A data link based unmanned aerial vehicle cluster security monitoring system

Abstract

The application discloses a kind of unmanned aerial vehicle cluster security monitoring systems based on data chain, it is related to unmanned aerial vehicle security monitoring technical field, including stable mobile range construction module, subgroup construction module, obstacle avoidance priority analysis module and safe obstacle avoidance module;Stable mobile range construction module is used to obtain the position state data of unmanned aerial vehicle in current period in unmanned aerial vehicle cluster;Subgroup construction module is used to establish the communication stable relationship of different unmanned aerial vehicles in current period in unmanned aerial vehicle cluster, and analyzes the performance change condition of different unmanned aerial vehicles under different time span, and constructs unmanned aerial vehicle subgroup in unmanned aerial vehicle cluster;Obstacle avoidance priority analysis module is used to analyze the obstacle avoidance priority condition of unmanned aerial vehicle subgroup in unmanned aerial vehicle cluster, obtain subgroup priority data, not only guarantee the stable progress of unmanned aerial vehicle cluster flight plan, but also strongly guarantee the safety of unmanned aerial vehicle cluster in flight process.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) security monitoring technology, specifically a UAV swarm security monitoring system based on a data link. Background Technology

[0002] Data links are information systems that use modern network technology, specific message formats, and communication protocols to transmit formatted messages in real time. The use of data link technology allows different drones in a drone swarm to form a whole, rather than individual drones. Traditional methods for handling anomalies in drone swarms require operators to monitor multiple screens simultaneously to detect anomalies in drone flight and then make autonomous judgments based on the anomalies. This is basically an impossible task in high-speed, highly dynamic drone swarms. Moreover, data links can treat the entire drone swarm as a whole, eliminating the need for operators to monitor individual drones in the swarm. Furthermore, data link technology can effectively solve the problem of information silos in drone swarms during flight, enabling true information sharing among all drones in the swarm.

[0003] While data links in drone swarms have solved the problems of information silos and data acquisition, the data transmission, processing, and reception processes within a swarm still require time. Traditional methods rely on the drone's state at the moment of data transmission to determine its flight trajectory, resulting in decisions based on past states. This is fatal in highly dynamic drone swarm scenarios. Furthermore, drone swarms require environmental analysis and evasive maneuvers due to varying conditions. Controlling numerous drones individually would consume vast resources, and the similarity of information among them can easily lead to conflicting obstacle avoidance strategies. All these issues contribute to safety incidents in drone swarms. Summary of the Invention

[0004] The purpose of this invention is to provide a data link-based drone swarm security monitoring system to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a data link-based unmanned aerial vehicle (UAV) swarm security monitoring system, the system comprising a stable movement range construction module, a sub-swarm construction module, an obstacle avoidance priority analysis module, and a safe obstacle avoidance module;

[0006] The stable movement range construction module is used to acquire the location status data of drones in the drone swarm in the current time period and construct the stable movement range of the drones.

[0007] The subgroup construction module is used to establish the stable communication relationship between different drones in the drone swarm during the current time period, analyze the performance changes of different drones over different time spans, elect the drone as the leader from the drone swarm, and construct the drone subgroup in the drone swarm.

[0008] The obstacle avoidance priority analysis module is used to analyze the obstacle avoidance priority status of drone subgroups in a drone swarm and obtain subgroup priority data.

[0009] The obstacle avoidance module is used to analyze the positional changes of drones, obtain target position data of drones, assess the collision risk of drones in the current time period, generate drone obstacle avoidance strategies, and control the drone swarm to perform safe obstacle avoidance.

[0010] Furthermore, the stable mobility range construction module includes a monitoring unit and a stable mobility range construction unit;

[0011] The monitoring unit is used to monitor the flight status of the drone swarm during the current time period and obtain the position status data of the drones in the drone swarm.

[0012] The stable movement range construction unit is used to construct the stable movement time range of the UAV within the current period, and combine it with position status data to analyze the flight status of the UAV at different time points within the stable movement time range, thereby constructing the stable movement range of the UAV.

[0013] Furthermore, by constructing a stable mobile range unit, the position status data of each drone in the drone swarm at the current time period is obtained, the coordinates of the drones are obtained from the position status data, and the straight-line distance between each drone in the drone swarm is obtained.

[0014] Obtain the preset stable communication duration t of the drone cluster from the platform. ▽ Obtain the drone's speed and maximum acceleration within the current time period, and calculate the duration t of the drone's speed change. υ ;

[0015] Obtain the stable communication duration t since the current time period. ▽ The time point is recorded as the target time point, and the stable movement time range of the drone swarm within the current time period is constructed based on the current time point and the target time point.

[0016] Several time points are extracted from the stable moving time range with a preset stable duration and recorded as the target time points in the stable moving time range.

[0017] Obtain the distance and duration between each target time point and the current time period within the stable movement time range, and obtain the stable flight distance of the UAV at each target time point;

[0018] Using the drone's current coordinates as the center and the drone's stable flight distance at the target time point as the radius, a circular area is constructed as the stable movement range of the drone at the target time point;

[0019] Obtain the stable movement range of the human and machine at each target time point within the stable movement time range.

[0020] Furthermore, the subgroup construction module includes a communication stability relationship establishment unit and a subgroup construction unit;

[0021] The communication stability relationship establishment unit is used to acquire the stable movement range of different drones in the drone swarm at various target time points within the stable movement range, analyze the communication impact of drones in the drone swarm on other drones at different time spans, and thus establish a stable communication relationship between drones in the drone swarm and other drones.

