Building surveying and mapping method based on unmanned aerial vehicle

Abstract

The invention provides a building surveying and mapping method based on an unmanned aerial vehicle, and the method comprises the steps: integrating data, constructing an intelligent algorithm server, planning a flight path, and distributing tasks; receiving and confirming flight tasks and path information, and flying to respective initial surveying and mapping points; flying along an intelligent algorithm dynamic optimization path, and synchronously collecting building data by a multi-source heterogeneous sensor according to preset parameters; encrypting and transmitting the processed surveying and mapping data to the block chain network, and recording a data hash value, a timestamp and an unmanned aerial vehicle identifier; generating a high-precision building three-dimensional model and a material distribution report; and completing the task, landing according to the plan, returning task details, and comprehensively checking and maintaining the unmanned aerial vehicle and equipment. An intelligent algorithm server is constructed by integrating various types of information, a flight path and a distribution task can be scientifically planned, multiple unmanned aerial vehicles cooperatively work and are linked with a data processing center and a block chain network, and a multi-source heterogeneous sensor collects data and performs real-time fusion processing and monitoring, so that the accuracy and efficiency of the data are guaranteed; and data security and traceability are ensured by using block chain storage, and the precision, efficiency, security and reliability of building surveying and mapping are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) building surveying and mapping, and in particular to a method for building surveying and mapping based on an unmanned aerial vehicle (UAV). Background Art

[0002] Drone building surveying and mapping technology can quickly cover large building areas, efficiently collect accurate data, is not restricted by complex terrain, and can obtain multi-angle information, greatly improving surveying and mapping efficiency, reducing labor costs and operational risks, and providing strong support for building planning, design, construction and monitoring.

[0003] The integration of building data, surrounding environment and drone performance parameters can enable drone building surveying and mapping to accurately plan paths and assign tasks. Data security and traceability can be guaranteed through blockchain. Improving and optimizing processes can improve surveying and mapping efficiency and quality, enhance overall reliability and adaptability, and promote continuous technological progress. However, existing drone building surveying and mapping technologies lack effective integration of building data, surrounding environment and drone performance parameters, making it difficult to scientifically plan flight paths and reasonably assign tasks. There is a lack of multi-source heterogeneous sensor collaboration and real-time data fusion processing and quality monitoring mechanisms. Blockchain is not used to ensure data security and traceability, and data loss and insufficient surveying and mapping accuracy are prone to occur. After the task is completed, there is a lack of improvement and optimization processes for the overall system and equipment, which is not conducive to continuously improving surveying and mapping effects and efficiency.

[0004] Therefore, it is necessary to design a drone-based architectural surveying method to solve the problems that the existing drone architectural surveying technology lacks effective integration of building data, surrounding environment and drone performance parameters, is difficult to scientifically plan flight paths and reasonably allocate tasks, lacks multi-source heterogeneous sensor collaboration and real-time data fusion processing and quality monitoring mechanisms, does not use blockchain to ensure data security and traceability, is prone to data loss, insufficient surveying and mapping accuracy, lacks a complete optimization process for the overall system and equipment, and is not conducive to continuously improving surveying and mapping effects and efficiency. Summary of the invention

[0005] In view of this, the present invention proposes a construction surveying and mapping method based on drones, aiming to solve the problems that the existing drone construction surveying and mapping technology lacks effective integration of building data, surrounding environment and drone performance parameters, is difficult to scientifically plan flight paths and reasonably allocate tasks, lacks multi-source heterogeneous sensor collaboration and real-time data fusion processing and quality monitoring mechanism, does not use blockchain to ensure data security and traceability, is prone to data loss and insufficient surveying and mapping accuracy, lacks a complete optimization process for the overall system and equipment, and is not conducive to continuously improving surveying and mapping effects and efficiency.

[0006] In one aspect, the present invention provides a construction surveying method based on a drone, comprising:

[0007] Integrate building data, surrounding environment information and multi-UAV performance parameters, build intelligent algorithm servers, plan flight paths and assign tasks, complete multi-source heterogeneous sensor and UAV system debugging, and build data processing centers and blockchain networks;

[0008] Multiple drones take off from the designated site in sequence, establish a two-way communication link with the intelligent algorithm server and the data processing center, receive and confirm flight mission and path information, and fly to their respective initial surveying and mapping points;

[0009] The drone flies along a path dynamically optimized by an intelligent algorithm, and multi-source heterogeneous sensors synchronously collect building data according to preset parameters and transmit it to the data processing center in real time for fusion processing and quality monitoring;

[0010] The processed surveying and mapping data is encrypted and transmitted to the blockchain network, and the data hash value, timestamp and drone identification are recorded;

[0011] Generate high-precision 3D building models and material distribution reports based on multi-source heterogeneous surveying and mapping data stored in blockchain;

[0012] The drone completes the mission and lands according to the plan, transmits back the mission details, comprehensively inspects and maintains the drone and equipment, and optimizes the performance of the intelligent algorithm server, the data processing center, and the blockchain network.

