Electric power construction-oriented heterogeneous unmanned aerial vehicle dynamic cooperative command and dispatch method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle dispatching, and discloses an electric power construction-oriented heterogeneous unmanned aerial vehicle dynamic cooperative command and dispatching method and system. The method comprises the following steps: decomposing task levels according to an environment tolerance threshold value and constructing an unmanned aerial vehicle capability matrix; collecting real-time environment parameters, calculating an environment coupling degree index, and generating a degradation task package when the environment coupling degree index exceeds a threshold value; calculating a single machine matching degree index, and if not, selecting the collaborative machines with the maximum capability complementation index to form a virtual collaborative group and distributing roles; position and wind speed vectors are obtained, and the time difference of arrival is eliminated by delaying takeoff after the flight speed is corrected. According to the invention, the environment adaptability, task completion rate and execution efficiency of heterogeneous unmanned aerial vehicle cooperative operation in an electric power construction scene are improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) scheduling technology, and in particular to a method and system for dynamic collaborative command and scheduling of heterogeneous UAVs for power construction. Background Technology

[0002] With the continuous expansion of power infrastructure construction and the rapid development of smart grid technology, drones have been widely used in power operation scenarios such as transmission line inspection, tower defect detection, and construction site monitoring. In existing technologies, drone power operation scheduling systems mainly adopt a task pre-allocation mode. This involves offline planning based on historical meteorological data and the nominal performance parameters of the drones before task execution, assigning specific tasks to specific drone models. This static scheduling method can meet basic operational requirements in scenarios with stable environmental conditions and clear task requirements. Typical implementation methods include path optimization scheduling based on genetic algorithms, serial allocation mechanisms based on task priorities, and fixed combination modes with pre-set cooperative formations. Some technical solutions introduce multi-drone cooperative mechanisms to complete complex tasks through formation flight or master-slave following, but the cooperative relationship is usually determined in the task planning stage and remains unchanged during execution.

[0003] However, the power operation environment is characterized by significant dynamism and uncertainty. Existing static scheduling methods have the following shortcomings: First, there is often a large discrepancy between the environmental prediction during the task planning phase and the actual environmental conditions during execution. When the actual wind speed, rainfall, or electromagnetic interference intensity exceeds expectations, the pre-assigned drones may be unable to complete the task or be forced to suspend operations due to insufficient performance, leading to an increased task failure rate and wasted resources. Second, fixed collaborative formation modes lack the ability to dynamically adapt to the performance differences of heterogeneous drones. When environmental conditions change, the roles of each drone cannot be flexibly adjusted according to the real-time status of each drone, resulting in the inability to fully leverage the capabilities of high-performance drones, while weaker drones may become bottlenecks in overall coordination due to tasks exceeding their capabilities. Summary of the Invention

[0004] This application provides a dynamic collaborative command and dispatch method and system for heterogeneous UAVs in power construction, which solves the problems of poor task adaptability under dynamic environmental changes, lack of quantitative assessment of collaborative UAV selection, mismatch between role allocation and performance characteristics, and insufficient timing synchronization accuracy of heterogeneous UAVs in existing UAV dispatching technology. It improves the environmental adaptability, task completion rate and execution efficiency of heterogeneous UAV collaborative operations in power construction scenarios.

[0005] Firstly, this application provides a dynamic collaborative command and dispatch method for heterogeneous unmanned aerial vehicles (UAVs) in power construction, the method comprising:

[0006] Step S1: Decompose the power operation task into multiple task levels according to the environmental tolerance threshold, and construct a UAV capability matrix that includes flight performance parameters and sensor performance parameters;

[0007] Step S2: Collect real-time environmental parameters and calculate the degree of deviation of the real-time environmental parameters. Obtain the environmental coupling index by weighted summation. When the environmental coupling index exceeds a preset threshold, generate a degradation task package.

[0008] Step S3: Calculate the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix. When the single-machine matching index does not meet the execution conditions, select the cooperating machine with the largest capability complementarity index from the standby UAV pool to form a virtual cooperative group, and assign the roles of stable platform machine and data acquisition machine to the virtual cooperative group according to the wind resistance level and sensor resolution.

[0009] Step S4: Obtain the position coordinates and wind speed vector of each UAV in the virtual collaborative group, correct the flight speed of each UAV, calculate the arrival time difference, and eliminate the arrival time difference by setting a delayed takeoff time.

[0010] Secondly, this application provides a heterogeneous UAV dynamic collaborative command and dispatch system for power construction, the heterogeneous UAV dynamic collaborative command and dispatch system for power construction comprising:

[0011] The decomposition module is used to decompose power operation tasks into multiple task levels according to environmental tolerance thresholds, and to construct a UAV capability matrix that includes flight performance parameters and sensor performance parameters.

[0012] The weighting module is used to collect real-time environmental parameters and calculate the degree of deviation of the real-time environmental parameters. The environmental coupling index is obtained by weighted summation. When the environmental coupling index exceeds a preset threshold, a degradation task package is generated.

[0013] The allocation module is used to calculate the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix. When the single-machine matching index does not meet the execution conditions, the module selects the cooperating machine with the largest capability complementarity index from the standby UAV pool to form a virtual cooperative group, and assigns the roles of stable platform machine and data acquisition machine to the virtual cooperative group according to the wind resistance level and sensor resolution.