[0022] The subgroup construction unit constructs a stable communication set of drones based on the stable communication relationship between different drones in the drone cluster and the drone cluster. It analyzes the performance changes of drones within the stable communication period, obtains key drones from the drone cluster, selects the group leader of the drone cluster based on the drone inclusion status in the stable communication set of the key drones in the drone cluster, and constructs the drone subgroup.

[0023] In the above steps, the construction of drone sub-swarms takes into account the need for stability over a certain period of time. If the construction process of drone sub-swarms lasts too long, it cannot flexibly adapt to the safe flight requirements of drones. On the other hand, frequent adjustments and replacements of drone sub-swarms would significantly consume the resources of the drone swarm. Therefore, the stability of communication relationships between drones is analyzed over a time span. Furthermore, the selection of a sub-swarm leader is considered, and the selection of the leader requires drones to maintain good performance stability. Therefore, historical records are used to obtain the performance changes of drones over a time span. This allows the constructed sub-swarms to be flexibly adjusted within the drone swarm while saving on drone swarm resource consumption, greatly improving the safety of drone swarm flight.

[0024] Furthermore, through the communication stability relationship establishment unit, the maximum value of the straight-line distance between any two coordinate points within the stable movement range of other drones in the drone swarm at the same target time point is obtained and recorded as the spatial straight-line distance between other drones at the target time point.

[0025] Obtain the maximum value of the spatial straight-line distance between the UAV and other UAVs at each target time point within the stable movement time range, and denote it as the extreme value of the spatial distance between the UAV and other UAVs, L. τ ;

[0026] Acquire all historical communication records of the drone, obtain the average communication distance and average signal strength indication when the drone communicates with other historical drones from each historical communication record, and obtain historical communication records that are greater than the preset average signal strength indication threshold from each historical communication record, and record them as target historical communication records.

[0027] The average communication distance obtained from the historical communication records of various targets of the UAV is greater than the extreme value of spatial distance L. τ The ratio of the number of historical communication records of several targets to the number of each historical communication record is used as the communication stability characteristic value ξ when the UAV communicates with other UAVs.

[0028] The target's historical communication records are removed from the various historical communication records of the UAV, resulting in several historical communication records for the UAV. From these historical communication records, the average communication distance is found to be less than the extreme value of the spatial distance L. τ The historical communication records are collected and marked. The ratio of the marked historical communication records to the number of several historical communication records is used as the communication anomaly characteristic value δ when the UAV communicates with other UAVs.

[0029] Divide the communication stability feature value ξ by the communication anomaly feature value δ to obtain the communication stability value U when the UAV communicates with other UAVs, and obtain the communication stability value U´ when other UAVs communicate with the UAV. When both the communication stability value U and the communication stability value U´ are greater than the preset communication stability threshold, a stable communication relationship is established between the UAV and other UAVs in the current time period.

[0030] Furthermore, through the sub-group construction unit, several other drones that have established stable communication relationships with the drone in the current time period are obtained from the drone swarm, and a stable drone communication set of the drone in the current time period is constructed;

[0031] Obtain the remaining flight time of the drone in the current time period, obtain each historical flight record of the drone, extract the historical flight records containing the remaining flight time e of the drone in the current time period from each historical flight record, and mark them;

[0032] Extract the time point when the drone has remaining flight time e from the marked historical flight records, and use it as the starting point. Then, record the stable communication duration t after the starting point. ▽ As the endpoint, the values ​​of various performance indicators of the UAV at the starting point and the endpoint in the marked historical flight records are extracted to obtain the difference between the performance indicators at the starting point and the endpoint in the historical flight records. The difference is then divided by the value of the performance indicator at the starting point to obtain the rate of change of the performance indicator in the historical flight records.

[0033] The average rate of change of the performance index of the UAV in each marked historical flight record is obtained. The value of the UAV's performance index in the current period is subtracted from the value of the average rate of change multiplied by the value of the UAV's performance index in the current period to obtain the stable value of the UAV's performance index.

[0034] Identify drones in the drone cluster whose stable value is greater than a preset threshold during the current time period and mark them as key drones;

[0035] Based on the average rate of change of various performance indicators of key drones, the key drones in the drone swarm are ranked.

[0036] According to the order, the stable communication set of each key UAV is obtained in turn. The UAVs included in the stable communication set of the UAV with higher order are removed from the stable communication set of the UAV with lower order, so as to obtain the characteristic stable communication set of the UAV with lower order.

[0037] The stable communication set of the highest-ranking critical drone in the drone swarm is denoted as the baseline stable communication set. The sets of drones included in the baseline stable communication set and the characteristic stable communication set of the critical drone are obtained in order until the sets of drones included in the baseline stable communication set and the characteristic stable communication set of the critical drone are added to the corresponding critical drone. This includes all drones in the drone swarm. The critical drones whose characteristic stable communication sets are empty among the several critical drones in the constructed set are removed.

[0038] The key drones that are retained are designated as group leaders, and drones in the characteristic communication stable set and the benchmark communication stable set are designated as group members according to the corresponding relationship, and drone subgroups in the drone cluster are constructed in sequence.

[0039] Furthermore, the obstacle avoidance priority analysis module includes an obstacle avoidance priority analysis unit;

[0040] The obstacle avoidance priority analysis unit is used to evaluate the obstacle avoidance priority status of drones in a drone swarm, obtain drone obstacle avoidance priority data, and analyze the obstacle avoidance priority status of drone subgroups in the drone swarm to obtain subgroup priority data of the drone swarm.