[0013] Furthermore, the integration of building data, surrounding environment information and multi-UAV performance parameters, building an intelligent algorithm server, planning flight paths and assigning tasks, includes:

[0014] Data collection and collation: collect building design drawings, geographic coordinate information, historical surveying and mapping data, and surrounding terrain, objects and meteorological environment data, and record in detail the models, flight performance, and types and performance parameters of multiple UAVs involved in the surveying and mapping;

[0015] Intelligent algorithm planning, building an intelligent algorithm server, inputting the collected information into the intelligent algorithm server, the intelligent algorithm server divides the surveying and mapping area into multiple sub-areas according to the scale, shape and complexity of the building, and plans the initial flight route for each drone according to the performance of the drone and the characteristics of the sub-areas, determines its flight altitude, speed and turning points at different stages, and assigns corresponding surveying and mapping tasks.

[0016] Furthermore, the completion of the debugging of multi-source heterogeneous sensors and drone systems, and the establishment of a data processing center and a blockchain network, includes:

[0017] Equipment debugging and calibration: accurately calibrate the accuracy of the spectral band of the hyperspectral camera sensor, set the image resolution, shooting mode and exposure parameters; adjust the scanning angle, scanning frequency and measurement accuracy of the lidar sensor to ensure that it can accurately obtain the three-dimensional spatial information of the building, and check the flight control system of the drone to test the drone's attitude stability, heading control and altitude holding functions; check the signal strength, transmission rate and stability of the data transmission device and the positioning accuracy of the positioning system;

[0018] The data and blockchain platform is established, and a data processing center is built. It is equipped with high-performance computers and professional data processing software to receive and process multi-source heterogeneous data from multiple drones. The data processing software has data fusion, quality inspection and format conversion functions; a blockchain network is built, data encryption algorithms are set up, data storage structures and access permission rules are determined, and preparations are made to receive and store surveying and mapping data.

[0019] Furthermore, the multiple drones take off from the designated site in sequence, establish a two-way communication link with the intelligent algorithm server and the data processing center, receive and confirm flight mission and path information, and fly to their respective initial mapping points, including:

[0020] Pre-takeoff inspection: at the designated takeoff site, before each drone takes off, check the battery power, motor operation status, propeller operation status, sensor operation status and parameters, and the connection status between the data transmission device and the intelligent algorithm server and the data processing center;

[0021] Take-off and communication connection, the UAVs take off in sequence according to a predetermined order. During the take-off process, the flight control system continuously monitors the flight status of the UAV and transmits the information to the intelligent algorithm server in real time. At the same time, the data transmission device establishes a stable two-way communication link with the data processing center to transmit the UAV's identity information, initial location information and system status information;

[0022] Mission confirmation and flying to the target. After receiving the information from the drone, the intelligent algorithm server fine-tunes the pre-planned flight path according to the current environmental information, and sends the final flight mission information and the optimized flight path information to the drone. After receiving and confirming the mission information, the drone flies to the initial mapping point for which it is responsible according to the flight path. During the flight, the flight altitude, speed and heading are adjusted in time according to the instructions of the intelligent algorithm server.

[0023] Furthermore, the UAV flies along a path dynamically optimized by an intelligent algorithm, including:

[0024] Intelligent path optimization and flight control: During the flight of the drone, the intelligent algorithm server dynamically optimizes the flight path of the drone based on the real-time location information, flight attitude information, and environmental perception data sent back by the drone, combined with the requirements of the building surveying and mapping task and changes in the surrounding environment;

[0025] When it is found that the mapping task of a certain sub-area is lagging behind, the intelligent algorithm server adjusts the task order of other drones and dispatches drones to assist.

[0026] Furthermore, the multi-source heterogeneous sensors synchronously collect building data according to preset parameters and transmit the data to the data processing center in real time for fusion processing and quality monitoring, including:

[0027] Multi-source heterogeneous sensor data acquisition: after arriving at the surveying area, the hyperspectral camera sensor and the lidar sensor start working at the same time. The hyperspectral camera sensor automatically captures the building surface image according to the preset shooting frequency and spectral band acquisition range. During the shooting process, the autofocus system inside the camera adjusts the lens focal length in real time according to the distance information between the drone and the building to ensure the image is clear. At the same time, the exposure parameters are automatically adjusted according to the light intensity to obtain high-quality hyperspectral image data. After the image data is compressed and preliminarily processed by adding shooting position and time marks inside the drone, it is transmitted to the data processing center in real time through the data transmission device. The lidar sensor emits laser pulses to the building and the surrounding environment at the set scanning frequency, calculates the distance and position information of the target object according to the reflected light signal, and constructs three-dimensional point cloud data. During the working process, the lidar sensor automatically adjusts the scanning angle and range according to the flight speed and altitude of the drone. The collected point cloud data is pre-processed by data compression and coordinate conversion and transmitted to the data processing center in real time.

[0028] Data processing center fusion and monitoring: The data processing center receives multi-source heterogeneous data from multiple drones, performs spatial registration of the data, and accurately matches the pixel coordinates in the hyperspectral image with the three-dimensional coordinates in the lidar point cloud data through the position and attitude information of the drone. The multi-source data fusion algorithm is used to combine the spectral features in the hyperspectral data with the geometric features in the lidar point cloud data. During the data fusion process, the quality and integrity of the data are monitored in real time, and outliers and missing values ​​in the data are identified and processed.

