Operation scene reconstruction data acquisition method and system based on city updating and reconstruction

Abstract

The invention discloses an operation scene reconstruction data acquisition method and system based on city updating and reconstruction. The method comprises the following steps: step 1, capturing a scene image; 2, performing real-time environment perception on the scene image, detecting an object to be reconstructed, and reconstructing three-dimensional representation data of the object to be reconstructed; 3, determining a reconstruction target based on the three-dimensional representation data and a preset distance threshold; 4, according to a preset reconstruction path planning algorithm, dynamically adjusting path planning by adopting an intelligent optimization technology, and generating an exploration path; 5, exploring the to-be-reconstructed area along the exploration path; step 6, on the basis of the step 1 and the step 5, when a reconstruction target is reached, acquiring reconstruction data according to a preset reconstruction path planning algorithm; and step 7, carrying out real-time processing and analysis on the collected data. Scene data are captured in real time through a multi-sensor fusion technology, and the collection efficiency is improved by combining three-dimensional reconstruction and intelligent path planning; a dynamic adaptation mechanism enhances flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of scene reconstruction data acquisition technology, and in particular to a method and system for acquiring operation scene reconstruction data based on urban renewal and transformation. Background Art

[0002] In current urban renewal projects, data collection for operational scene reconstruction is crucial. Traditional methods rely on prior geometric information, such as building models and topographic maps, combined with drone technology for data collection. However, these methods have significant limitations: some require additional flights to obtain prior information, increasing the reconstruction cycle and difficulty. For example, the DJI-Terra algorithm, while not relying on prior information, has poor scene adaptability. Furthermore, most algorithms utilize offline path planning. Inaccurate scene information can degrade path quality, leading to lengthy data collection times and the risk of collisions.

[0003] Traditional methods face problems such as low data collection efficiency, poor flexibility, weak path planning adaptability and safety hazards, which increase costs and affect project progress. Therefore, a data collection method and system for reconstructing operational scenarios based on urban renewal and transformation are proposed. Summary of the Invention

[0004] In response to the deficiencies of the existing technology, the present invention provides a method and system for collecting data for reconstructing operational scenarios based on urban renewal and transformation, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a data collection method for reconstructing an operation scenario based on urban renewal and transformation, comprising the following steps: Step 1: Capture scene images and use multi-sensor fusion technology to obtain multi-source data of the scene; Step 2: Perform real-time environmental perception on the scene image, detect the object to be reconstructed, and reconstruct its 3D representation data; Step 3: Determine the reconstruction target based on the 3D representation data and the preset distance threshold; Step 4: Based on the preset reconstruction path planning algorithm, intelligent optimization technology is used to dynamically adjust the path planning and generate an exploration path; Step 5: Explore the area to be reconstructed along the exploration path and adjust the exploration strategy in real time based on the dynamic scene adaptation mechanism; Step 6: Based on Steps 1 and 5, the progress of the reconstruction of the operational scenarios of the urban renewal project is evaluated in real time. When the data collection work meets the predetermined requirements for completeness, accuracy, and coverage, that is, when the reconstruction target is achieved, reconstruction data is collected according to the preset reconstruction path planning algorithm. If the reconstruction target is not achieved, the collection path and strategy are adjusted based on the evaluation results, and data collection is continued until the reconstruction target is met. Step 7: Process and analyze the collected data in real time, and use artificial intelligence algorithms to assist in decision-making and obtain analytical results; Step 8: Store the processed data in a designated location for subsequent analysis and application; Step 9: Provide support for decision-making on urban renewal projects based on the collected data and analysis results of the artificial intelligence algorithm; By acquiring multi-source scene data through multi-sensor fusion technology and combining it with 3D reconstruction and intelligent optimization path planning algorithms, data acquisition efficiency is significantly improved. This addresses the long reconstruction cycles and poor scene adaptability associated with traditional methods that rely on prior geometric information. The dynamic scene adaptation mechanism enables the system to perceive environmental changes in real time and adjust its exploration strategy, enhancing the flexibility of the acquisition process, avoiding path quality degradation and collision risks caused by inaccurate scene information, and improving path planning adaptability. The real-time processing and analysis of collected data by artificial intelligence algorithms provides precise support for project decision-making and reduces safety hazards. In addition, this method ensures that data collection meets the predetermined completeness, accuracy and coverage requirements by real-time evaluation of reconstruction progress, providing an efficient, flexible and safe solution for the reconstruction of operational scenarios of urban renewal and renovation projects, effectively promoting project progress, reducing costs and helping to carry out urban renewal and renovation work efficiently.

