A remote sensing survey data transmission method and system based on unmanned aerial vehicle

Abstract

The present invention discloses a remote sensing survey data transmission method and system based on unmanned aerial vehicles, belonging to the technical field of data transmission; obtaining the distance between unmanned aerial vehicles, constructing a group of unmanned aerial vehicles; calculating the individual flight mean of the unmanned aerial vehicles and the group flight mean of the group of unmanned aerial vehicles, and calculating the degree of deviation; calculating the failure risk rate of the unmanned aerial vehicle based on the degree of deviation, presetting the failure risk rate threshold, analyzing and transferring the data transmission; calculating the flight time adjustment amount of the unmanned aerial vehicle, and managing all unmanned aerial vehicles for the next remote sensing survey mission. The present invention realizes real-time identification and data transmission transfer of faulty unmanned aerial vehicles by calculating the failure risk rate and combining the preset threshold value, and at the same time adjusts the flight time according to the degree of deviation, dynamically optimizes the task allocation of unmanned aerial vehicles, effectively solves the problem of low efficiency of unmanned aerial vehicle management and data transmission in remote sensing survey tasks, and significantly improves the reliability of survey data and the service life of unmanned aerial vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a remote sensing survey data transmission method and system based on an unmanned aerial vehicle. Background Art

[0002] With the rapid development of UAV technology and its wide application in the field of remote sensing and surveying, UAVs have become one of the important tools for data collection. At present, remote sensing and surveying UAVs are widely used in geological exploration, agricultural monitoring, disaster assessment and other fields. They have the remarkable characteristics of high flexibility, strong spatial resolution and good real-time performance. However, in practical applications, remote sensing tasks often involve the coordinated operation of multiple UAVs and the real-time transmission of massive data. Current technologies mostly focus on single-machine data collection and transmission management, while insufficient research has been conducted on information integration and transmission efficiency issues in the process of collaborative operations of multiple UAVs. Especially in complex environments, the dynamic flight status and potential failure risks of UAV groups may directly affect the integrity and transmission reliability of data.

[0003] Existing technologies mainly rely on fixed transmission protocols and path planning methods for UAV remote sensing data transmission, but have exposed some shortcomings in multi-UAV collaborative tasks; for example, they fail to fully consider the dynamic nature of the UAV's flight status, resulting in frequent data loss or delays during the transmission process; in the event of a possible UAV failure, there is a lack of a fast and effective risk assessment and data transfer mechanism, which in turn affects the stability of the overall mission; in addition, existing technologies lack data management strategies optimized for the characteristics of UAV groups, and fail to effectively combine individual and group flight behaviors to dynamically adjust task allocation. Summary of the invention

[0004] The purpose of the present invention is to provide a remote sensing survey data transmission method and system based on unmanned aerial vehicle to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A remote sensing survey data transmission method based on unmanned aerial vehicles (UAVs), the method comprising the following steps: tracking the position information of all UAVs dispatched for a single remote sensing survey mission, and obtaining the distance between the UAVs; constructing a UAV group based on the distance between the UAVs; calculating the individual flight mean of the UAVs and the group flight mean of the UAV group, and calculating the degree of deviation between the individual flight mean and the group flight mean; calculating the failure risk rate of the UAV based on the degree of deviation, presetting a failure risk rate threshold, analyzing and transferring data transmission; calculating the flight time adjustment amount of the UAVs, and managing all UAVs for the next remote sensing survey mission.

[0007] As a preferred solution of the remote sensing survey data transmission method based on drones described in the present invention, the position information of all drones dispatched for a single remote sensing survey mission is tracked to form the dynamic position information of each drone, and the dynamic position information of any two drones is obtained, which are recorded as and ,in, represents the ath time point, Indicates the i-th drone at time point Dynamic location information, Indicates the jth UAV at time point Dynamic location information.

[0008] Based on the i-th UAV at time point Dynamic location information and the jth UAV at time point Dynamic location information , get the i-th UAV and the j-th UAV at time point The distance between .

[0009] As a preferred solution of the remote sensing survey data transmission method based on drones described in the present invention, based on the distance between drones , build a drone group, as follows:

[0010] Randomly select the i-th UAV as the core UAV, build a UAV group based on the i-th UAV, and preset the distance threshold between UAVs , the distance between the i-th UAV Less than distance threshold All drones of join the drone group of the i-th drone.

