A communication data management system and method for unmanned aerial vehicle

Abstract

The invention discloses a communication data management system and method applied to unmanned aerial vehicles, which relates to the technical field of unmanned aerial vehicles, and comprises a historical flight event extraction module, a target flight set storage module, a communication data risk characteristic value analysis module, an unmanned aerial vehicle type analysis module, an optimal storage node determination module and a data copy storage module; the historical flight event extraction module is used to extract historical flight events of applying unmanned aerial vehicle clusters to perform tasks; the target flight set storage module is used to summarize and store historical flight events recorded for the same unmanned aerial vehicle scale and the same target task as a target flight set; the communication data risk characteristic value analysis module is used to analyze the corresponding communication data risk characteristic value for each target flight set; the unmanned aerial vehicle type analysis module is used to determine suspicious risk unmanned aerial vehicles and unmanned aerial vehicles to be analyzed; and the optimal storage node determination module is used to output the optimal storage node of the unmanned aerial vehicle to be analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a communication data management system and method applied to unmanned aerial vehicles. Background Art

[0002] The management of drone communication data is a complex process involving multiple aspects, mainly including data collection, transmission, storage, processing and application. Data collection is the first step in drone communication data management, which is mainly carried out through various sensors and mission payloads installed on drones; data transmission is the process of transmitting the collected drone data to the ground control station or data center wirelessly. Data storage is the process of saving data transmitted to the ground control station or data center. In the storage of drone communication data, redundant storage strategies are often used. However, the application of redundant storage strategies will also bring storage pressure to drone groups, generate a lot of unnecessary data storage, and the available storage nodes are not differentiated based on different drone groups performing different flight missions, and there is no definition of the response time that must be stored, which increases the storage pressure. Summary of the invention

[0003] The purpose of the present invention is to provide a communication data management system and method applied to unmanned aerial vehicles to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: a communication data management method applied to a drone, the method comprising the following steps:

[0005] Step S100: extracting historical flight events in which drone clusters are used to perform tasks, wherein the historical flight events record the scale of drones, the target tasks to be performed by the drone clusters, and the data records during the task execution process; using the scale of drones and the target tasks as search items, summarizing historical flight events with the same scale of drones and the same target tasks and storing them as a target flight set; analyzing the corresponding communication data risk feature value for each target flight set;

[0006] Step S200: Based on the risk feature value of the communication data, determine the suspicious risk drones in each target flight set, and other drones excluding the suspicious risk drones under the corresponding drone scale as drones to be analyzed;

[0007] Step S300: extracting data records of the drone to be analyzed during the mission execution process of the flight event, analyzing and sorting the storage nodes of the drone to be analyzed, and outputting the optimal storage node of the drone to be analyzed;

[0008] Step S400: When a suspicious risk drone is determined, a copy of the data recorded by the corresponding drone is triggered and stored in the optimal storage node of the drone to be analyzed.

[0009] Furthermore, step S100 includes the following specific steps:

[0010] Step S110: Mark the historical flight events in which the drone communication data abnormality is recorded in the same target flight set as abnormal flight events, where the communication data abnormality refers to the drone system sending an error code or warning information through the communication data; extract the data records of the abnormal flight events during the mission execution process, where the data records include data transmission rate, flight status parameters, and sensor parameters loaded on the drone;

[0011] Step S120: trace back the abnormal moment of the communication data response to each abnormal flight event in the same target flight set, extract the preset monitoring period T before the abnormal moment, T=[t1,t2], t1 represents the starting time of the preset monitoring period, t2 represents the abnormal moment; obtain the average data transmission rate V0, the normalized average flight state parameter G0 and the normalized average sensor parameter D0 of the drone cluster recorded in the preset monitoring period T per unit time;

[0012] V0=∑V / m, V represents the data transmission rate of each drone in the drone cluster;

