A building surveying method based on a drone

Abstract

The application provides a building surveying and mapping method based on a UAV, which comprises the following steps: integrating information to build an intelligent algorithm server, planning a flight path and assigning a task; receiving and confirming flight task and path information, flying to respective initial surveying and mapping points; flying along an intelligent algorithm dynamically optimized path, and synchronously collecting building data by a multi-source heterogeneous sensor according to preset parameters; transmitting processed surveying and mapping data to a blockchain network, and recording data hash values, timestamps and UAV identifiers; generating a high-precision building three-dimensional model and a material distribution report; landing according to planning after completing the task, returning task details, and comprehensively checking and maintaining the UAV and equipment. By integrating multiple types of information to build an intelligent algorithm server, the flight path can be scientifically planned and the task can be assigned, multiple UAVs can be cooperatively operated, the data processing center and the blockchain network can be linked, data can be collected by a multi-source heterogeneous sensor and be fused, processed and monitored in real time, the data accuracy and efficiency can be ensured, the data safety and traceability can be ensured by using the blockchain storage, and the precision, efficiency, safety and reliability of building surveying and mapping are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle (UAV) building surveying, and in particular, to a building surveying method based on UAV. BACKGROUND

[0002] The UAV building surveying technology can quickly cover a large area of building area, efficiently collect accurate data, is not limited by complex terrain, can obtain multi-angle information, greatly improves the surveying efficiency, reduces the labor cost and operation risk, and provides strong support for building planning, design, construction and monitoring.

[0003] The integration of building data, surrounding environment and UAV performance parameters can enable the UAV building surveying to accurately plan the path and task allocation, the data security and traceability can be guaranteed by the blockchain, the perfect optimization process can improve the surveying efficiency and quality, and the overall reliability and adaptability are enhanced, the technology is continuously improved, however, the existing UAV building surveying technology lacks effective integration of building data, surrounding environment and UAV performance parameters, it is difficult to scientifically plan the flight path and reasonably allocate tasks, lacks multi-source heterogeneous sensor cooperation and real-time data fusion processing and quality monitoring mechanism, does not use blockchain to guarantee data security and traceability, is prone to data loss and insufficient surveying accuracy, and lacks perfect optimization process for the overall system and equipment after the completion of the task, which is not conducive to continuously improving the surveying effect and efficiency.

[0004] Therefore, it is necessary to design a building surveying method based on UAV to solve the problems that the existing UAV building surveying technology lacks effective integration of building data, surrounding environment and UAV performance parameters, it is difficult to scientifically plan the flight path and reasonably allocate tasks, lacks multi-source heterogeneous sensor cooperation and real-time data fusion processing and quality monitoring mechanism, does not use blockchain to guarantee data security and traceability, is prone to data loss and insufficient surveying accuracy, lacks perfect optimization process for the overall system and equipment, and is not conducive to continuously improving the surveying effect and efficiency. SUMMARY

[0005] In view of this, the present application provides a building surveying method based on UAV, which aims to solve the problems that the existing UAV building surveying technology lacks effective integration of building data, surrounding environment and UAV performance parameters, it is difficult to scientifically plan the flight path and reasonably allocate tasks, lacks multi-source heterogeneous sensor cooperation and real-time data fusion processing and quality monitoring mechanism, does not use blockchain to guarantee data security and traceability, is prone to data loss and insufficient surveying accuracy, lacks perfect optimization process for the overall system and equipment, and is not conducive to continuously improving the surveying effect and efficiency.

[0006] In one aspect, the present application provides a building surveying method based on UAV, comprising:

[0007] Integrating building data, surrounding environment information and multi- UAV performance parameters, constructing an intelligent algorithm server, planning flight paths and assigning tasks, completing multi-source heterogeneous sensor and UAV system debugging, building a data processing center and a blockchain network;

[0008] Multiple UAVs take off from designated locations in sequence, establish bidirectional communication links with the intelligent algorithm server and the data processing center, receive and confirm flight tasks and path information, and fly to their initial mapping points;

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

[0010] The processed mapping data is transmitted to the blockchain network after encryption, and the data hash value, timestamp and UAV identifier are recorded;

[0011] Based on the multi-source heterogeneous mapping data stored in the blockchain, a high-precision building three-dimensional model and material distribution report are generated;

[0012] The UAVs complete the task according to the planned landing, return the task details, and comprehensively check and maintain the UAVs and equipment, and optimize the performance of the intelligent algorithm server, the data processing center and the blockchain network.

