Multi-uav cooperative mission control method against dos attacks and fault disturbances

Abstract

This invention discloses a multi-UAV collaborative task control method for DoS attacks and fault disturbances, specifically relating to the field of UAV swarm collaborative control technology. This invention calculates the DoS control impact value by weighting four types of communication data: heartbeat packet loss ratio, link packet loss rate, adjacent non-update time difference, and communication interruption duration. Simultaneously, it calculates the fault disturbance impact value by weighting two types of operational data: the proportion of abnormal track deviations and the proportion of abnormal control responses. The two impact values ​​are then fused to obtain an abnormal impact value for screening abnormal UAVs. This achieves a quantitative fusion assessment of the DoS attack impact and the inherent fault disturbance, breaking through the limitations of traditional single-dimensional binary judgment, improving the accuracy and timeliness of abnormal UAV identification, and providing a reliable basis for subsequent task scheduling.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm collaborative control technology, and more specifically, to a multi-UAV collaborative task control method for DoS attacks and fault disturbances. Background Technology

[0002] With the rapid development of UAV swarm collaborative control technology, the mode of multi-UAV collaborative task execution has been widely applied in many fields such as regional inspection, environmental monitoring, communication relay, and target tracking. The stable execution of multi-UAV collaborative tasks depends on the communication interaction links within the swarm and the flight control capabilities of each UAV. Connectivity and the reliability of individual operation determine the quality of task completion and execution efficiency. In complex and open operating environments, UAV swarms face security threats from external network attacks as well as operational risks from equipment failures and environmental disturbances. How to maintain the continuity and stability of collaborative tasks under abnormal operating conditions has become a key research direction in the field of UAV swarm control. However, existing technologies for multi-UAV cooperative task control in response to DoS attacks and fault disturbances still have the following shortcomings: First, the identification of DoS attacks is mostly based on the binary judgment result of communication connectivity, without quantitative integration of multi-dimensional information such as missing heartbeat packets, link packet loss, lag in adjacent state updates, and duration of communication interruption. This makes it impossible to accurately assess the actual impact of DoS attacks on collaborative control, and it is easy to have a lag in anomaly identification or misjudgment, making it difficult to support refined task scheduling decisions.

[0003] Secondly, the monitoring of the drone's own fault disturbance and the monitoring of communication anomalies are independent of each other. There is no integrated assessment mechanism for the impact of the two types of anomalies. It is impossible to comprehensively determine the feasibility of a single drone continuing to perform its mission, and it is also difficult to distinguish the root cause of mission anomalies, resulting in insufficient accuracy in screening abnormal drones. Furthermore, the task takeover mechanism after an abnormal drone exits is rather crude, often relying on preset backup nodes or global task replanning. It fails to categorize task vacancies by type or classify their urgency, and the selection of takeover drones does not comprehensively consider multiple factors such as distance response, power availability, task load margin, and capability matching. In scenarios with multiple task vacancies, it lacks an orderly priority scheduling logic, which can easily lead to excessive disturbance to the original tasks and low takeover response efficiency.

[0004] Therefore, a multi-UAV collaborative mission control method for DoS attacks and fault disturbances is proposed to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multi-UAV cooperative mission control method for DoS attacks and fault disturbances.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A multi-UAV cooperative mission control method for dealing with DoS attacks and fault disturbances includes the following steps: Identifying Abnormal Drones: During the collaborative task execution, DoS impact information and fault disturbance information of each drone are acquired, and then weighted and fused to obtain the DoS control impact value and fault disturbance impact value. The fusion is then used to generate an abnormal impact value. Drones with an abnormal impact value greater than a preset threshold are identified as abnormal drones. Among them, DoS impact information includes missing heartbeat packets, link packet loss, adjacent state not updated data, and continuous communication interruption data; fault disturbance information includes abnormal track deviation data and abnormal control response data. Task gap determination: After identifying the abnormal drone, calculate the remaining task value of the corresponding task execution unit and determine whether a task gap has been formed; distinguish between single task gaps and multiple task gaps. A single gap is output as a whole takeover object, while multiple gaps are broken down into gap task segments. Calculate the gap urgency value of each segment and sort them to obtain the gap urgency order. Determine the drone takeover suitability value: Process the target vacant task segments in order of urgency, screen candidate drones, and calculate the takeover suitability value by combining distance response value, power guarantee value, task load margin value, and task capability matching value; select the candidate drone with the highest suitability value and that meets the threshold as the takeover drone, and complete the takeover assignment of all vacant task segments. Takeover control command generation: Generate corresponding takeover control commands based on the takeover allocation results.

[0007] Specifically, the process for obtaining the DoS control impact value is as follows: Within a pre-set monitoring time window, the number of heartbeat packets that UAV i should have received from neighboring UAV j and the actual number received are counted. The difference between the two is taken as the number of missing heartbeat packets. The ratio of the missing number to the number that should have been received is taken as the heartbeat packet missing ratio. Within the same monitoring window, the total number of data packets that should be received between UAV i and its neighboring UAV j and the number of data packets that are actually successfully received are counted. The proportion of data packets that are not successfully received to the total number that should be received is calculated to obtain the link packet loss rate. The update time is recorded as the moment when UAV i last successfully received state data from neighboring UAV j. The difference between the current time and the update time is calculated to obtain the difference between adjacent non-updated times.