[0041] Furthermore, through the obstacle avoidance priority analysis unit, the battery life and task data of each drone in the drone swarm in the current time period are acquired. The task data includes the preset time period for each task to be performed by the drone.

[0042] Calculate the drone's endurance score V and mission score S for the current time period;

[0043] Calculate the obstacle avoidance priority value H of the drone in the current time period, obtain the obstacle avoidance priority value of each drone in the drone cluster and aggregate them to obtain the obstacle avoidance priority data of the drone cluster.

[0044] Obtain the average obstacle avoidance priority value of several drones in a drone subgroup within a drone swarm, and record it as the subgroup obstacle avoidance priority value of the drone subgroup.

[0045] Obtain the obstacle avoidance priority values ​​of each drone subgroup in the drone swarm and aggregate them to obtain the drone swarm subgroup obstacle avoidance priority data.

[0046] Furthermore, the safety obstacle avoidance module includes a safety obstacle avoidance unit;

[0047] The obstacle avoidance unit is used to analyze the positional changes of the UAV, obtain the target of the UAV, assess the collision risk of the UAV in the current time period, obtain collision risk data, generate UAV obstacle avoidance strategy, obtain target obstacle avoidance strategy, and control the UAV swarm to perform safe obstacle avoidance according to the target obstacle avoidance strategy.

[0048] Furthermore, through the safety obstacle avoidance unit, the historical data transmission records of drones in the drone cluster are obtained, and the data transmission time of the drones is obtained from the historical data transmission records;

[0049] The average data transmission time is obtained from various historical data transmission records of the drone, and used as the target data transmission time t of the drone in the current time period. Γ ;

[0050] Based on the position status data of drones in the drone swarm, analyze the position changes of drones to obtain the target position data of drones in the current time period.

[0051] Acquire the target location data of the drone cluster within the current time period, and construct the safe range of the drones within the current time period;

[0052] Obtain the safe range of each drone in the drone swarm during the current time period, analyze the collision risk of the ζ-th drone in the drone swarm, and obtain the collision risk value of the ζ-th drone.

[0053] Obtain the collision risk values ​​of each drone subgroup in the drone swarm, and sum the collision risk values ​​of several drones in each drone subgroup to obtain the subgroup collision risk value of the drone subgroup.

[0054] Based on the collision risk values ​​of each drone in the drone swarm and the collision risk values ​​of the subgroups of drones, an obstacle avoidance strategy for drones is generated to obtain the target obstacle avoidance strategy. Based on the target obstacle avoidance strategy of the drone swarm, the drone subgroups and drones in the drone swarm are controlled to perform safe obstacle avoidance.

[0055] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention not only solves the problem of information silos in drone swarms, but also analyzes the spatial communication stability between drones in the swarm by using historical communication records and current location information of drones, thereby constructing drone subgroups within the swarm. This makes drone safety control more flexible. Furthermore, it considers the time consumption of drone location information transmission and reception, analyzes drone location changes, and ensures that the platform receives drone location information close to the location received by the drone. It also assesses the collision risk of drones and drone subgroups in the swarm during flight and generates obstacle avoidance strategies based on collision risk and drone priority. This not only ensures the stable execution of drone swarm flight plans but also effectively guarantees the safety of drone swarms during flight. Attached Figure Description

[0056] Figure 1 This is a unit flowchart of a data link-based unmanned aerial vehicle (UAV) swarm security monitoring system according to the present invention.

[0057] Figure 2 This is a flowchart of a data link-based drone swarm security monitoring system according to the present invention. Detailed Implementation

[0058] 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.

[0059] Example: Figures 1-2As shown, the present invention provides a technical solution, a data link-based unmanned aerial vehicle (UAV) swarm security monitoring system, the system including a stable mobile range construction module, a sub-swarm construction module, an obstacle avoidance priority analysis module, and a safe obstacle avoidance module;

[0060] The stable movement range construction module is used to acquire the location status data of drones in the drone swarm in the current time period and construct the stable movement range of the drones.

[0061] The stable movement range construction module includes a monitoring unit and a stable movement range construction unit.

[0062] The monitoring unit is used to monitor the flight status of the drone swarm during the current time period and obtain the position status data of the drones in the drone swarm.

[0063] The stable movement range construction unit is used to construct the stable movement time range of the UAV within the current period, and combine it with position status data to analyze the flight status of the UAV at different time points within the stable movement time range, thereby constructing the stable movement range of the UAV.

[0064] Among them, by constructing a stable mobile range unit, the position status data of each drone in the drone cluster at the current time period is obtained, the coordinates of the drones are obtained from the position status data, and the straight-line distance between each drone in the drone cluster is obtained.

[0065] For example, calculating the straight-line distance d between the a-th drone and the b-th drone in a drone swarm. (a,c) The specific calculation formula is as follows:

[0066] ,

[0067] Among them, (x a ,y a ,z a (x) represents the coordinates of the a-th drone; b ,y b ,z b Let ) be the coordinates of the b-th drone;

[0068] Obtain the preset stable communication duration t of the drone cluster from the platform. ▽ Obtain the drone's speed and maximum acceleration within the current time period, and calculate the duration t of the drone's speed change. υ ;

[0069] Obtain the stable communication duration t since the current time period. ▽ The time point is recorded as the target time point, and the stable movement time range of the drone swarm within the current time period is constructed based on the current time point and the target time point.

[0070] Several time points are extracted from the stable moving time range with a preset stable duration and recorded as the target time points in the stable moving time range.