[0013] Further, the integration of building data, surrounding environment information and multi- UAV performance parameters, the construction of an intelligent algorithm server, the planning of flight paths and the assignment of tasks, comprises:

[0014] Data collection and arrangement, collecting design drawings, geographic coordinate information, historical mapping data of buildings, and surrounding terrain, ground objects and meteorological environment data, and recording in detail the model, flight performance of multiple UAVs participating in mapping, and the type and performance parameters of multi-source heterogeneous sensors;

[0015] Intelligent algorithm planning, constructing an intelligent algorithm server, inputting the collected information into the intelligent algorithm server, the intelligent algorithm server dividing the mapping area into multiple sub-areas according to the size, shape and complexity of the building, planning the initial flight route for each UAV according to the UAV performance and sub- area characteristics, determining the flight height, speed and turning point at different stages, and assigning the corresponding mapping task.

[0016] Further, the completion of multi-source heterogeneous sensor and UAV system debugging, the construction of a data processing center and a blockchain network, comprises:

[0017] Device debugging and calibration: accurate calibration of the spectral band of the hyperspectral camera sensor, setting the image resolution, shooting mode and exposure parameters; adjusting the scanning angle, scanning frequency and measurement accuracy of the laser radar sensor to ensure that it can accurately obtain the three-dimensional spatial information of the building, and checking the flight control system of the unmanned aerial vehicle to test the functions of attitude stability, heading control and height maintenance; checking the signal strength, transmission rate and stability of the data transmission device and the positioning accuracy of the positioning system;

[0018] Data and blockchain platform building, building a data processing center equipped with high-performance computers and professional data processing software for receiving and processing multi-source heterogeneous data from multiple unmanned aerial vehicles, said data processing software has data fusion, quality detection and format conversion functions; build a blockchain network, set up data encryption algorithm, determine data storage structure and access permission rules, prepare to receive and store surveying and mapping data.

[0019] Further, the multiple unmanned aerial vehicles take off from the designated site in sequence, establish a bidirectional communication link with the intelligent algorithm server and the data processing center, receive and confirm the flight task and path information, and fly to their initial mapping points, including:

[0020] Pre-flight inspection, at the designated take-off site, each unmanned aerial vehicle checks the battery level, motor operation status, propeller working status, sensor working status and parameters, and the connection status of the data transmission device with the intelligent algorithm server and the data processing center before take-off;

[0021] Take-off and communication connection, the unmanned aerial vehicles take off in the predetermined order one by one, in the take-off process, the flight control system continuously monitors the flight state of the unmanned aerial vehicle and transmits the information to the intelligent algorithm server in real time, at the same time, the data transmission device establishes a stable bidirectional communication link with the data processing center to transmit the identity information, initial position information and system state information of the unmanned aerial vehicle;

[0022] Task confirmation and flight to target, after the intelligent algorithm server receives the information of the unmanned aerial vehicle, it fine-tunes the pre-planned flight path according to the current environmental information, and sends the final flight task information and optimized flight path information to the unmanned aerial vehicle, after the unmanned aerial vehicle receives and confirms the task information, it flies to the initial mapping point it is responsible for according to the flight path, in the flight process, it adjusts the flight height, speed and heading according to the instructions of the intelligent algorithm server.

[0023] Further, the unmanned aerial vehicle flies along the intelligent algorithm dynamically optimized path, including:

[0024] Intelligent path optimization and flight control, during the flight of the unmanned aerial vehicle, the intelligent algorithm server dynamically optimizes the flight path of the unmanned aerial vehicle according to the position information, flight attitude information and environmental perception data returned by the unmanned aerial vehicle in real time, combines the building surveying and mapping task requirements and the changes of the surrounding environment, and adjusts the task order of other unmanned aerial vehicles and dispatches unmanned aerial vehicles to assist.

[0025] When it is found that the surveying and mapping task progress of a certain sub-area lags behind, the intelligent algorithm server adjusts the task order of other unmanned aerial vehicles and dispatches unmanned aerial vehicles to assist.

[0026] Further, the multi-source heterogeneous sensor synchronously collects building data according to preset parameters and transmits the building data to the data processing center in real time for fusion processing and quality monitoring, including:

[0027] Multi-source heterogeneous sensor data collection, after reaching the surveying and mapping area, the hyperspectral camera sensor and the laser radar sensor start working at the same time, the hyperspectral camera sensor automatically photographs the building surface image according to the preset photographing frequency and spectral band collection range, in the photographing process, the automatic focusing system inside the camera adjusts the lens focal length in real time according to the distance information of the unmanned aerial vehicle and the building, to ensure the clarity of the image, and automatically adjusts the exposure parameter according to the light intensity, to obtain high-quality hyperspectral image data, the photographed image data is preliminarily processed by data compression, adding photographing position and time mark in the unmanned aerial vehicle, and then transmitted to the data processing center in real time through the data transmission device, the laser radar sensor emits laser pulses to the building and the surrounding environment at a 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, the laser radar sensor automatically adjusts the scanning angle and range according to the flight speed and height of the unmanned aerial vehicle during the working process, and the collected point cloud data is preprocessed by data compression and coordinate conversion and then 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 unmanned aerial vehicles, performs spatial registration of the data, accurately matches the pixel coordinates in the hyperspectral image with the three-dimensional coordinates in the laser radar point cloud data through the position and attitude information of the unmanned aerial vehicle, combines the spectral features in the hyperspectral data with the geometric features in the laser radar point cloud data by using a multi-source data fusion algorithm, and monitors the quality and integrity of the data in real time during the data fusion process, and identifies and processes abnormal values and missing values in the data.