[0029] Furthermore, the processed surveying and mapping data is encrypted and transmitted to the blockchain network, and the data hash value, timestamp and drone identification are recorded, including:

[0030] Data encryption processing: after the data processing center completes data fusion and quality monitoring, it uses an asymmetric encryption algorithm to encrypt the data, generates a digital signature, and converts the encrypted surveying and mapping data into a format ready for transmission to the blockchain network;

[0031] Blockchain data storage, the encrypted data is transmitted to the blockchain node according to the protocol specifications of the blockchain network. After receiving the data, the blockchain node verifies the integrity of the data, calculates the hash value of the data, and compares it with the hash value attached during data transmission to verify the legitimacy of the source of the data. By checking the drone identification information in the data packet header and matching it with the legal drone information pre-registered in the blockchain network, it is confirmed that the data comes from an authorized drone device; after the verification is passed, the blockchain node stores the data in the distributed ledger of the blockchain and generates a data block containing the data hash value, timestamp, drone identification, and data source. Each data block is linked to the previous data block through a hash pointer to form an unalterable blockchain data chain.

[0032] Furthermore, the multi-source heterogeneous surveying and mapping data stored on the blockchain generates a high-precision three-dimensional building model and material distribution report, including:

[0033] The results are generated based on the multi-source heterogeneous surveying and mapping data stored in the blockchain. The surveying and mapping analysis software is used to generate results. The security-verified data is read from the blockchain network. The three-dimensional framework model of the building is constructed according to the geometric information in the data. The discrete points are connected into surfaces through the triangulation algorithm of the point cloud data, and then the three-dimensional shape model of the building is constructed. Combined with the material information in the hyperspectral data, the areas of different materials are distinguished and labeled in the three-dimensional model, and a building material distribution report is generated, which shows in detail the distribution position and area information of different materials on the building surface.

[0034] Furthermore, the drone completes the mission and lands according to the plan, transmits back the mission details, and comprehensively inspects and maintains the drone and equipment, including:

[0035] After the mission is completed, all drones complete the surveying and mapping flight according to the scheduled mission, and the drones land at the designated site in turn according to the landing path planned by the intelligent algorithm server. During the landing process, the flight control system continuously monitors the descent speed, attitude stability and distance to the landing site during the landing process, and the drones transmit the detailed execution status of this mission to the data processing center;

[0036] Equipment inspection and maintenance: After the drone is landed and recovered, the hyperspectral camera sensor and lidar sensor are cleaned, calibrated and performance tested, the physical condition of the drone's mechanical parts are checked, the battery is charged and replaced, and the flight control system and data transmission device are updated and debugged, and the equipment maintenance information and test results are recorded in the equipment management system.

[0037] Furthermore, the optimizing the performance of the intelligent algorithm server, the data processing center and the blockchain network includes:

[0038] System performance optimization: According to the data processing status and system operation status of the surveying and mapping tasks, the intelligent algorithm server, the data processing center and the blockchain network are optimized; the intelligent algorithm server adjusts the optimization strategy and parameters according to the effect and efficiency of path optimization during task execution; the data processing center updates the data processing algorithm and model according to the problems found in data fusion and quality monitoring; the blockchain network expands nodes and optimizes network bandwidth according to the performance bottlenecks of data storage and transmission.

[0039] Compared with the prior art, the beneficial effect of the present invention lies in that a drone-based building surveying and mapping method of the present invention can scientifically plan flight paths and assign tasks by integrating multiple types of information to build an intelligent algorithm server. Multiple drones work together and are linked with a data processing center and a blockchain network. Multi-source heterogeneous sensors collect data and perform real-time fusion processing and monitoring to ensure data accuracy and efficiency. Blockchain storage is used to ensure data security and traceability, ultimately generating high-precision results. After the task is completed, the equipment can be comprehensively inspected and maintained and system performance can be optimized, greatly improving the accuracy, efficiency, safety and reliability of building surveying and mapping, and effectively promoting the development and progress of building surveying and mapping technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0041] Figure 1 A flowchart of a method for building surveying and mapping based on a drone according to an embodiment of the present invention; DETAILED DESCRIPTION

[0042] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to be able to fully convey the scope of the present invention to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the implementation regulations.

[0043] Reference Figure 1 As shown, in some embodiments of the present application, a construction surveying method based on a drone includes:

[0044] Integrate building data, surrounding environment information and multi-UAV performance parameters, build intelligent algorithm servers, plan flight paths and assign tasks, complete multi-source heterogeneous sensor and UAV system debugging, and build data processing centers and blockchain networks;

[0045] Multiple drones take off from designated sites in sequence, establish two-way communication links with the intelligent algorithm server and data processing center, receive and confirm flight mission and path information, and fly to their respective initial surveying points;

[0046] The drone flies along a path dynamically optimized by intelligent algorithms, and multi-source heterogeneous sensors synchronously collect building data according to preset parameters and transmit it to the data processing center in real time for fusion processing and quality monitoring;

[0047] The processed mapping data is encrypted and transmitted to the blockchain network, recording the data hash value, timestamp and drone identification;

[0048] Generate high-precision 3D building models and material distribution reports based on multi-source heterogeneous surveying and mapping data stored in blockchain;

[0049] After the drone completes the mission, it lands as planned, transmits back mission details, conducts comprehensive inspections and maintenance of the drone and equipment, and optimizes the performance of the intelligent algorithm server, data processing center, and blockchain network.

[0050] Specifically, the building's design drawings, geographic coordinate information, historical surveying and mapping data, and surrounding terrain, objects, weather and other environmental information were collected. At the same time, the models and flight performance of the multiple drones involved in the surveying and mapping, as well as the types and performance parameters of the sensors carried were recorded in detail; flight performance includes: endurance, maximum flight speed and flight altitude limit, etc. Sensor types include: spectral range and resolution of hyperspectral cameras; scanning accuracy and range of laser radars, etc.