[0014] The acquisition module is used to acquire the position coordinates and wind speed vector of each UAV in the virtual collaborative group, calculate the arrival time difference after correcting the flight speed of each UAV, and eliminate the arrival time difference by setting a delayed takeoff time.

[0015] The technical solution provided in this application decomposes power operation tasks into multiple task levels according to environmental tolerance thresholds and constructs a UAV capability matrix containing flight performance parameters and sensor performance parameters. This establishes a structured mapping relationship between task requirements and UAV capabilities, enabling the system to clearly define the tolerance boundaries of different task levels to environmental conditions during the task planning stage, providing a quantitative basis for subsequent dynamic degradation decisions. By collecting real-time environmental parameters and calculating their deviation, and then obtaining an environmental coupling index through weighted summation, this application achieves a quantitative assessment of the comprehensive impact of multi-dimensional environmental factors. The mechanism of generating a degradation task package when the environmental coupling index exceeds a preset threshold allows the system to automatically adjust task requirements rather than simply suspend operations during sudden environmental changes, ensuring operational continuity while avoiding safety risks associated with exceeding capabilities.

[0016] Based on the parameter requirements of the downgraded task package and the method of calculating the single-drone matching index using the drone capability matrix, the adaptability of drones in key dimensions such as resolution, wind resistance, and endurance is comprehensively evaluated through multiplication of multi-dimensional ratios, providing an objective criterion for determining whether a single drone's capabilities are sufficient. When the single-drone matching index does not meet the execution conditions, a virtual collaborative group is formed by selecting the collaborative drone with the highest capability complementarity index from the standby drone pool. By quantifying the differences between the candidate drone and the currently executing drone in multiple performance dimensions, the group ensures the strongest capability complementarity among its members, thereby enabling comprehensive performance that a single drone cannot achieve through role division.

[0017] The strategy of assigning stable platform and data acquisition roles to the virtual collaborative group based on wind resistance level and sensor resolution achieves precise matching between UAV performance characteristics and task roles. This allows UAVs with high maneuverability but weak wind resistance to focus on providing stable support, while UAVs with strong wind resistance focus on environmental adaptation and data acquisition, avoiding the efficiency loss caused by the mismatch between role allocation and performance characteristics in traditional fixed formations. By acquiring the position coordinates and wind speed vectors of each UAV in the virtual collaborative group, correcting the flight speed of each UAV, and calculating the arrival time difference, a timing synchronization mechanism that eliminates the arrival time difference by setting a delayed takeoff time fully considers the differentiated impact of wind field on the flight speed of heterogeneous UAVs. By dynamically calculating the effective flight speed and adjusting the takeoff sequence accordingly, precise spatiotemporal synchronization of collaborative group members at the target work point is achieved, creating conditions for the smooth conduct of subsequent collaborative operations. Overall, this significantly improves the environmental adaptability, task completion rate, and execution efficiency of heterogeneous UAV collaborative operations in power construction scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an embodiment of the heterogeneous UAV dynamic collaborative command and dispatch method for power construction in this application.

[0020] Figure 2 This is a schematic diagram comparing the collaborative execution efficiency under different wind speed conditions in the embodiments of this application;

[0021] Figure 3 This is a schematic diagram illustrating the relationship between the capability complementarity index and synergistic performance in an embodiment of this application. Detailed Implementation

[0022] This application provides a method and system for dynamic collaborative command and dispatch of heterogeneous unmanned aerial vehicles (UAVs) for power construction. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0023] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the heterogeneous UAV dynamic collaborative command and dispatch method for power construction in this application includes:

[0024] Step S1: Decompose the power operation task into multiple task levels according to the environmental tolerance threshold, and construct a UAV capability matrix that includes flight performance parameters and sensor performance parameters;

[0025] Specifically, for power construction scenarios, transmission line inspection tasks are divided into three levels based on defect detection accuracy. The basic level task identifies macroscopic defects such as conductor breaks, with an image resolution requirement of 5 cm / pixel and a corresponding wind speed threshold of 15 m / s. The standard level task identifies surface cracks on insulators, with the resolution requirement increased to 2 cm / pixel and the wind speed threshold reduced to 10 m / s. The fine level task detects millimeter-level discharge traces through ultraviolet or infrared imaging, with a resolution requirement of 0.5 cm / pixel, a wind speed threshold limited to within 5 m / s, and an electromagnetic interference intensity below -80 dBm. The UAV capability matrix is ​​constructed by collecting seven-dimensional parameters for each UAV, including endurance, payload, speed, sensor resolution, wind resistance level, communication distance, and maneuverability index, and arranging them according to UAV number to form a matrix structure.

[0026] Step S2: Collect real-time environmental parameters and calculate the degree of deviation of real-time environmental parameters. Obtain the environmental coupling index by weighted summation. When the environmental coupling index exceeds the preset threshold, generate a degradation task package.

