Unmanned aerial vehicle curtain wall operation and maintenance task intelligent distribution system under low-altitude economy

Abstract

The invention relates to the technical field of resource scheduling optimization, in particular to an unmanned aerial vehicle curtain wall operation and maintenance task intelligent distribution system under low-altitude economy, which comprises a direction density analysis module, a direction matching and screening module, a wind disturbance data construction module, a correlation degree updating module and a task sorting and distribution module. According to the method, the boundary points of the remaining task blocks of the curtain wall are obtained, angle division and density statistics are carried out, and the angle difference is extracted in combination with the current operation direction of the unmanned aerial vehicle, so that dynamic adjustment of the flight path and directional screening of the task blocks can be realized; through measuring point acquisition and disturbance calculation of wind direction and wind speed data in a target direction coverage area, the wind field stability of different curtain wall blocks in a current time period can be identified, the task emergency degree is further corrected based on the relevance of a wind disturbance factor sequence and a reference sequence, dynamic adjustment of priority parameters is realized, and the task emergency degree is further corrected. And linkage scheduling of the operation and maintenance task of the unmanned aerial vehicle on a space path and a time window is realized.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling and optimization technology, and in particular to an intelligent allocation system for the operation and maintenance of unmanned aerial vehicle (UAV) curtain walls under low-altitude economic conditions. Background Art

[0002] The field of resource scheduling optimization technology involves methods for efficient allocation and scheduling of resources under multi-task and multi-resource conditions, including task identification and classification, resource matching strategies, scheduling algorithm design, execution plan generation and real-time adjustment.

[0003] Among them, the intelligent allocation system for drone curtain wall operation and maintenance tasks under the low-altitude economy refers to the use of drones as operation carriers in urban low-altitude airspace to complete operation and maintenance tasks such as cleaning and inspection of high-rise building curtain walls. Based on preset building information and task parameters, the system completes the allocation of operation tasks for multiple drones through methods such as manual arrangement of task lists, fixed route dispatch, and time-based task allocation.

[0004] Because existing technologies mainly rely on manual task list arrangement, fixed route assignment, and time sequence scheduling, task allocation is prone to directional deviations and overlapping operation paths when the distribution density of the operation area is uneven or sudden wind disturbances occur frequently. Especially in urban low-altitude airspace where high-rise building curtain walls are widely distributed and wind fields fluctuate significantly, manually designed routes are difficult to respond to dynamically changing operation conditions in a timely manner, resulting in overlapping coverage of some areas and delayed execution of some tasks. For example, when operating in certain high-wind-speed sections, tasks are still executed according to the original plan, which may lead to operation failure or increased risk. At the same time, because the urgency level is set statically and fixedly, there is a lack of linkage mechanism with changes in the actual operation environment, and task scheduling lacks pertinence and flexibility, ultimately affecting the overall efficiency and safety of UAV operation and maintenance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent allocation system for the operation and maintenance of curtain walls under low-altitude economic conditions using unmanned aerial vehicles (UAVs).

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A low-altitude, economical UAV curtain wall maintenance task intelligent allocation system includes: The orientation density analysis module obtains the boundary point matrix of the remaining task blocks corresponding to the curtain wall to be operated, divides equal-angle sectors with the current drone operation position as the center, maps the boundary point matrix to the corresponding angle sector, calculates the task density of each angle sector, and filters the preferred orientation set. The direction matching and filtering module compares each angle sector in the preferred direction set with the current UAV flight direction angle and selects the target dispatch direction sector. The wind disturbance data construction module obtains the set of curtain wall blocks covered by the target relocation direction sector, as well as the current wind direction and wind speed at multiple measuring points on the facade of each curtain wall block, and calculates the corresponding wind field disturbance factor to construct a wind disturbance feature set. The correlation update module extracts the wind field disturbance factors of all measuring points in the wind disturbance feature set, analyzes the correlation between the wind field stability reference sequence and the preset wind field stability reference sequence, adjusts the original urgency threshold of the task according to the correlation, and obtains the updated task priority parameter set. The task sorting and allocation module sorts the curtain wall task blocks under the UAV target direction path according to the updated task priority parameter set, and obtains the operation and maintenance task allocation result.

[0007] As a further embodiment of the present invention, the preferred direction set includes angle sector number, direction density value, and continuously increasing interval identifier; the target dispatch direction sector specifically includes direction angle difference and minimum angle sector index; the wind disturbance feature set includes measuring point wind direction data, measuring point wind speed data, and wind field disturbance factor; the updated task priority parameter set includes original task urgency, correlation analysis results, and adjusted urgency threshold; and the operation and maintenance task allocation result includes sorted block number and task priority order.

[0008] As a further aspect of the present invention, the directional density analysis module includes: The task block acquisition submodule acquires the boundary point matrix data of the remaining task blocks corresponding to the curtain wall to be operated, detects the current UAV operation position as the center point to establish a polar coordinate system, divides the spatial coordinate system by angle to generate an angle partition structure, and establishes the polar coordinate sector division result. The angle sector mapping submodule calculates the azimuth angle formed by each boundary point data and the center point in the polar coordinate system according to the polar coordinate sector division result, maps the boundary point data to the corresponding angle sector according to the angle classification method, and counts the number of boundary points in each angle sector as the task density to obtain the angle sector task density dataset. The preferred direction filtering submodule calls the angle sector task density dataset, constructs a sliding window in angle order and compares the task density in each sector in turn. It uses the nearest neighbor search algorithm to determine whether consecutive sectors meet the density value increasing condition, and integrates consecutive angle sectors that meet the condition by numbering them to obtain the preferred direction set.