[0051] It is understandable that collecting building-related design drawings, geographic coordinates, historical data and surrounding environment information, and recording multiple drone models, flight performance and sensor parameters, will help drone-based architectural surveying methods to scientifically plan flight paths, reasonably allocate tasks, achieve accurate and efficient data collection and processing, improve the quality and reliability of surveying and mapping results, ensure the smooth implementation of surveying and mapping operations, and provide a comprehensive and detailed basis for subsequent analysis.

[0052] Specifically, an intelligent algorithm server is built and the collected information is input into the intelligent algorithm server. The algorithm divides the surveying and mapping area into multiple sub-areas based on the size, shape, complexity, etc. of the building. The initial flight route is planned for each drone based on the drone performance and the characteristics of the sub-areas, and its flight altitude, speed, turning points and other information at different stages are determined. Corresponding surveying and mapping tasks are assigned, such as a specific drone being responsible for lidar scanning of the high-rise areas of the building, and another being responsible for hyperspectral image collection on the side of the building.

[0053] It is understandable that building an intelligent algorithm server and inputting information can divide the surveying and mapping sub-areas according to the situation of buildings and drones, plan the initial flight route, determine the flight details and assign tasks, realize accurate and efficient task division and path planning, give full play to the performance advantages of drones, greatly improve the scientificity, orderliness and accuracy of surveying and mapping operations, and reduce resource waste and operational errors.

[0054] Specifically, for the hyperspectral camera carried by the drone, accurately calibrate the accuracy of its spectral band, set the appropriate image resolution (for example, set it to 0.1 meters per pixel in the field according to the mapping accuracy requirements), shooting mode (you can choose to trigger shooting by time interval or distance interval), and exposure parameters; for the laser radar, adjust its scanning angle (horizontal and vertical coverage), scanning frequency (such as emitting 100,000-1 million laser pulses per second), measurement accuracy (such as reaching centimeter-level accuracy) and other key parameters to ensure that it can accurately obtain the three-dimensional spatial information of the building. At the same time, check the flight control system of the drone, including testing the drone's attitude stability, heading control, altitude maintenance and other functions; check the signal strength, transmission rate and stability of the data transmission system; and verify the positioning accuracy of the positioning system (such as RTK-GPS), etc., to ensure the normal operation of each system of the drone.

[0055] Specifically, a data processing center is built, equipped with high-performance computers and professional data processing software to receive and process multi-source heterogeneous data from multiple drones. The software has functional modules such as data fusion, quality inspection, and format conversion. A blockchain network is built, and data encryption algorithms are set up (such as using advanced elliptic curve encryption technology), data storage structures are determined (such as storing surveying and mapping data and related metadata in the form of blocks), and access rights rules (strictly limiting the read and write rights of different roles to data) to prepare for receiving and storing safe and reliable surveying and mapping data.

[0056] It is understandable that equipment debugging and calibration can ensure accurate data collection by each sensor, stable flight of the drone and stable data transmission. The construction of the data and blockchain platform can use high-performance equipment and professional software to process multi-source heterogeneous data. The blockchain network ensures secure data storage and permission management, which together improves the accuracy, efficiency and data reliability of drone building surveying and mapping, and provides solid guarantee and strong support for the entire process of building surveying and mapping.

[0057] Specifically, at the designated takeoff site, each drone will undergo a comprehensive self-inspection before takeoff. The inspection items include whether the battery power is sufficient (to ensure that the power demand of the expected flight mission is met), whether the motor is running normally (by testing the motor's speed, torque and other parameters), whether the propeller is damaged or loose, whether the sensor is working properly and the parameters are correct (such as the cleanliness of the hyperspectral camera lens, the status of the laser radar's transmitting and receiving devices), and whether the data transmission device is connected to the intelligent algorithm server and data processing center.

[0058] Specifically, the drones take off in a predetermined order. During the takeoff process, the flight control system continuously monitors the flight status of the drones, such as altitude, speed, attitude, etc., and transmits this information to the intelligent algorithm server in real time. At the same time, the data transmission device establishes a stable two-way communication link with the data processing center to transmit the drone's identity information (such as drone number, model, etc.), initial location information (latitude and longitude, altitude), and system status information (such as the working status of each sensor, battery power percentage, etc.).

[0059] Specifically, after receiving the information from the drone, the intelligent algorithm server fine-tunes the pre-planned flight path based on the current environmental information (such as real-time wind speed, wind direction, surrounding airspace conditions, etc.), and sends the final flight mission information (including specific mapping sub-area boundaries, key mapping targets, data collection requirements, etc.) and optimized flight path information to the drone. After receiving and confirming the mission information, the drone flies to the initial mapping point for which it is responsible according to the flight path. During the flight, according to the instructions of the intelligent algorithm server, the flight altitude, speed and heading are adjusted in time to ensure accurate arrival at the target point and prepare for subsequent mapping operations.

[0060] It is understandable that multiple drones take off in an orderly manner and establish two-way communication links with the intelligent algorithm server and data processing center, which can ensure that the drones accurately receive mission and path information, fly to the predetermined initial surveying and mapping points, realize collaborative operations, improve the accuracy and timeliness of surveying and mapping initiation, and lay a solid foundation for the subsequent efficient and orderly completion of architectural surveying and mapping work.

