feature areas in the 3D model are automatically identified, such as edges, corners, and texture-rich areas. The sampling density of each area is dynamically planned based on the importance and complexity of the feature area. For key feature areas, the sampling points are increased to improve accuracy. For smooth areas, the sampling points are appropriately reduced to reduce data redundancy.

[0115] Based on the planning results, an adaptive sampling algorithm is used to dynamically adjust the sampling density during model building or optimization;

[0116] Acquire external auxiliary data (ground control points, high-precision maps) and perform preprocessing, including coordinate conversion, denoising, accuracy assessment, etc., and perform data fusion operations to fuse the external auxiliary data with the existing 3D model data; after the fusion is completed, perform accuracy assessment on the fusion results;

[0117] Use multi-source data (such as data collected by different sensors, data at different time points, etc.) to verify each other, discover and correct potential errors in the model, and use surface reconstruction technology (such as surface reconstruction based on point clouds, mesh optimization, etc.) to refine the model surface;

[0118] Establish a real-time feedback mechanism to monitor and evaluate each step in the model building and optimization process, and provide feedback and adjustments if problems or deficiencies are found;

[0119] Based on the real-time feedback results, an iterative optimization strategy is formulated, and the accuracy and detail expression of the model are improved through multiple iterations.

[0120] The working principle of the above technical solution is as follows: using image processing and machine learning algorithms, automatically identify key feature areas in the three-dimensional model, such as edges, corners, and texture-rich areas. These areas usually have an important impact on the accuracy and visual effects of the model; dynamically plan the sampling density of each area according to the importance and complexity of the feature area. For key feature areas, increase the sampling points to improve accuracy; for smooth areas, appropriately reduce the sampling points to reduce data redundancy; in the process of model construction or optimization, use an adaptive sampling algorithm to dynamically adjust the sampling density according to the planning results. This can not only ensure the fineness of the model, but also improve processing efficiency; obtain external auxiliary data (such as ground control points, high-precision maps), and perform preprocessing, including coordinate transformation, denoising, accuracy assessment, etc., to ensure the accuracy and availability of the data; fuse the preprocessed external auxiliary data with the existing three-dimensional model data. The compatibility, consistency and accuracy requirements of the data need to be considered during the fusion process; perform accuracy assessment on the fused model to ensure that the fusion operation achieves the expected effect in improving the accuracy and integrity of the model; use multi-source data (such as data collected by different sensors, data at different time points, etc.) for mutual verification to discover and correct potential errors in the model. This helps improve the accuracy and reliability of the model; surface reconstruction technologies such as point cloud-based surface reconstruction and mesh optimization are used to refine the model surface. These technologies can improve the smoothness and realism of the model surface, making it more in line with the actual scene; a real-time feedback mechanism is established to monitor and evaluate each link in the model construction and optimization process. Once problems or deficiencies are found, feedback and adjustments are made immediately; based on the real-time feedback results, an iterative optimization strategy is formulated. Through multiple iterations, the accuracy and detail expression of the model are continuously improved until the expected goal is achieved. This iterative optimization method helps to gradually approach the optimal solution and improve the overall quality of the model.