[0029] Further, the processed surveying and mapping data is transmitted to the blockchain network, and the data hash value, timestamp and unmanned aerial vehicle identifier are recorded, including:

[0030] Data encryption processing, after the data processing center completes data fusion and quality monitoring, the data is encrypted using an asymmetric encryption algorithm, a digital signature is generated, and the encrypted surveying and mapping data is converted 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 specification of the blockchain network, the blockchain node verifies the integrity of the data after receiving 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 data source, checks the UAV identification information in the data packet header and the legal UAV information pre-registered in the blockchain network to match, confirms that the data comes from authorized UAV equipment; after verification, the blockchain node stores the data in the distributed ledger of the blockchain, and generates a data block containing the data hash value, timestamp, UAV identification, and data source. Each data block is linked to the previous data block through a hash pointer, forming an unalterable blockchain data chain.

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

[0033] Result generation, based on the multi-source heterogeneous surveying and mapping data stored in the blockchain, the surveying and mapping analysis software is used for result generation operation, the data that has passed security verification 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 a surface through the triangulation algorithm of 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 with different materials are distinguished and labeled in the three-dimensional model, and a building material distribution report is generated to show the distribution position and area information of different materials on the building surface in detail.

[0034] Further, the UAV completes the task according to the planned landing, returns the task details, and comprehensively checks and maintains the UAV and equipment, including:

[0035] Task completion landing, after all UAVs complete the surveying and mapping flight according to the predetermined task, the UAVs land in the designated area 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 of the landing site during the landing process, and the UAV transmits the detailed execution of this task to the data processing center;

[0036] The equipment is checked and maintained, the hyperspectral camera sensor and the laser radar sensor are cleaned, calibrated and performance detected after the unmanned aerial vehicle is landed and recycled, the physical state of the mechanical parts of the unmanned aerial vehicle is checked, the battery is charged and replaced, and the software of the flight control system and the data transmission device is updated and debugged, and the maintenance information and detection results of the equipment are recorded in the equipment management system.

[0037] Further, the performance of the intelligent algorithm server, the data processing center and the blockchain network is optimized, including:

[0038] The system performance is optimized, the intelligent algorithm server, the data processing center and the blockchain network are optimized according to the data processing situation and the system running situation of the surveying and mapping task, the intelligent algorithm server adjusts the optimization strategy and parameters according to the effect and efficiency of path optimization in the task execution process, the data processing center updates the data processing algorithm and model according to the problems found in data fusion and quality monitoring, and the blockchain network performs node expansion and network bandwidth optimization according to the performance bottleneck of data storage and transmission.

[0039] Compared with the prior art, the building surveying and mapping method based on the unmanned aerial vehicle has the advantages that the intelligent algorithm server is constructed by integrating multiple types of information, the flight path can be scientifically planned and the task can be distributed, multiple unmanned aerial vehicles can be cooperatively operated and linked with the data processing center and the blockchain network, the data collected by the multiple source heterogeneous sensors can be fused and processed in real time and monitored, the data accuracy and efficiency can be ensured, the data safety and traceability can be ensured by using the blockchain storage, high-precision results can be finally generated, the equipment can be comprehensively checked and maintained after the task is completed, and the system performance can be optimized, the precision, efficiency, safety and reliability of the building surveying and mapping are greatly improved, and the development and progress of the building surveying and mapping technology are effectively promoted. BRIEF DESCRIPTION OF DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference made to the accompanying drawings. The drawings are for purposes of illustration only and are not intended to be limiting in any respect. Moreover, the use of the same reference symbols in different drawings indicates similar or identical items.

[0041] Figure 1 The flowchart of the embodiment of the building surveying and mapping method based on the unmanned aerial vehicle is shown in the following detailed description of the preferred embodiments. DETAILED DESCRIPTION

[0042] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms without being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0043] Referring to Figure 1 As shown in some embodiments of the present application, a UAV-based building surveying method comprises:

[0044] Integrating building data, surrounding environment information and multi-UAV performance parameters, constructing an intelligent algorithm server, planning a flight path and assigning tasks, completing multi-source heterogeneous sensor and UAV system debugging, building a data processing center and a blockchain network;

[0045] Multiple UAVs take off from a designated site in sequence, establish a bidirectional communication link with the intelligent algorithm server and the data processing center, receive and confirm flight tasks and path information, and fly to their initial surveying points;

[0046] The UAVs fly along the dynamically optimized path of the intelligent algorithm, and the multi-source heterogeneous sensors synchronously collect building data according to preset parameters, which are transmitted in real time to the data processing center for fusion processing and quality monitoring;

[0047] The processed surveying data is encrypted and transmitted to the blockchain network, and the data hash value, timestamp and UAV identifier are recorded;

[0048] Based on the multi-source heterogeneous surveying data stored in the blockchain, a high-precision building three-dimensional model and a material distribution report are generated;

[0049] The UAVs complete the task according to the planned landing, return the task details, and comprehensively check and maintain the UAVs and equipment, and optimize the performance of the intelligent algorithm server, data processing center and blockchain network.