[0071] Obtain the distance and duration between each target time point and the current time period within the stable movement time range, and obtain the stable flight distance of the UAV at each target time point;

[0072] Stable flight distance L of the UAV at the target time point Δ The specific acquisition process is as follows:

[0073] Get the maximum speed v of the drone max Get the duration t of the distance between the target time point and the current time period. λ Calculate the time t for the change in the speed of the drone. υ =(v max -v) / a, where a is the acceleration of the drone in the current time period;

[0074] When t λ >t υ Calculate the stable flight distance L of the drone during the current time period. Δ :

[0075] ,

[0076] When t ▽ ≤t υ Calculate the stable flight distance L of the drone during the current time period. Δ :

[0077] ,

[0078] Using the drone's current coordinates as the center and the drone's stable flight distance at the target time point as the radius, a circular area is constructed as the stable movement range of the drone at the target time point;

[0079] Obtain the stable movement range of the human and machine at each target time point within the stable movement time range.

[0080] The subgroup construction module is used to establish the stable communication relationship between different drones in the drone swarm during the current time period, analyze the performance changes of different drones over different time spans, elect the drone as the leader from the drone swarm, and construct the drone subgroup in the drone swarm.

[0081] The subgroup construction module includes a communication stability relationship establishment unit and a subgroup construction unit;

[0082] The communication stability relationship establishment unit is used to acquire the stable movement range of different drones in the drone swarm at various target time points within the stable movement range, analyze the communication impact of drones in the drone swarm on other drones at different time spans, and thus establish a stable communication relationship between drones in the drone swarm and other drones.

[0083] The subgroup construction unit constructs a stable communication set of drones based on the stable communication relationship between different drones in the drone cluster and the drone cluster. It analyzes the performance changes of drones within the stable communication period, obtains key drones from the drone cluster, selects the group leader of the drone cluster based on the drone inclusion status in the stable communication set of the key drones in the drone cluster, and constructs the drone subgroup.

[0084] Among them, through the communication stability relationship establishment unit, the maximum value of the straight-line distance between any two coordinate points within the stable movement range of the drones in the drone cluster and other drones at the same target time point is obtained and recorded as the spatial straight-line distance between the drones and other drones at the target time point.

[0085] Obtain the maximum value of the spatial straight-line distance between the UAV and other UAVs at each target time point within the stable movement time range, and denote it as the extreme value of the spatial distance between the UAV and other UAVs, L. τ ;

[0086] Acquire all historical communication records of the drone, obtain the average communication distance and average signal strength indication when the drone communicates with other historical drones from each historical communication record, and obtain historical communication records that are greater than the preset average signal strength indication threshold from each historical communication record, and record them as target historical communication records.

[0087] The average communication distance obtained from the historical communication records of various targets of the UAV is greater than the extreme value of spatial distance L. τ The ratio of the number of historical communication records of several targets to the number of each historical communication record is used as the communication stability characteristic value ξ when the UAV communicates with other UAVs.

[0088] The target's historical communication records are removed from the various historical communication records of the UAV, resulting in several historical communication records for the UAV. From these historical communication records, the average communication distance is found to be less than the extreme value of the spatial distance L. τ The historical communication records are collected and marked. The ratio of the marked historical communication records to the number of several historical communication records is used as the communication anomaly characteristic value δ when the UAV communicates with other UAVs.

[0089] Divide the communication stability feature value ξ by the communication anomaly feature value δ to obtain the communication stability value U when the UAV communicates with other UAVs, and obtain the communication stability value U´ when other UAVs communicate with the UAV. When both the communication stability value U and the communication stability value U´ are greater than the preset communication stability threshold, a stable communication relationship is established between the UAV and other UAVs in the current time period.

[0090] For example, the communication stability value U when a drone communicates with other drones is specifically the communication stability value U when a drone transmits data to other drones.

[0091] For example, let's say drone A is a drone, and other drones are drone B. We need to obtain the stable communication value U when drone A communicates with drone B. A Obtain the communication stability value U when UAV B communicates with UAV A. B When the communication stability value U A and communication stability value U B If both values ​​are greater than the preset communication stability threshold, it indicates that there is a stable communication relationship between UAV A and UAV B during the data transmission process. Therefore, a stable communication relationship will be established between UAV A and UAV B in the current time period.

[0092] Specifically, through the subgroup construction unit, several other drones that have established stable communication relationships with the drone in the current time period are obtained from the drone cluster, and a stable drone communication set of the drone in the current time period is constructed.

[0093] Obtain the remaining flight time of the drone in the current time period, obtain each historical flight record of the drone, extract the historical flight records containing the remaining flight time e of the drone in the current time period from each historical flight record, and mark them;

[0094] Extract the time point when the drone has remaining flight time e from the marked historical flight records, and use it as the starting point. Then, record the stable communication duration t after the starting point. ▽ As the endpoint, the values ​​of various performance indicators of the UAV at the starting point and the endpoint in the marked historical flight records are extracted to obtain the difference between the performance indicators at the starting point and the endpoint in the historical flight records. The difference is then divided by the value of the performance indicator at the starting point to obtain the rate of change of the performance indicator in the historical flight records.

[0095] For example, the performance indicators of a drone include remaining battery power, signal-to-noise ratio, and CPU load;

[0096] The average rate of change of the performance index of the UAV in each marked historical flight record is obtained. The value of the UAV's performance index in the current period is subtracted from the value of the average rate of change multiplied by the value of the UAV's performance index in the current period to obtain the stable value of the UAV's performance index.

[0097] Identify drones in the drone cluster whose stable value is greater than a preset threshold during the current time period and mark them as key drones;

[0098] Based on the average rate of change of various performance indicators of key drones, the key drones in the drone swarm are ranked.