[0121] The effect of the above technical solution is: automatically identify key feature areas through image processing and machine learning algorithms, and dynamically plan sampling density according to their importance and complexity. This strategy ensures that key feature areas get enough sampling points to improve accuracy, while smooth areas reduce sampling points to reduce data redundancy. This not only ensures the fineness of the model, but also improves processing efficiency; using multi-source data for mutual verification can discover and correct potential errors in the model. At the same time, through surface reconstruction technology, such as surface reconstruction and mesh optimization based on point clouds, the model surface is refined to improve the smoothness and realism of the surface. These measures jointly improve the accuracy and detail expression of the model; in the process of model construction or optimization, an adaptive sampling algorithm is used to dynamically adjust the sampling density. This algorithm can automatically adjust the sampling strategy according to the actual situation of the model, avoid unnecessary computational waste, and improve processing efficiency; establish a real-time feedback mechanism to monitor and evaluate each link in the process of model construction and optimization. Once problems or deficiencies are found, feedback and adjustments are made immediately. At the same time, an iterative optimization strategy is formulated based on the real-time feedback results, and the accuracy and detail expression of the model are continuously improved through multiple iterations. This iterative optimization method can quickly approach the optimal solution and improve the efficiency of model construction and optimization; through surface reconstruction technology, such as point cloud-based surface reconstruction and mesh optimization, the model surface is refined. These technologies can significantly improve the smoothness and realism of the model surface, making it more in line with the actual scene; external auxiliary data (such as ground control points, high-precision maps) are fused with existing 3D model data. These external data usually have higher accuracy and integrity, and the accuracy and integrity of the model can be further improved through fusion. At the same time, the fused model is more realistic and rich in visual effects; using multi-source data for mutual verification can discover and correct potential errors in the model. This verification mechanism improves the reliability and stability of the model and reduces model problems caused by data errors or inconsistencies; the real-time feedback mechanism can promptly discover problems or deficiencies in the model construction and optimization process, and provide feedback and adjustments. This mechanism ensures the stability and controllability of the model during the construction and optimization process.

[0122] In one embodiment of the present invention, the S4 includes:

[0123] S41. Comprehensively monitor, aggregate and integrate data from various links such as data acquisition system, processing system, storage system, etc. through real-time monitoring system;

[0124] S42. Collect real-time feedback data through the monitoring system, including sensor status, data processing efficiency, model quality assessment results, etc.; conduct in-depth analysis and evaluation of the collected feedback data to identify potential problems and bottlenecks;

[0125] S43. Present the analysis results to relevant personnel (project managers or technical personnel) in a visual manner, and formulate dynamic adjustment strategies based on real-time feedback data and changes in project requirements;

[0126] S44, sending the formulated strategy to each execution unit (such as a sensor, a processing node, etc.) through the monitoring system, and automatically executing the strategy;

[0127] S45. Evaluate and verify the effectiveness of strategy execution based on the execution results, and adjust and optimize the strategy according to the evaluation results.

[0128] The working principle of the above technical solution is: through the real-time monitoring system, the data of each link such as the data acquisition system, processing system, storage system, etc. are comprehensively monitored. These monitoring systems can capture the operating status and performance indicators of each link in real time, such as the working status of the sensor, the speed and efficiency of data processing, the capacity and stability of data storage, etc. The collected data will be aggregated and integrated into a unified data platform to provide a basis for subsequent data analysis and evaluation; the monitoring system not only collects real-time operation data, but also collects real-time feedback data from various links, including sensor status, data processing efficiency, model quality evaluation results, etc. These feedback data reflect the actual operation and performance of the system. Subsequently, these collected feedback data are deeply analyzed and evaluated, and potential problems and bottlenecks are identified by using technical means such as data mining and machine learning. These analysis results provide an important basis for subsequent strategy formulation; based on real-time feedback data and changes in project requirements, dynamic adjustment strategies are formulated. These strategies are aimed at solving current problems, optimizing system performance, and improving data quality and processing efficiency. In order to more intuitively display the analysis results and strategy solutions, the analysis results are presented to relevant personnel (such as project managers or technicians) in a visual way. Visual presentation helps relevant personnel quickly understand the problem and accurately grasp the direction and focus of the strategy; the formulated strategy is sent to each execution unit (such as sensors, processing nodes, etc.) through the monitoring system. After receiving the strategy, these execution units will automatically adjust the working status and parameter settings according to the strategy requirements, thereby realizing the automatic execution of the strategy. This automatic execution mechanism improves the efficiency and accuracy of strategy implementation and reduces the possibility of manual intervention and errors; the strategy execution effect is evaluated and verified based on the execution results. By comparing and analyzing the data changes and performance indicators before and after execution, it is evaluated whether the actual effect of the strategy has achieved the expected goal. If the strategy effect is not ideal or there are problems, the strategy is adjusted and optimized according to the evaluation results. This cyclic iterative process helps to continuously optimize system performance and improve data quality and processing efficiency.