[0009] As a further aspect of the present invention, the direction matching and filtering module includes: The azimuth angle extraction submodule extracts the center azimuth angle value of each angle sector in the preferred azimuth set, detects the real-time angle of the current UAV flight direction, and establishes an angle sector azimuth angle set. The angle difference calculation submodule calculates the difference between the current UAV flight direction angle and the center direction angle of each angle sector based on the set of directional angles of the angle sector, and organizes all angle differences into a directional angle difference sequence. The target sector determination submodule calls the direction angle difference sequence, identifies the angle sector number corresponding to the smallest value, determines it as the direction closest to the current flight direction of the UAV, and outputs the smallest angle sector number as the target dispatch direction sector.

[0010] As a further aspect of the present invention, the wind disturbance data construction module includes: The block set extraction submodule locates the corresponding curtain wall block spatial location based on the target dispatch direction sector coverage path range, filters all curtain wall blocks to be operated and organizes them into an independent block set, and generates curtain wall block set data. The wind element acquisition submodule, based on the set data of the curtain wall blocks, calls the set measuring points on the exterior of each curtain wall block, collects the wind direction and wind speed data of each measuring point at the current moment, organizes the collected data according to the measuring point number and time order, and establishes a measuring point wind direction and wind speed record set; The disturbance factor calculation submodule calls the wind direction and speed record set of the measuring points, counts the number of times the wind direction changes at each measuring point within a set time period and calculates the frequency of change, and calculates the standard deviation and range of wind speed as the fluctuation amplitude. Based on the frequency of wind direction change and the fluctuation amplitude of wind speed, the wind field disturbance factor is jointly constructed to generate a wind disturbance feature set.

[0011] As a further aspect of the present invention, the correlation update module includes: The disturbance factor extraction submodule extracts the wind field disturbance factors corresponding to all measurement points in each task block of the wind disturbance feature set, constructs a continuous temporal structure of the measurement point disturbance factors in chronological order, and generates a wind field disturbance factor sequence. The correlation analysis submodule, based on the wind field disturbance factor sequence, uses a preset wind field stability reference sequence as a comparison benchmark, inputs the two types of sequences into the grey relational discrimination model to perform numerical comparison, calculates the corresponding matching degree between the disturbance factor sequence and the reference sequence under each task block, and obtains the disturbance factor matching degree information. The urgency adjustment submodule obtains the original urgency threshold of the task, adjusts the urgency threshold of the corresponding task block according to the perturbation factor matching information, summarizes and outputs the adjustment results by task block number, and generates an updated task priority parameter set.

[0012] As a further aspect of the present invention, the task sorting and allocation module includes: The priority sorting submodule extracts the updated task priority parameter set, sorts the curtain wall task blocks covered by the target direction path according to the updated task priority order in the parameter set, and generates a block sorting index list. The task parameter setting submodule sets the task number, execution time period and path coverage fields for each task block according to the block sorting index list and in the sorting order, forming a sorted task parameter set. The task instruction generation submodule calls the sorted task parameter set, combines the current operation status of the UAV with the remaining flight time information to determine the matching time of the operation, constructs a scheduling structure format for all sorted tasks, and generates operation and maintenance task allocation results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by acquiring the boundary points of the remaining task blocks of the curtain wall and performing angle division and density statistics, and combining the angle difference extracted from the current working direction of the UAV, dynamic adjustment of the flight path and directional screening of task blocks can be achieved. By collecting and calculating the wind direction and wind speed data at measurement points within the target direction coverage area, the wind field stability of different curtain wall blocks in the current time period can be identified. Based on the correlation between the sequence of wind disturbance factors and the reference sequence, the urgency of the task is further corrected, and the priority parameters are dynamically adjusted. By using the updated priority parameters to sort and allocate the task blocks covered by the path, the UAV maintenance tasks can be linked and scheduled in spatial path and time window. In complex low-altitude environments such as uneven resource allocation and severe wind field disturbance, the rationality and efficiency of task execution are improved, task conflicts and repeated flights are reduced, the real-time performance and security of maintenance task allocation are enhanced, and path decoupling and task load balancing are ensured in the collaborative operation of multiple UAVs. Attached Figure Description

[0014] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the directional density analysis module of the present invention; Figure 3 This is a flowchart of the directional matching and filtering module of the present invention; Figure 4 This is a flowchart of the wind disturbance data construction module of the present invention; Figure 5 This is a flowchart of the correlation update module of the present invention; Figure 6 This is a flowchart of the task sorting and allocation module of the present invention. Detailed Implementation

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Please see Figure 1 The intelligent allocation system for the operation and maintenance of curtain walls using drones in a low-altitude economy includes: The orientation density analysis module obtains the boundary point matrix of the remaining task blocks corresponding to the curtain wall to be operated, divides equal-angle sectors with the current drone operation position as the center, maps the boundary point matrix to the corresponding angle sector, calculates the task density of each angle sector, and filters the preferred orientation set. The direction matching and filtering module compares each angle sector in the preferred direction set with the current flight direction angle of the UAV and selects the target dispatch direction sector. The wind disturbance data construction module obtains the set of curtain wall blocks covered by the sector of the target relocation direction, as well as the current wind direction and wind speed at multiple measuring points on the facade of each curtain wall block, and calculates the corresponding wind field disturbance factor to construct a set of wind disturbance features. The correlation update module extracts the wind field disturbance factors of all measuring points in the wind disturbance feature set, analyzes the correlation between the wind field stability reference sequence and the preset wind field stability reference sequence, adjusts the original urgency threshold of the task according to the correlation, and obtains the updated task priority parameter set. The task sorting and allocation module sorts the curtain wall task blocks under the UAV target direction path according to the updated task priority parameter set, and obtains the operation and maintenance task allocation result. The preferred direction set includes angle sector number, direction density value, and continuously increasing interval identifier. The target dispatch direction sector specifically includes direction angle difference and minimum angle sector index. The wind disturbance feature set includes measurement point wind direction data, measurement point wind speed data, and wind field disturbance factor. The updated task priority parameter set includes the original urgency of the task, correlation analysis results, and adjusted urgency threshold. The operation and maintenance task allocation results include the sorted block number and task priority order.