[0050] Specifically, the design drawings of the building, geographic coordinate information, historical surveying data and environmental data such as surrounding terrain, ground objects and weather are collected. At the same time, the models and flight performance of multiple UAVs participating in the surveying are recorded in detail, as well as the types and performance parameters of the sensors carried; the flight performance includes: endurance time, maximum flight speed and flight height limit, etc., and the sensor types include: spectral range and resolution of hyperspectral camera; scanning accuracy and range of laser radar, etc.

[0051] It can be understood that collecting building-related design drawings, geographic coordinates, historical data, and surrounding environment information, recording multiple unmanned aerial vehicle models, flight performance, and sensor parameters, helps the unmanned aerial vehicle-based building surveying and mapping method to scientifically plan flight paths, reasonably allocate tasks, achieve precise and efficient data collection and processing, improve surveying and mapping result quality and reliability, ensure smooth development of surveying and mapping operations, and provide comprehensive and detailed basis for subsequent analysis.

[0052] Specifically, an intelligent algorithm server is constructed, and the collected information is input into the intelligent algorithm server. The algorithm divides the surveying and mapping area into multiple sub-areas according to the size, shape, and complexity of the building, plans an initial flight route for each unmanned aerial vehicle according to the performance of the unmanned aerial vehicle and the characteristics of the sub-area, determines the flight height, speed, turning point, and other information of the unmanned aerial vehicle at different stages, and allocates corresponding surveying and mapping tasks, such as a specific unmanned aerial vehicle responsible for laser radar scanning of the high-rise area of the building, and another unmanned aerial vehicle responsible for hyperspectral image acquisition of the side of the building.

[0053] It can be understood that the construction of the intelligent algorithm server and the input of the information can divide the surveying and mapping sub-area according to the building and the unmanned aerial vehicle, plan the initial flight route, determine the flight details, and allocate the tasks, which can realize precise and efficient task division and path planning, fully exert the performance advantages of the unmanned aerial vehicle, greatly improve the scientificity, orderliness, and accuracy of the surveying and mapping operation, and reduce resource waste and operation errors.

[0054] Specifically, for the hyperspectral camera carried by the unmanned aerial vehicle, the accuracy of the spectral band is accurately calibrated, and appropriate image resolution (such as setting to 0.1 meters per pixel in real ground according to the surveying and mapping accuracy requirement), shooting mode (which can be triggered to shoot according to time interval or distance interval), and exposure parameters are set; for the laser radar, the key parameters such as scanning angle (horizontal and vertical coverage range), scanning frequency (such as 100,000-1,000,000 laser pulses per second), and measurement accuracy (such as reaching centimeter-level accuracy) are adjusted to ensure that it can accurately obtain the three-dimensional spatial information of the building. At the same time, the flight control system of the unmanned aerial vehicle is checked, including testing the functions such as attitude stabilization, heading control, and height maintenance of the unmanned aerial vehicle; checking the signal strength, transmission rate, and stability of the data transmission system; and verifying the positioning accuracy of the positioning system (such as RTK-GPS), to ensure the normal operation of each system of the unmanned aerial vehicle.

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

[0056] It can be understood that equipment debugging and calibration can ensure accurate data collection by sensors, stable flight of UAVs and stable data transmission. Data and blockchain platform construction can process multi-source heterogeneous data using high-performance equipment and professional software. Blockchain network ensures safe data storage and permission management, which together improves the accuracy, efficiency and data reliability of UAV building surveying and mapping, and provides solid protection and strong support for the whole process of building surveying and mapping.

[0057] Specifically, at the designated take-off site, each UAV performs a comprehensive self-check before take-off. The inspection items include whether the battery has sufficient power (to ensure that the power demand of the planned flight task is met), whether the motor is running normally (by testing the speed, torque and other parameters of the motor), whether the propeller is damaged or loose, whether the sensor is working normally and the parameters are correct (such as the cleanliness of the lens of the hyperspectral camera, the state of the laser radar's transmitting and receiving device), and whether the data transmission device is connected smoothly with the intelligent algorithm server and the data processing center, etc.