[0013] G0=∑{[(∑a) / m-amin] / (amax-amin)}, a represents any state parameter in the flight state parameters, ∑{[(∑a) / m-amin] / (amax-amin)} represents the normalized sum of the average values ​​of all flight state parameters in the UAV cluster; amin represents the minimum value recorded for the same flight state parameter, amax represents the maximum value recorded for the same flight state parameter; m represents the number of UAVs in the UAV cluster;

[0014] D0=∑{[(∑b) / m-bmin] / (bmax-bmin)}, b represents any sensor parameter among the sensor parameters loaded on the drone, ∑{[(∑b) / m-bmin] / (bmax-bmin)} represents the normalized sum of the average values ​​of all sensor parameters in the drone cluster, bmin represents the minimum value recorded for the same sensor parameter, and bmax represents the maximum value recorded for the same sensor parameter;

[0015] Step S130: Calculate the comprehensive parameter index U(t1, t2) of the drone cluster at time t1 and time t2 of the preset monitoring period, U(t1, t2)=k1×V0(t1, t2)+k2×G0(t1, t2)+k3×D0(t1, t2); use the formula: f=(Ut1-Ut2) / T to calculate the index change rate f of the same target flight set within the preset monitoring period T; k1, k2, k3 represent the corresponding reference coefficients respectively;

[0016] Step S140: If f≥0, select the comprehensive parameter index Ut2 as the communication data risk characteristic value corresponding to the target flight set analysis and issue an early warning when it is less than or equal to Ut2; if f<0, select the comprehensive parameter index Ut2 as the communication data risk characteristic value corresponding to the target flight set analysis and issue an early warning when it is greater than Ut2.

[0017] Furthermore, step S200 includes the following specific steps:

[0018] Step S210: extracting data records of the drone cluster of each flight event record in the target flight set, and calculating the comprehensive parameter index U of each drone at the corresponding unit flight time of the drone cluster in the corresponding flight event;

[0019] Step S220: When the index change rate recorded in the corresponding target flight set is greater than or equal to zero, calculate the first difference u1 between the comprehensive parameter index of each drone in the drone cluster and the communication data risk characteristic value Ut2 of the target flight set with the same historical record, where u1=U-Ut2; extract the corresponding drones when u1≥0 and u1 is less than the preset first difference threshold and u1<0 as suspicious risk drones;

[0020] When the change rate of the indicators recorded in the corresponding target flight set is less than zero, calculate the second difference u2 between the comprehensive parameter indicators of each UAV in the UAV cluster and the communication data risk characteristic value Ut2 of the target flight set with the same historical record, u2=Ut2-U; extract the corresponding UAVs when u2≥0 and u2 is less than the second difference threshold and u2<0 as suspicious risk UAVs.

[0021] Further, step S300 includes the following:

[0022] Step S310: Mark the target drone as the target time, and extract the comprehensive parameter index U recorded by each drone to be analyzed at the target time.

[0023] Step S320: When the index change rate recorded in the corresponding target flight set is greater than or equal to zero, obtain the first difference u1 of each drone to be analyzed at the target time, and the corresponding storage space remaining capacity c1, and use the formula: R1=r1×u1+r2×c1 to calculate the first effective storage index R1 of each drone to be analyzed at the target time, and sort the first effective storage index R1 in descending order to generate a first storage sequence; based on the first storage sequence, the first corresponding drone to be analyzed is output as the optimal drone storage node to be analyzed;

[0024] When the index change rate recorded in the corresponding target flight set is less than zero, the second difference u2 of each drone to be analyzed at the target time and the corresponding remaining capacity c1 of the storage space are obtained, and the second effective storage index R2 of each drone to be analyzed at the target time is calculated using the formula: R2=r1×u2+r2×c1, and the second effective storage index R2 is sorted in descending order to generate a second storage sequence; based on the second storage sequence, the drone to be analyzed corresponding to the first sequence is output as the optimal drone storage node to be analyzed.