[0018] Please see Figure 2 The directional density analysis module includes: The task block acquisition submodule acquires the boundary point matrix data of the remaining task blocks corresponding to the curtain wall to be operated, detects the current UAV operation position as the center point to establish a polar coordinate system, divides the spatial coordinate system by angle to generate an angle partition structure, and establishes the polar coordinate sector division result. To obtain the boundary point matrix data of the remaining task blocks corresponding to the curtain wall to be constructed, it is necessary to acquire 3D point cloud data by scanning with a drone. Then, the curtain wall surface is modeled and the boundary point matrix of the unfinished area is extracted using software such as a LiDAR point cloud processing platform. This process requires filtering out completed areas based on construction logs and comparing the predetermined curtain wall BIM model boundary with the actual boundary to select the "remaining task blocks". Then, the current position of the drone is set as the polar coordinate origin using laser point cloud software, and its spatial coordinates, such as the horizontal, vertical and longitudinal coordinates, are extracted. This origin information is located in the spatial 3D model and used as the reference center point for subsequent calculations. The entire 3D space is divided into equally spaced angles around the origin, with each angle set to a fixed angle value, such as 20 degrees, forming multiple polar coordinate sectors. Each sector contains a certain directional range and is distributed around the origin. Then, the spatial coordinate system is converted to a polar coordinate system. This process is accomplished by setting the radial distance from the origin to each point and the angle with the reference direction. Each point corresponds to a unique azimuth angle and distance value. Finally, a complete polar coordinate sector division result is generated.

[0019] The angle sector mapping submodule calculates the azimuth angle formed by each boundary point data and the center point in the polar coordinate system according to the polar coordinate sector division results. It maps the boundary point data to the corresponding angle sector according to the angle classification method, counts the number of boundary points in each angle sector as the task density, and obtains the angle sector task density dataset. Based on the polar coordinate sector division results, the azimuth angle of each point in the boundary point matrix needs to be calculated. The calculation method is to set the straight line segment formed between the current center point and the boundary point as the radius direction, and take the angle between it and the set reference direction (such as due east) as the azimuth angle of the point. The angle can be obtained through trigonometric relations or arctangent function. In the specific operation, the horizontal difference and vertical difference between the coordinates of the boundary point and the coordinates of the center point are read, and the angle is obtained by "arctangent function (vertical difference ÷ horizontal difference)". The angle range is corrected to 0 to 360 degrees according to the quadrant. Each point is classified into its corresponding sector number. Each sector number is defined according to the starting angle and the division step size. Then, the points in each angle sector are counted to obtain the number of boundary points it contains. This is the task density. The task density value can be obtained by recording "total number of boundary points contained in this sector". This density value is used to reflect the degree of task concentration and form a complete angle sector task density dataset.

[0020] The preferred direction filtering submodule calls the angle sector task density dataset, constructs a sliding window in angle order and compares the task density in each sector in turn. It uses the nearest neighbor search algorithm to determine whether consecutive sectors meet the condition of increasing density value. It then numbers and integrates consecutive angle sectors that meet the condition to obtain the preferred direction set. Based on the task density dataset of angle sectors, all angle sectors are traversed in order from the starting angle to the ending angle. A fixed-width sliding window, such as three sectors, is set and slid backwards one by one, recording the task density value corresponding to each sector within the window. The density values ​​in the window are compared to determine whether a continuous increasing relationship is satisfied. For example, if the density of the first sector is less than the second, and the second is less than the third, then an increasing trend is considered to be satisfied. The judgment criterion is set as "the next density value - the previous density value > the task density increasing threshold". The increasing threshold is set based on experience, such as each increase should be greater than the number of 3 boundary points. If satisfied, these consecutive sector numbers are recorded and used as a set of preferred directions. The window continues to slide backwards until the entire angle range is processed. Finally, all numbered segments that meet the conditions are integrated as the set of preferred directions, completing the preferred direction selection process.

[0021] Please see Figure 3 The direction matching filtering module includes: The azimuth angle extraction submodule extracts the center azimuth angle value of each angle sector in the preferred azimuth set, detects the real-time angle of the current UAV flight direction, and establishes an azimuth angle set for the angle sector. Extracting the center direction angle value of each angle sector in the preferred direction set requires first determining the starting angle and step size of each angle sector. For example, if the preferred direction set contains angle sector number 3, and the angle span of each angle sector is set to 20 degrees, then the starting angle of sector number 3 is (3-1)×20=40 degrees. The center direction angle value of this sector is calculated as (starting angle + starting angle + sector span)÷2, that is, (40+60)÷2=50 degrees. Therefore, the center direction angle value of this sector is 50 degrees. This process is repeated for each preferred direction sector number to obtain a set containing all center direction angle values. Then, the actual flight direction of the current UAV needs to be detected. The real-time angle is derived from the attitude sensor module in the flight control system, such as the IMU module or the combined estimation of GPS and magnetometer. It is obtained by recording the angle of the line connecting the flight position coordinates at the previous time point and the current flight position coordinates. For example, the direction angle formed from point A to point B is the current flight direction angle. Assuming that the coordinates of point A are (100, 200) and the coordinates of point B are (130, 260), then based on the longitudinal coordinate difference of 60 and the lateral coordinate difference of 30, the direction angle can be obtained by the arctangent function, that is, arctangent (60 ÷ 30) = 63.43 degrees. This real-time angle is recorded and formed with the center direction angle values ​​of all extracted angle sectors to form the angle sector direction angle set.