[0008] Specifically, the process of obtaining the DoS control impact value also includes: After normalizing the heartbeat packet missing ratio, link packet loss rate, and adjacent non-update time difference, the results are multiplied by the corresponding preset weight factors and summed to obtain the link communication anomaly value. Pre-set corresponding thresholds for heartbeat packet loss ratio, link packet loss rate, and the time difference between adjacent unupdated packets; When any parameter exceeds the corresponding preset threshold, or when the abnormal value of the link communication exceeds the preset threshold, it is determined that the communication link between the UAV and the neighboring UAV is in a communication interruption state. The start time of the communication interruption is recorded, and the difference between the current time and that time is calculated to obtain the duration of the communication interruption. The DoS control impact value for each UAV is obtained by comprehensively processing the heartbeat packet missing ratio, link packet loss rate, adjacent non-update time difference, and communication interruption duration.

[0009] Specifically, the process of obtaining information about the abnormal drone is as follows: The actual flight position of UAV i is collected according to the preset sampling period, and the deviation distance between the actual position and the planned flight path at each sampling time is calculated. If the deviation distance is greater than the preset allowable flight path deviation threshold, it is marked as a flight path deviation anomaly point. The ratio of the flight path deviation anomaly point to the total number of sampling points is calculated to obtain the flight path deviation anomaly percentage. The total number of flight control commands received by UAV i is counted to identify abnormal control response commands. The ratio of abnormal control response commands to the total number of flight control commands is calculated to obtain the percentage of abnormal control response commands. The fault disturbance impact value is obtained by comprehensively processing the proportion of abnormal track deviation and the proportion of abnormal response. The abnormal impact value is obtained by combining the DoS control impact value and the fault disturbance impact value; Drones whose abnormal impact values ​​exceed a pre-set abnormal impact threshold are considered abnormal drones.

[0010] Specifically, the process for identifying missing task segments is as follows: Once the abnormal drone is identified, its corresponding task execution information before the abnormality occurred is read; The first The total number of tasks in each task execution unit is denoted as . The amount of work completed at the time the exception occurred is recorded as follows: Through formula Get the first Remaining task value for each task execution unit ; Remaining task value =Preset task gap determination threshold When a comparison is performed, Greater than or equal to Then determine the first A task vacancy is created by a task execution unit; when Less than Then determine the first No task vacancies were created in any of the task execution units; The number of task execution units that create task vacancies is counted. If the number of task vacancies is one, it is determined to be a single task vacancies, and the task vacancies are output as the overall takeover object. If there are multiple task vacancies, or if the abnormal drone involves multiple task types and forms task vacancies respectively, it is determined to be a multi-task vacancy, and each task vacancy is identified as a vacant task segment.

[0011] Specifically, the process for determining the urgency order of vacancies is as follows: Identify the pre-defined task levels in the task scheduling plan, pre-set a task importance value corresponding to each task level, and calculate the task importance values ​​corresponding to different vacant task segments. ; The first The time when each missing task fragment is formed is recorded as Record the current time as The maximum allowable waiting time for this task type is denoted as . Then use the formula Get the duration of the vacancy. ; The first The remaining value of the task corresponding to each vacant task fragment is denoted as . The number of collaborative units affected by this vacancy is denoted as The total number of collaborative units in the current collaborative task is denoted as . Then use the formula Obtain the vacancy impact value ; The urgency value of a vacancy is obtained by comprehensively processing the task importance value, vacancy duration value, and vacancy impact value. ; Calculate the vacancy urgency value for each vacancy, and sort the vacancy urgency values ​​from largest to smallest to obtain the vacancy urgency order.

[0012] Specifically, the process of analyzing and matching data is as follows: Read the vacancy urgency order, and determine the target vacancy task fragments to be taken over in order of target sequence. Record the current target vacancy task fragment to be taken over as the th vacancy. A missing task segment; For the first For each missing mission segment, candidate drones are selected from the vicinity of the abnormal drone, the same mission group, or the preset collaborative range. Divide the distance from the candidate drone's current location to the takeover location by the maximum allowable takeover distance for the vacant mission segment, take the smaller value between the ratio and 1, and then subtract this value from 1 to obtain the distance response value; Divide the candidate drone's current remaining battery power by the required battery power to complete the takeover mission and retain a return margin, and take the smaller value between the ratio and 1 to obtain the battery power guarantee value; Subtract the task load pressure value from 1 to obtain the task load margin value; The first The total number of ability tags required for each missing task fragment is denoted as . , will the The number of capability tags that perfectly match the candidate drones is denoted as The number of partially matching capability tags is recorded as Then use the formula Get task capability matching value .