[0129] The effect of the above technical solution is: through the real-time monitoring system, data from various links such as data collection, processing and storage can be obtained in real time, ensuring the immediate grasp of the system status. At the same time, collecting real-time feedback data enables the project team to respond quickly to system changes and identify potential problems, thereby achieving dynamic adjustment and optimization; based on real-time feedback data and changes in project requirements, a dynamic adjustment strategy is formulated. This flexibility ensures that the system can continue to adapt to changes in the external environment and internal conditions and maintain an efficient and stable operating state; the data of each link is fully monitored, aggregated and integrated to form a comprehensive data view. This provides the project team with rich data resources to support the data-based decision-making process; the collected feedback data is deeply analyzed and evaluated to identify potential problems and bottlenecks. This data-driven approach improves the accuracy and scientificity of decision-making, reduces subjective assumptions and blindness; and presents the analysis results to relevant personnel in a visual way. This intuitive display method helps project managers and technical personnel quickly understand the system status and problems, improves communication efficiency and decision-making speed; through the visualization platform, team members with different roles can share information and views, promote cross-departmental collaboration and communication, and form a joint force to jointly promote project progress; the formulated strategy is sent to each execution unit through the monitoring system to achieve automatic execution of the strategy. This automation mechanism reduces the possibility of manual intervention and errors, and improves execution efficiency and accuracy; through real-time monitoring and data analysis, resource bottlenecks and optimization points can be discovered in time, thereby optimizing resource allocation, improving resource utilization and overall efficiency; based on the execution results, the strategy execution effect is evaluated and verified, and the strategy is adjusted and optimized according to the evaluation results. This continuous improvement mechanism helps to continuously improve system performance and data quality, and ensure the smooth realization of project goals; through the close collaboration of real-time monitoring, data analysis, dynamic adjustment and continuous improvement, the S4 technical solution provides strong support for the project team, which helps to improve the project success rate and customer satisfaction.

[0130] One embodiment of the present invention is an electronic device, including a memory, a processor, and a computer program stored in and executable on the memory, wherein the processor executes the program to implement a method for rapid data acquisition for real-life three-dimensional technology as described in any one of the above.

[0131] One embodiment of the present invention is a non-temporary computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement any of the above-described methods for rapid data acquisition for real-scene three-dimensional technology.

[0132] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for rapid data acquisition for real-scene three-dimensional technology, characterized in that: The method comprises: S1. Collect raw data through a data acquisition system based on multi-source sensors, and plan and schedule raw data acquisition tasks using a cloud computing platform combined with GIS data; S2. During data collection, the raw data is pre-processed in real time through edge computing nodes, and the multi-source raw data is fused through data fusion algorithms to generate a three-dimensional model; S3. Use artificial intelligence algorithms to automatically optimize and correct the fused 3D model; and combine lighting simulation and texture mapping technology to enhance the realism and visual effects of the model; S4. Monitor the entire data collection and processing process through a real-time monitoring system; dynamically adjust data collection and processing strategies based on real-time feedback data.

2. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 1, characterized in that: Said S1 comprises: S11. Clarify the purpose and requirements of data collection, select data sources from multiple GIS data sources, and pre-process the selected GIS data; S12, integrating the pre-processed GIS data to form a unified spatial database; S13. According to the demand analysis results, the relevant indicators of the required sensors are listed to calibrate the selected sensors and determine the sensor deployment plan; S14. Based on GIS data and sensor deployment plan, plan the data collection route and set the time window for data collection based on multiple factors; and formulate a collaborative working strategy between multi-source sensors; S15, assigning the planned tasks to each sensor, and setting the task priority and execution order; establishing a real-time monitoring mechanism to monitor the working status of the sensor in real time through a wireless communication network; S16. Based on the real-time monitoring results, the emergency plan is activated and handled for any abnormal situation that occurs.

3. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 2, characterized in that: The S12 comprises: Convert GIS data from different sources into a unified format and enhance key features in GIS data; Perform data fusion operations based on the preset fusion scheme, and use the selected algorithms and tools to fuse the pre-processed GIS data; Perform quality assessment on the fused data and iteratively optimize the fusion algorithm or parameters based on the assessment results; Import the fused GIS data into the spatial database, perform indexing and metadata management, manage the data securely, and develop a data backup strategy.

4. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 1, characterized in that: The S2 comprises: S21. Select edge computing node devices according to data processing requirements; deploy edge computing nodes at the data collection site, and connect the edge computing nodes to sensors through the Internet of Things; S22, the edge computing node receives the raw data from the sensor, verifies the data integrity and consistency, denoises the raw data through a filtering algorithm, and compresses the denoised data; S23. Accurately match multi-source data through feature extraction and matching algorithms, and fuse data through data fusion strategies; and perform quality assessment on the fused data; S24. Based on the fused data, a three-dimensional reconstruction algorithm is used to reconstruct the data; a preliminary three-dimensional model framework is generated according to the reconstructed data, and the preliminary model is verified.

5. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 1, characterized in that: The S3 includes: S31, classifying and analyzing the errors in the three-dimensional model, and automatically identifying the errors in the three-dimensional model through a non-invasive recognition algorithm; S32. Develop corresponding elimination strategies for different types of errors; S33. Dynamically adjust the sampling density according to different regions and features of the model, introduce external auxiliary data, and integrate it with existing data to further improve the accuracy and integrity of the model; S34, identifying and processing repeated structures in the three-dimensional model, and reducing redundant information through algorithm optimization; S35. Based on physical principles and scene characteristics, build a lighting model to simulate the lighting effects under different time and weather conditions; S36. Acquire texture materials through the texture material library, and accurately map the texture materials to the model surface based on texture mapping technology.

6. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 5, characterized in that: The S33 comprises: Through image processing and machine learning algorithms, key feature areas in the 3D model are automatically identified, and the sampling density of each area is dynamically planned according to the importance and complexity of the feature area; Based on the planning results, an adaptive sampling algorithm is used to dynamically adjust the sampling density during model building or optimization; Acquire external auxiliary data, perform preprocessing, and perform data fusion operations to fuse the external auxiliary data with the existing 3D model data; after the fusion is completed, perform accuracy evaluation on the fusion results; Use multi-source data to verify each other, discover and correct potential errors in the model, and use surface reconstruction technology to refine the model surface; Establish a real-time feedback mechanism to monitor and evaluate each step in the model building and optimization process, and provide feedback and adjustments if problems or deficiencies are found; Based on the real-time feedback results, an iterative optimization strategy is formulated, and the accuracy and detail expression of the model are improved through multiple iterations.

7. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 5, characterized in that: The corresponding elimination strategies include: for geometric errors, correction by interpolation, smoothing or resampling; for texture errors, resolution by texture repair, replacement or remapping; for semantic errors, adjustment by combining contextual information and prior knowledge.

8. The method for rapid data acquisition for real-scene three-dimensional technology according to claim 1, characterized in that: The S4 comprises: S41. Comprehensively monitor, aggregate and integrate data from all links through a real-time monitoring system; S42. Collect real-time feedback data through the monitoring system, conduct in-depth analysis and evaluation of the collected feedback data, and identify potential problems and bottlenecks; S43. Present the analysis results to relevant personnel in a visual manner and formulate dynamic adjustment strategies based on real-time feedback data and changes in project requirements; S44, sending the formulated strategy to each execution unit through the monitoring system, and automatically executing the strategy; S45. Evaluate and verify the effectiveness of strategy execution based on the execution results, and adjust and optimize the strategy according to the evaluation results.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the memory, wherein the processor executes the program to implement a method for rapid data acquisition for real-scene three-dimensional technology as described in any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a method for rapid data acquisition for real-scene three-dimensional technology as described in any one of claims 1-8.

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