[0022] The angle difference calculation submodule calculates the difference between the current UAV flight direction angle and the center direction angle of each angle sector based on the set of directional angles of the angle sector, and organizes all angle differences into a directional angle difference sequence. The angle difference calculation must be based on the current flight direction angle of the drone. For example, if the current angle is 63.43 degrees, the difference is calculated between this angle and the center direction angle value of each sector in the angle sector direction angle set. The calculation method is: current angle value - sector center angle value. Taking sector number 3 with a center angle value of 50 degrees as an example, the difference is 63.43 - 50 = 13.43 degrees. If a sector has a center angle of 70 degrees, the difference is 63.43 - 70 = -6.57 degrees. In this case, to avoid negative angle differences... The value needs to be corrected by 360 degrees, i.e., -6.57 + 360 = 353.43 degrees. Similarly, if a calculation result exceeds 360 degrees, then 360 degrees needs to be subtracted, for example, 370.12 degrees - 360 = 10.12 degrees. Therefore, all differences are uniformly organized between 0 and 360 degrees. Finally, each calculation result is recorded to form a direction angle difference sequence, where each item consists of "angle difference" and "corresponding sector number". This sequence fully reflects the degree of proximity between the current flight direction and each preferred direction.

[0023] The target sector determination submodule calls the direction angle difference sequence, identifies the angle sector number corresponding to the smallest value, determines the direction closest to the current flight direction of the UAV, and outputs the smallest angle sector number as the target dispatch direction sector; Identifying the sector number corresponding to the minimum angle difference in the angle difference sequence is a crucial step. Each difference needs to be compared with the current minimum value. Initially, the first angle difference is set as the current minimum value. For example, if the first difference is 13.43 degrees, it is compared with the next difference one by one, using the comparison method: current difference - current minimum difference. If the result is less than 0, the minimum value and its corresponding sector number are updated. If the comparison result is positive or equal to 0, the original minimum value is retained. For example, if the second value is 10.12 degrees, then 10.12 - 13.43 = -3.31, which is less than 0. Therefore, the minimum difference is updated to 10.12 degrees, and the sector number is updated to that value. This process is repeated until the end of the sequence. The final minimum difference and its corresponding sector number are the target reassignment direction sector. This number is output for task path adjustment and command control direction selection.

[0024] Please see Figure 4 The wind disturbance data construction module includes: The block set extraction submodule locates the corresponding curtain wall block spatial location based on the sector coverage path range of the target transfer direction, filters all curtain wall blocks to be operated and organizes them into independent block sets, and generates curtain wall block set data; Based on the target dispatch direction sector coverage path range, the spatial range corresponding to that direction needs to be clearly defined in the 3D building model. By calculating the width of the projected path and the forward distance covered by the direction the current UAV is facing, for example, setting the path width to 20 meters and the forward extension length to 50 meters, a rectangular coverage area is formed. This coverage area is projected onto the curtain wall coordinate model, and spatial intersection detection is performed with the 3D coordinate data of the building facade. The geometric center point coordinates of each curtain wall block are used to determine the point within the coverage area. If the geometric center point is inside the coverage area, the block is considered to belong to the coverage area of ​​the current direction. These block numbers are extracted to form a preliminary set. Then, blocks with the same number are selected from the list of all curtain wall blocks to be operated, forming an independent block set. This set is then structured and organized, for example, by establishing data fields according to attributes such as serial number, 3D coordinates, area, and orientation, and integrated and output as curtain wall block set data.

[0025] The wind element acquisition submodule, based on the curtain wall block set data, calls the set measuring points on the exterior of each curtain wall block, collects the wind direction and wind speed data of each measuring point at the current moment, organizes the collected data according to the measuring point number and time order, and establishes a measuring point wind direction and wind speed record set; Based on the curtain wall block set data, a scheme for calling the facade measuring points is established for each block. For example, the spacing between measuring points is set according to the side length of each block, such as one point every 10 meters in the horizontal direction and one point every 8 meters in the vertical direction. Then, multiple measuring points can be set up in each curtain wall block. The measuring point number is generated by adding the measuring point index to the block number, such as "Block 5 - Measuring Point 3". Then, at each set measuring point location, the sensor is called or the corresponding spatial coordinate wind element data provided by the weather data platform is obtained. The wind direction and wind speed values ​​at the current moment are collected in real time. The wind direction unit is angle and the wind speed unit is meters per second. The collection results are stored in the format of "measuring point number - time - wind direction - wind speed" and the data of each measuring point is sorted and organized in chronological order to form a complete set of measuring point wind direction and wind speed records.