[0013] Specifically, the process of obtaining the task load pressure value is as follows: Add the remaining execution time of the candidate drone's original mission to the new execution time of taking over the vacant mission segment, then divide by the scheduling time window allowed for the current mission of the drone, and take the smaller value between the result and 1 to obtain the time occupation value. The estimated delay time caused to the original mission after the candidate drone takes over the vacant mission segment is divided by the maximum allowable delay time of the original mission, and the smaller value between the result and 1 is taken to obtain the original mission disturbance value. Add the current task resource occupancy value of the candidate UAV to the additional resource occupancy value required to take over the vacant task segment, and take the smaller value between the result and 1 to obtain the task resource occupancy value; The task load pressure value is obtained by comprehensively processing the time consumption value, the original task disturbance value, and the task resource consumption value.

[0014] Specifically, the process of obtaining the output takeover allocation result is as follows: The takeover adaptation value for each candidate drone is obtained by comprehensively processing the distance response value, power reserve value, task load margin value, and task capability matching value. ; Compare takeover adaptation values ​​within the candidate drone set The candidate drone with the highest value and not lower than the preset takeover adaptation threshold is selected as the takeover drone for the kth vacant task segment. If there are no candidate drones that meet the threshold requirements, it is determined that the kth vacant task segment cannot be automatically taken over at present, a maintenance prompt message is generated and pushed to the management personnel terminal, and the untaken status of the vacant task segment is output simultaneously. After determining the drone to take over the kth vacant mission segment, update the drone's remaining battery power, mission load, mission occupancy status and expected location, and continue matching the next vacant mission segment according to the urgency of the vacancy. After the status is updated, if the same candidate drone still meets the screening criteria for the next vacant mission segment and has the highest takeover fit value, it can continue to take over; if the takeover fit value is lower than the threshold, no new vacant mission segment will be assigned to it. Repeat the matching process until all missing task segments have been assigned to takeover, and output the takeover assignment results.

[0015] The technical effects and advantages of this invention are as follows: This invention calculates the DoS control impact value by weighting four types of communication data: heartbeat packet loss ratio, link packet loss rate, difference between adjacent non-update times, and communication interruption duration. Simultaneously, it calculates the fault disturbance impact value by weighting two types of operational data: the proportion of abnormal track deviations and the proportion of abnormal control responses. The two types of impact values ​​are then fused to obtain an abnormal impact value to screen abnormal UAVs. This invention achieves a quantitative fusion assessment of the impact of DoS attacks and the inherent fault disturbances, breaking through the limitations of traditional single-dimensional binary judgment, improving the accuracy and timeliness of abnormal UAV identification, and providing a reliable judgment basis for subsequent task scheduling. This invention quantifies the task vacancy status of abnormal drones by using task remaining value, classifies the scenarios into single task vacancy and multiple task vacancy, and calculates the urgency value of the vacancy from three dimensions: task importance, vacancy duration, and vacancy impact, and sorts them to determine the takeover priority. This realizes the classification, identification and hierarchical scheduling of task vacancy, ensuring that high-importance and high-impact task vacancy is handled first, and avoiding the scheduling disorder problem in multiple vacancy scenarios. This invention comprehensively calculates the takeover adaptation value of candidate UAVs from four dimensions: distance response, power guarantee, task load margin, and task capability matching. It completes task takeover matching in the order of vacancy urgency and dynamically updates the UAV's operating status. Based on the matching results, it generates local takeover control commands containing path, timing, and safety constraints, realizing multi-constraint fine-grained adaptation of task takeover. While ensuring the executability of the takeover task, it minimizes the disturbance of the takeover action to the original collaborative task and improves the overall stability and continuity of UAV swarm task execution. Attached Figure Description