[0006] Preferably, the multi-sensor fusion technology includes data fusion of lidar, camera and IMU; Multi-sensor fusion technology, which integrates lidar, camera, and IMU data, significantly improves the accuracy, robustness, and efficiency of data acquisition. Lidar provides high-precision distance measurement, helping to create accurate 3D models; the camera provides rich visual information, helping to identify and classify objects; and the IMU provides device motion information, helping to stabilize images and compensate for motion blur. By fusing the data from these three sensors, the limitations of a single sensor can be compensated for and the accuracy and robustness of data acquisition can be improved. For example, in complex environments, lidar may be interfered with by light, while the camera can provide supplementary information. When the device moves quickly, the IMU can stabilize the image and avoid motion blur. This multi-sensor fusion technology enables the system to adapt to different scenarios and environments, enhancing the system's flexibility and applicability. At the same time, by real-time processing and analysis of the collected data, the required information can be obtained more quickly, improving the efficiency of data acquisition.

[0007] Preferably, the intelligent optimization technology includes genetic algorithm and ant colony algorithm; The intelligent optimization technology of genetic algorithm and ant colony algorithm significantly improves the efficiency and adaptability of path planning. The genetic algorithm simulates the natural selection process and searches for the optimal solution globally through selection, crossover and mutation operations, avoiding falling into local optimality, providing a high-quality solution for path planning. The ant colony algorithm simulates the foraging behavior of ants and uses the pheromone update mechanism to find the shortest path in complex environments. Its distributed computing and self-organizing characteristics enhance the flexibility and robustness of path planning. By integrating these two algorithms, the system can automatically adjust the path planning strategy according to different scenarios and environmental characteristics to generate efficient and safe exploration paths. For example, in urban renewal and renovation projects, faced with complex and changeable building layouts and traffic conditions, intelligent optimization technology can adjust the data collection path in real time, avoid obstacles and dangerous areas, and ensure the smooth progress of data collection. At the same time, by optimizing path planning, it reduces invalid collection and duplication of work, improves data collection efficiency, reduces costs and safety risks, and provides strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0008] Preferably, the dynamic scene adaptation mechanism includes real-time monitoring of scene changes and adjusting path planning and data collection strategies according to the changes; The dynamic scene adaptation mechanism significantly improves the flexibility and efficiency of data collection by monitoring scene changes in real time and adjusting path planning and data collection strategies accordingly. In traditional data collection methods, scene changes are often difficult to detect and handle in a timely manner, resulting in low data collection efficiency or even failure. The dynamic scene adaptation mechanism can monitor scene changes in real time, such as building demolition and new obstacles. Once a change is detected, the system immediately adjusts path planning and data collection strategies according to the preset algorithm to ensure smooth data collection. This mechanism enables the system to adapt to different scenarios and environments, enhancing the system's flexibility and applicability. For example, in urban renewal and renovation projects, faced with ever-changing building layouts and traffic conditions, the dynamic scene adaptation mechanism can adjust the data collection path in real time, avoid obstacles and dangerous areas, and ensure the safety and effectiveness of data collection. At the same time, by adaptively adjusting path planning and data collection strategies, it reduces invalid collection and duplication of work, improves data collection efficiency, reduces costs and safety risks, and provides strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0009] Preferably, the artificial intelligence algorithm includes machine learning and deep learning algorithms; The use of artificial intelligence algorithms, including machine learning and deep learning algorithms, has significantly improved the quality and efficiency of data collection and processing. Machine learning algorithms can learn patterns and regularities from large amounts of data, automatically identify and process abnormal data, and improve the accuracy and completeness of data collection. Deep learning algorithms, through deep neural networks, can process complex data structures, further explore useful information in the data, and improve the quality of data collection and processing. At the same time, AI algorithms can automatically process and analyze collected data, generating analysis results in real time, improving the efficiency of data collection and processing. For example, in urban renewal and renovation projects, AI algorithms can automatically identify buildings, roads, and other facilities, quickly generate three-dimensional models, and support project decision-making. Furthermore, AI algorithms can automatically adjust data collection and processing strategies based on different scenarios and environments, enhancing the system's intelligence and adaptability. By adopting artificial intelligence algorithms, this method not only improves the quality and efficiency of data collection and processing, but also reduces the cost and safety risks of manual intervention, providing strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0010] Preferably, the reconstruction of the three-dimensional representation data includes feature point extraction, image matching and depth information analysis; The 3D data reconstruction method using feature point extraction, image matching, and depth information analysis significantly improves the accuracy and integrity of the 3D model. Feature point extraction technology can accurately extract key feature points in the scene, providing an accurate data foundation for 3D reconstruction. Image matching technology can generate a more complete and accurate 3D model by matching images from different perspectives, reducing omissions and errors. Depth information analysis uses depth information to further improve the accuracy and integrity of the 3D model, making it closer to the real scene. The three-dimensional representation data reconstruction method not only improves the accuracy and completeness of the three-dimensional model, but also improves the efficiency of data collection and processing. By automatically processing and parsing the collected data, the required three-dimensional model can be obtained more quickly to provide support for project decision-making. For example, in urban renewal and renovation projects, this reconstruction method can quickly generate accurate three-dimensional models, helping planners better understand the scene layout and structural characteristics, thereby formulating more reasonable renovation plans. At the same time, high-precision three-dimensional models can also provide strong support for subsequent engineering construction and operation management, thereby improving the overall benefits of the project.