[0027] Specifically, the wind speed deviation rate is obtained by dividing the difference between the measured wind speed and the predicted wind speed by the predicted wind speed. When the predicted wind speed is 3 m / s but the measured wind speed suddenly changes to 8 m / s, the wind speed deviation rate reaches 1.67. This deviation rate is multiplied by the weighting coefficient 0.35 and then added to the rainfall ratio multiplied by 0.25 and the electromagnetic interference ratio multiplied by 0.20. If the rainfall is 0 at a certain moment, the electromagnetic interference intensity is -75 dBm and the threshold is -80 dBm, then the environmental coupling index is 0.583 plus 0.188 equals 0.771. After exceeding the preset threshold of 0.7, a downgrade decision is triggered. When the downgrade task package is generated, the real-time wind speed of 8 m / s is compared with the wind speed threshold of each level. It does not meet the requirements of 5 m / s for the fine layer and 10 m / s for the standard layer. Therefore, the task is downgraded to the basic layer and the operation parameters are modified.

[0028] Step S3: Calculate the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix. When the single-machine matching index does not meet the execution conditions, select the cooperating machine with the largest capability complementarity index from the standby UAV pool to form a virtual cooperative group, and assign the roles of stable platform machine and data acquisition machine to the virtual cooperative group according to the wind resistance level and sensor resolution.

[0029] Specifically, a multi-dimensional ratio multiplication method is used to extract the resolution requirement of 5 cm / pixel, wind resistance requirement of 15 m / s, and estimated mission duration of 30 minutes from the downgraded mission package. The sensor resolution of the currently executing drone is extracted from the drone capability matrix, which is 1 cm / pixel, wind resistance level of 8 m / s, and remaining flight time of 45 minutes. The three ratios are calculated as follows: 5 divided by 1 equals 5, 8 divided by 15 equals 0.53, and 45 divided by 30 equals 1.5. Multiplying the three results in a matching index of 3.975. When this index is less than the preset threshold of 1.2, it is determined that collaboration is required. The capability complementarity index is calculated to select candidate drones with remaining battery power greater than 40% and distance less than 10 kilometers from the standby pool.

[0030] Step S4: Obtain the position coordinates and wind speed vector of each drone in the virtual collaborative group, correct the flight speed of each drone, calculate the arrival time difference, and eliminate the arrival time difference by setting a delayed takeoff time.

[0031] Specifically, considering the speed differences of heterogeneous drones, the current location of the stabilizing platform is 1000 meters east, 2000 meters north, and 100 meters high; the location of the data acquisition drone is 3000 meters east, 2000 meters north, and 200 meters high; and the target operation point is 2000 meters east, 2000 meters north, and 150 meters high. The calculated straight-line distance between the stabilizing platform and the data acquisition drone is approximately 1005 meters. The wind speed vector is -5 m / s eastward (headwind). The maximum speed of the stabilizing platform is 50 km / h, which is converted to 13. With a speed of 89 m / s and a flight direction completely against the wind, the effective speed is 13.89 minus 5 equals 8.89 m / s. The estimated arrival time is 1005 divided by 8.89, approximately 113 seconds. The data acquisition aircraft has a speed of 80 km / h, or 22.22 m / s. Also against the wind, its effective speed is 17.22 m / s, and its arrival time is approximately 58 seconds. The time difference is 55 seconds. Multiplying the time difference by a delay factor of 0.9 gives a delayed takeoff time of 49.5 seconds. A delay command is then sent to the data acquisition aircraft to delay its takeoff by 49.5 seconds to achieve synchronous arrival.

[0032] In one specific embodiment, step S1 includes:

[0033] The scope, accuracy requirements and timeliness level of power operation tasks are obtained. Power operation tasks are decomposed into basic tasks, standard tasks and fine tasks. Wind speed threshold, rainfall threshold and electromagnetic interference intensity threshold are set as environmental tolerance thresholds for each task level.

[0034] Collect data on the flight time, payload, maximum flight speed, sensor resolution, wind resistance level, communication distance, and maneuverability index of each UAV;

[0035] The drone capability matrix is ​​constructed by arranging the flight time, payload, maximum flight speed, sensor resolution, wind resistance level, communication distance, and maneuverability index by drone number as the row and performance parameter type as the column.

[0036] Specifically, the environmental tolerance thresholds in the task decomposition process are set based on safety regulations and equipment performance constraints in power operation scenarios. The basic layer tasks target macroscopic inspection needs such as conductor continuity detection. The wind speed threshold is set at 15 m / s because at this wind speed, fixed-wing UAVs can still maintain stable flight and image jitter does not affect the identification of large-scale defects. The rainfall threshold is set at 10 mm / h to ensure that the visible light sensor does not fail due to raindrop obstruction. The wind speed threshold for the standard layer tasks is reduced to 10 m / s to ensure that the attitude stability of the multi-rotor UAV meets the resolution requirement of 2 cm / pixel when shooting at close range. The wind speed threshold for the fine layer tasks is strictly limited to 5 m / s because the ultraviolet or infrared sensor requires extremely high platform stability to capture weak discharge signals. The electromagnetic interference intensity threshold of -80 dBm is set based on the signal-to-noise ratio requirements of the UAV communication link. When the interference intensity exceeds this threshold, the data transmission error rate increases significantly.