[0061] Specifically, during the flight of the drone, the intelligent algorithm server dynamically optimizes the flight path of the drone based on the real-time location information (latitude, longitude and altitude accurate to the centimeter level), flight attitude information (pitch angle, roll angle, heading angle) and environmental perception data (such as wind speed, wind direction, light intensity, temperature, humidity, etc.) sent back by the drone, combined with the requirements of the building surveying and mapping task and changes in the surrounding environment (such as temporary obstacles, other aircraft activities, etc.). For example, if encountering a strong wind area, the algorithm server will recalculate the path and guide the drone to bypass the area or adjust the flight altitude and speed to reduce the impact of wind on flight stability and data collection accuracy; if it is found that the progress of the surveying and mapping task in a certain sub-area is lagging behind, the server will adjust the task order of other drones and dispatch a suitable drone to assist. At the same time, the flight control system of the drone accurately controls the flight attitude and speed of the drone according to the instructions of the intelligent algorithm server, ensuring stable flight under different flight conditions and maintaining a suitable distance and angle from the building, providing good conditions for sensors to collect data.

[0062] Specifically, after arriving at the surveying area, the hyperspectral camera and laser radar carried by the drone start working at the same time. The hyperspectral camera automatically captures the surface image of the building according to the preset shooting frequency (such as taking a photo every 2 meters of flight or taking 3 photos per second) and the spectral band acquisition range (such as the 400-1000nm band, which can be subdivided into multiple bands at the nanometer level). During the shooting process, the autofocus system inside the camera adjusts the lens focal length in real time according to the distance information between the drone and the building to ensure the image is clear, and automatically adjusts the exposure parameters according to the light intensity to obtain high-quality hyperspectral image data. The image data after shooting is initially processed inside the drone, including data compression (using efficient lossless compression algorithms, such as LZW algorithm), adding shooting location (accurate to centimeter-level latitude and longitude and altitude) and time stamping, and then transmitted to the data processing center in real time through the data transmission device. The laser radar emits laser pulses to the building and the surrounding environment at a set scanning frequency (such as 500,000-1 million laser pulses per second), calculates the distance and position information of the target object based on the reflected light signal, and constructs three-dimensional point cloud data. During operation, the laser radar will automatically adjust the scanning angle and range according to the flight speed and altitude of the drone. For example, when approaching complex structures or detailed parts of buildings, it will automatically reduce the scanning angle (such as adjusting from 360 degrees horizontally to 180 degrees) and increase the scanning frequency (such as increasing to 1.5 million laser pulses per second) to obtain more detailed point cloud data; when flying away from buildings or over relatively open areas, it will appropriately expand the scanning range to improve surveying and mapping efficiency. The collected point cloud data is also pre-processed, including data compression (using compression algorithms suitable for point cloud data, such as octree compression algorithm), coordinate conversion (converting the laser radar coordinate system to a unified geographic coordinate system), etc., and then transmitted to the data processing center in real time.

[0063] Specifically, after the data processing center receives multi-source heterogeneous data from multiple drones, it first performs spatial registration of the data. Through the position and attitude information of the drone, the pixel coordinates in the hyperspectral image are accurately matched with the three-dimensional coordinates in the lidar point cloud data, so that the two can accurately correspond to the same spatial position, and the error is controlled within the centimeter level. Then, the multi-source data fusion algorithm is used to combine the spectral features in the hyperspectral data with the geometric features in the lidar point cloud data. For example, the difference in reflectivity of specific spectral bands in the hyperspectral data is used to identify different materials on the building surface (such as concrete, glass, metal, etc.), and these material information is annotated to the corresponding lidar point cloud data, so as to construct a more comprehensive and accurate digital model of the building, which not only includes the geometric shape of the building, but also covers the material information of the building surface. During the data fusion process, the quality and integrity of the data are monitored in real time, and outliers and missing values ​​in the data are identified and processed. For example, abnormal points in the lidar point cloud data (such as points that are too far or too close) can be determined by setting a threshold, and processed using interpolation or other data repair methods; for noise points in hyperspectral image data (such as abnormal pixel values ​​caused by light interference), a filtering algorithm (such as median filtering) is used to remove them to ensure that the quality of the fused data meets the surveying and mapping accuracy requirements, with an accuracy of up to centimeters or even higher.

[0064] It is understandable that the drone flies along a path dynamically optimized by an intelligent algorithm and collects data from multi-source heterogeneous sensors for real-time transmission, processing and monitoring. It can flexibly adjust the path according to actual conditions, ensure comprehensive and accurate collection, achieve efficient data integration and quality control, greatly improve the accuracy, completeness and timeliness of surveying and mapping data, and effectively ensure the high-quality completion of architectural surveying and mapping.

[0065] Specifically, after the data processing center completes data fusion and quality monitoring, it encrypts the data. It uses an asymmetric encryption algorithm (such as the RSA algorithm or a more advanced elliptic curve encryption algorithm) to encrypt the data and generate a digital signature to ensure the security of the data during transmission and prevent the data from being stolen or tampered with. The encrypted surveying and mapping data is converted into a specific format (such as a binary format suitable for blockchain storage) and is ready to be transmitted to the blockchain network.