[0011] Preferably, the planning of the exploration path includes decomposing the area to be reconstructed into a plurality of non-overlapping sub-areas and performing exploration according to a full coverage path planning algorithm; The area to be reconstructed is decomposed into multiple non-overlapping sub-areas and explored using a full-coverage path planning algorithm, significantly improving the efficiency and coverage of data collection. By decomposing the area to be reconstructed, the path planning problem can be simplified, making the exploration path of each sub-area more optimized and reducing duplication and omissions. The full-coverage path planning algorithm ensures that each sub-area is fully covered, avoiding blind spots in data collection and improving the integrity of data collection. The exploration path planning method not only improves the efficiency and coverage of data collection, but also enhances the flexibility and applicability of the system. For example, in urban renewal and renovation projects, faced with complex and changeable building layouts and traffic conditions, this method can adaptively adjust the exploration path to ensure the smooth progress of data collection. At the same time, by optimizing the exploration path, it reduces invalid collection and duplication of work, reduces costs and safety risks, and provides strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0012] Preferably, the acquisition of the reconstruction data includes generating a reconstruction path surrounding the reconstruction target based on the three-dimensional representation data of the reconstruction target and preset reconstruction path parameters, and acquiring data along the reconstruction path; Based on the three-dimensional representation data of the reconstruction target and the preset reconstruction path parameters, a reconstruction path surrounding the reconstruction target is generated, and data is collected along the reconstruction path, which significantly improves the accuracy and efficiency of data collection. By generating a reconstruction path surrounding the reconstruction target, the comprehensiveness and accuracy of data collection can be ensured, and blind spots and omissions in data collection can be avoided. Data collection along the generated reconstruction path reduces duplication and invalid collection, and improves the efficiency of data collection. The reconstruction data collection method not only improves the accuracy and efficiency of data collection, but also enhances the flexibility and applicability of the system. For example, in urban renewal and renovation projects, facing different reconstruction goals and scenario environments, this method can adaptively adjust the reconstruction path to ensure the smooth progress of data collection. At the same time, by optimizing the collection path, it reduces costs and safety risks, providing strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0013] Preferably, the real-time processing and analysis of the data includes performing denoising, registration and fusion processing on the collected data using an algorithm; The real-time processing and analysis method of using algorithms to denoise, align and fuse the collected data significantly improves the accuracy and availability of data. Denoising effectively removes noise from the collected data, improves the accuracy and reliability of the data, and makes the data closer to the real scene. Registration accurately aligns data collected from different sources or at different times, improving the accuracy and consistency of the data and avoiding deviations and conflicts between data. Fusion further fuses data collected by multiple sensors or multiple times, integrating the advantages of different data, improving the integrity and availability of data, and providing more comprehensive and accurate data support for project decision-making; This real-time processing and analysis method not only improves the accuracy and availability of data, but also enhances the flexibility and applicability of the system. For example, in urban renewal and renovation projects, faced with complex and changing data collection environments, this method can adaptively adjust data processing strategies to ensure data accuracy and availability. At the same time, by optimizing the data processing process, it reduces costs and security risks, providing strong support for the reconstruction of operational scenarios for urban renewal and renovation projects.