[0016] Figure 1 This is a flowchart of the multi-UAV collaborative task control method for DoS attacks and fault disturbances according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the steps of the multi-UAV cooperative mission control method for DoS attacks and fault disturbances are as follows: Identifying Abnormal Drones: During collaborative tasks performed by multiple drones, DoS impact information between each drone and its neighboring drones is acquired, along with fault disturbance information for each drone itself. A comprehensive analysis of the DoS impact information yields the DoS control impact value for each drone, and a comprehensive analysis of the fault disturbance information yields the fault disturbance impact value for each drone. The DoS control impact value and the fault disturbance impact value are then fused to obtain the abnormal impact value for each drone. When the abnormal impact value of a drone exceeds a pre-set abnormal impact threshold, that drone is identified as an abnormal drone. Among them, DoS impact information includes missing heartbeat data, lost link data, unupdated adjacent state data, and persistent communication interruption data; fault disturbance information includes abnormal track deviation data and abnormal control response data. Specifically: A monitoring time window is preset. The number of heartbeat packets that UAV i should receive from neighboring UAV j is counted, and the actual number of heartbeat packets received is also counted. The difference between the expected and actual heartbeat packets is taken as the missing heartbeat packet count. The heartbeat packet missing ratio is calculated by dividing the missing heartbeat packet count by the expected heartbeat packet count. ; Additional explanation: The heartbeat packet missing ratio is used to characterize the basic connectivity status of the communication link between UAV i and its neighboring UAV j. The larger the heartbeat packet missing value, the more likely the link is to be subject to DoS attack, communication blockage, or link unavailability within the current monitoring window. If heartbeat packets are missing continuously, it means that UAV i can no longer reliably obtain the existence status of its neighboring UAV j. Within the same monitoring window, the number of data packets that should have been received between UAV i and its neighboring UAV j, and the number of data packets that were actually successfully received, are counted. The proportion of unsuccessfully received data packets to the total number of data packets that should have been received is then calculated to obtain the link packet loss rate. ; Additional explanation: The link packet loss rate is used to characterize the data transmission integrity of the communication link. The higher the link packet loss value, the more difficult it is to transmit the status data, mission instructions, or collaborative control data between UAVs completely. When the link packet loss value continues to rise, even if the heartbeat packet is not completely interrupted, it may lead to unreliable adjacent status information required for collaborative control. The update time is defined as the time when drone i most recently successfully received state data from neighboring drone j, and the difference between the current time and the update time is calculated as the difference between adjacent unupdated times. ; Additional explanation: The time difference between adjacent unupdated states is used to represent the visibility of UAV i to the current state of adjacent UAV j. The larger the value, the longer UAV i has failed to obtain the effective state of adjacent UAV j. When the value of the adjacent state not being updated continues to increase, even if the link is not completely determined to be disconnected, it can be considered that the UAV in the adjacent state is no longer suitable as the basis for the current cooperative control. The link communication anomaly value is obtained by normalizing the heartbeat packet missing ratio, link packet loss rate, and adjacent non-update time difference, multiplying them by a pre-set weighting factor, and summing them. Preset thresholds for heartbeat packet loss ratio, link packet loss rate, and the difference between adjacent unupdated times; When any parameter, such as the heartbeat packet loss ratio, link packet loss rate, or the difference between adjacent unupdated times, exceeds the corresponding preset threshold, or when the abnormal value of the link communication exceeds the preset threshold, the communication link between the current UAV and its neighboring UAVs is determined to be in a communication interruption state. The start time of the communication interruption state is recorded, and the communication interruption duration value is calculated by subtracting the current time from the start time of the communication interruption state. ; Additional notes: The communication interruption duration value characterizes the duration of the impact of a DoS attack or communication blockage on collaborative tasks. A higher communication interruption duration value indicates a longer period of time the link is unable to provide effective neighboring UAV information for collaborative control, and the more likely the task role corresponding to that UAV may be unable to continue to execute stably. After normalizing the heartbeat packet loss ratio, link packet loss rate, adjacent non-update time difference, and communication interruption duration, the formula was used. After weighted calculation, the DoS control impact value for each UAV is obtained. ,in , , , These are the corresponding preset weighting factors; Within the current monitoring time window, the actual flight position of UAV i is acquired according to a preset sampling period, and the deviation distance between the actual flight position and the planned flight path at each sampling moment is calculated. When the deviation distance at a certain sampling moment exceeds a preset allowable flight path deviation threshold, that sampling moment is marked as a flight path deviation anomaly. The total number of sampling points within the current monitoring time window is recorded as follows: The number of track deviations from anomalies is recorded as Then use the formula Obtain the percentage of track deviation anomalies ; Among them, the proportion of abnormal track deviations Used to indicate the degree of abnormality of UAV i deviating from the planned mission trajectory within the current monitoring time window; The larger the value, the more likely the drone i is to be affected by wind disturbance, positioning abnormalities, power output abnormalities, or flight control abnormalities. The system counts the number of flight control commands received by UAV i, and within the response judgment time after each flight control command is issued, it determines whether the actual speed change, heading change, or position change of UAV i meets the corresponding command requirements. If the actual motion change does not meet the corresponding command requirements, the flight control command is marked as a control response abnormal command. The total number of flight control commands within the current monitoring time window is recorded as follows: The number of control response exception commands is recorded as Then use the formula The percentage of abnormal control responses was obtained. ; For example, within the current monitoring time window, 20 flight control commands are continuously issued to UAV i, including speed adjustment commands, heading adjustment commands, and altitude adjustment commands. For a speed adjustment command, if the command requires UAV i to adjust its flight speed from 5 m / s to 7 m / s within 2 seconds, with an allowable speed error of ±0.5 m / s, but the actual speed of UAV i at the end of the response judgment time is only 5.8 m / s, then this speed adjustment command has not met the corresponding command requirements and is marked as a control response abnormal command. For a heading adjustment command, if the command requires UAV i to adjust 15° to the right within 3 seconds, with an allowable heading error of ±3°, but the actual heading adjustment of UAV i is only 6° at the end of the response judgment time, then this heading adjustment command is also marked as a control response abnormal command. After normalizing the proportions of track deviation anomalies and response anomalies respectively, the formula was used. The impact value of the fault disturbance is obtained after weighted calculation. ,in , These are the corresponding preset weighting factors; After normalizing the DoS control impact value and the fault disturbance impact value respectively, the formula is used. The abnormal impact value is obtained after weighted calculation. ,in , These are the corresponding preset weighting factors; Among them, the DoS control impact value Hck and the fault disturbance impact value Gdk are both positive anomaly indicators; the larger Hck is, the more serious the impact of communication blockage, link packet loss or adjacent state missing on cooperative control; the larger Gdk is, the more serious the impact of abnormal track deviation or abnormal control response on the stable execution of the UAV mission; therefore, the larger the anomaly impact value Yck is, the weaker the corresponding UAV's ability to continue to stably execute cooperative missions. The abnormal impact values ​​corresponding to each drone The abnormal impact value is compared with a pre-set threshold for abnormal impact. When the impact exceeds the abnormality threshold, the corresponding drone will be classified as an abnormal drone. Task Vacancy Determination: After identifying the abnormal drone, based on the drone's task type, task execution unit, task progress, and time of occurrence, the remaining task value of the corresponding task execution unit is obtained according to a pre-built task remaining value determination rule. Based on the pre-built task vacancy determination rule, it is determined whether the drone's failure creates a task vacancy. If a task vacancy is created, it is determined whether the vacancy is a single task vacancy or a multi-task vacancy according to the pre-built task vacancy type determination rule. If it is a single task vacancy, it is output as a whole takeover object. If it is a multi-task vacancy, multiple vacancy segments are treated as vacant task fragments, and emergency analysis data is extracted and comprehensively analyzed to obtain the vacancy emergency value of each vacant task fragment, which is then sorted. The emergency analysis data includes the mission importance value, the duration of the vacancy value, and the impact value of the vacancy value. Specifically: After identifying the abnormal drone, read the task execution information of the abnormal drone before the abnormality occurred; The task execution information includes task type, task execution unit, total task volume, completed task volume, task progress, and time of exception occurrence. The task type is used to determine the calculation rules for the remaining task value, the task execution unit is used to limit the calculation object of the remaining task value, the task progress is used to determine the degree to which the task has been completed when the exception occurs, and the time of exception occurrence is used to determine the time when the task vacancy is formed. The first The total number of tasks in each task execution unit is denoted as . The amount of work completed at the time the exception occurred is recorded as Then use the formula Get the first Remaining task value for each task execution unit ; in, Indicates the first The proportion of tasks that have not yet been completed or guaranteed by the individual task execution units; This indicates the total amount of tasks that the task execution unit should complete or guarantee within the current task cycle; This indicates the amount of work that had been completed or guaranteed at the time the exception occurred. For different