[0025] By analyzing the storage capacity and the risk level of the drone's communication data at the response time, when the suspicious risk drone may be abnormal, the data copy can be transmitted to the optimal storage node of the drone to be analyzed at the response time, which improves the effective preservation and security of the communication data. The larger the storage capacity, the smaller the impact on the operation of the transmission target drone, the lower the risk of communication data, and the higher the security of the data. It can effectively use multiple storage nodes to store data copies, improve data redundancy, and reduce the risk of single point failure through decentralized storage. At the same time, the storage selection of the copy has a certain rationality and directionality.

[0026] A communication data management system applied to unmanned aerial vehicles, the system includes a historical flight event extraction module, a target flight set storage module, a communication data risk characteristic value analysis module, a unmanned aerial vehicle type analysis module, an optimal storage node determination module and a data copy storage module;

[0027] The historical flight event extraction module is used to extract historical flight events of drone clusters performing tasks;

[0028] The target flight set storage module is used to use the drone scale and target mission as search items, summarize the historical flight events recorded with the same drone scale and the same target mission and store them as a target flight set;

[0029] The communication data risk characteristic value analysis module is used to analyze the corresponding communication data risk characteristic value for each target flight set;

[0030] The drone type analysis module is used to determine the suspicious risk drones in each target flight set, and other drones excluding the suspicious risk drones under the corresponding drone scale as drones to be analyzed;

[0031] The optimal storage node determination module is used to extract the data records of the drone to be analyzed during the mission execution process of the flight event, analyze and sort the storage nodes of the drone to be analyzed, and output the optimal storage node of the drone to be analyzed;

[0032] The data copy storage module is used to trigger the storage of the data copy recorded by the corresponding drone to the optimal drone storage node to be analyzed when a suspicious risk drone is determined.

[0033] Further, the communication data risk characteristic value analysis module includes an abnormal flight event extraction unit, a data record analysis unit, a comprehensive parameter index calculation unit and a communication data risk characteristic value output unit;

[0034] The abnormal flight event extraction unit is used to mark the historical flight events with abnormal UAV communication data in the same target flight set as abnormal flight events;

[0035] The data record analysis unit is used to extract data records of abnormal flight events during the mission execution process, and the data records include data transmission rate, flight status parameters and sensor parameters loaded on the UAV;

[0036] The comprehensive parameter index calculation unit is used to calculate the comprehensive parameter index of the UAV cluster at time t1 and time t2 of the preset monitoring period;

[0037] The communication data risk characteristic value output unit is used to select comprehensive parameter indicators as the communication data risk characteristic values ​​corresponding to the target flight set analysis and to issue early warnings based on the indicator change rate.

[0038] Further, the drone type analysis module includes a difference data calculation unit and a suspicious drone determination unit;

[0039] The difference data calculation unit is used to calculate the first difference and the second difference between the comprehensive parameter index of each drone in the drone cluster and the communication data risk characteristic value of the target flight set with the same historical record;

[0040] The suspicious drone determining unit is used to determine a suspicious drone based on the difference data output by the difference data calculating unit.

[0041] Further, the optimal storage node determination module includes a target time determination unit, an effective storage index calculation unit and an optimal storage node output unit;

[0042] The target time determination unit is used to mark the time when the drone to be analyzed records the marking response of the suspicious risk drone in the drone cluster in the same target flight set as the target time;

[0043] The effective storage index calculation unit is used to calculate the first effective storage index and the second effective storage index of each drone to be analyzed at the target time;