[0014] The system for collecting data on reconstruction of operation scenarios based on urban renewal and transformation, based on the above-mentioned method for collecting data on reconstruction of operation scenarios based on urban renewal and transformation, includes: A data acquisition module is used to capture scene images and obtain multi-source data of the scene using multi-sensor fusion technology; The data processing module is used to perform real-time environmental perception on scene images, detect objects to be reconstructed, and reconstruct their three-dimensional representation data; The path planning module is used to reconstruct the path planning algorithm according to the preset, dynamically adjust the path planning using intelligent optimization technology, and generate the exploration path; The exploration execution module is used to explore the area to be reconstructed along the exploration path and adjust the exploration strategy in real time according to the dynamic scene adaptation mechanism; A data acquisition execution module is used to collect reconstruction data according to a preset reconstruction path planning algorithm when reaching the reconstruction target; Data analysis module, used to process and analyze collected data in real time and use artificial intelligence algorithms to assist in decision-making; A data storage module, used to store the processed data in a designated location; A decision support module, which is used to provide support for decision-making in urban renewal projects based on the collected data and analysis results of artificial intelligence algorithms; By integrating multiple modules, including data acquisition, processing, path planning, exploration execution, data analysis, storage, and decision support, the system significantly improves the efficiency and accuracy of data collection for operational scene reconstruction in urban renewal projects. The data acquisition module uses multi-sensor fusion technology to acquire multi-source data of the scene, improving the accuracy and robustness of data collection. The data processing module performs real-time environmental perception of scene images, detects objects to be reconstructed, and reconstructs their 3D representation data, providing a precise foundation for subsequent path planning and data acquisition. The path planning module uses intelligent optimization technology to dynamically adjust path planning and generate exploration paths, improving the efficiency of data collection. The exploration execution module explores the area to be reconstructed along the exploration path and adjusts the exploration strategy in real time based on the dynamic scene adaptation mechanism, enhancing the flexibility and adaptability of the system. When the reconstruction target is reached, the data acquisition execution module collects reconstruction data according to the preset reconstruction path planning algorithm, ensuring the accuracy and completeness of data collection. The data analysis module processes and analyzes the collected data in real time, and uses artificial intelligence algorithms to assist decision-making, thereby improving the accuracy and efficiency of data analysis; the data storage module stores the processed data in a designated location for subsequent analysis and application; the decision support module provides support for decision-making in urban renewal and transformation projects based on the collected data and the analysis results of artificial intelligence algorithms, thereby improving the scientific nature and accuracy of decision-making.

[0015] In summary, compared with the prior art, the present invention provides a method and system for collecting data for reconstructing operational scenarios based on urban renewal and transformation, which has the following beneficial effects: This invention uses multi-sensor fusion technology to capture multi-source scene data in real time. Combining 3D reconstruction with an intelligent optimization path planning algorithm, it effectively solves the problems of traditional methods that rely on prior geometric information, resulting in long reconstruction cycles and poor scene adaptability, significantly improving data collection efficiency. Secondly, the dynamic scene adaptation mechanism enables the system to perceive environmental changes in real time and adjust its exploration strategy, enhancing the flexibility of the acquisition process, avoiding path quality degradation and collision risks caused by inaccurate scene information, and significantly improving the adaptability of path planning. In addition, the real-time processing and analysis of collected data by artificial intelligence algorithms provides precise support for project decision-making and further reduces safety hazards. Compared with traditional methods, this method not only shortens data collection time, but also reduces costs. It provides an efficient, flexible and safe solution for the reconstruction of operational scenarios of urban renewal and renovation projects, and effectively promotes project progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a step diagram of the data collection method for reconstructing operational scenarios based on urban renewal and transformation of the invention.