[0037] The construction of the UAV capability matrix organizes the performance parameters of each UAV in a row-column structure. The row index corresponds to the UAV number to distinguish different individuals in the cluster, and the column index corresponds to seven types of performance parameters to facilitate subsequent matching calculations by parameter dimension. The endurance reflects the UAV's ability to complete long-distance or long-duration tasks, the payload determines the type and number of sensors that can be carried, the maximum flight speed affects the mission response time and coordination sequence, the sensor resolution is directly related to the executable mission level, the wind resistance level characterizes the flight stability under different wind speed environments, the communication distance limits the feasible range of beyond-visual-range operations, and the maneuverability index quantifies the UAV's acceleration capability and attitude adjustment speed. The value of each element in the matrix comes from the technical parameters or measured flight data provided by the UAV manufacturer. After the matrix is ​​constructed, specific performance parameters can be quickly extracted for mission matching through matrix operations.

[0038] In one specific embodiment, step S2 includes:

[0039] The wind speed vector, rainfall, and visibility of the work area are collected by ground meteorological monitoring stations, and the electromagnetic signal strength is collected by airborne electromagnetic interference detection module to obtain real-time environmental parameters.

[0040] The wind speed deviation is calculated by comparing the measured and predicted values ​​of the wind speed vector; the rainfall ratio is calculated by comparing the rainfall amount with the rainfall threshold; and the electromagnetic signal strength ratio is calculated by comparing the electromagnetic signal strength with the electromagnetic interference strength threshold.

[0041] The environmental coupling index is obtained by multiplying the wind speed deviation, the rainfall ratio, and the electromagnetic signal intensity ratio by weighting coefficients and then summing them.

[0042] When the environmental coupling index is greater than or equal to the preset coupling threshold, the real-time environmental parameters are matched with the environmental tolerance threshold of each task level to generate a downgraded task package containing the downgraded task level identifier and the modified job parameters.

[0043] Specifically, real-time environmental parameters are collected through multi-source sensor collaboration. Ground meteorological monitoring stations are deployed around the operational area and report the three-dimensional components of the wind speed vector, including the eastward, northward, and vertical components, every 5 minutes. Rainfall is measured in millimeters per unit time using rain gauges. Visibility is measured by atmospheric transparency using laser scattering instruments. An airborne electromagnetic interference detection module scans electromagnetic signals in the 500MHz to 6GHz frequency band and records the maximum signal strength value. These parameters together constitute a set of real-time environmental parameters describing the dynamic characteristics of the operational environment. The predicted wind speed vector value comes from the expected wind speed generated during the mission planning phase based on historical meteorological data and numerical weather prediction models. The difference between the measured value and the predicted value is divided by the predicted value to obtain the wind speed deviation. This deviation reflects the degree of dynamic change of the environment relative to the expectation rather than the absolute wind speed.

[0044] The environmental coupling index is calculated by integrating the influence of multi-dimensional environmental factors through weighted summation. The weight coefficients are set based on the analysis of the degree of influence of each environmental factor on the power operation task. The weight coefficient of wind speed deviation is the largest at 0.35 because sudden wind speed changes have the most significant impact on the flight stability of UAVs and the imaging quality of sensors. The weight of rainfall ratio is the second largest at 0.25 because rainfall mainly affects visible light sensors but has a smaller impact on infrared sensors. The weight of electromagnetic signal strength ratio is 0.20 to reflect the degree of impact of electromagnetic interference on the communication link.

[0045] The environmental coupling index obtained after weighted summation is a dimensionless value between 0 and 1. When the index reaches or exceeds the preset coupling threshold of 0.7, a degradation decision is triggered. The generation of the degradation task package requires comparing the real-time environmental parameters with the environmental tolerance threshold of each task level. If the real-time wind speed of 8 m / s exceeds the fine layer threshold of 5 m / s but does not exceed the standard layer threshold of 10 m / s, the task is downgraded from the fine layer to the standard layer. At the same time, the operation parameters are modified, including relaxing the image resolution requirement from 0.5 cm / pixel to 2 cm / pixel, increasing the flight altitude from 5 meters to 10 meters, and switching the sensor type from ultraviolet imaging to visible light high-definition video. The downgraded task level identifier and the modified operation parameters are jointly packaged into a degradation task package for subsequent matching degree calculation.

[0046] Figure 2 This is a schematic diagram comparing the collaborative execution efficiency under different wind speed conditions in the embodiments of this application. Figure 2 As shown, under different wind speed conditions, the dynamic collaborative method of the present invention exhibits significant performance advantages compared with single-machine execution and fixed collaborative methods. Figure 2 The paper compares the execution efficiency of three execution methods within a wind speed range of 5 m / s to 15 m / s. Under low wind speed conditions of 5 m / s, the single-machine execution efficiency is 92%, fixed collaboration is 94%, and dynamic collaboration reaches 96%, with minimal differences among the three. As the wind speed increases to 8 m / s, the single-machine execution efficiency drops to 78%, fixed collaboration reaches 84%, while dynamic collaboration maintains a high efficiency of 91%. When the wind speed reaches strong wind conditions of 12 m / s, the single-machine execution efficiency plummets to 48%, fixed collaboration to 62%, while dynamic collaboration still maintains an execution efficiency of 82%. Under extreme wind speeds of 15 m / s, the single-machine execution efficiency is only 32%, fixed collaboration is 51%, while the dynamic collaboration method, through real-time role allocation and capability matching, still achieves an execution efficiency of 76%, representing a 137.5% improvement compared to single-machine execution and a 49% improvement compared to fixed collaboration. This fully verifies the robustness and adaptability advantages of the method in harsh environmental conditions.