[0044] The optimal storage node output unit is used to output the optimal storage node based on the result of the effective storage index calculation unit.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: when a drone cluster is performing a flight mission, the present application can quickly and in real time analyze drones that may have data storage risks, select other drones corresponding to the optimal data storage nodes, and implement distributed storage to ensure data security management, thereby reducing the impact of a single storage on the overall flight data when a single point failure occurs, and when selecting a distributed storage node, a reasonable selection is made based on the current operating status of each drone to be analyzed, thereby reducing storage pressure while improving the security of data management, and there is no need to blindly increase data pressure for distributed storage; and the selection of distributed storage nodes for drone clusters does not depend on the ground station, which can effectively avoid the additional storage pressure on copy data storage when the drone interacts with uplink data and downlink data, and reduce the communication protocol between the drone and the ground station. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a structural schematic diagram of a communication data management system applied to an unmanned aerial vehicle. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example: Figure 1 As shown, the present invention provides a technical solution for a communication data management system applied to unmanned aerial vehicles, the system includes a historical flight event extraction module, a target flight set storage module, a communication data risk characteristic value analysis module, a unmanned aerial vehicle type analysis module, an optimal storage node determination module and a data copy storage module;

[0049] The historical flight event extraction module is used to extract historical flight events of drone clusters performing tasks;

[0050] The target flight set storage module is used to use the drone scale and target mission as search items, summarize the historical flight events recorded with the same drone scale and the same target mission and store them as a target flight set;

[0051] The communication data risk characteristic value analysis module is used to analyze the corresponding communication data risk characteristic value for each target flight set;

[0052] The drone type analysis module is used to determine the suspicious risk drones in each target flight set, and other drones excluding the suspicious risk drones under the corresponding drone scale as drones to be analyzed;

[0053] The optimal storage node determination module is used to extract the data records of the drone to be analyzed during the mission execution process of the flight event, analyze and sort the storage nodes of the drone to be analyzed, and output the optimal storage node of the drone to be analyzed;

[0054] The data copy storage module is used to trigger the storage of the data copy recorded by the corresponding drone to the optimal drone storage node to be analyzed when a suspicious risk drone is determined.

[0055] The communication data risk characteristic value analysis module includes an abnormal flight event extraction unit, a data record analysis unit, a comprehensive parameter index calculation unit and a communication data risk characteristic value output unit;

[0056] The abnormal flight event extraction unit is used to mark the historical flight events with abnormal UAV communication data in the same target flight set as abnormal flight events;

[0057] The data record analysis unit is used to extract data records of abnormal flight events during the mission execution process, and the data records include data transmission rate, flight status parameters and sensor parameters loaded on the UAV;

[0058] The comprehensive parameter index calculation unit is used to calculate the comprehensive parameter index of the UAV cluster at time t1 and time t2 of the preset monitoring period;

[0059] The communication data risk characteristic value output unit is used to select comprehensive parameter indicators as the communication data risk characteristic values ​​corresponding to the target flight set analysis and to issue early warnings based on the indicator change rate.

[0060] The drone type analysis module includes a difference data calculation unit and a suspicious drone determination unit;

[0061] The difference data calculation unit is used to calculate the first difference and the second difference between the comprehensive parameter index of each drone in the drone cluster and the communication data risk characteristic value of the target flight set with the same historical record;

[0062] The suspicious drone determining unit is used to determine a suspicious drone based on the difference data output by the difference data calculating unit.

[0063] The optimal storage node determination module includes a target time determination unit, an effective storage index calculation unit and an optimal storage node output unit;

[0064] The target time determination unit is used to mark the time when the drone to be analyzed records the marking response of the suspicious risk drone in the drone cluster in the same target flight set as the target time;

[0065] The effective storage index calculation unit is used to calculate the first effective storage index and the second effective storage index of each drone to be analyzed at the target time;

[0066] The optimal storage node output unit is used to output the optimal storage node based on the result of the effective storage index calculation unit.

[0067] A communication data management method applied to an unmanned aerial vehicle, the method comprising the following steps:

[0068] Step S100: extracting historical flight events in which drone clusters are used to perform tasks, wherein the historical flight events record the scale of drones, the target tasks to be performed by the drone clusters, and the data records during the task execution process; using the scale of drones and the target tasks as search items, summarizing historical flight events with the same scale of drones and the same target tasks and storing them as a target flight set; analyzing the corresponding communication data risk feature value for each target flight set;

[0069] Step S200: Based on the risk feature value of the communication data, determine the suspicious risk drones in each target flight set, and other drones excluding the suspicious risk drones under the corresponding drone scale as drones to be analyzed;

[0070] Step S300: extracting data records of the drone to be analyzed during the mission execution process of the flight event, analyzing and sorting the storage nodes of the drone to be analyzed, and outputting the optimal storage node of the drone to be analyzed;

[0071] Step S400: When a suspicious risk drone is determined, a copy of the data recorded by the corresponding drone is triggered and stored in the optimal storage node of the drone to be analyzed.