[0017] Figure 2 This is a system diagram of the invention for reconstructing data collection for operational scenarios based on urban renewal and transformation. DETAILED DESCRIPTION

[0018] This invention provides a technical solution, a data collection method for reconstructing the operation scene of urban renewal and transformation, please refer to Figure 1 and Figure 2 , including the following steps: Step 1: Capture scene images and use multi-sensor fusion technology to obtain multi-source data of the scene; Step 2: Perform real-time environmental perception on the scene image, detect the object to be reconstructed, and reconstruct its 3D representation data; Step 3: Determine the reconstruction target based on the 3D representation data and the preset distance threshold; Step 4: Based on the preset reconstruction path planning algorithm, intelligent optimization technology is used to dynamically adjust the path planning and generate an exploration path; Step 5: Explore the area to be reconstructed along the exploration path and adjust the exploration strategy in real time based on the dynamic scene adaptation mechanism; Step 6: Based on Steps 1 and 5, the progress of the reconstruction of the operational scenarios of the urban renewal project is evaluated in real time. When the data collection work meets the predetermined requirements for completeness, accuracy, and coverage, that is, when the reconstruction target is achieved, reconstruction data is collected according to the preset reconstruction path planning algorithm. If the reconstruction target is not achieved, the collection path and strategy are adjusted based on the evaluation results, and data collection is continued until the reconstruction target is met. Step 7: Process and analyze the collected data in real time, and use artificial intelligence algorithms to assist in decision-making and obtain analytical results; Step 8: Store the processed data in a designated location for subsequent analysis and application; Step 9: Based on the collected data and the analysis results of the artificial intelligence algorithm, support is provided for decision-making on urban renewal and transformation projects; By acquiring multi-source scene data through multi-sensor fusion technology and combining 3D reconstruction with an intelligently optimized path planning algorithm, data acquisition efficiency is significantly improved. This addresses the long reconstruction cycles and poor scene adaptability associated with traditional methods that rely on prior geometric information. The dynamic scene adaptation mechanism enables the system to perceive environmental changes in real time and adjust its exploration strategy, enhancing the flexibility of the acquisition process, avoiding path quality degradation and collision risks caused by inaccurate scene information, and improving path planning adaptability. The real-time processing and analysis of collected data by artificial intelligence algorithms provides precise support for project decision-making and reduces safety risks. In addition, this method ensures that data collection meets the predetermined completeness, accuracy and coverage requirements by real-time evaluation of reconstruction progress, providing an efficient, flexible and safe solution for the reconstruction of operational scenarios of urban renewal and renovation projects, effectively promoting project progress, reducing costs, and helping to carry out urban renewal and renovation work efficiently.

[0019] See also Figure 1 and Figure 2 ,Multi-sensor fusion technology includes data fusion of lidar, camera and IMU; Multi-sensor fusion technology, which integrates lidar, camera, and IMU data, significantly improves the accuracy, robustness, and efficiency of data acquisition. Lidar provides high-precision distance measurement, helping to create accurate 3D models; the camera provides rich visual information, helping to identify and classify objects; and the IMU provides device motion information, helping to stabilize images and compensate for motion blur. By fusing the data from these three sensors, the limitations of a single sensor can be compensated for and the accuracy and robustness of data acquisition can be improved. For example, in complex environments, lidar may be interfered with by light, while the camera can provide supplementary information. When the device moves quickly, the IMU can stabilize the image and avoid motion blur. This multi-sensor fusion technology enables the system to adapt to different scenarios and environments, enhancing the system's flexibility and applicability. At the same time, by real-time processing and analysis of the collected data, the required information can be obtained more quickly, improving the efficiency of data acquisition.