[0099] According to the order, the stable communication set of each key UAV is obtained in turn. The UAVs included in the stable communication set of the UAV with higher order are removed from the stable communication set of the UAV with lower order, so as to obtain the characteristic stable communication set of the UAV with lower order.

[0100] The stable communication set of the highest-ranking critical drone in the drone swarm is denoted as the baseline stable communication set. The sets of drones included in the baseline stable communication set and the characteristic stable communication set of the critical drone are obtained in order until the sets of drones included in the baseline stable communication set and the characteristic stable communication set of the critical drone are added to the corresponding critical drone. This includes all drones in the drone swarm. The critical drones whose characteristic stable communication sets are empty among the several critical drones in the constructed set are removed.

[0101] The key drones that are retained are designated as group leaders, and drones in the characteristic communication stable set and the benchmark communication stable set are designated as group members according to the corresponding relationship, and drone subgroups in the drone cluster are constructed in sequence.

[0102] The obstacle avoidance priority analysis module is used to analyze the obstacle avoidance priority status of drone subgroups in a drone swarm and obtain subgroup priority data.

[0103] The obstacle avoidance priority analysis module includes an obstacle avoidance priority analysis unit.

[0104] The obstacle avoidance priority analysis unit is used to evaluate the obstacle avoidance priority status of drones in a drone swarm, obtain drone obstacle avoidance priority data, and analyze the obstacle avoidance priority status of drone subgroups in the drone swarm to obtain subgroup priority data of the drone swarm.

[0105] Among them, the obstacle avoidance priority analysis unit acquires the endurance and task data of each drone in the drone cluster in the current time period. The task data includes the preset time period of each task to be performed by the drone.

[0106] Calculate the drone's endurance score V and mission score S for the current time period;

[0107] For example, the specific formula for calculating the battery life score V is:

[0108] ,

[0109] Among them, L min and L max These represent the maximum and minimum flight times of each drone in the drone swarm during the current time period, respectively; L represents the flight time of the drone during the current time period.

[0110] For example, the specific formula for calculating the task score S is:

[0111] Set the unit duration t, obtain the preset value k, where k is a positive integer greater than zero. When the preset time period of the task data of the drone that is closest to the current time period includes the current time period, k will be used as the task time value of the drone.

[0112] If the preset time period of the task closest to the current time period in the drone's task data does not include the current time period, obtain the duration t between the preset time period of the task closest to the current time period and the current time period. △ When t △ If the time value is greater than t, then the drone's mission time value is 0; otherwise, calculate the drone's mission time value T within the current time period.

[0113] ,

[0114] Get the maximum value T of the task time of each drone in the drone cluster during the current time period. max and minimum value T min Calculate the drone's task score S for the current time period:

[0115] ,

[0116] Calculate the obstacle avoidance priority value H of the drone in the current time period, obtain the obstacle avoidance priority value of each drone in the drone cluster and aggregate them to obtain the obstacle avoidance priority data of the drone cluster.

[0117] For example, the specific formula for calculating the obstacle avoidance priority value H is:

[0118] ,

[0119] Where, η v η is the preset battery life priority factor; S These are preset task weight coefficients;

[0120] Obtain the average obstacle avoidance priority value of several drones in a drone subgroup within a drone swarm, and record it as the subgroup obstacle avoidance priority value of the drone subgroup.

[0121] Obtain the obstacle avoidance priority values ​​of each drone subgroup in the drone swarm and aggregate them to obtain the drone swarm subgroup obstacle avoidance priority data.

[0122] The obstacle avoidance module is used to analyze the position changes of drones, obtain drone target position data, assess the collision risk of drones in the current time period, generate drone obstacle avoidance strategies, and control the drone swarm to perform safe obstacle avoidance.

[0123] The safety obstacle avoidance module includes a safety obstacle avoidance unit;

[0124] The obstacle avoidance unit is used to analyze the positional changes of the UAV, obtain the target of the UAV, assess the collision risk of the UAV in the current time period, obtain collision risk data, generate UAV obstacle avoidance strategy, obtain target obstacle avoidance strategy, and control the UAV swarm to perform safe obstacle avoidance according to the target obstacle avoidance strategy.

[0125] Among them, the safety obstacle avoidance unit obtains the historical data transmission records of drones in the drone cluster, and obtains the data transmission consumption time of the drones from the historical data transmission records;

[0126] For example, the data transmission time of the drone can be obtained from the historical data transmission record. Specifically, it is the time interval between the drone sending historical data and the data processed by the drone receiving platform after the historical data is processed.

[0127] The average data transmission time is obtained from various historical data transmission records of the drone, and used as the target data transmission time t of the drone in the current time period. Γ ;

[0128] Based on the position status data of drones in the drone swarm, analyze the position changes of drones to obtain the target position data of drones in the current time period.

[0129] For example, analyzing the positional changes of drones yields the target position data of the drone swarm within the current time period. The specific analysis process is as follows:

[0130] Obtain the coordinates (x, y, z) of the UAV from its position status data during the current time period, and obtain the components of the velocity and acceleration of the UAV on the x, y, and z axes in the preset coordinate system during the current time period;

[0131] Calculate the displacements Δx, Δy, and Δz of the UAV on the x, y, and z axes of the coordinate system during the current time period;

[0132] The specific formula for calculating △x is as follows:

[0133] ,

[0134] Among them, a x v represents the component of the UAV's acceleration along the x-axis in a preset coordinate system during the current time period; x This represents the component of the drone's velocity along the x-axis in the preset coordinate system during the current time period;

[0135] The specific formula for calculating △y is as follows:

[0136] ,

[0137] Among them, a y v represents the component of the UAV's acceleration along the y-axis in a preset coordinate system during the current time period; y This represents the component of the drone's velocity along the y-axis in the preset coordinate system during the current time period.