[0066] Specifically, the encrypted data is transmitted to the blockchain node in accordance with the protocol specifications of the blockchain network. After receiving the data, the blockchain node first verifies the integrity of the data by calculating the hash value of the data and comparing it with the hash value attached to the data during transmission to ensure that the data has not been tampered with during transmission. At the same time, the legitimacy of the source of the data is verified by checking the drone identification information in the data packet header and matching it with the legal drone information pre-registered in the blockchain network to confirm that the data comes from an authorized drone device. After verification, the blockchain node stores the data in the distributed ledger of the blockchain and generates a data block containing data hash value, timestamp, drone identification, data source and other information. Each data block is linked to the previous data block through a hash pointer to form an unalterable blockchain data chain, ensuring the traceability of the data. For example, if you need to query the source and processing process of the surveying and mapping data of a certain part of a building at a certain moment in the future, you can use the traceability function of the blockchain to extract relevant information from the data block to understand which drone collected it, when it was collected, and what data processing steps it went through, thereby ensuring the reliability and auditability of the data.

[0067] It is understandable that the data processing center transmits the encrypted surveying and mapping data to the blockchain network and records key information, effectively preventing data tampering and leakage, ensuring data security and integrity. Its traceability facilitates accurate tracing of data sources and processing processes, greatly enhancing the credibility and reliability of drone building surveying and mapping data, and providing solid data support for the full life cycle management of construction projects.

[0068] Specifically, based on the multi-source heterogeneous surveying and mapping data stored in the blockchain, professional surveying and mapping analysis software (such as geographic information system software with powerful three-dimensional modeling and data analysis functions) is used to generate results. The software first reads the data that has been verified by security from the blockchain network, builds a three-dimensional framework model of the building based on the geometric information in the data (lidar point cloud data), connects discrete points into surfaces through algorithms such as triangulation of point cloud data, and then builds a three-dimensional shape model of the building. The accuracy can reach the centimeter level or even the millimeter level, and can accurately restore the building's appearance, structural features, etc. At the same time, combined with the material information in the hyperspectral data, the areas of different materials are distinguished and marked in the three-dimensional model, and a building material distribution report is generated, which shows in detail the distribution location, area and other information of different materials on the building surface. In addition, the software can also perform other surveying and mapping analysis work, such as building area calculation (accurate building area is obtained through geometric calculation of the three-dimensional model), building height measurement (obtaining the height difference between the highest and lowest points of the building from the model), building structure analysis (analyzing the structural stability, symmetry and other characteristics of the building), etc.

[0069] Specifically, the generated high-precision surveying and mapping results can be widely used in the field of architectural design, providing designers with accurate information on the current status of buildings, assisting them in the design of building renovation, expansion or new construction projects; in terms of construction supervision, it can be used to compare the construction progress with the design requirements, monitor the deformation and displacement during the construction process, and ensure the quality and safety of construction; in the field of historical building protection, it can provide detailed historical information and current status data of buildings for cultural relics protection experts, and help formulate scientific and reasonable protection plans. At the same time, since the data is stored in the blockchain network, users in different fields can easily share these surveying and mapping results on the premise of satisfying data access rights, promote information exchange and collaboration between various fields of the construction industry, and improve the work efficiency and scientific decision-making of the entire industry.

[0070] It is understandable that multi-source heterogeneous surveying and mapping data stored on blockchain can generate high-precision building three-dimensional models and material distribution reports and other results with the help of professional software. These results can accurately present the overall picture and details of the building, provide creative inspiration and precise parameters for architectural design, and effectively assist construction supervision to ensure project quality and progress, and comprehensively promote efficient and scientific development in multiple fields of the construction industry.

[0071] Specifically, when all drones complete the mapping flight according to the scheduled tasks, the drones will land at the designated site in turn according to the landing path planned by the intelligent algorithm server. During the landing process, the drone's flight control system continuously monitors various parameters during the landing process, such as descent speed, attitude stability, distance from the landing site, etc., to ensure a safe and smooth landing. At the same time, the drone transmits the detailed execution status of this mission (including total flight mileage, total time, total data collection of each sensor, battery consumption, abnormal events encountered during the flight and how to deal with them, etc.) to the data processing center.

[0072] Specifically, after the drone is landed and recovered, it will be fully inspected and maintained. This includes cleaning, calibrating and performance testing of sensors such as hyperspectral cameras and lidars, checking whether the mechanical parts of the drone (such as propellers, motors, landing gear, etc.) are damaged or worn, charging or replacing batteries, and updating and debugging the software of the drone's flight control system and data transmission system. For example, for hyperspectral cameras, recalibrate their spectral response curves and check whether the optical performance of the lens has declined; for lidars, check whether the sensitivity and accuracy of the transmitting and receiving devices meet the requirements; update the drone's flight control system software, fix known vulnerabilities or optimize flight control algorithms, and improve flight stability and safety; test the data transmission system to ensure that the rate and stability of data transmission meet the requirements of the next surveying and mapping mission. At the same time, the maintenance information and test results of the equipment are recorded in the equipment management system to promptly discover potential problems with the equipment and ensure that the drone can operate normally during the next mission.

[0073] Specifically, according to the data processing and system operation of this surveying and mapping task, the intelligent algorithm server, data processing center and blockchain network are optimized. The intelligent algorithm server adjusts the optimization strategy and parameters according to the effect and efficiency of path optimization during task execution, such as improving the way of considering environmental factors and optimizing the task allocation algorithm of multi-machine collaboration, so as to improve the accuracy and flexibility of path planning; the data processing center updates the data processing algorithm and model according to the problems found in data fusion and quality monitoring, such as optimizing the multi-source data fusion algorithm, improving the speed and accuracy of data processing, improving the data quality monitoring model, and enhancing the ability to identify and process abnormal data; the blockchain network performs node expansion, network bandwidth optimization and other operations according to the performance bottleneck of data storage and transmission, such as increasing the number of blockchain nodes, increasing data storage capacity and processing capacity, upgrading network equipment, and increasing the data transmission rate, so as to improve the performance and reliability of the entire surveying and mapping system and make full preparations for the next surveying and mapping task.