[0020] See also Figure 1 and Figure 2 ,Intelligent optimization techniques include genetic algorithms and ant colony algorithms; The intelligent optimization technology of genetic algorithm and ant colony algorithm significantly improves the efficiency and adaptability of path planning. The genetic algorithm simulates the natural selection process and searches for the optimal solution globally through selection, crossover and mutation operations, avoiding falling into local optimality, providing a high-quality solution for path planning. The ant colony algorithm simulates the foraging behavior of ants and uses the pheromone update mechanism to find the shortest path in complex environments. Its distributed computing and self-organizing characteristics enhance the flexibility and robustness of path planning. By integrating these two algorithms, the system can automatically adjust the path planning strategy according to different scenarios and environmental characteristics to generate efficient and safe exploration paths. For example, in urban renewal and renovation projects, faced with complex and changeable building layouts and traffic conditions, intelligent optimization technology can adjust the data collection path in real time, avoid obstacles and dangerous areas, and ensure the smooth progress of data collection. At the same time, by optimizing path planning, it reduces invalid collection and duplication of work, improves data collection efficiency, reduces costs and safety risks, and provides strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0021] See also Figure 1 and Figure 2 ,The dynamic scene adaptation mechanism includes real-time monitoring of scene changes and ,adjusting path planning and data collection strategies according to the changes; The dynamic scene adaptation mechanism significantly improves the flexibility and efficiency of data collection by monitoring scene changes in real time and adjusting path planning and data collection strategies accordingly. In traditional data collection methods, scene changes are often difficult to detect and handle in a timely manner, resulting in low data collection efficiency or even failure. The dynamic scene adaptation mechanism can monitor scene changes in real time, such as building demolition and new obstacles. Once a change is detected, the system immediately adjusts path planning and data collection strategies according to preset algorithms to ensure smooth data collection. This mechanism enables the system to adapt to different scenarios and environments, enhancing the system's flexibility and applicability. For example, in urban renewal and renovation projects, faced with ever-changing building layouts and traffic conditions, the dynamic scene adaptation mechanism can adjust the data collection path in real time, avoid obstacles and dangerous areas, and ensure the safety and effectiveness of data collection. At the same time, by adaptively adjusting path planning and data collection strategies, it reduces invalid collection and duplication of work, improves data collection efficiency, reduces costs and safety risks, and provides strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0022] See also Figure 1 and Figure 2 ,artificial intelligence algorithms include machine learning and deep learning algorithms; The use of artificial intelligence algorithms, including machine learning and deep learning algorithms, has significantly improved the quality and efficiency of data collection and processing. Machine learning algorithms can learn patterns and regularities from large amounts of data, automatically identify and process abnormal data, and improve the accuracy and completeness of data collection. Deep learning algorithms, through deep neural networks, can process complex data structures, further explore useful information in the data, and improve the quality of data collection and processing. At the same time, AI algorithms can automatically process and analyze collected data, generating analysis results in real time, improving the efficiency of data collection and processing. For example, in urban renewal and renovation projects, AI algorithms can automatically identify buildings, roads, and other facilities, quickly generate three-dimensional models, and support project decision-making. Furthermore, AI algorithms can automatically adjust data collection and processing strategies based on different scenarios and environments, enhancing the system's intelligence and adaptability. By adopting artificial intelligence algorithms, this method not only improves the quality and efficiency of data collection and processing, but also reduces the cost and safety risks of manual intervention, providing strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0023] See also Figure 1 and Figure 2 ,The reconstruction of three-dimensional representation data includes feature point extraction, image matching and depth information analysis; The 3D data reconstruction method using feature point extraction, image matching, and depth information analysis significantly improves the accuracy and integrity of the 3D model. Feature point extraction technology can accurately extract key feature points in the scene, providing an accurate data foundation for 3D reconstruction. Image matching technology can generate a more complete and accurate 3D model by matching images from different perspectives, reducing omissions and errors. Depth information analysis uses depth information to further improve the accuracy and integrity of the 3D model, making it closer to the real scene. The three-dimensional representation data reconstruction method not only improves the accuracy and completeness of the three-dimensional model, but also improves the efficiency of data collection and processing. By automatically processing and parsing the collected data, the required three-dimensional model can be obtained more quickly to provide support for project decision-making. For example, in urban renewal and renovation projects, this reconstruction method can quickly generate accurate three-dimensional models, helping planners better understand the scene layout and structural characteristics, thereby formulating more reasonable renovation plans. At the same time, high-precision three-dimensional models can also provide strong support for subsequent engineering construction and operation management, thereby improving the overall benefits of the project.