[0026] The disturbance factor calculation submodule calls the wind direction and speed record set of the measuring points, counts the number of times the wind direction changes at each measuring point within a set time period and calculates the frequency of change, and calculates the standard deviation and range of wind speed as the fluctuation amplitude. Based on the frequency of wind direction change and the fluctuation amplitude of wind speed, the wind field disturbance factor is jointly constructed to generate a set of wind disturbance characteristics. After accessing the wind direction and speed records from the measurement points, the frequency of wind direction changes, the standard deviation of wind speed, and the range of wind speed need to be jointly processed within a set time period T to construct a wind field disturbance factor under a unified metric. First, the number of times the wind direction changes significantly at each measurement point within the time period T is counted. The standard for change is that the absolute value of the change in wind direction angle between any two consecutive sampling times exceeds a set threshold (e.g., 15°), and this is accumulated as the number of changes. The frequency of wind direction change is calculated as follows: Where f: wind direction change frequency, in times per second (1 / s), indicating how frequently the wind direction changes at that measuring point; : Number of significant wind direction changes (unit: times), derived from abrupt changes in wind direction time series statistics; T: Total duration of the analysis period (unit: seconds), for example, 300 seconds can be used as a calculation window.

[0027] Next, the wind speed fluctuations are measured, mainly including: the standard deviation of wind speed σ, which reflects the average degree to which the wind speed at the measuring point deviates from the average value within the time period T, in meters per second (m / s); and the range of wind speed R, which is the difference between the maximum and minimum wind speed values, also in meters per second (m / s). Since f, σ, and R have different units and cannot be directly added, standardization is used to convert each term to a dimensionless form. First, the statistical baseline values ​​for all measuring points within the time period T need to be obtained, including: The average frequency of wind direction changes at all measuring points; Standard deviation of the frequency of wind direction changes at all measuring points; : The mean of the standard deviations of wind speeds at all measuring points; : The standard deviation of the standard deviation of wind speed at all measuring points; : The mean of the wind speed range at all measuring points; Standard deviation of the range of wind speeds at all measuring points.

[0028] Based on this, the disturbance index of each measuring point is standardized, and then the three dimensionless results are superimposed to obtain the wind field disturbance factor W, which is calculated as follows: ; Where W: wind field disturbance factor (dimensionless), used to characterize the wind field disturbance intensity at the current measuring point; f: wind direction change frequency at the current measuring point (unit: times per second), derived from abrupt change analysis of the record set; The average frequency of wind direction change at all measuring points, used for benchmark comparison; : Standard deviation of wind direction frequency, reflecting the range of wind direction frequency fluctuations across all measuring points; σ: Standard deviation of wind speed at the current measuring point (unit: m / s), calculated from wind speed record fluctuations; : The average of the standard deviations of wind speed, used for normalization; : Standard deviation of wind speed standard deviation, a unified dimension scale; R: Current wind speed range at the measuring point (unit: m / s), i.e., maximum wind speed - minimum wind speed; : Mean of the wind speed range at all measuring points; Standard deviation of wind speed range, with consistent dimensions.

[0029] The final calculated W value is a dimensionless real number. The larger the value, the more frequent the wind direction changes and the more drastic the wind speed fluctuations at the measuring point.

[0030] Suppose we select a measuring point to calculate the wind disturbance factor and collect the following raw data: Measuring point analysis time period: T=300 seconds; Number of sudden wind direction changes at the measuring point: =9, Standard deviation of wind speed at measuring points: σ = 1.6 m / s, Range of wind speed at measuring points: R = 4.0 m / s. Mean frequency of wind direction change: =0.025 (unit: times / second), standard deviation of wind direction change frequency: =0.01, mean standard deviation of wind speed: =1.2m / s, Standard deviation of wind speed: =0.3m / s, mean wind speed range: =3.2m / s, wind speed range standard deviation: =0.5m / s.

[0031] Calculate the frequency of wind direction change f: times / second; Substitute into the standardized perturbation factor formula .

[0032] The final calculated wind field disturbance factor for this measuring point is W = 3.433. This value is dimensionless and represents the overall disturbance level of this measuring point during this time period. In subsequent processes, a threshold can be set to filter high-disturbance areas. For example, if the high-disturbance threshold is set to W > 3.0, then this measuring point can be classified into a significantly disturbed area.

[0033] First, by setting a time period, the number of times the wind direction changed significantly at each measuring point within that period is counted. Dividing this number by the time length yields the wind direction change frequency, which measures whether the wind direction fluctuates frequently. Then, the standard deviation and range of the wind speed at each measuring point are calculated within that time period. The standard deviation represents the dispersion of wind speed fluctuations, while the range reflects the span between the maximum and minimum wind speeds, describing the instantaneous amplitude of wind speed changes. Since these three parameters have different physical units—one representing the frequency of change and the other two representing the amplitude of speed fluctuations—they cannot be directly weighted or combined. Therefore, they are normalized using the mean and standard deviation of all measuring points, transforming them into dimensionless standard scores. These three standard scores are then added together to obtain a disturbance factor with a unified dimension. The significance of this normalization and addition lies in balancing the influence of different dimensional indicators on the overall disturbance level, preventing a single indicator's value from dominating the calculation result, thus fairly reflecting the comprehensive disturbance level of wind direction frequency, wind speed fluctuation intensity, and instantaneous amplitude of change. The final calculated disturbance factor result is used to determine whether a certain measuring point is located in an area with unstable wind field or strong disturbance, and can serve as the input basis for subsequent path adjustment, task scheduling and safety warning.