[0138] The specific formula for calculating △z is as follows:

[0139] ,

[0140] Among them, a z v represents the component of the UAV's acceleration along the z-axis in a preset coordinate system during the current time period; z This represents the component of the drone's velocity along the z-axis in the preset coordinate system during the current time period.

[0141] Obtain the target coordinates (x´,y´,z´) of the UAV in the current time period, where x´=x+△x, y´=y+△y, z´=z+△z;

[0142] The target coordinates of each drone in the drone swarm are obtained and aggregated to obtain the drone target location data of the drone swarm in the current time period.

[0143] Acquire the target location data of the drone cluster within the current time period, and construct the safe range of the drones within the current time period;

[0144] Obtain the target coordinates (x´,y´,z´) of the drones in the drone swarm during the current time period from the drone target location data;

[0145] Set the feature duration, obtain the coordinates of the UAV after the target coordinates for the specified duration, and record them as the UAV's feature coordinates (x, y) within the current time period. Δ ,y Δ ,z Δ );

[0146] Obtain the feature coordinates (x) of the drone in the current time period. Δ ,y Δ ,z Δ ) in xΔ y Δ and z Δ The acquisition methods are the same, where the x-coordinate in the feature coordinates Δ =x´+△x´, where the specific formula for calculating △x´ is:

[0147] ,

[0148] Among them, a x v represents the component of the UAV's acceleration along the x-axis in a preset coordinate system during the current time period; x tψ represents the component of the UAV's velocity along the x-axis in the preset coordinate system during the current time period; tψ represents the characteristic duration.

[0149] Based on the characteristic coordinates and target coordinates of the UAV in the current time period, obtain the center point W=(x) of the safe range of the UAV in the current time period. κ ,y κ ,z κ );

[0150] Where, x κ =(x Δ +x´) / 2,y κ =(y Δ +y´) / 2,z κ =(z Δ +z´) / 2;

[0151] Calculate the safe radius R of the drone in the current time period, and construct the safe range of the drone in the current time period with the safe center point W as the center and the safe radius R as the radius;

[0152] Obtain the safe range of each drone in the drone swarm during the current time period, analyze the collision risk of the ζ-th drone in the drone swarm, and obtain the collision risk value of the ζ-th drone.

[0153] For example, the specific analysis process for analyzing the collision risk of the ζ-th drone in a drone swarm is as follows:

[0154] If the safe range of the ζ-th drone intersects with the safe ranges of other drones in the drone cluster, then the ζ-th drone is determined to be at risk of collision. The total number N of other drones in the drone cluster that intersect with the safe range of the ζ-th drone is obtained.

[0155] Get the total number N of drones in the drone swarm. sum Calculate the collision risk value P = N / N for the ζ-th drone. sum ;

[0156] Obtain the collision risk values ​​of each drone subgroup in the drone swarm, and sum the collision risk values ​​of several drones in each drone subgroup to obtain the subgroup collision risk value of the drone subgroup.

[0157] Based on the collision risk values ​​of each drone in the drone swarm and the collision risk values ​​of the subgroups of drones, a drone obstacle avoidance strategy is generated to obtain the target obstacle avoidance strategy. Based on the target obstacle avoidance strategy of the drone swarm, the drone subgroups and drones in the drone swarm are controlled to perform safe obstacle avoidance.

[0158] For example, generating an obstacle avoidance strategy for a drone to obtain a target obstacle avoidance strategy involves the following specific generation process:

[0159] Obtain the subgroup collision risk value of each drone subgroup in the drone cluster. If the absolute value of the difference between the subgroup collision risk values ​​of one drone subgroup and another drone subgroup is less than a preset absolute value, then it is determined that the collision risk of one drone subgroup and another drone subgroup is similar.

[0160] Obtain the obstacle avoidance priority value of each drone subgroup in the drone cluster from the subgroup obstacle avoidance priority data. If the obstacle avoidance priority value of one drone subgroup is greater than that of another drone subgroup, then the obstacle avoidance priority of one drone subgroup is higher than that of the other drone.

[0161] Based on the collision risk value of each drone subgroup in the drone swarm, the obstacle avoidance order of each drone subgroup is sorted, the obstacle avoidance order of each drone subgroup in the drone swarm is obtained and aggregated to obtain the subgroup obstacle avoidance data in the drone swarm.

[0162] Obtain the obstacle avoidance priority values ​​of several drones in the drone subgroup. When there are two drones in the drone subgroup with the same collision risk value, the obstacle avoidance order of the drone with the higher obstacle avoidance priority value is higher than that of the drone with the lower obstacle avoidance priority value.

[0163] Based on the collision risk values ​​of several drones in the drone subgroup, the drones in the drone subgroup are sorted, the obstacle avoidance order of several drones in the drone subgroup is obtained and aggregated to obtain the obstacle avoidance data of the drone subgroup.

[0164] Among them, the higher the obstacle avoidance order of a drone subgroup in a drone swarm and the higher the obstacle avoidance order of a drone in a drone subgroup, the higher the priority for obstacle avoidance.

[0165] Obstacle avoidance data of subgroups and individual drone subgroups are acquired and aggregated to obtain the target obstacle avoidance strategy of the drone swarm.