[0047] In one specific embodiment, step S3, which calculates the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix, includes:

[0048] Extract the downgraded image resolution requirements, downgraded wind resistance level requirements, and downgraded task duration from the downgrade task package;

[0049] Extract the sensor resolution, wind resistance level, and remaining flight time of the currently executing drone from the drone capability matrix;

[0050] The ratio of the downgraded image resolution requirement to the sensor resolution is calculated, the ratio of the wind resistance level to the downgraded wind resistance level requirement is calculated, and the ratio of the remaining battery life to the downgraded mission duration is calculated. The product of these three ratios is then used to obtain the single-unit matching index.

[0051] Specifically, the single-machine matching index is calculated by multiplying multi-dimensional ratios to quantify the degree of fit between the drone's capabilities and the requirements of the downgraded task. The downgraded image resolution requirement, downgraded wind resistance requirement, and downgraded task duration extracted from the downgraded task package represent the minimum performance requirements of the task for the drone. The sensor resolution, wind resistance, and remaining flight time of the currently executing drone in the drone capability matrix represent the actual capabilities the drone can currently provide. The three ratios are calculated by dividing the drone's capabilities by the task requirements. When the sensor resolution is 1 cm / pixel... A ratio of 0.2 when the downgraded requirement is 5 cm / pixel indicates that the drone's resolution far exceeds the mission requirements. A ratio of 0.53 when the wind resistance level is 8 m / s and the downgraded requirement is 15 m / s indicates that the drone's wind resistance is insufficient. A ratio of 1.5 when the remaining flight time is 45 minutes and the downgraded mission duration is 30 minutes indicates that the flight time is sufficient. The single-drone matching index obtained by multiplying the three ratios comprehensively reflects the drone's overall adaptability in the three key dimensions of resolution, wind resistance, and flight time. When the index is less than the preset threshold of 1.2, it is determined that the single-drone capability is insufficient and a collaborative mechanism needs to be activated.

[0052] In one specific embodiment, when the single-machine matching index in step S3 does not meet the execution conditions, a virtual collaborative group is formed by selecting the collaborative drone with the largest capability complementarity index from the standby drone pool, including:

[0053] Select drones from the standby drone pool whose remaining battery power is greater than 40% of the total battery power and whose distance from the currently executing drone is less than a preset distance to form a candidate drone set;

[0054] Extract the wind resistance level, sensor resolution, and maneuverability index of each candidate drone in the candidate drone set and the currently executing drone;

[0055] Calculate the ratio of the larger to the smaller values ​​of each candidate UAV and the currently executing UAV in terms of wind resistance level, sensor resolution, and maneuverability index. Multiply the three ratios to obtain the capability complementarity index of each candidate UAV.

[0056] The candidate UAV with the highest capability complementarity index is selected as the collaborating UAV, and it forms a virtual collaborative group with the currently executing UAV.

[0057] Specifically, the selection process for the collaborative drone quantifies the performance difference between candidate drones and the currently executing drone through a capability complementarity index. Drones with more than 40% remaining battery power are selected from the standby drone pool to ensure the collaborative drone has sufficient power to complete the collaborative task. A distance limit of less than 10 kilometers ensures the collaborative drone can reach the work point within a reasonable time. The wind resistance level, sensor resolution, and maneuverability index of each candidate drone in the candidate drone set are extracted and paired with the corresponding parameters of the currently executing drone. The capability complementarity index is calculated by dividing the larger value by the smaller value. The ratio is 1.5 when the candidate aircraft has a wind resistance rating of 12 m / s and the current aircraft has a wind resistance rating of 8 m / s. The ratio is 5 when the candidate aircraft has a resolution of 5 cm / pixel and the current aircraft has a resolution of 1 cm / pixel. The ratio is 2 when the candidate aircraft has a maneuverability index of 2.5 and the current aircraft has a maneuverability index of 5.0. Multiplying these three ratios gives a complementarity index of 15, indicating that the performance difference between the two aircraft is significant and their complementarity is strong. The candidate aircraft with the largest complementarity index is selected as the cooperating aircraft because the greater the performance difference, the more the ability to form a capability superposition effect through role division. The virtual cooperative group composed of the cooperating aircraft and the current executing drone achieves comprehensive performance that cannot be achieved by a single aircraft through task division.

[0058] Figure 3 This is a schematic diagram illustrating the relationship between the capability complementarity index and synergistic performance in an embodiment of this application. For example... Figure 3 As shown, the collaborative machine selection process quantifies the performance difference between the candidate UAV and the currently executing UAV through a capability complementarity index. Figure 3 The graph illustrates the relationship between collaboration success rate and resource consumption index as a function of capability complementarity index. The collaboration success rate curve exhibits a logarithmic growth trend, reaching approximately 65% ​​when the capability complementarity index is 1, and peaking at approximately 92% when the index increases to 15, after which the growth trend flattens out. The resource consumption index grows approximately linearly with the complementarity index, increasing from approximately 50 when the index is 1 to approximately 100 when the index is 25. The optimal operating point can be determined by calculating the ratio of collaboration success rate to resource consumption. The optimal operating point, marked by a red star in the graph, lies in the range of capability complementarity index between approximately 8 and 12. At this point, the system maintains a reasonable level of resource consumption while ensuring a high collaboration success rate, achieving the best balance between performance and cost.