[0074] It is understandable that the drone lands according to the plan and transmits back the mission details, which facilitates the comprehensive inspection and maintenance of equipment, timely detection and resolution of problems, and optimization of the performance of intelligent algorithm servers, data processing centers and blockchain networks. It can improve the overall system stability and accuracy, ensure more efficient execution of the next surveying and mapping mission, and realize the continuous iteration and improvement of drone building surveying and mapping technology.

[0075] It should be noted that:

[0076] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0077] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present application and to form different embodiments.

[0078] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A building surveying and mapping method based on drone, characterized in that: include: Integrate building data, surrounding environment information and multi-UAV performance parameters, build intelligent algorithm servers, plan flight paths and assign tasks, complete multi-source heterogeneous sensor and UAV system debugging, and build data processing centers and blockchain networks; Multiple drones take off from the designated site in sequence, establish a two-way communication link with the intelligent algorithm server and the data processing center, receive and confirm flight mission and path information, and fly to their respective initial surveying and mapping points; The drone flies along a path dynamically optimized by an intelligent algorithm, and multi-source heterogeneous sensors synchronously collect building data according to preset parameters and transmit it to the data processing center in real time for fusion processing and quality monitoring; The processed surveying and mapping data is encrypted and transmitted to the blockchain network, and the data hash value, timestamp and drone identification are recorded; Generate high-precision 3D building models and material distribution reports based on multi-source heterogeneous surveying and mapping data stored in blockchain; The drone completes the mission and lands according to the plan, transmits back the mission details, comprehensively inspects and maintains the drone and equipment, and optimizes the performance of the intelligent algorithm server, the data processing center, and the blockchain network.

2. The method for building surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The above mentioned process integrates building data, surrounding environment information and multi-UAV performance parameters, builds an intelligent algorithm server, plans flight paths and assigns tasks, including: Data collection and collation: collect building design drawings, geographic coordinate information, historical surveying and mapping data, and surrounding terrain, landforms, and meteorological environment data, and record in detail the models, flight performance, and types and performance parameters of multiple UAVs involved in the surveying and mapping; Intelligent algorithm planning, building an intelligent algorithm server, inputting the collected information into the intelligent algorithm server, the intelligent algorithm server divides the surveying and mapping area into multiple sub-areas according to the scale, shape and complexity of the building, and plans the initial flight route for each drone according to the performance of the drone and the characteristics of the sub-areas, determines its flight altitude, speed and turning points at different stages, and assigns corresponding surveying and mapping tasks.

3. The method for building surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The completion of the debugging of multi-source heterogeneous sensors and drone systems, and the construction of data processing centers and blockchain networks include: Equipment debugging and calibration: accurately calibrate the accuracy of the spectral band of the hyperspectral camera sensor, set the image resolution, shooting mode and exposure parameters; adjust the scanning angle, scanning frequency and measurement accuracy of the lidar sensor to ensure that it can accurately obtain the three-dimensional spatial information of the building, and check the flight control system of the drone to test the drone's attitude stability, heading control and altitude holding functions; check the signal strength, transmission rate and stability of the data transmission device and the positioning accuracy of the positioning system; The data and blockchain platform is established, and a data processing center is built. It is equipped with high-performance computers and professional data processing software to receive and process multi-source heterogeneous data from multiple drones. The data processing software has data fusion, quality inspection and format conversion functions; a blockchain network is built, data encryption algorithms are set up, data storage structures and access permission rules are determined, and preparations are made to receive and store surveying and mapping data.

4. The method for building surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The multiple drones take off from the designated site in sequence, establish a two-way communication link with the intelligent algorithm server and the data processing center, receive and confirm flight mission and path information, and fly to their respective initial mapping points, including: Pre-takeoff inspection: at the designated takeoff site, before each drone takes off, check the battery power, motor operation status, propeller operation status, sensor operation status and parameters, and the connection status between the data transmission device and the intelligent algorithm server and the data processing center; Take-off and communication connection, the UAVs take off in sequence according to a predetermined order. During the take-off process, the flight control system continuously monitors the flight status of the UAV and transmits the information to the intelligent algorithm server in real time. At the same time, the data transmission device establishes a stable two-way communication link with the data processing center to transmit the UAV's identity information, initial location information and system status information; Mission confirmation and flying to the target. After receiving the information from the drone, the intelligent algorithm server fine-tunes the pre-planned flight path according to the current environmental information, and sends the final flight mission information and the optimized flight path information to the drone. After receiving and confirming the mission information, the drone flies to the initial mapping point for which it is responsible according to the flight path. During the flight, the flight altitude, speed and heading are adjusted in time according to the instructions of the intelligent algorithm server.