[0166] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A data link-based unmanned aerial vehicle (UAV) swarm security monitoring system, characterized in that, The system includes a stable movement range construction module, a subgroup construction module, an obstacle avoidance priority analysis module, and a safe obstacle avoidance module. The stable movement range construction module is used to acquire the location status data of drones in the drone cluster at the current time period and construct the stable movement range of the drones. The subgroup construction module is used to establish the stable communication relationship between different drones in the drone cluster during the current time period, analyze the performance changes of different drones over different time spans, elect a drone as the group leader from the drone cluster, and construct a drone subgroup in the drone cluster. The obstacle avoidance priority analysis module is used to analyze the obstacle avoidance priority status of drone subgroups in a drone swarm and obtain subgroup priority data. The safety obstacle avoidance module is used to analyze the position changes of the UAV, obtain the target position data of the UAV, assess the collision risk of the UAV in the current time period, generate the UAV obstacle avoidance strategy, and control the UAV cluster to perform safe obstacle avoidance. The stable movement range construction module includes a monitoring unit and a stable movement range construction unit; The monitoring unit is used to monitor the flight status of the drone cluster during the current time period and obtain the position status data of the drones in the drone cluster. The stable movement range construction unit is used to construct the stable movement time range of the UAV in the current period, and combine it with position status data to analyze the flight status of the UAV at different time points within the stable movement time range, thereby constructing the stable movement range of the UAV. The stable movement range construction unit obtains the position status data of each drone in the drone cluster at the current time period, obtains the coordinates of the drones from the position status data, and obtains the straight-line distance between each drone in the drone cluster. Obtain the preset stable communication duration t of the drone cluster from the platform. ▽ Obtain the drone's speed and maximum acceleration within the current time period, and calculate the duration t of the drone's speed change. υ ; Obtain the stable communication duration t since the current time period. ▽ The time point is recorded as the target time point, and the stable movement time range of the drone swarm within the current time period is constructed based on the current time point and the target time point. Several time points are extracted from the stable moving time range with a preset stable duration and recorded as the target time points in the stable moving time range. Obtain the distance and duration between each target time point and the current time period within the stable movement time range, and obtain the stable flight distance of the UAV at each target time point; Using the drone's current coordinates as the center and the drone's stable flight distance at the target time point as the radius, a circular area is constructed as the stable movement range of the drone at the target time point; Obtain the stable movement range of the human-machine interface at each target time point within the stable movement time range; The subgroup construction module includes a communication stability relationship establishment unit and a subgroup construction unit; The communication stability relationship establishment unit is used to acquire the stable movement range of different drones in the drone swarm at each target time point within the stable movement range, analyze the communication impact of drones in the drone swarm on other drones at different time spans, and thus establish a stable communication relationship between drones in the drone swarm and other drones. The subgroup construction unit constructs a stable communication set of drones based on the stable communication relationship between drones and different drones in the drone cluster, analyzes the performance changes of drones within the stable communication period, obtains key drones from the drone cluster, selects the group leader of the drone cluster based on the drone inclusion status in the stable communication set of the key drones in the drone cluster, and constructs a drone subgroup. The communication stability relationship establishment unit obtains the maximum value of the straight-line distance between any two coordinate points within the stable movement range of a drone in the drone cluster and other drones at the same target time point, and records it as the spatial straight-line distance between the drone and other drones at the target time point. Obtain the maximum value of the spatial straight-line distance between the UAV and other UAVs at each target time point within the stable movement time range, and denote it as the extreme value of the spatial distance between the UAV and other UAVs, L. τ ; Acquire all historical communication records of the drone, obtain the average communication distance and average signal strength indication when the drone communicates with other historical drones from each historical communication record, and obtain historical communication records that are greater than the preset average signal strength indication threshold from each historical communication record, and record them as target historical communication records. The average communication distance obtained from the historical communication records of various targets of the UAV is greater than the extreme value of spatial distance L. τ The ratio of the number of historical communication records of several targets to the number of each historical communication record is used as the communication stability characteristic value ξ when the UAV communicates with other UAVs. The target's historical communication records are removed from the various historical communication records of the UAV, resulting in several historical communication records for the UAV. From these historical communication records, the average communication distance is found to be less than the extreme value of the spatial distance L. τ The historical communication records are collected and marked. The ratio of the marked historical communication records to the number of the plurality of historical communication records is used as the communication anomaly characteristic value δ when the UAV communicates with other UAVs. Divide the communication stability feature value ξ by the communication anomaly feature value δ to obtain the communication stability value U when the UAV communicates with other UAVs, and obtain the communication stability value U´ when other UAVs communicate with the UAV. When both the communication stability value U and the communication stability value U´ are greater than the preset communication stability threshold, a stable communication relationship is established between the UAV and other UAVs in the current time period. The sub-group construction unit obtains several other drones that have established stable communication relationships with the drone in the current time period from the drone cluster, and constructs a stable drone communication set for the drone in the current time period. Obtain the remaining flight time of the drone in the current time period, obtain each historical flight record of the drone, extract the historical flight records containing the remaining flight time e of the drone in the current time period from each historical flight record, and mark them; Extract the time point when the drone has remaining flight time e from the marked historical flight records, and use it as the starting point. Then, record the stable communication duration t after the starting point. ▽ As the endpoint, the values ​​of various performance indicators of the UAV at the starting point and the endpoint in the marked historical flight records are extracted to obtain the difference between the performance indicators at the starting point and the endpoint in the historical flight records. The difference is then divided by the value of the performance indicator at the starting point to obtain the rate of change of the performance indicator in the historical flight records. The average rate of change of the performance index of the UAV in each marked historical flight record is obtained. The value of the UAV's performance index in the current period is subtracted from the value of the average rate of change multiplied by the value of the UAV's performance index in the current period to obtain the stable value of the UAV's performance index. Identify drones in the drone cluster whose stable value is greater than a preset threshold during the current time period and mark them as key drones; Based on the average rate of change of various performance indicators of key drones, the key drones in the drone swarm are ranked. According to the order, the stable communication set of each key UAV is obtained in turn. The UAVs included in the stable communication set of the UAV with higher order are removed from the stable communication set of the UAV with lower order, so as to obtain the characteristic stable communication set of the UAV with lower order. The stable communication set of the highest-ranking critical drone in the drone swarm is denoted as the baseline stable communication set. The sets of drones included in the baseline stable communication set and the characteristic stable communication set of the critical drone are obtained in order until the sets of drones included in the baseline stable communication set and the characteristic stable communication set of the critical drone are added to the corresponding critical drone. This includes all drones in the drone swarm. The critical drones whose characteristic stable communication sets are empty among the several critical drones in the constructed set are removed. The key drones that are retained are designated as group leaders, and drones in the characteristic communication stable set and the benchmark communication stable set are designated as group members according to the corresponding relationship, and drone subgroups in the drone cluster are constructed in sequence.