[0072] Step S100 includes the following specific steps:

[0073] Step S110: Mark the historical flight events in which the drone communication data abnormality is recorded in the same target flight set as abnormal flight events, where the communication data abnormality refers to the drone system sending an error code or warning information through the communication data; extract the data records of the abnormal flight events during the mission execution process, where the data records include data transmission rate, flight status parameters, and sensor parameters loaded on the drone;

[0074] Step S120: trace back the abnormal moment of the communication data response to each abnormal flight event in the same target flight set, extract the preset monitoring period T before the abnormal moment, T=[t1,t2], t1 represents the starting time of the preset monitoring period, t2 represents the abnormal moment; obtain the average data transmission rate V0, the normalized average flight state parameter G0 and the normalized average sensor parameter D0 of the drone cluster recorded in the preset monitoring period T per unit time;

[0075] V0=∑V / m, V represents the data transmission rate of each drone in the drone cluster;

[0076] G0=∑{[(∑a) / m-amin] / (amax-amin)}, a represents any state parameter in the flight state parameters, ∑{[(∑a) / m-amin] / (amax-amin)} represents the normalized sum of the average values ​​of all flight state parameters in the UAV cluster; amin represents the minimum value recorded for the same flight state parameter, amax represents the maximum value recorded for the same flight state parameter; m represents the number of UAVs in the UAV cluster;

[0077] D0=∑{[(∑b) / m-bmin] / (bmax-bmin)}, b represents any sensor parameter among the sensor parameters loaded on the drone, ∑{[(∑b) / m-bmin] / (bmax-bmin)} represents the normalized sum of the average values ​​of all sensor parameters in the drone cluster, bmin represents the minimum value recorded for the same sensor parameter, and bmax represents the maximum value recorded for the same sensor parameter;

[0078] Step S130: Calculate the comprehensive parameter index U(t1, t2) of the drone cluster at time t1 and time t2 of the preset monitoring period, U(t1, t2)=k1×V0(t1, t2)+k2×G0(t1, t2)+k3×D0(t1, t2); use the formula: f=(Ut1-Ut2) / T to calculate the index change rate f of the same target flight set within the preset monitoring period T; k1, k2, k3 represent the corresponding reference coefficients respectively;

[0079] As shown in the embodiment: when calculating the comprehensive parameter index of the drone cluster at time t1, the corresponding data is substituted into Ut1=k1×V0(t1)+k2×G0(t1)+k3×D0(t1); similarly, at time t2, it is Ut2=k1×V0(t2)+k2×G0(t2)+k3×D0(t2);

[0080] Step S140: If f≥0, select the comprehensive parameter index Ut2 as the communication data risk characteristic value corresponding to the target flight set analysis and issue an early warning when it is less than or equal to Ut2; if f<0, select the comprehensive parameter index Ut2 as the communication data risk characteristic value corresponding to the target flight set analysis and issue an early warning when it is greater than Ut2.

[0081] Step S200 includes the following specific steps:

[0082] Step S210: extracting data records of the drone cluster of each flight event record in the target flight set, and calculating the comprehensive parameter index U of each drone at the corresponding unit flight time of the drone cluster in the corresponding flight event;

[0083] As shown in the embodiment: when calculating the comprehensive parameter index U of each drone, the three types of parameters are the data transmission rate V of the drone per unit time, the normalized flight state parameter G, and the normalized sensor parameter D loaded on the drone.