[0034] Please see Figure 5 The correlation update module includes: The disturbance factor extraction submodule extracts the wind field disturbance factors corresponding to all measuring points in each task block of the wind disturbance feature set, constructs a continuous temporal structure of the measuring point disturbance factors in chronological order, and generates a wind field disturbance factor sequence. To extract the wind field disturbance factors from all measuring points in each task block of the wind disturbance feature set, the calculation results for each measuring point in the set must first be read according to the block number. Each measuring point has already had its dimensionless disturbance factor value calculated by the preceding module. The task block number, measuring point number, timestamp, and disturbance factor value can be read through structured storage fields. Then, all measuring point data within the same block are organized chronologically. Using each unified time point as a benchmark, the disturbance factor values ​​of all measuring points within the block at that time point are summarized, constructing a continuous record with time as the horizontal axis and disturbance factor values ​​as the vertical axis. Data from multiple consecutive time points are then sequentially concatenated to form a time-series structure. For data synchronization requirements, the minimum common time interval can be used to interpolate or resample data from different measuring points to ensure an equally spaced sequence structure on a unified timeline. Each task block corresponds to an independent wind field disturbance factor sequence. This sequence reflects the trend of local spatial disturbance intensity over time within the entire set analysis period and serves as the basis for subsequent matching analysis with standard stable wind field sequences. Finally, the time sequence of all task blocks is organized into a dictionary structure or a numbered index mapping set according to the block number.

[0035] The correlation analysis submodule, based on the wind field disturbance factor sequence and using the preset wind field stability reference sequence as the comparison benchmark, inputs the two types of sequences into the grey relational discrimination model to perform numerical comparison, calculates the corresponding matching degree between the disturbance factor sequence and the reference sequence under each task block, and obtains the disturbance factor matching degree information. In the correlation analysis submodule, the time series sequence of disturbance factors generated for each task block needs to be compared with a preset wind field stability reference sequence. This comparison process is based on the grey relational analysis model in grey system theory, used to quantify whether the overall trend of the two sequences is consistent in the time dimension. The disturbance factor sequence comes from the aggregated disturbance results of measurement points constructed by time in the previous module, representing the fluctuation process of wind disturbance in the current task block over time; the reference sequence is the "ideal wind stability" standard extracted from historical wind field data or simulation data. The difference between the two at each time point will be normalized and then averaged to obtain the final matching degree value, as shown in the following formula: ; Where, r: perturbation factor matching degree (dimensionless), the final calculation result, with a value ranging from 0 to 1. The larger the value, the closer the current block's perturbation trend is to the reference wind field's stable mode. n: sequence length, i.e., the number of time points contained in the perturbation factor sequence. For example, if data is collected once per second, 300 seconds would contain 300 time points. k: time index, used to represent the current k-th time point, ranging from 1 to n. : The perturbation factor value of the reference sequence at time point k, dimensionless, derived from the preset standard wind field in the system. : The value of the disturbance factor sequence of the current task block at time point k, dimensionless, from the dynamic record after aggregation of measurement points. The absolute difference between the two sequences at time point k represents the degree of deviation between the current task block and the ideal wind field at this moment. : The minimum difference between any two sequences at time point k in the set of all block perturbation sequences, where i represents the i-th task block or reference sequence; j represents the other task block or reference sequence being compared with it; the two are used to enumerate all combinations of measurement points or blocks, find the theoretically possible minimum difference value among all time points, and form the normalization lower bound. The maximum difference within the same range as above is used to form the upper limit of normalization, which also controls the scale of the denominator. The resolution coefficient is used to adjust the weight of the maximum difference in the normalization calculation, and its value is generally in the range of 0.2 to 0.8. The setting is based on the following: when the overall disturbance of the system is small and it is desirable to improve the model's sensitivity to subtle differences, a smaller value (e.g., 0.3) is set; when there are drastic disturbances in the system and it is necessary to improve the stability of trend judgment, a larger value (e.g., 0.6) is set; a commonly used median value of 0.5 is recommended to balance the model response and the tolerance to fluctuations.

[0036] Let task block number A be. Five consecutive time points of perturbation factor sequences were collected during the analysis period, and grey relational matching was performed with a stable reference sequence of corresponding length. The settings are as follows: the perturbation factor sequences for the task block are: 0.35, 0.40, 0.38, 0.45, 0.50; the reference sequence is: 0.30, 0.32, 0.35, 0.37, 0.40; all values ​​are dimensionless perturbation factors, and the resolution coefficient is... =0.5.

[0037] Calculate the difference between the task block and the reference sequence at each time point. : Point 1: ; Point 2: ; Point 3: ; Point 4: ; Point 5: .

[0038] Find the minimum and maximum differences among all sequence combinations: (From point 3); (From point 5).

[0039] The formula for the correlation coefficient is: ; Right now: ; Calculate each point: Point 1: ; Point 2: ; Point 3: ; Point 4: ; Point 5: .

[0040] The average correlation coefficients at the five time points are used to obtain the perturbation factor matching degree r: The matching degree between this task block and the stable wind field reference sequence is 0.7126.

[0041] First, the disturbance factor value at each time point is extracted and subtracted from the corresponding value in the reference sequence to obtain the disturbance deviation at each time point. Then, the maximum and minimum possible differences across all sequences are calculated as the upper and lower limits for normalization. Next, at each time point, the actual difference is combined with the maximum difference proportionally to construct a normalized local correlation coefficient, reflecting how close the disturbance trend of the current block is to the ideal state. The correlation coefficients at all time points are averaged to obtain a final matching degree index. This matching degree is a dimensionless value, and its magnitude directly represents the degree of consistency between the wind disturbance fluctuations of the block and the stability model over the entire time period. The closer the final calculation result is to the upper limit, the more stable the disturbance pattern of the block is, and the closer it is to the wind field stability standard defined in the system. Conversely, it indicates abnormal disturbances, deviating from the normal change state. This value will serve as the basis for judging the task adjustment strategy, determining whether the block needs to be reordered or given special attention.