5. The method for building surveying and mapping based on a drone according to claim 1, characterized in that: The UAV flies along a path dynamically optimized by an intelligent algorithm, including: Intelligent path optimization and flight control: During the flight of the drone, the intelligent algorithm server dynamically optimizes the flight path of the drone based on the real-time location information, flight attitude information, and environmental perception data sent back by the drone, combined with the requirements of the building surveying and mapping task and changes in the surrounding environment; When it is found that the mapping task of a certain sub-area is lagging behind, the intelligent algorithm server adjusts the task order of other drones and dispatches drones to assist.

6. The method for building surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The multi-source heterogeneous sensors synchronously collect building data according to preset parameters and transmit the data to the data processing center in real time for fusion processing and quality monitoring, including: Multi-source heterogeneous sensor data acquisition: after arriving at the surveying area, the hyperspectral camera sensor and the lidar sensor start working at the same time. The hyperspectral camera sensor automatically captures the building surface image according to the preset shooting frequency and spectral band acquisition range. During the shooting process, the autofocus system inside the camera adjusts the lens focal length in real time according to the distance information between the drone and the building to ensure the image is clear. At the same time, the exposure parameters are automatically adjusted according to the light intensity to obtain high-quality hyperspectral image data. After the image data is compressed and preliminarily processed by adding shooting position and time marks inside the drone, it is transmitted to the data processing center in real time through the data transmission device. The lidar sensor emits laser pulses to the building and the surrounding environment at the set scanning frequency, calculates the distance and position information of the target object according to the reflected light signal, and constructs three-dimensional point cloud data. During the working process, the lidar sensor automatically adjusts the scanning angle and range according to the flight speed and altitude of the drone. The collected point cloud data is pre-processed by data compression and coordinate conversion and transmitted to the data processing center in real time. Data processing center fusion and monitoring: The data processing center receives multi-source heterogeneous data from multiple drones, performs spatial registration of the data, and accurately matches the pixel coordinates in the hyperspectral image with the three-dimensional coordinates in the lidar point cloud data through the position and attitude information of the drone. The multi-source data fusion algorithm is used to combine the spectral features in the hyperspectral data with the geometric features in the lidar point cloud data. During the data fusion process, the quality and integrity of the data are monitored in real time, and outliers and missing values ​​in the data are identified and processed.

7. The method for building surveying and mapping based on a drone according to claim 1, characterized in that: The processed surveying and mapping data is encrypted and transmitted to the blockchain network, and the data hash value, timestamp and drone identification are recorded, including: Data encryption processing: after the data processing center completes data fusion and quality monitoring, it uses an asymmetric encryption algorithm to encrypt the data, generates a digital signature, and converts the encrypted surveying and mapping data into a format ready for transmission to the blockchain network; Blockchain data storage, the encrypted data is transmitted to the blockchain node according to the protocol specifications of the blockchain network. After receiving the data, the blockchain node verifies the integrity of the data, calculates the hash value of the data, and compares it with the hash value attached during data transmission to verify the legitimacy of the source of the data. By checking the drone identification information in the data packet header and matching it with the legal drone information pre-registered in the blockchain network, it is confirmed that the data comes from an authorized drone device; after the verification is passed, the blockchain node stores the data in the distributed ledger of the blockchain and generates a data block containing the data hash value, timestamp, drone identification, and data source. Each data block is linked to the previous data block through a hash pointer to form an unalterable blockchain data chain.

8. The method for building surveying and mapping based on a drone according to claim 1, characterized in that: The multi-source heterogeneous surveying and mapping data stored on blockchain generates high-precision building 3D models and material distribution reports, including: The results are generated based on the multi-source heterogeneous surveying and mapping data stored in the blockchain. The surveying and mapping analysis software is used to generate results. The security-verified data is read from the blockchain network. The three-dimensional framework model of the building is constructed according to the geometric information in the data. The discrete points are connected into surfaces through the triangulation algorithm of the point cloud data, and then the three-dimensional shape model of the building is constructed. Combined with the material information in the hyperspectral data, the areas of different materials are distinguished and labeled in the three-dimensional model, and a building material distribution report is generated, which shows in detail the distribution position and area information of different materials on the building surface.

9. The method for building surveying and mapping based on a drone according to claim 1, characterized in that: The drone completes the mission and lands according to the plan, transmits the mission details, and comprehensively inspects and maintains the drone and equipment, including: After the mission is completed, all drones complete the surveying and mapping flight according to the scheduled mission, and the drones land at the designated site in turn according to the landing path planned by the intelligent algorithm server. During the landing process, the flight control system continuously monitors the descent speed, attitude stability and distance to the landing site during the landing process, and the drones transmit the detailed execution status of this mission to the data processing center; Equipment inspection and maintenance: After the drone is landed and recovered, the hyperspectral camera sensor and lidar sensor are cleaned, calibrated and performance tested, the physical condition of the drone's mechanical parts are checked, the battery is charged and replaced, and the flight control system and data transmission device are updated and debugged, and the equipment maintenance information and test results are recorded in the equipment management system.

10. The method for building surveying and mapping based on a drone according to claim 1, characterized in that: The optimizing the performance of the intelligent algorithm server, the data processing center and the blockchain network includes: System performance optimization: According to the data processing status and system operation status of the surveying and mapping tasks, the intelligent algorithm server, the data processing center and the blockchain network are optimized; the intelligent algorithm server adjusts the optimization strategy and parameters according to the effect and efficiency of path optimization during task execution; the data processing center updates the data processing algorithm and model according to the problems found in data fusion and quality monitoring; the blockchain network expands nodes and optimizes network bandwidth according to the performance bottlenecks of data storage and transmission.

Images