[0084] For example, G=∑[(a-amin) / (amax-amin)];

[0085] Step S220: When the index change rate recorded in the corresponding target flight set is greater than or equal to zero, calculate the first difference u1 between the comprehensive parameter index of each drone in the drone cluster and the communication data risk characteristic value Ut2 of the target flight set with the same historical record, where u1=U-Ut2; extract the corresponding drones when u1≥0 and u1 is less than the preset first difference threshold and u1<0 as suspicious risk drones;

[0086] When the change rate of the indicators recorded in the corresponding target flight set is less than zero, calculate the second difference u2 between the comprehensive parameter indicators of each UAV in the UAV cluster and the communication data risk characteristic value Ut2 of the target flight set with the same historical record, u2=Ut2-U; extract the corresponding UAVs when u2≥0 and u2 is less than the second difference threshold and u2<0 as suspicious risk UAVs.

[0087] Step S300 includes the following:

[0088] Step S310: Mark the target drone as the target time, and extract the comprehensive parameter index U recorded by each drone to be analyzed at the target time.

[0089] Step S320: When the index change rate recorded in the corresponding target flight set is greater than or equal to zero, obtain the first difference u1 of each drone to be analyzed at the target time, and the corresponding storage space remaining capacity c1, and use the formula: R1=r1×u1+r2×c1 to calculate the first effective storage index R1 of each drone to be analyzed at the target time, and sort the first effective storage index R1 in descending order to generate a first storage sequence; based on the first storage sequence, the first corresponding drone to be analyzed is output as the optimal drone storage node to be analyzed;

[0090] When the index change rate recorded in the corresponding target flight set is less than zero, the second difference u2 of each drone to be analyzed at the target time and the corresponding remaining capacity c1 of the storage space are obtained, and the second effective storage index R2 of each drone to be analyzed at the target time is calculated using the formula: R2=r1×u2+r2×c1, and the second effective storage index R2 is sorted in descending order to generate a second storage sequence; based on the second storage sequence, the drone to be analyzed corresponding to the first sequence is output as the optimal drone storage node to be analyzed.

[0091] By analyzing the storage capacity and the risk level of the drone's communication data at the response time, when the suspicious risk drone may be abnormal, the data copy can be transmitted to the optimal storage node of the drone to be analyzed at the response time, which improves the effective preservation and security of the communication data. The larger the storage capacity, the smaller the impact on the operation of the transmission target drone, the lower the risk of communication data, and the higher the security of the data. It can effectively use multiple storage nodes to store data copies, improve data redundancy, and reduce the risk of single point failure through decentralized storage. At the same time, the storage selection of the copy has a certain rationality and directionality.

[0092] When there are a large number of suspicious risk drones, the unanalyzed drones in the storage sequence can be selected as candidate storage nodes as needed and in order.