[0042] The urgency adjustment submodule obtains the original urgency threshold of the task, adjusts the urgency threshold of the corresponding task block according to the perturbation factor matching information, summarizes and outputs the adjustment results by task block number, and generates an updated task priority parameter set. First, the system calls the preset initial urgency threshold for each task block. This threshold is generally set during the construction planning phase and represents the priority order of tasks in each block under conditions of no external interference. It can be represented by numerical levels, standard coefficients, or sorting numbers. Then, based on the disturbance factor matching information calculated in the previous module, the matching value and the initial urgency threshold are jointly processed to adjust the task priority. This adjustment process requires logical judgment and correction strategies. Specifically, a disturbance-sensitive rule can be set. For example, when the matching value of a block is lower than the preset stable benchmark value, the current wind disturbance intensity of that block is considered significant, and its urgency should be reduced to avoid scheduling conflicts; while when the matching value is higher than the benchmark value, its urgency is retained or appropriately increased. A linear adjustment method can be used during execution, that is, calculating a combined value of the initial urgency and the disturbance matching value according to weights, and then using this as the new urgency parameter. For example, the initial value is set to account for 70% of the weight, and the disturbance correction accounts for 30%, and the final result is obtained through numerical transformation. After the adjustment is completed, the task number of each block and the updated urgency need to be mapped into a mapping structure to form a task priority parameter set, which is used to provide the task scheduling system or path planning module with real-time scheduling reference.

[0043] Please see Figure 6 The task sorting and allocation module includes: The priority sorting submodule extracts the updated task priority parameter set, sorts the curtain wall task blocks covered by the target direction path according to the updated task priority order in the parameter set, and generates a block sorting index list. First, the updated task priority parameter set is extracted. This set includes the number of each task block and its urgency level adjusted for perturbation matching. After extraction, the system cross-filters these parameters against task blocks within the current target direction path coverage area, retaining only those blocks actually within the current direction's operational range. Next, the filtered task blocks are sorted according to their priority values, either in ascending or descending order, based on the system's definition of priority direction represented by the urgency value (e.g., lower urgency values ​​represent higher priority, thus sorted in ascending order). If task blocks with the same urgency value are encountered during sorting, they can be further subdivided based on secondary sorting criteria such as block number, spatial location, or modeling time. After sorting, the unique numbers of the task blocks are output sequentially according to the sorting results, forming an ordered block index, which serves as the basic structure for subsequent scheduling parameter preparation.

[0044] The task parameter setting submodule sets the task number, execution time period and path overlay fields for each task block according to the block sorting index list and in the sorting order, forming a sorted task parameter set. After receiving the block sorting index list, detailed scheduling execution parameters need to be configured for each task block according to the sorting order. First, a unique task number is generated for each block. This number can be automatically generated by combining task type, timestamp, or path location to ensure the uniqueness of the task identity in the scheduling instructions. Next, an expected execution time period is assigned to each block. The time period setting needs to consider the time required for each UAV operation to complete. Parameter mapping can be performed based on block area, expected disturbance level, and task type. For example, larger areas or high disturbance levels require longer time periods, while ordinary blocks can be set to a standard time length. In addition, a path overlay field needs to be set for each task block. This field describes how the UAV's operational path enters, traverses, and exits the block, typically determined by spatial matching between the block's 3D spatial coordinates and the UAV's flight trajectory model. After setting these parameters, the task numbers, execution time periods, and path fields of all sorted blocks are structurally combined to form a complete set of sorted task parameters.

[0045] The task instruction generation submodule calls the sorted task parameter set, combines the current operation status of the UAV with the remaining flight time information to match and judge the operation time, constructs a scheduling structure format for all sorted tasks, and generates operation and maintenance task allocation results. Upon receiving the sorted task parameter set, the system first needs to perform a task reachability and time matching judgment based on the current UAV's operational status and its remaining available flight time. This judgment process mainly analyzes whether the sum of the time required for the UAV to return to the current path starting point after completing the current task, the remaining flight time, the flight path time, and the task execution time is less than the remaining flight time. If some tasks are determined to be executable, they are retained and added to the scheduling list; otherwise, they are marked as unreachable or pending allocation. The system then constructs a scheduling structure format based on the retained tasks in sequence. This format must include fields such as task number, pre-allocated time period, takeoff time, end time, target block coordinates, and path trajectory index, and outputs it as a unified scheduling structured instruction set. Each task instruction corresponds to a record in the sorted parameters and provides specific scheduling instructions to the execution system based on the real-time flight status. Finally, the scheduling result can be directly sent to the flight control system for UAV task scheduling and control, or fed back to the task management platform for display and subsequent updates.

[0046] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A smart allocation system for the operation and maintenance of curtain walls using unmanned aerial vehicles (UAVs) under low-altitude economic conditions, characterized in that: The system includes: The orientation density analysis module obtains the boundary point matrix of the remaining task blocks corresponding to the curtain wall to be operated, divides equal-angle sectors with the current drone operation position as the center, maps the boundary point matrix to the corresponding angle sector, calculates the task density of each angle sector, and filters the preferred orientation set. The direction matching and filtering module compares each angle sector in the preferred direction set with the current UAV flight direction angle and selects the target dispatch direction sector. The wind disturbance data construction module obtains the set of curtain wall blocks covered by the target relocation direction sector, as well as the current wind direction and wind speed at multiple measuring points on the facade of each curtain wall block, and calculates the corresponding wind field disturbance factor to construct a wind disturbance feature set. The correlation update module extracts the wind field disturbance factors of all measuring points in the wind disturbance feature set, analyzes the correlation between the wind field stability reference sequence and the preset wind field stability reference sequence, adjusts the original urgency threshold of the task according to the correlation, and obtains the updated task priority parameter set. The task sorting and allocation module sorts the curtain wall task blocks under the UAV target direction path according to the updated task priority parameter set, and obtains the operation and maintenance task allocation result.