[0059] In one specific embodiment, step S3 assigns the roles of stable platform machine and data acquisition machine to the virtual collaborative group based on wind resistance level and sensor resolution, including:

[0060] Compare the maneuverability index and wind resistance level of each drone in the virtual collaborative group. Drones with higher maneuverability index and lower wind resistance level are assigned to the role of stable platform drones, while drones with higher wind resistance level are assigned to the role of data acquisition drones.

[0061] To stabilize the platform, the machine generates hovering task instructions, which include the target hovering coordinates and attitude angle thresholds.

[0062] Generate follow-up acquisition task instructions for the data acquisition unit. The follow-up acquisition task instructions include the trigger conditions for waiting for the stable platform to send a stable ready signal and the sensor start instruction.

[0063] Specifically, the role allocation of the virtual collaborative group is based on the differentiated utilization of the performance characteristics of each UAV. UAVs with higher maneuverability have faster attitude adjustment capabilities and hovering stability, but their lower wind resistance means that they are difficult to complete long-distance flights in strong wind environments. Therefore, they are assigned as stabilization platform UAVs to hover at the target point and provide a stable measurement platform for precision sensors. UAVs with higher wind resistance can maintain flight path stability in harsh wind environments and are suitable for being assigned as data acquisition UAVs to carry sensors and complete data acquisition. The stabilization platform UAV's hovering task instruction includes the target hovering coordinates, specifying its hovering position as 5 meters directly below the target guideline. The attitude angle threshold is set to ensure that both the pitch and roll angles are less than 3 degrees to ensure that the platform stability meets the sensor's working requirements. The data acquisition UAV's follow-up acquisition task instruction is set to be triggered when it receives the stabilization ready signal sent by the stabilization platform UAV and monitors the stabilization platform UAV's attitude angle to be less than 3 degrees in real time, and then starts the sensor to collect data. This role division mechanism allows UAVs with weak wind resistance but high maneuverability to focus on providing stable support, while UAVs with strong wind resistance focus on environmental adaptation and data acquisition. The collaboration between the two UAVs enables the completion of precision detection tasks in strong wind environments.

[0064] In one specific embodiment, step S4 includes:

[0065] Obtain the current position coordinates and target operation point coordinates of each drone in the virtual collaborative group, and calculate the straight-line distance between the current position of each drone and the target operation point;

[0066] Obtain the wind speed vector of the work area, calculate the angle between the flight direction of each UAV and the wind speed vector, and calculate the corrected effective flight speed based on the angle and the maximum flight speed of each UAV.

[0067] Divide the straight-line distance by the effective flight speed to obtain the estimated arrival time of each UAV, and calculate the arrival time difference by calculating the difference between the estimated arrival times of each UAV.

[0068] Multiply the arrival time difference by the delay factor to obtain the delayed takeoff time of the fast aircraft, and issue a delayed takeoff instruction to the fast aircraft.

[0069] Specifically, the timing synchronization mechanism eliminates speed differences among heterogeneous UAVs by dynamically calculating the actual arrival time of each UAV under the influence of the wind field. After obtaining the three-dimensional position coordinates of each UAV in the virtual collaborative group and the coordinates of the target operation point, it calculates the Euclidean distance between the two points as the straight-line flight distance. The wind speed vector is obtained from the three-dimensional wind speed components reported in real time by the ground meteorological monitoring station. The flight direction vector of each UAV points from the current position to the target point. The cosine value of the angle between the two vectors is calculated by dividing the dot product of the two vectors by their respective moduli. When the cosine value of the angle between the flight direction and the wind speed direction is negative one, it indicates that the UAV is flying completely against the wind and is effective. Flight speed equals maximum flight speed minus wind speed. When the cosine of the angle is positive 1, it indicates that the effective speed for tailwind flight is the maximum speed plus the wind speed. The straight-line distance divided by the effective flight speed gives the estimated arrival time after considering wind resistance. The estimated arrival times of the stable platform machine and the data acquisition machine are subtracted to obtain the arrival time difference. This time difference is multiplied by a delay factor of 0.9, retaining a 10% safety redundancy, to obtain the delayed takeoff time of the fast machine. A delayed takeoff instruction is issued to the fast machine that is expected to arrive first, so that it postpones its takeoff, ensuring that the two machines achieve time and space synchronization at the target operation point to create conditions for subsequent collaborative operations.

[0070] The above describes the heterogeneous UAV dynamic collaborative command and dispatch method for power construction in the embodiments of this application. The following describes the heterogeneous UAV dynamic collaborative command and dispatch system for power construction in the embodiments of this application. One embodiment of the heterogeneous UAV dynamic collaborative command and dispatch system for power construction in the embodiments of this application includes:

[0071] The decomposition module is used to decompose power operation tasks into multiple task levels according to environmental tolerance thresholds, and to construct a UAV capability matrix that includes flight performance parameters and sensor performance parameters.