[0058] Specifically, the UAVs take off in the predetermined order, and in the take-off process, the flight control system continuously monitors the flight state of the UAV, such as height, speed, attitude and other information, and transmits these 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, and transmits the UAV's identity information (such as UAV number, model, etc.), initial position information (latitude, longitude, altitude), and system state information (such as the working state of each sensor, battery percentage, etc.).

[0059] Specifically, after receiving the information of the UAV, the intelligent algorithm server adjusts the pre-planned flight path according to the current environmental information (such as real-time wind speed, wind direction, surrounding airspace situation, etc.), and sends the final flight task information (including specific surveying and mapping sub-area boundary, key surveying and mapping target, data collection requirements, etc.) and the optimized flight path information to the UAV. After receiving and confirming the task information, the UAV flies to the initial surveying and mapping point according to the flight path, and adjusts the flight height, speed and heading in time according to the instructions of the intelligent algorithm server to ensure accurate arrival at the target point and preparation for subsequent surveying and mapping operations.

[0060] It can be understood that the orderly takeoff of multiple drones and the establishment of a bidirectional communication link with the intelligent algorithm server and the data processing center can ensure that the drones accurately receive task and path information, fly to the predetermined initial mapping point, realize cooperative operation, improve the accuracy and timeliness of mapping startup, and lay a solid foundation for subsequent efficient and orderly completion of building mapping work.

[0061] Specifically, during flight, the intelligent algorithm server dynamically optimizes the flight path of the drones based on real-time position information (latitude and longitude accurate to centimeters and height), flight attitude information (pitch angle, roll angle, heading angle), and environmental perception data (such as wind speed, wind direction, light intensity, temperature, humidity, etc.) returned by the drones, combined with building mapping task requirements and changes in the surrounding environment (such as temporary obstacles, other aircraft activities, etc.). For example, if a strong wind area is encountered, the algorithm server will recalculate the path, guide the drone to bypass the area or adjust the flight height and speed to reduce the impact of wind on flight stability and data collection accuracy; if it is found that the mapping task progress of a certain sub-area is lagging behind, the server adjusts the task order of other drones and dispatches appropriate drones 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 to provide good conditions for sensor data collection.

[0062] Specifically, upon reaching the survey area, the hyperspectral camera and the lidar carried by the UAV are simultaneously activated. The hyperspectral camera automatically captures images of the building surface according to the preset shooting frequency (e.g., one photo every 2 meters of flight or 3 photos per second) and the spectral band acquisition range (e.g., 400-1000 nm band, which can be subdivided into multiple bands at the nanometer level). During the shooting process, the automatic focusing system inside the camera adjusts the lens focal length in real time according to the distance information between the UAV and the building, ensuring clear images, and automatically adjusts the exposure parameters according to the light intensity to obtain high-quality hyperspectral image data. The captured image data is preliminarily processed inside the UAV, including data compression (using efficient lossless compression algorithms such as LZW algorithm), adding shooting position (latitude and longitude accurate to centimeter level and height) and time stamping, etc., and then transmitted to the data processing center in real time through the data transmission device. The lidar emits laser pulses to the building and surrounding environment at a set scanning frequency (e.g., 0.5-1 million laser pulses per second), 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 automatically adjusts the scanning angle and range according to the flight speed and height of the UAV, for example, when approaching the complex structure or detailed part of the building, it automatically reduces the scanning angle (e.g., from 360 degrees to 180 degrees horizontally) and increases the scanning frequency (e.g., to 1.5 million laser pulses per second) to obtain more detailed point cloud data; when far away from the building or flying over relatively open areas, it appropriately expands the scanning range to improve the survey efficiency. The collected point cloud data is also preprocessed, including data compression (using compression algorithms suitable for point cloud data such as octree compression algorithm), coordinate conversion (converting the lidar coordinate system to a unified geographic coordinate system), etc., and then transmitted to the data processing center in real time.

[0063] Specifically, after receiving multi-source heterogeneous data from multiple UAVs, the data processing center first performs spatial registration of the data. Through the position and attitude information of the UAV, the pixel coordinates in the hyperspectral image are accurately matched with the three-dimensional coordinates in the laser radar point cloud data, so that they can be accurately corresponded to the same spatial position with an error controlled within centimeters. Then, a multi-source data fusion algorithm is used to combine the spectral features in the hyperspectral data with the geometric features in the laser radar point cloud data. For example, the differences in reflectivity of specific spectral bands in the hyperspectral data are used to identify different materials (such as concrete, glass, metal, etc.) on the surface of the building, and these material information is labeled to the corresponding laser radar point cloud data, thereby constructing a more comprehensive and accurate digital model of the building, which not only contains the geometric shape of the building, but also covers the material information of the building surface. During data fusion, the quality and integrity of the data are monitored in real time, and abnormal values, missing values, etc. in the data are identified and processed. For example, by setting a threshold to judge the abnormal points (such as points with too far or too close distance) in the laser radar point cloud data, and using interpolation method or other data repair method for processing; for the noise points (such as abnormal pixel values due to light interference) in the hyperspectral image data, filter algorithm (such as median filter) is used for removal, to ensure that the quality of the fused data meets the surveying and mapping accuracy requirements, with an accuracy of centimeters or even higher.