2. The intelligent allocation system for the operation and maintenance of UAV curtain walls under low-altitude economic conditions according to claim 1, characterized in that, The preferred direction set includes angle sector number, direction density value, and continuously increasing interval identifier. The target dispatch direction sector specifically includes direction angle difference and minimum angle sector index. The wind disturbance feature set includes measurement point wind direction data, measurement point wind speed data, and wind field disturbance factor. The update task priority parameter set includes the original urgency of the task, correlation analysis results, and adjusted urgency threshold. The operation and maintenance task allocation result includes the sorted block number and task priority order.

3. The intelligent allocation system for the operation and maintenance of UAV curtain walls under low-altitude economic conditions according to claim 1, characterized in that, The directional density analysis module includes: The task block acquisition submodule acquires the boundary point matrix data of the remaining task blocks corresponding to the curtain wall to be operated, detects the current UAV operation position as the center point to establish a polar coordinate system, divides the spatial coordinate system by angle to generate an angle partition structure, and establishes the polar coordinate sector division result. The angle sector mapping submodule calculates the azimuth angle formed by each boundary point data and the center point in the polar coordinate system according to the polar coordinate sector division result, maps the boundary point data to the corresponding angle sector according to the angle classification method, and counts the number of boundary points in each angle sector as the task density to obtain the angle sector task density dataset. The preferred direction filtering submodule calls the angle sector task density dataset, constructs a sliding window in angle order and compares the task density in each sector in turn. It uses the nearest neighbor search algorithm to determine whether consecutive sectors meet the density value increasing condition, and integrates consecutive angle sectors that meet the condition by numbering them to obtain the preferred direction set.

4. The intelligent allocation system for the operation and maintenance of UAV curtain walls under low-altitude economic conditions according to claim 3, characterized in that, The direction matching and filtering module includes: The azimuth angle extraction submodule extracts the center azimuth angle value of each angle sector in the preferred azimuth set, detects the real-time angle of the current UAV flight direction, and establishes an angle sector azimuth angle set. The angle difference calculation submodule calculates the difference between the current UAV flight direction angle and the center direction angle of each angle sector based on the set of directional angles of the angle sector, and organizes all angle differences into a directional angle difference sequence. The target sector determination submodule calls the direction angle difference sequence, identifies the angle sector number corresponding to the smallest value, determines it as the direction closest to the current flight direction of the UAV, and outputs the smallest angle sector number as the target dispatch direction sector.

5. The intelligent allocation system for the operation and maintenance of UAV curtain walls under low-altitude economic conditions according to claim 4, characterized in that, The wind disturbance data construction module includes: The block set extraction submodule locates the corresponding curtain wall block spatial location based on the target dispatch direction sector coverage path range, filters all curtain wall blocks to be operated and organizes them into an independent block set, and generates curtain wall block set data. The wind element acquisition submodule, based on the set data of the curtain wall blocks, calls the set measuring points on the exterior of each curtain wall block, collects the wind direction and wind speed data of each measuring point at the current moment, organizes the collected data according to the measuring point number and time order, and establishes a measuring point wind direction and wind speed record set; The disturbance factor calculation submodule calls the wind direction and speed record set of the measuring points, counts the number of times the wind direction changes at each measuring point within a set time period and calculates the frequency of change, and calculates the standard deviation and range of wind speed as the fluctuation amplitude. Based on the frequency of wind direction change and the fluctuation amplitude of wind speed, the wind field disturbance factor is jointly constructed to generate a wind disturbance feature set.

6. The intelligent allocation system for the operation and maintenance of UAV curtain walls under low-altitude economic conditions according to claim 5, characterized in that, The correlation update module includes: The disturbance factor extraction submodule extracts the wind field disturbance factors corresponding to all measurement points in each task block of the wind disturbance feature set, constructs a continuous temporal structure of the measurement point disturbance factors in chronological order, and generates a wind field disturbance factor sequence. The correlation analysis submodule, based on the wind field disturbance factor sequence, uses a preset wind field stability reference sequence as a comparison benchmark, inputs the two types of sequences into the grey relational discrimination model to perform numerical comparison, calculates the corresponding matching degree between the disturbance factor sequence and the reference sequence under each task block, and obtains the disturbance factor matching degree information. The urgency adjustment submodule obtains the original urgency threshold of the task, adjusts the urgency threshold of the corresponding task block according to the perturbation factor matching information, summarizes and outputs the adjustment results by task block number, and generates an updated task priority parameter set.

7. The intelligent allocation system for the operation and maintenance of UAV curtain walls under low-altitude economic conditions according to claim 6, characterized in that, The task sorting and allocation module includes: The priority sorting submodule extracts the updated task priority parameter set, sorts the curtain wall task blocks covered by the target direction path according to the updated task priority order in the parameter set, and generates a block sorting index list. The task parameter setting submodule sets the task number, execution time period and path coverage fields for each task block according to the block sorting index list and in the sorting order, forming a sorted task parameter set. The task instruction generation submodule calls the sorted task parameter set, combines the current operation status of the UAV with the remaining flight time information to determine the matching time of the operation, constructs a scheduling structure format for all sorted tasks, and generates operation and maintenance task allocation results.

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