[0072] The weighting module is used to collect real-time environmental parameters and calculate the degree of deviation of the real-time environmental parameters. The environmental coupling index is obtained by weighted summation. When the environmental coupling index exceeds a preset threshold, a degradation task package is generated.

[0073] The allocation module is used to calculate the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix. When the single-machine matching index does not meet the execution conditions, the module selects the cooperating machine with the largest capability complementarity index from the standby UAV pool to form a virtual cooperative group, and assigns the roles of stable platform machine and data acquisition machine to the virtual cooperative group according to the wind resistance level and sensor resolution.

[0074] The acquisition module is used to acquire the position coordinates and wind speed vector of each UAV in the virtual collaborative group, calculate the arrival time difference after correcting the flight speed of each UAV, and eliminate the arrival time difference by setting a delayed takeoff time.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic collaborative command and dispatch method for heterogeneous unmanned aerial vehicles (UAVs) for power construction, characterized in that, The method includes: Step S1: Decompose the power operation task into multiple task levels according to the environmental tolerance threshold, and construct a UAV capability matrix that includes flight performance parameters and sensor performance parameters; Step S2: Collect real-time environmental parameters and calculate the degree of deviation of the real-time environmental parameters. Obtain the environmental coupling index by weighted summation. When the environmental coupling index exceeds a preset threshold, generate a degradation task package. Step S3: Calculate the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix. When the single-machine matching index does not meet the execution conditions, select the cooperating machine with the largest capability complementarity index from the standby UAV pool to form a virtual cooperative group, and assign the roles of stable platform machine and data acquisition machine to the virtual cooperative group according to the wind resistance level and sensor resolution. Step S4: Obtain the position coordinates and wind speed vector of each UAV in the virtual collaborative group, correct the flight speed of each UAV, calculate the arrival time difference, and eliminate the arrival time difference by setting a delayed takeoff time.

2. The heterogeneous UAV dynamic collaborative command and dispatch method for power construction according to claim 1, characterized in that, Step S1 includes: The scope, accuracy requirements, and timeliness level of the power operation tasks are obtained. The power operation tasks are decomposed into basic tasks, standard tasks, and fine-grained tasks. Wind speed threshold, rainfall threshold, and electromagnetic interference intensity threshold are set for each task level as the environmental tolerance threshold. Collect data on the flight time, payload, maximum flight speed, sensor resolution, wind resistance level, communication distance, and maneuverability index of each UAV; The flight time, payload, maximum flight speed, sensor resolution, wind resistance level, communication distance, and maneuverability index are arranged by UAV number as rows and by performance parameter type as columns. The UAV capability matrix is ​​constructed based on the arrangement results.

3. The heterogeneous UAV dynamic collaborative command and dispatch method for power construction according to claim 2, characterized in that, Step S2 includes: The wind speed vector, rainfall, and visibility of the work area are collected by ground meteorological monitoring stations, and the electromagnetic signal strength is collected by airborne electromagnetic interference detection module to obtain the real-time environmental parameters. The wind speed deviation is calculated by the difference between the measured value and the predicted value of the wind speed vector; the rainfall is calculated by the ratio of the rainfall amount to the rainfall threshold; and the electromagnetic signal strength is calculated by the ratio of the electromagnetic signal strength to the electromagnetic interference strength threshold. The environmental coupling index is obtained by multiplying the wind speed deviation, rainfall ratio, and electromagnetic signal intensity ratio by weighting coefficients and then summing them. When the environmental coupling index is greater than or equal to the preset coupling threshold, the real-time environmental parameters are matched with the environmental tolerance threshold of each task level to generate the downgraded task package, which includes the downgraded task level identifier and the modified job parameters.

4. The heterogeneous UAV dynamic collaborative command and dispatch method for power construction according to claim 1, characterized in that, Step S3, which calculates the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix, includes: Extract the downgraded image resolution requirements, downgraded wind resistance level requirements, and downgraded task duration from the downgraded task package; Extract the sensor resolution, wind resistance level, and remaining flight time of the currently executing drone from the drone capability matrix; The ratio of the downgraded image resolution requirement to the sensor resolution is calculated, the ratio of the wind resistance level to the downgraded wind resistance level requirement is calculated, and the ratio of the remaining battery life to the downgraded mission duration is calculated. The product of these three ratios is then used to obtain the single-machine matching index.

5. The heterogeneous UAV dynamic collaborative command and dispatch method for power construction according to claim 4, characterized in that, In step S3, when the single-machine matching index does not meet the execution conditions, a virtual collaborative group is formed by selecting the collaborative drone with the largest capability complementarity index from the standby drone pool, including: Select drones from the standby drone pool whose remaining battery power is greater than 40% of the total battery power and whose distance from the currently executing drone is less than a preset distance to form a candidate drone set; Extract the wind resistance level, sensor resolution, and maneuverability index of each candidate drone in the candidate drone set and the currently executing drone; Calculate the ratio of the larger to the smaller values ​​of each candidate UAV and the currently executing UAV in terms of wind resistance level, sensor resolution, and maneuverability index. Multiply the three ratios together to obtain the capability complementarity index of each candidate UAV. The candidate UAV with the largest capability complementarity index is selected as the cooperating machine, and it forms the virtual cooperative group with the currently executing UAV.