[0064] It can be understood that the UAV flies along the dynamically optimized path by the intelligent algorithm and collects data by the multi-source heterogeneous sensors for real-time transmission, processing and monitoring, which can flexibly adjust the path according to the actual situation, ensure comprehensive and accurate collection, realize efficient integration and quality control of data, greatly improve the accuracy, integrity and timeliness of surveying and mapping data, and effectively guarantee the high-quality completion of building surveying and mapping.

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

[0066] Specifically, the encrypted data is transmitted to the blockchain node according to the protocol specification 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 legality of the data source is verified by checking the UAV identification information in the data packet header and matching it with the legal UAV information pre-registered in the blockchain network to confirm that the data comes from an authorized UAV device. After verification, the blockchain node stores the data in the distributed ledger of the blockchain and generates a data block containing information such as data hash value, timestamp, UAV identification, data source, etc. Each data block is linked to the previous data block through a hash pointer, forming an unalterable blockchain data chain, ensuring the traceability of the data. For example, if it is necessary to query the mapping data source and processing process of a certain part of the building at a certain time in the future, the relevant information can be extracted from the data block through the traceability function of the blockchain to understand which UAV collected the data, at what time, and what data processing steps were taken, thereby ensuring the reliability and auditability of the data.

[0067] It can be understood that the data processing center transmits the encrypted mapping data to the blockchain network and records key information, effectively preventing data tampering and leakage, ensuring data security and integrity, and its traceability facilitates accurate tracing of data sources and processing processes, greatly enhancing the credibility and reliability of UAV building mapping data, providing solid data support for building project life cycle management.

[0068] Specifically, based on the multi-source heterogeneous mapping data stored in the blockchain, professional mapping analysis software (such as geographic information system software with powerful three-dimensional modeling and data analysis functions) is used for result generation operations. The software first reads the securely verified data from the blockchain network, constructs a three-dimensional framework model of the building based on the geometric information (laser radar point cloud data) in the data, connects the discrete points into surfaces through algorithms such as point cloud triangulation, and then constructs a three-dimensional shape model of the building, with an accuracy of centimeters or even millimeters, which can accurately restore the shape contour, structural features, etc. of the building. At the same time, combined with the material information in the hyperspectral data, the areas of different materials are distinguished and labeled in the three-dimensional model, generating a building material distribution report that details the distribution location, area, etc. of different materials on the building surface. In addition, the software can also perform other mapping analysis work, such as building area calculation (accurately calculating the building area 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, etc. of the building), etc.

[0069] Specifically, the generated high-precision mapping results can be widely applied in the field of architectural design, providing accurate building status information for designers to assist them in designing building renovation, expansion or new projects; in terms of construction supervision, it can be used to compare construction progress with design requirements, monitor deformation, displacement and other conditions during the construction process, and ensure construction quality and safety; in the field of historical building protection, it can provide detailed building history information and current data for cultural heritage protection experts to help develop 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 mapping results under the premise of meeting data access permissions, promoting information exchange and cooperation between various fields of the construction industry, and improving the work efficiency and decision-making scientificity of the entire industry.

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

[0071] Specifically, after all the drones complete the mapping flight according to the predetermined tasks, the drones land in the designated area according to the landing path planned by the intelligent algorithm server. During the landing process, the flight control system of the drone continuously monitors various parameters during the landing process, such as descent speed, attitude stability, distance to the landing site, etc., to ensure safe and smooth landing. At the same time, the drone transmits the detailed execution of this task (including total mileage, total time consumption, total amount of data collection of each sensor, battery consumption, abnormal events encountered during flight and handling methods, etc.) to the data processing center.

[0072] Specifically, after the drone is landed and recovered, it is subjected to comprehensive inspection and maintenance. This includes cleaning, calibration and performance testing of sensors such as hyperspectral cameras and lidar, checking whether mechanical components (such as propellers, motors, landing gear, etc.) of the drone are damaged or worn out, charging or replacing the battery, and updating and debugging the flight control system and data transmission system software of the drone. For example, for the hyperspectral camera, recalibrate its spectral response curve and check whether the optical performance of the lens has declined; for the lidar, test the sensitivity and accuracy of the transmitting and receiving devices to see if they meet the requirements; update the flight control system software, fix known vulnerabilities or optimize the flight control algorithm to improve flight stability and safety; test the data transmission system to ensure that the data transmission rate and stability meet the requirements of the next mapping task. At the same time, the maintenance information and test results of the equipment are recorded in the equipment management system to detect potential problems in the equipment in a timely manner and ensure that the drone can operate normally for the next task.