[0093] It will be apparent to those skilled in the art that the 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 the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A communication data management method applied to an unmanned aerial vehicle, characterized in that: The method comprises the following steps: Step S100: extracting historical flight events for executing tasks using drone clusters, wherein the historical flight events record the scale of drones, the target tasks to be executed by the drone cluster, and the data records during the task execution process; using the scale of drones and the target tasks as search items, summarizing historical flight events recorded with the same scale of drones and the same target tasks and storing them as a target flight set; analyzing the corresponding communication data risk feature value for each target flight set; The step S100 includes the following specific steps: Step S110: Mark the historical flight events in which abnormal UAV communication data is recorded in the same target flight set as abnormal flight events, wherein the abnormal communication data refers to the UAV system sending an error code or warning information through communication data; extract the data record of the abnormal flight event during the mission execution process, wherein the data record includes data transmission rate, flight state parameters, and sensor parameters loaded on the UAV; Step S120: Tracing back the abnormal moment of the communication data response to each abnormal flight event in the same target flight set, extracting the preset monitoring period T before the abnormal moment, T = [t1, t2], t1 represents the starting moment of the preset monitoring period, t2 represents the abnormal moment; obtaining the average data transmission rate V0, the normalized average flight state parameter G0 and the normalized average sensor parameter D0 of the drone cluster recorded in the preset monitoring period T per unit time; V0 = ∑V / m, where V represents the data transmission rate of each drone in the drone cluster; G0 = ∑{[(∑a) / m-amin] / (amax-amin)}, where a represents any state parameter in the flight state parameters, ∑{[(∑a) / m-amin] / (amax-amin)} represents the normalized sum of the average values ​​of all flight state parameters in the UAV cluster; amin represents the minimum value recorded for the same flight state parameter, amax represents the maximum value recorded for the same flight state parameter; m represents the number of UAVs in the UAV cluster; D0 = ∑{[(∑b) / m-bmin] / (bmax-bmin)}, where b represents any sensor parameter among the sensor parameters carried by the drone, ∑{[(∑b) / m-bmin] / (bmax-bmin)} represents the normalized sum of the average values ​​of all sensor parameters in the drone cluster, bmin represents the minimum value recorded for the same sensor parameter, and bmax represents the maximum value recorded for the same sensor parameter; Step S130: Calculate the comprehensive parameter index U(t1, t2) of the drone cluster at time t1 and time t2 of the preset monitoring period, U(t1, t2) = k1×V0(t1, t2)+k2×G0(t1, t2)+k3×D0(t1, t2); use the formula: f=(Ut1-Ut2) / T to calculate the index change rate f of the same target flight set within the preset monitoring period T; k1, k2, k3 represent the corresponding reference coefficients respectively; Step S140: if f≥0, select the comprehensive parameter index Ut2 as the communication data risk characteristic value corresponding to the target flight set analysis and issue an early warning when it is less than or equal to Ut2; if f<0, select the comprehensive parameter index Ut2 as the communication data risk characteristic value corresponding to the target flight set analysis and issue an early warning when it is greater than Ut2; Step S200: Based on the risk feature value of the communication data, determine the suspicious risk drones in each target flight set, and other drones excluding the suspicious risk drones under the corresponding drone scale as drones to be analyzed; Step S300: extracting data records of the drone to be analyzed during the mission execution process of the flight event, analyzing and sorting the storage nodes of the drone to be analyzed, and outputting the optimal storage node of the drone to be analyzed; Step S400: When a suspicious risk drone is determined, a copy of the data recorded by the corresponding drone is triggered and stored in the optimal storage node of the drone to be analyzed.

2. The communication data management method for an unmanned aerial vehicle according to claim 1, characterized in that: The step S200 includes the following specific steps: Step S210: extracting data records of the drone cluster of each flight event record in the target flight set, and calculating the comprehensive parameter index U of each drone at the corresponding unit flight time of the drone cluster in the corresponding flight event; Step S220: When the index change rate recorded in the corresponding target flight set is greater than or equal to zero, calculate the first difference u1 between the comprehensive parameter index of each drone in the drone cluster and the communication data risk characteristic value Ut2 of the target flight set with the same historical record, u1=U-Ut2; extract the corresponding drones when u1≥0 and u1 is less than the preset first difference threshold and u1<0 as suspicious risk drones; When the change rate of the indicators recorded in the corresponding target flight set is less than zero, calculate the second difference u2 between the comprehensive parameter indicators of each UAV in the UAV cluster and the communication data risk characteristic value Ut2 of the target flight set with the same historical record, u2=Ut2-U; extract the corresponding UAVs when u2≥0 and u2 is less than the second difference threshold and u2<0 as suspicious risk UAVs.