task types and The workload is determined according to the measurable workload of the corresponding task. For example, the patrol task corresponds to the patrol workload, the coverage task corresponds to the coverage workload, the tracking task corresponds to the tracking support workload, the relay task corresponds to the relay support workload, and the formation task corresponds to the formation support workload. Remaining task value =Preset task gap determination threshold When a comparison is performed, Greater than or equal to Then determine the first A task vacancy is created by a task execution unit; when Less than Then determine the first No task vacancies were created in any of the task execution units; The number of task execution units that create task vacancies is counted. If the number of task vacancies is one, it is determined to be a single task vacancies, and the task vacancies are output as the overall takeover object. If there are multiple task vacancies, or if the abnormal drone involves multiple task types and forms task vacancies respectively, it is determined to be a multi-task vacancies, and each task vacancies is identified as a vacant task segment. Additional notes: When the task vacancy is a single task vacancy, the entire takeover object will be directly used as the current target vacant task fragment and entered into the drone takeover adaptation value calculation. When there are multiple task vacancies, the drone takeover adaptation value is calculated sequentially according to the urgency of each vacant task segment. When a task is identified as vacant, the urgency value of each vacant task segment is obtained by comprehensively analyzing the urgency value evaluation data extracted from each vacant task segment: Identify the pre-defined task levels in the task scheduling plan, pre-set a task importance value corresponding to each task level, and calculate the task importance values ​​corresponding to different vacant task segments. ; The first The time when each missing task fragment is formed is recorded as Record the current time as The maximum allowable waiting time for this task type is denoted as . Then use the formula Get the duration of the vacancy. ; The first The remaining value of the task corresponding to each vacant task fragment is denoted as . The number of collaborative units affected by this vacancy is denoted as The total number of collaborative units in the current collaborative task is denoted as . Then use the formula Obtain the vacancy impact value ,in , These are pre-defined weighting factors; After normalizing the task importance value, vacancy duration value, and vacancy impact value respectively, the formula is used. The vacancy emergency value is obtained after weighted calculation. ,in , , These are the corresponding preset weighting factors; Calculate the vacancy urgency value for each vacancy, sort the vacancy urgency values ​​from largest to smallest, and obtain the vacancy urgency order. Determine the takeover suitability value of the drone: Determine the target empty task segments to be taken over in order of urgency of the empty segment. For each target empty task segment, select candidate drones from the neighboring area of ​​the abnormal drone or the same task group. After comprehensive analysis of the matching data of each drone, the takeover suitability value of each candidate drone is obtained. The matching data includes distance response value, power guarantee value, task load margin value, and task capability matching value. The candidate drone with the highest takeover fit value and that meets the takeover threshold is selected as the takeover drone for the corresponding vacant task segment. Then, the takeover matching is performed on the next vacant task segment until the takeover assignment of all vacant task segments is completed. Specifically: Read the vacancy urgency order, and determine the target vacancy task fragment to be taken over in sequence according to the vacancy urgency order. Record the target vacancy task fragment to be taken over as the th vacancy. A missing task segment; For the first For each missing mission segment, candidate drones are selected from the vicinity of the abnormal drone, the same mission group, or the preset collaborative range. Additional notes: UAVs that meet the following criteria will be considered as candidate UAVs and will form a candidate UAV set: their abnormal impact value is lower than the pre-set abnormal judgment threshold, their remaining power is higher than the minimum takeover power threshold, their communication status is available, and their flight status is normal. Get the The current location of the candidate drone and the first The distance between the takeover locations of the vacant task segments and the first The maximum takeover distance allowed for a single vacant task segment is denoted as . Then use the formula Obtain distance response value ; in, Indicates the first Candidate drones against the first The ability to quickly access missing task segments; The larger the value, the closer the candidate drone is to the takeover location and the faster its response time. Get the The candidate drone has the following remaining battery power. The required power is determined based on the takeover location, the execution of the missing mission segments, and the power required to maintain a safety margin for return. Then use the formula Get power guarantee value ; in, Indicates the first The candidate drone completed its first The battery availability of each missing task segment; The larger the value, the better the candidate drone can meet the endurance requirements for takeover missions; Get the The candidate drone's current original mission remaining execution time Takeover of the The additional execution time required for each missing task segment , No. Scheduling time window allowed for the current mission of the candidate UAV ,in This includes the time required to fly from the current location to the takeover location and to execute the... Time required for each missing task segment; Using formula Obtain the time occupied after takeover ;in, The larger the value, the higher the value. Candidate drones take over the first The longer the time is taken up after each missing task segment; Get the Candidate drones take over the first The estimated delay time caused to the original task after the missing task fragment. And obtain the maximum allowed delay time for the original task. Using the formula Obtain the original task perturbation value ;in, The larger the value, the more likely it is to take over the first... The greater the impact of a missing mission segment on the original mission of the candidate drone; Get the Current resource usage of candidate drones and takeover of the The resource usage required for each missing task fragment Using the formula Obtain the task resource usage value after takeover ; After normalizing the time consumption value, the original task disturbance value, and the task resource consumption value respectively, the formula is used. The task load stress value is obtained after weighted calculation. ,in , , These are the corresponding preset weighting factors; using the formula Obtain task load margin value ; in, The larger the value, the higher the value. Candidate drones take over the first Even after a missing task segment, there is still a higher task capacity margin; According to the For each missing task segment, the task type and task execution unit are determined, and the corresponding set of capability requirement tags is retrieved from the pre-built task capability requirement table, denoted as... Simultaneously read the first The set of capability configuration tags for a candidate drone, denoted as ; Additional notes: The task capability requirement table is configured with capability requirement tags according to task type. For example, the inspection task corresponds to perception and data acquisition capability, positioning and navigation capability, and trajectory execution capability. Among them, the capability requirement label is used to indicate the basic capability type required to complete the missing task segment, and the capability configuration label is used to indicate the actual task execution capability of the candidate UAV. The matching results of the capability labels are divided into three categories: complete match, partial match, and no match: complete match is recorded as 1, partial match is recorded as 0.5, and no match is recorded as 0. If the kth vacant task segment has a core capability tag, and the ith candidate drone does not fully match the core capability tag, then the task capability matching value of the candidate drone for the kth vacant task segment is set to 0, and it will no longer participate in the takeover adaptation value sorting of the vacant task segment. The first The total number of ability tags required for each missing task fragment is denoted as . , will the The number of capability tags that perfectly match the candidate drones is denoted as The number of partially matching capability tags is recorded as Then use the formula Get task capability matching value ; After normalizing the distance response value, power guarantee value, task load margin value, and task capability matching value, the formula is used. After weighted calculation, the takeover adaptation value of each candidate drone is obtained. ,in , , , These are the corresponding preset weighting factors; The takeover adaptation value corresponding to each candidate drone in the candidate drone set. Compare the candidates and select the candidate drone with the highest takeover adaptation value that is greater than or equal to the preset takeover adaptation threshold as the takeover drone for the kth vacant task segment. If there are no candidate drones with a takeover adaptation value greater than or equal to the preset takeover adaptation threshold, then the first... If a vacant task segment cannot be automatically taken over at present, a maintenance prompt message will be generated and sent to the administrator's terminal, and the untaken status of the vacant task segment will be output. In determining the first After taking over a vacant mission segment, update the remaining battery power, mission load, mission occupancy status and expected location of the taking over drone, and continue to match the next vacant mission segment with takeover according to the urgency of the vacancy. If the same candidate drone still meets the screening criteria for the next vacant task segment after the status update, and the takeover adaptation value is still the highest, then the candidate drone is allowed to continue to take over the next vacant task segment; if the takeover adaptation value is lower than the preset takeover adaptation threshold after the status update, then no new vacant task segment will be assigned to the corresponding candidate drone. Repeat the above takeover matching process until all vacant task segments are assigned to takeover, and output the takeover assignment results; the takeover assignment results include the vacant task segment identifier, vacant urgency order, takeover drone identifier, takeover adaptation value, takeover order, takeover location, and drone status update results. Takeover control command generation: Based on the takeover allocation results, the matching relationship between each vacant task segment and the corresponding takeover UAV is converted into executable local takeover control commands using trajectory planning technology. The local takeover control commands include the takeover UAV identifier, vacant task segment identifier, takeover sequence, takeover path, takeover time, takeover speed, task switching method, safety constraints, and task execution parameters. In the process of generating takeover control commands, the takeover drone is not reselected; instead, the takeover allocation result is read directly, and the matching relationship between the takeover drone and the vacant task segment is converted into a local takeover control command.