[0073] Specifically, according to the data processing and system running of this survey task, the intelligent algorithm server, data processing center and blockchain network are optimized. The intelligent algorithm server adjusts and optimizes the strategy and parameters according to the effect and efficiency of path optimization during task execution, such as improving the consideration of environmental factors, optimizing the task allocation algorithm of multi-machine cooperation, etc., 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 multi-source data fusion algorithm, improving the speed and accuracy of data processing, improving data quality monitoring model, enhancing the identification and processing ability of abnormal data; the blockchain network performs node expansion, network bandwidth optimization, etc. according to the performance bottleneck of data storage and transmission, such as increasing the number of blockchain nodes, improving data storage capacity and processing capacity, upgrading network equipment, and improving data transmission rate, to improve the performance and reliability of the entire survey system and make full preparation for the next survey task.

[0074] It can be understood that the unmanned aerial vehicle lands according to the plan and returns the task details, which is convenient for comprehensive inspection and maintenance of equipment, timely discovery and solution of problems, optimization of intelligent algorithm server, data processing center and blockchain network performance, improvement of overall system stability and accuracy, guarantee of more efficient execution of next survey task, and realization of continuous iteration and improvement of unmanned aerial vehicle building survey technology.

[0075] It should be noted that:

[0076] In the specification provided herein, a large number of specific details are described. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some examples, well-known structures and techniques are not shown in detail in order not to obscure the understanding of the present specification.

[0077] In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments but not others, the combination of features of different embodiments means within the scope of the present application and forms different embodiments.

[0078] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited to this. Any changes or replacements within the technical scope disclosed by the present application can be easily thought by those skilled in the art, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A building surveying method based on unmanned aerial vehicles (UAVs), characterized in that, include: Integrate building data, surrounding environment information and performance parameters of multiple drones, build an intelligent algorithm server, plan flight paths and assign tasks, complete the debugging of multi-source heterogeneous sensors and drone systems, and build a data processing center and blockchain network. Multiple drones take off sequentially from the designated site, 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. 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, which is then transmitted in real time to the data processing center for fusion processing and quality monitoring. The processed mapping data is encrypted and transmitted to the blockchain network, recording the data hash value, timestamp, and drone identifier; High-precision 3D building models and material distribution reports are generated from multi-source heterogeneous surveying and mapping data stored on blockchain. The drone completes its mission and lands according to the plan, transmits mission details, conducts a comprehensive inspection and maintenance of the drone and equipment, and optimizes the performance of the intelligent algorithm server, the data processing center, and the blockchain network. When multi-source heterogeneous sensors synchronously collect building data according to preset parameters and transmit it in real time to the data processing center for fusion processing and quality monitoring, the process includes: Data acquisition from multiple heterogeneous sensors: Upon reaching the survey area, both the hyperspectral camera sensor and the lidar sensor simultaneously activate. The hyperspectral camera sensor automatically captures images of the building surface according to a preset shooting frequency and spectral band acquisition range. During the shooting process, the camera's internal autofocus system adjusts the lens focal length in real time based on the distance information between the drone and the building to ensure image clarity. Simultaneously, it automatically adjusts exposure parameters based on light intensity to obtain high-quality hyperspectral image data. After initial processing within the drone, including data compression and the addition of shooting position and time stamps, the captured image data is transmitted in real time to the data processing center via a data transmission device. The lidar sensor emits laser pulses at a set scanning frequency towards the building and its surrounding environment. Based on the reflected light signals, it calculates the distance and position information of the target object, constructing three-dimensional point cloud data. During operation, the lidar sensor automatically adjusts the scanning angle and range according to the drone's flight speed and altitude. The collected point cloud data is preprocessed through data compression and coordinate transformation before being transmitted in real time to the data processing center. The data processing center integrates and monitors data. It 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 using the position and attitude information of the drones. It then uses a multi-source data fusion algorithm to combine the spectral features in the hyperspectral data with the geometric features in the lidar point cloud data. During the data fusion process, it monitors the quality and integrity of the data in real time and identifies and processes outliers and missing values ​​in the data.

2. The building surveying method based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The integration of building data, surrounding environmental information, and multi-UAV performance parameters to construct an intelligent algorithm server for planning flight paths and assigning tasks includes: Data collection and organization includes collecting architectural design drawings, geographic coordinate information, historical survey data, and surrounding topography, land features, and meteorological environment data. It also involves recording in detail the models, flight performance, and types and performance parameters of the multiple drones involved in the survey. The system employs intelligent algorithm planning and constructs an intelligent algorithm server. The collected information is input into the intelligent algorithm server, which divides the survey area into multiple sub-regions based on the scale, shape, and complexity of the buildings. According to the performance of the UAVs and the characteristics of the sub-regions, the intelligent algorithm server plans an initial flight route for each UAV, determines its flight altitude, speed, and turning points at different stages, and assigns corresponding survey tasks.