3. The communication data management method applied to an unmanned aerial vehicle according to claim 1, characterized in that: The step S300 includes the following: Step S310: Mark the target drone as the target time, and extract the comprehensive parameter index U recorded by each drone to be analyzed at the target time. Step S320: When the index change rate recorded in the corresponding target flight set is greater than or equal to zero, obtain the first difference u1 of each drone to be analyzed at the target time, and the corresponding storage space remaining capacity c1, and use the formula: R1 = r1 × u1 + r2 × c1 to calculate the first effective storage index R1 of each drone to be analyzed at the target time, and sort the first effective storage index R1 in descending order to generate a first storage sequence; based on the first storage sequence, the first corresponding drone to be analyzed is output as the optimal drone storage node to be analyzed; When the index change rate recorded in the corresponding target flight set is less than zero, the second difference u2 of each drone to be analyzed at the target time and the corresponding storage space remaining capacity c1 are obtained, and the second effective storage index R2 of each drone to be analyzed at the target time is calculated using the formula: R2=r1×u2+r2×c1, and the second effective storage index R2 is sorted in descending order to generate a second storage sequence; based on the second storage sequence, the drone to be analyzed corresponding to the first sequence is output as the optimal drone storage node to be analyzed.

4. A communication data management system for an unmanned aerial vehicle, such as a communication data management method for an unmanned aerial vehicle according to any one of claims 1 to 3, characterized in that: The system includes a historical flight event extraction module, a target flight set storage module, a communication data risk characteristic value analysis module, a drone type analysis module, an optimal storage node determination module and a data copy storage module; The historical flight event extraction module is used to extract historical flight events of applying drone clusters to perform tasks; The target flight set storage module is used to use the drone scale and target mission as search items, summarize the historical flight events recorded with the same drone scale and the same target mission and store them as a target flight set; The communication data risk characteristic value analysis module is used to analyze the corresponding communication data risk characteristic value for each target flight set; The drone type analysis module is used to determine the suspicious risk drones in each target flight set, and other drones excluding the suspicious risk drones under the corresponding drone scale as drones to be analyzed; The optimal storage node determination module is used to extract data records of the drone to be analyzed during the task execution process of the flight event, analyze and sort the storage nodes of the drone to be analyzed, and output the optimal storage node of the drone to be analyzed; The data copy storage module is used to trigger the storage of the data copy recorded by the corresponding drone into the optimal drone storage node to be analyzed when a suspicious risk drone is determined.

5. A communication data management system for unmanned aerial vehicles according to claim 4, characterized in that: The communication data risk characteristic value analysis module includes an abnormal flight event extraction unit, a data record analysis unit, a comprehensive parameter index calculation unit and a communication data risk characteristic value output unit; The abnormal flight event extraction unit is used to mark historical flight events in which abnormal UAV communication data is recorded in the same target flight set as abnormal flight events; The data record analysis unit is used to extract data records of abnormal flight events during mission execution, wherein the data records include data transmission rate, flight state parameters, and sensor parameters loaded on the UAV; The comprehensive parameter index calculation unit is used to calculate the comprehensive parameter index of the drone cluster at time t1 and time t2 of the preset monitoring period; The communication data risk characteristic value output unit is used to select a comprehensive parameter index as the communication data risk characteristic value corresponding to the target flight set analysis and to issue an early warning based on the index change rate.

6. A communication data management system for unmanned aerial vehicles according to claim 5, characterized in that: The drone type analysis module includes a difference data calculation unit and a suspicious drone determination unit; The difference data calculation unit is used to calculate a first difference and a second difference between the comprehensive parameter index of each drone in the drone cluster and the communication data risk characteristic value of the same target flight set in the historical record; The suspicious drone determination unit is used to determine a suspicious drone based on the difference data output by the difference data calculation unit.

7. A communication data management system for unmanned aerial vehicles according to claim 6, characterized in that: The optimal storage node determination module includes a target time determination unit, an effective storage index calculation unit and an optimal storage node output unit; The target time determination unit is used to mark the time when the drone to be analyzed records the marking response of the suspicious risk drone in the drone cluster in the same target flight set as the target time; The effective storage index calculation unit is used to calculate the first effective storage index and the second effective storage index of each drone to be analyzed at the target time; The optimal storage node output unit is used to output the optimal storage node based on the result of the effective storage index calculation unit.

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