[0024] See also Figure 1 and Figure 2 ,The exploration path planning includes decomposing the area to be reconstructed into multiple ,nonoverlapping sub-areas and exploring them according to the full coverage ,path planning algorithm; The area to be reconstructed is decomposed into multiple non-overlapping sub-areas and explored using a full-coverage path planning algorithm, significantly improving the efficiency and coverage of data collection. By decomposing the area to be reconstructed, the path planning problem can be simplified, making the exploration path of each sub-area more optimized and reducing duplication and omissions. The full-coverage path planning algorithm ensures that each sub-area is fully covered, avoiding blind spots in data collection and improving the integrity of data collection. The exploration path planning method not only improves the efficiency and coverage of data collection, but also enhances the flexibility and applicability of the system. For example, in urban renewal and renovation projects, faced with complex and changeable building layouts and traffic conditions, this method can adaptively adjust the exploration path to ensure the smooth progress of data collection. At the same time, by optimizing the exploration path, it reduces invalid collection and duplication of work, reduces costs and safety risks, and provides strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0025] See also Figure 1 and Figure 2 , the acquisition of reconstruction data includes generating a reconstruction path surrounding the reconstruction target according to the three-dimensional representation data of the reconstruction target and preset reconstruction path parameters, and acquiring data along the reconstruction path; Based on the three-dimensional representation data of the reconstruction target and the preset reconstruction path parameters, a reconstruction path surrounding the reconstruction target is generated, and data is collected along the reconstruction path, which significantly improves the accuracy and efficiency of data collection. By generating a reconstruction path surrounding the reconstruction target, the comprehensiveness and accuracy of data collection can be ensured, and blind spots and omissions in data collection can be avoided. Data collection along the generated reconstruction path reduces duplication and invalid collection, and improves the efficiency of data collection. The reconstruction data collection method not only improves the accuracy and efficiency of data collection, but also enhances the flexibility and applicability of the system. For example, in urban renewal and renovation projects, facing different reconstruction goals and scenario environments, this method can adaptively adjust the reconstruction path to ensure the smooth progress of data collection. At the same time, by optimizing the collection path, it reduces costs and safety risks, providing strong support for the reconstruction of operational scenarios of urban renewal and renovation projects.

[0026] See also Figure 1 and Figure 2 ,Real-time data processing and analysis include using algorithms to denoise, ,register and fuse the collected data; The real-time processing and analysis method of using algorithms to denoise, align and fuse the collected data significantly improves the accuracy and availability of data. Denoising effectively removes noise from the collected data, improves the accuracy and reliability of the data, and makes the data closer to the real scene. Registration accurately aligns data collected from different sources or at different times, improving the accuracy and consistency of the data and avoiding deviations and conflicts between data. Fusion further fuses data collected from multiple sensors or multiple times, integrating the advantages of different data, improving the integrity and availability of data, and providing more comprehensive and accurate data support for project decision-making; This real-time processing and analysis method not only improves the accuracy and availability of data, but also enhances the flexibility and applicability of the system. For example, in urban renewal and renovation projects, faced with complex and changing data collection environments, this method can adaptively adjust data processing strategies to ensure data accuracy and availability. At the same time, by optimizing the data processing process, it reduces costs and security risks, providing strong support for the reconstruction of operational scenarios for urban renewal and renovation projects.

[0027] The system for collecting data on reconstruction of operation scenarios based on urban renewal and transformation, based on the above-mentioned method for collecting data on reconstruction of operation scenarios based on urban renewal and transformation, please refer to Figure 1 and Figure 2 ,include: A data acquisition module is used to capture scene images and obtain multi-source data of the scene using multi-sensor fusion technology; The data processing module is used to perform real-time environmental perception on scene images, detect objects to be reconstructed, and reconstruct their three-dimensional representation data; The path planning module is used to reconstruct the path planning algorithm according to the preset, dynamically adjust the path planning using intelligent optimization technology, and generate the exploration path; The exploration execution module is used to explore the area to be reconstructed along the exploration path and adjust the exploration strategy in real time according to the dynamic scene adaptation mechanism; A data acquisition execution module is used to collect reconstruction data according to a preset reconstruction path planning algorithm when reaching the reconstruction target; Data analysis module, used to process and analyze collected data in real time and use artificial intelligence algorithms to assist in decision-making; A data storage module, used to store the processed data in a designated location; A decision support module, which is used to provide support for decision-making in urban renewal projects based on the collected data and analysis results of artificial intelligence algorithms; By integrating multiple modules, including data acquisition, processing, path planning, exploration execution, data analysis, storage, and decision support, the system significantly improves the efficiency and accuracy of data collection for operational scene reconstruction in urban renewal projects. The data acquisition module uses multi-sensor fusion technology to acquire multi-source data of the scene, improving the accuracy and robustness of data collection. The data processing module performs real-time environmental perception of scene images, detects objects to be reconstructed, and reconstructs their 3D representation data, providing a precise foundation for subsequent path planning and data acquisition. The path planning module uses intelligent optimization technology to dynamically adjust path planning and generate exploration paths, improving the efficiency of data collection. The exploration execution module explores the area to be reconstructed along the exploration path and adjusts the exploration strategy in real time based on the dynamic scene adaptation mechanism, enhancing the flexibility and adaptability of the system. When the reconstruction target is reached, the data acquisition execution module collects reconstruction data according to the preset reconstruction path planning algorithm, ensuring the accuracy and completeness of data collection. The data analysis module processes and analyzes the collected data in real time, and uses artificial intelligence algorithms to assist decision-making, thereby improving the accuracy and efficiency of data analysis; the data storage module stores the processed data in a designated location for subsequent analysis and application; the decision support module provides support for decision-making in urban renewal and transformation projects based on the collected data and the analysis results of artificial intelligence algorithms, thereby improving the scientific nature and accuracy of decision-making.