6. The heterogeneous UAV dynamic collaborative command and dispatch method for power construction according to claim 5, characterized in that, In step S3, the roles of stable platform machine and data acquisition machine are assigned to the virtual collaborative group according to the wind resistance level and sensor resolution, including: Compare the maneuverability index and wind resistance level of each drone in the virtual collaborative group, assign drones with higher maneuverability index and lower wind resistance level to the role of stable platform drones, and assign drones with higher wind resistance level to the role of data acquisition drones. Generate a hovering task instruction for the stabilized platform machine, the hovering task instruction including the target hovering coordinates and attitude angle threshold; Generate a follow-up acquisition task instruction for the data acquisition machine. The follow-up acquisition task instruction includes a trigger condition for waiting for the stable platform machine to send a stable ready signal and a sensor start instruction.

7. The heterogeneous UAV dynamic collaborative command and dispatch method for power construction according to claim 1, characterized in that, Step S4 includes: Obtain the current position coordinates and target work point coordinates of each UAV in the virtual collaborative group, and calculate the straight-line distance between the current position of each UAV and the target work point; Obtain the wind speed vector of the work area, calculate the angle between the flight direction of each UAV and the wind speed vector, and calculate the corrected effective flight speed based on the angle and the maximum flight speed of each UAV. Divide the straight-line distance by the effective flight speed to obtain the estimated arrival time of each UAV, and calculate the arrival time difference by taking the difference between the estimated arrival times of each UAV. The arrival time difference is multiplied by a delay factor to obtain the delayed takeoff time of the fast aircraft, and a delayed takeoff command is issued to the fast aircraft.

8. A heterogeneous unmanned aerial vehicle (UAV) dynamic collaborative command and dispatch system for power construction, characterized in that: The method for dynamic collaborative command and dispatch of heterogeneous unmanned aerial vehicles (UAVs) for power construction as described in any one of claims 1-7, wherein the heterogeneous UAV dynamic collaborative command and dispatch system for power construction comprises: The decomposition module is used to decompose power operation tasks into multiple task levels according to environmental tolerance thresholds, and to construct a UAV capability matrix that includes flight performance parameters and sensor performance parameters. The weighting module is used to collect real-time environmental parameters and calculate the degree of deviation of the real-time environmental parameters. The environmental coupling index is obtained by weighted summation. When the environmental coupling index exceeds a preset threshold, a degradation task package is generated. The allocation module is used to calculate the single-machine matching index based on the parameter requirements of the downgraded task package and the UAV capability matrix. When the single-machine matching index does not meet the execution conditions, the module selects the cooperating machine with the largest capability complementarity index from the standby UAV pool to form a virtual cooperative group, and assigns the roles of stable platform machine and data acquisition machine to the virtual cooperative group according to the wind resistance level and sensor resolution. The acquisition module is used to acquire the position coordinates and wind speed vector of each UAV in the virtual collaborative group, calculate the arrival time difference after correcting the flight speed of each UAV, and eliminate the arrival time difference by setting a delayed takeoff time.

9. The system according to claim 8, characterized in that, Power operation tasks are decomposed into multiple task levels based on environmental tolerance thresholds, and a UAV capability matrix containing flight performance parameters and sensor performance parameters is constructed, including: The scope, accuracy requirements, and timeliness level of the power operation tasks are obtained. The power operation tasks are decomposed into basic tasks, standard tasks, and fine-grained tasks. Wind speed threshold, rainfall threshold, and electromagnetic interference intensity threshold are set for each task level as the environmental tolerance threshold. Collect data on the flight time, payload, maximum flight speed, sensor resolution, wind resistance level, communication distance, and maneuverability index of each UAV; The flight time, payload, maximum flight speed, sensor resolution, wind resistance level, communication distance, and maneuverability index are arranged by UAV number as rows and by performance parameter type as columns. The UAV capability matrix is ​​constructed based on the arrangement results.

10. The system according to claim 8, characterized in that, Real-time environmental parameters are collected and their deviation is calculated. An environmental coupling index is obtained through weighted summation. When the environmental coupling index exceeds a preset threshold, a degradation task package is generated, including: The wind speed vector, rainfall, and visibility of the work area are collected by ground meteorological monitoring stations, and the electromagnetic signal strength is collected by airborne electromagnetic interference detection module to obtain the real-time environmental parameters. The wind speed deviation is calculated by the difference between the measured value and the predicted value of the wind speed vector; the rainfall is calculated by the ratio of the rainfall amount to the rainfall threshold; and the electromagnetic signal strength is calculated by the ratio of the electromagnetic signal strength to the electromagnetic interference strength threshold. The environmental coupling index is obtained by multiplying the wind speed deviation, rainfall ratio, and electromagnetic signal intensity ratio by weighting coefficients and then summing them. When the environmental coupling index is greater than or equal to the preset coupling threshold, the real-time environmental parameters are matched with the environmental tolerance threshold of each task level to generate the downgraded task package, which includes the downgraded task level identifier and the modified job parameters.

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