2. The data link-based unmanned aerial vehicle (UAV) swarm security monitoring system according to claim 1, characterized in that, The obstacle avoidance priority analysis module includes an obstacle avoidance priority analysis unit; The obstacle avoidance priority analysis unit is used to evaluate the obstacle avoidance priority status of drones in the drone swarm, obtain drone obstacle avoidance priority data, and analyze the obstacle avoidance priority status of drone subgroups in the drone swarm to obtain subgroup priority data of the drone swarm.

3. The data link-based unmanned aerial vehicle (UAV) swarm security monitoring system according to claim 2, characterized in that: The obstacle avoidance priority analysis unit acquires the endurance and task data of each drone in the drone swarm in the current time period. The task data includes the preset time period for each task that the drone needs to perform. Calculate the drone's endurance score V and mission score S for the current time period; The specific formula for calculating the battery life score V is as follows: ， Among them, L min and L max These represent the maximum and minimum flight times of each drone in the drone swarm during the current time period, respectively; L represents the flight time of the drone during the current time period. The specific formula for calculating the task score S is as follows: Set the unit duration t, obtain the preset value k, where k is a positive integer greater than zero. When the preset time period of the task data of the drone that is closest to the current time period includes the current time period, k will be used as the task time value of the drone. If the preset time period of the task closest to the current time period in the drone's task data does not include the current time period, obtain the duration t between the preset time period of the task closest to the current time period and the current time period. △ When t △ If the time value is greater than t, then the drone's mission time value is 0; otherwise, calculate the drone's mission time value T within the current time period. ， Get the maximum value T of the task time of each drone in the drone cluster during the current time period. max and minimum value T min Calculate the drone's task score S for the current time period: ， Calculate the obstacle avoidance priority value H of the drone in the current time period, obtain the obstacle avoidance priority value of each drone in the drone cluster and aggregate them to obtain the obstacle avoidance priority data of the drone cluster. The specific formula for calculating the obstacle avoidance priority value H of the drone in the current time period is as follows: ， Where, η v η is the preset battery life priority factor; S These are preset task weight coefficients; Obtain the average obstacle avoidance priority value of several drones in a drone subgroup within a drone swarm, and record it as the subgroup obstacle avoidance priority value of the drone subgroup. Obtain the obstacle avoidance priority values ​​of each drone subgroup in the drone swarm and aggregate them to obtain the drone swarm subgroup obstacle avoidance priority data.

4. The data link-based unmanned aerial vehicle (UAV) swarm security monitoring system according to claim 3, characterized in that, The safety obstacle avoidance module includes a safety obstacle avoidance unit; The obstacle avoidance unit is used to analyze the positional changes of the UAV, obtain the target of the UAV, assess the collision risk of the UAV in the current time period, obtain collision risk data, generate UAV obstacle avoidance strategy, obtain target obstacle avoidance strategy, and control the UAV swarm to perform safe obstacle avoidance according to the target obstacle avoidance strategy.

5. The data link-based unmanned aerial vehicle (UAV) swarm security monitoring system according to claim 4, characterized in that: The system uses a safety obstacle avoidance unit to obtain historical data transmission records of drones in the drone swarm, and then retrieves the data transmission time of the drones from these records. The average data transmission time is obtained from various historical data transmission records of the drone, and used as the target data transmission time t of the drone in the current time period. Γ ; Based on the position status data of drones in the drone swarm, analyze the position changes of drones to obtain the target position data of drones in the current time period. Acquire the target location data of the drone cluster within the current time period, and construct the safe range of the drones within the current time period; Obtain the safe range of each drone in the drone swarm during the current time period, analyze the collision risk of the ζ-th drone in the drone swarm, and obtain the collision risk value of the ζ-th drone. Obtain the collision risk values ​​of each drone subgroup in the drone swarm, and sum the collision risk values ​​of several drones in each drone subgroup to obtain the subgroup collision risk value of the drone subgroup. Based on the collision risk values ​​of each drone in the drone swarm and the collision risk values ​​of the subgroups of drones, an obstacle avoidance strategy for drones is generated to obtain the target obstacle avoidance strategy. Based on the target obstacle avoidance strategy of the drone swarm, the drone subgroups and drones in the drone swarm are controlled to perform safe obstacle avoidance.

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