[0028] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0029] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data collection method for reconstructing operational scenarios based on urban renewal and transformation, characterized in that: The steps include: Step 1: Capture scene images and use multi-sensor fusion technology to obtain multi-source data of the scene; Step 2: Perform real-time environmental perception on the scene image, detect the object to be reconstructed, and reconstruct its 3D representation data; Step 3: Determine the reconstruction target based on the 3D representation data and the preset distance threshold; Step 4: Based on the preset reconstruction path planning algorithm, intelligent optimization technology is used to dynamically adjust the path planning and generate an exploration path; Step 5: Explore the area to be reconstructed along the exploration path and adjust the exploration strategy in real time based on the dynamic scene adaptation mechanism; Step 6: Based on Steps 1 and 5, evaluate the progress of the reconstruction of the operational scenarios of the urban renewal project in real time. When the data collection meets the predetermined requirements for completeness, accuracy, and coverage, that is, when the reconstruction goals are achieved, collect reconstruction data according to the preset reconstruction path planning algorithm; Step 7: Process and analyze the collected data in real time, and use artificial intelligence algorithms to assist in decision-making and obtain analytical results; Step 8: Store the processed data in the designated location; Step 9: Provide support for decision-making on urban renewal projects based on the collected data and analysis results of artificial intelligence algorithms.

2. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The multi-sensor fusion technology includes data fusion of lidar, camera and IMU.

3. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The intelligent optimization technology includes genetic algorithm and ant colony algorithm.

4. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The dynamic scene adaptation mechanism includes real-time monitoring of scene changes and adjusting path planning and data collection strategies according to the changes.

5. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The artificial intelligence algorithms include machine learning and deep learning algorithms.

6. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The reconstruction of the three-dimensional representation data includes feature point extraction, image matching and depth information analysis.

7. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The planning of the exploration path includes decomposing the area to be reconstructed into multiple non-overlapping sub-areas and performing exploration according to a full coverage path planning algorithm.

8. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The acquisition of the reconstruction data includes generating a reconstruction path surrounding the reconstruction target according to the three-dimensional representation data of the reconstruction target and preset reconstruction path parameters, and acquiring data along the reconstruction path.

9. The data collection method for reconstructing operation scenarios based on urban renewal and transformation according to claim 1 is characterized by: The real-time processing and analysis of the data includes using algorithms to perform denoising, registration and fusion processing on the collected data.

10. A system for collecting data on reconstruction of operation scenarios based on urban renewal and transformation, based on the method for collecting data on reconstruction of operation scenarios based on urban renewal and transformation according to any one of claims 1 to 9, comprising: A data acquisition module is used to capture scene images and obtain multi-source data of the scene using multi-sensor fusion technology; The data processing module is used to perform real-time environmental perception on scene images, detect objects to be reconstructed, and reconstruct their three-dimensional representation data; The path planning module is used to reconstruct the path planning algorithm according to the preset, dynamically adjust the path planning using intelligent optimization technology, and generate the exploration path; The exploration execution module is used to explore the area to be reconstructed along the exploration path and adjust the exploration strategy in real time according to the dynamic scene adaptation mechanism; A data acquisition execution module is used to collect reconstruction data according to a preset reconstruction path planning algorithm when reaching the reconstruction target; Data analysis module, used to process and analyze collected data in real time and use artificial intelligence algorithms to assist in decision-making; A data storage module, used to store the processed data in a designated location; The decision support module is used to provide support for decision-making in urban renewal and transformation projects based on the collected data and analysis results of artificial intelligence algorithms.

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