[0019] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0020] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0021] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0022] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0023] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0024] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0025] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0026] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0027] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-UAV cooperative mission control method for DoS attacks and fault disturbances, characterized in that, Includes the following steps: Identifying Abnormal Drones: During the collaborative task execution, DoS impact information and fault disturbance information of each drone are acquired, and then weighted and fused to obtain the DoS control impact value and fault disturbance impact value. The fusion is then used to generate an abnormal impact value. Drones with an abnormal impact value greater than a preset threshold are identified as abnormal drones. Among them, DoS impact information includes missing heartbeat packets, link packet loss, adjacent state not updated data, and continuous communication interruption data; fault disturbance information includes abnormal track deviation data and abnormal control response data. Task gap determination: After identifying the abnormal drone, calculate the remaining task value of the corresponding task execution unit and determine whether a task gap has been formed; distinguish between single task gaps and multiple task gaps. A single gap is output as a whole takeover object, while multiple gaps are broken down into gap task segments. Calculate the gap urgency value of each segment and sort them to obtain the gap urgency order. Determine the drone takeover suitability value: Process the target vacant task segments in order of urgency, screen candidate drones, and calculate the takeover suitability value by combining distance response value, power guarantee value, task load margin value, and task capability matching value; select the candidate drone with the highest suitability value and that meets the threshold as the takeover drone, and complete the takeover assignment of all vacant task segments. Takeover control command generation: Generate corresponding takeover control commands based on the takeover allocation results.

2. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 1, characterized in that, The specific process for obtaining the DoS control impact value is as follows: Within a pre-set monitoring time window, the number of heartbeat packets that UAV i should have received from neighboring UAV j and the actual number received are counted. The difference between the two is taken as the number of missing heartbeat packets. The ratio of the missing number to the number that should have been received is taken as the heartbeat packet missing ratio. Within the same monitoring window, the total number of data packets that should be received between UAV i and its neighboring UAV j and the number of data packets that are actually successfully received are counted. The proportion of data packets that are not successfully received to the total number that should be received is calculated to obtain the link packet loss rate. The update time is recorded as the moment when UAV i last successfully received state data from neighboring UAV j. The difference between the current time and the update time is calculated to obtain the difference between adjacent non-updated times.

3. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 2, characterized in that, The specific process of obtaining the DoS control impact value also includes: After normalizing the heartbeat packet missing ratio, link packet loss rate, and adjacent non-update time difference, the results are multiplied by the corresponding preset weight factors and summed to obtain the link communication anomaly value. Pre-set corresponding thresholds for heartbeat packet loss ratio, link packet loss rate, and the time difference between adjacent unupdated packets; When any parameter exceeds the corresponding preset threshold, or when the abnormal value of the link communication exceeds the preset threshold, it is determined that the communication link between the UAV and the neighboring UAV is in a communication interruption state. The start time of the communication interruption is recorded, and the difference between the current time and that time is calculated to obtain the duration of the communication interruption. The DoS control impact value for each UAV is obtained by comprehensively processing the heartbeat packet missing ratio, link packet loss rate, adjacent non-update time difference, and communication interruption duration.

4. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 1, characterized in that, The specific process of obtaining the abnormal drone is as follows: The actual flight position of UAV i is collected according to the preset sampling period, and the deviation distance between the actual position and the planned flight path at each sampling time is calculated. If the deviation distance is greater than the preset allowable flight path deviation threshold, it is marked as a flight path deviation anomaly point. The ratio of the flight path deviation anomaly point to the total number of sampling points is calculated to obtain the flight path deviation anomaly percentage. The total number of flight control commands received by UAV i is counted to identify abnormal control response commands. The ratio of abnormal control response commands to the total number of flight control commands is calculated to obtain the percentage of abnormal control response commands. The fault disturbance impact value is obtained by comprehensively processing the proportion of abnormal track deviation and the proportion of abnormal response. The abnormal impact value is obtained by combining the DoS control impact value and the fault disturbance impact value; Drones whose abnormal impact values ​​exceed a pre-set abnormal impact threshold are considered abnormal drones.

5. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 1, characterized in that, The specific process for identifying missing task segments is as follows: Once the abnormal drone is identified, its corresponding task execution information before the abnormality occurred is read; The first The total number of tasks in each task execution unit is denoted as . The amount of work completed at the time the exception occurred is recorded as follows: Through formula Get the first Remaining task value for each task execution unit ; Remaining task value =Preset task gap determination threshold When a comparison is performed, Greater than or equal to Then determine the first A task vacancy is created by a task execution unit; when Less than Then determine the first No task vacancies were created in any of the task execution units; The number of task execution units that create task vacancies is counted. If the number of task vacancies is one, it is determined to be a single task vacancies, and the task vacancies are output as the overall takeover object. If there are multiple task vacancies, or if the abnormal drone involves multiple task types and forms task vacancies respectively, it is determined to be a multi-task vacancy, and each task vacancy is identified as a vacant task segment.

6. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 1, characterized in that, The specific process for determining the urgency order of vacancies is as follows: Identify the pre-defined task levels in the task scheduling plan, pre-set a task importance value corresponding to each task level, and calculate the task importance values ​​corresponding to different vacant task segments. ; The first The time when each missing task fragment is formed is recorded as follows: Record the current time as The maximum allowable waiting time for this task type is denoted as . Then use the formula Get the duration of the vacancy. ; The first The remaining value of the task corresponding to each vacant task fragment is denoted as . The number of collaborative units affected by this vacancy is denoted as The total number of collaborative units in the current collaborative task is denoted as . Then use the formula Obtain the value of the impact of the vacancy. ; The urgency value of a vacancy is obtained by comprehensively processing the task importance value, vacancy duration value, and vacancy impact value. ; Calculate the vacancy urgency value for each vacancy, and sort the vacancy urgency values ​​from largest to smallest to obtain the vacancy urgency order.

7. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 1, characterized in that, The specific process of analyzing and matching data is as follows: Read the vacancy urgency order, and determine the target vacancy task fragments to be taken over in order of target sequence. Record the current target vacancy task fragment to be taken over as the th vacancy. A missing task segment; For the For each missing mission segment, candidate drones are selected from the vicinity of the abnormal drone, the same mission group, or the preset collaborative range. Divide the distance from the candidate drone's current location to the takeover location by the maximum allowable takeover distance for the vacant mission segment, take the smaller value between the ratio and 1, and then subtract this value from 1 to obtain the distance response value; Divide the candidate drone's current remaining battery power by the required battery power to complete the takeover mission and retain a return margin, and take the smaller value between the ratio and 1 to obtain the battery power guarantee value; Subtract the task load pressure value from 1 to obtain the task load margin value; The first The total number of ability tags required for each missing task fragment is denoted as . , will the The number of capability tags that perfectly match the candidate drones is denoted as The number of partially matched capability tags is recorded as Then use the formula Get task capability matching value .

8. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 7, characterized in that, The specific process for obtaining the task load pressure value is as follows: Add the remaining execution time of the candidate drone's original mission to the new execution time of taking over the vacant mission segment, then divide by the scheduling time window allowed for the current mission of the drone, and take the smaller value between the result and 1 to obtain the time occupation value. The estimated delay time caused to the original mission after the candidate drone takes over the vacant mission segment is divided by the maximum allowable delay time of the original mission, and the smaller value between the result and 1 is taken to obtain the original mission disturbance value. Add the current task resource occupancy value of the candidate UAV to the additional resource occupancy value required to take over the vacant task segment, and take the smaller value between the result and 1 to obtain the task resource occupancy value; The task load pressure value is obtained by comprehensively processing the time consumption value, the original task disturbance value, and the task resource consumption value.

9. The multi-UAV cooperative mission control method for DoS attacks and fault disturbances according to claim 1, characterized in that, The specific process for obtaining the output control allocation result is as follows: The takeover adaptation value for each candidate drone is obtained by comprehensively processing the distance response value, power reserve value, task load margin value, and task capability matching value. ; Compare takeover adaptation values ​​within the candidate drone set The candidate drone with the highest value and not lower than the preset takeover adaptation threshold is selected as the takeover drone for the kth vacant task segment. If there are no candidate drones that meet the threshold requirements, it is determined that the kth vacant task segment cannot be automatically taken over at present, a maintenance prompt message is generated and pushed to the management personnel terminal, and the untaken status of the vacant task segment is output simultaneously. After determining the drone to take over the kth vacant mission segment, update the drone's remaining battery power, mission load, mission occupancy status and expected location, and continue matching the next vacant mission segment according to the urgency of the vacancy. After the status is updated, if the same candidate drone still meets the screening criteria for the next vacant mission segment and has the highest takeover fit value, it can continue to take over; if the takeover fit value is lower than the threshold, no new vacant mission segment will be assigned to it. Repeat the matching process until all missing task segments have been assigned to takeover, and output the takeover assignment results.

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