3. The UAV-based building surveying method according to claim 1, characterized in that, The completion of the debugging of multi-source heterogeneous sensors and UAV systems, and the establishment of a data processing center and blockchain network, includes: Equipment debugging and calibration: Accurately calibrate the spectral band accuracy 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 it can accurately acquire the three-dimensional spatial information of the building; at the same time, check the UAV's flight control system and test the UAV's attitude stability, heading control, and altitude maintenance functions; check the signal strength, transmission rate, and stability of the data transmission device and the positioning accuracy of the positioning system. A data and blockchain platform is established, and 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 UAVs. The data processing software has data fusion, quality detection, and format conversion functions. A blockchain network is built, data encryption algorithms are set, data storage structure and access permission rules are determined, and preparations are made to receive and store mapping data.

4. The building surveying method based on unmanned aerial vehicles according to claim 3, characterized in that, The multiple drones take off sequentially from the designated site, 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-flight checks: At the designated takeoff site, each drone undergoes checks before takeoff, including battery level, motor operation status, propeller operation status, sensor operation status and parameters, and the connection status of the data transmission device with the intelligent algorithm server and the data processing center. Takeoff and communication connection: The UAVs take off in a predetermined order. During takeoff, the flight control system continuously monitors the flight status of the UAVs 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 UAVs' identification information, initial position information and system status information. After receiving the information from the UAV, the intelligent algorithm server fine-tunes the pre-planned flight path based on the current environmental information and sends the final flight mission information and the optimized flight path information to the UAV. After receiving and confirming the mission information, the UAV flies to its assigned initial mapping point according to the flight path. During the flight, the UAV adjusts its flight altitude, speed and heading in a timely manner according to the instructions of the intelligent algorithm server.

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

6. The building surveying method based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The process of encrypting and transmitting the processed mapping data to the blockchain network, and recording the data hash value, timestamp, and drone identifier, includes: 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 mapping data into a format, ready to be transmitted to the blockchain network. In blockchain data storage, encrypted data is transmitted to blockchain nodes according to the protocol specifications of the blockchain network. After receiving the data, the blockchain nodes verify the integrity of the data, calculate the hash value of the data, and compare it with the hash value attached during data transmission to verify the legality of the data source. By checking the drone identification information in the data packet header and matching it with the information of legitimate drones pre-registered in the blockchain network, it is confirmed that the data comes from an authorized drone device. After successful verification, the blockchain nodes store the data in the distributed ledger of the blockchain and generate data blocks containing data hash values, timestamps, drone identification, and data source. Each data block is linked to the previous data block through a hash pointer, forming an immutable blockchain data chain.

7. A building surveying method based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The multi-source heterogeneous surveying and mapping data stored on the blockchain generates a high-precision 3D building model and material distribution report, including: The results are generated based on multi-source heterogeneous surveying and mapping data stored in the blockchain. Surveying and mapping analysis software is used to generate the results. The data is read from the blockchain network and securely verified. The geometric information in the data is used to construct a three-dimensional framework model of the building. The discrete points are connected into surfaces through the triangulation algorithm of the point cloud data, thereby constructing a three-dimensional shape model of the building. Combined with the material information in the hyperspectral data, the regions of different materials are distinguished and labeled in the three-dimensional model, generating a building material distribution report that shows in detail the distribution location and area information of different materials on the building surface.

8. A building surveying method based on unmanned aerial vehicles (UAVs) according to claim 4, characterized in that, The drone completes its mission, lands according to plan, transmits mission details, and performs a comprehensive inspection and maintenance of the drone and equipment, including: After the mission is completed and all UAVs have completed the mapping flight according to the predetermined mission, the UAVs will land in sequence at the designated site 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. The UAVs will transmit the detailed execution status of this mission to the data processing center. Equipment inspection and maintenance: After the UAV lands and is recovered, the hyperspectral camera sensor and lidar sensor are cleaned, calibrated, and their performance is tested. The physical condition of the UAV's mechanical components is checked, the battery is charged and replaced, and the software of the flight control system and data transmission device is updated and debugged. The equipment maintenance information and test results are recorded in the equipment management system.

9. A building surveying method based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, Optimizing the performance of the intelligent algorithm server, the data processing center, and the blockchain network includes: System performance optimization involves optimizing the intelligent algorithm server, the data processing center, and the blockchain network based on the data processing and system operation of the surveying and mapping task. The intelligent algorithm server adjusts its optimization strategies and parameters based on the effectiveness and efficiency of path optimization during task execution. The data processing center updates its data processing algorithms and models based on issues identified during data fusion and quality monitoring. The blockchain network expands its nodes and optimizes its network bandwidth based on performance bottlenecks in data storage and transmission.

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