[0011] Continue to randomly select core drones from the remaining drones until all drones are added to the drone group, and the remaining drones include all drones outside the drone group of the i-th drone.

[0012] As a preferred solution of the remote sensing survey data transmission method based on unmanned aerial vehicle described in the present invention, obtaining the unmanned aerial vehicle Flight records of remote sensing survey missions dispatched at the past N time points, including drones The total number of flights and drones dispatched for remote sensing missions at the past N time points The duration of each remote sensing mission.

[0013] Computing drones The individual flight mean of the drone The average flight time of remote sensing missions dispatched at the past N time points is calculated as follows:

[0014] ;

[0015] in, Indicates drone The individual flight mean, B represents the drone The total number of flights dispatched for remote sensing missions at the past N time points, Indicates drone The flight duration of the bth remote sensing survey mission.

[0016] It should be noted that in long-term remote sensing survey missions, the flight time of a drone is an important indicator reflecting its work intensity and potential failure risks. By calculating the average flight time, the working status of the drone can be comprehensively evaluated. For example, if the average flight time of a drone is long, this indicates that it is under high work intensity, and its internal mechanical parts such as motors and rotors will experience more wear, and electronic components will also accumulate heat after long-term operation. These factors may increase the probability of failure. Therefore, by monitoring the average flight time, maintenance work for such high-intensity drones can be predicted and arranged in advance to avoid mission interruptions due to mechanical failures or failures of electronic components during mission execution.

[0017] Calculate drone groups based on individual flight averages The group flight mean is the group flight mean of the drone group The average of the individual flight means of the drones is calculated as follows:

[0018] ;

[0019] in, Represents a drone group The group flight mean.

[0020] Computing drones The individual flight mean Swarming with drones The group flight mean The degree of deviation between them is as follows:

[0021] ;

[0022] in, Indicates the degree of deviation.

[0023] It should be noted that this formula intuitively calculates the difference between the working time of the drone and the average level within the group by taking the difference; the longer the flight time, the higher the loss of the drone, and the greater the probability of failure caused by the loss risk. At the same time, the failure probability of the individual drone is indirectly fed back through the individual flight mean. The group risk caused by the failure probability of the individual drone is the key technical means of the present invention. Therefore, the failure risk rate is quantified by the degree of deviation between the individual flight mean and the group flight mean, which comprehensively reflects the severity of the failure rate and the consequences of the failure. The larger the failure risk rate, the greater the flight risk of the group caused by the individual drone; for example, if a drone's If the value is large and positive, it means that its individual flight mean is long, then the higher the loss of the drone, the greater the probability of failure caused by loss risk; conversely, if the loss of a drone is The smaller it is, the smaller its individual flight mean is. The data transmission task of the drone with a longer individual flight mean can be assigned to this drone.

[0024] As a preferred solution of the remote sensing survey data transmission method based on drone described in the present invention, based on drone For drone swarms The degree of deviation , computing drones The failure risk rate is calculated as follows:

[0025] ;

[0026] in, Indicates drone The failure risk rate, represents the preset basic failure risk rate, Indicates the rate at which the preset failure risk rate changes with the degree of deviation.

[0027] It should be noted that in remote sensing survey missions, the failure of UAVs will lead to interruption or loss of data collection, affecting the quality of mission completion. By calculating the failure risk rate of UAVs, measures can be taken in advance, such as transferring remote sensing survey data in advance for UAVs with higher failure risk rates. For example, if the failure risk rate of a certain UAV is calculated to be higher according to the formula, its important data collection tasks can be transferred to other UAVs with lower failure risk rates. Task allocation and maintenance plans can be optimized to reduce losses caused by UAV failures and improve the reliability of the entire remote sensing survey mission.

[0028] Preset failure risk rate threshold, if the drone Failure risk rate If the fault risk rate is greater than the threshold, the drone is judged to be If there is a fault, the drone The remote sensing survey data is sent to the drone group first There is no fault in the UAV for transmission of remote sensing survey data.

[0029] Based on drone For drone swarms The degree of deviation , computing drones The flight duration adjustment at the next time point is calculated as follows:

[0030] ;

[0031] in, Indicates drone The flight duration adjustment at the next time point, Represents the preset flight duration regression coefficient.

[0032] It should be noted that this formula is used to calculate the UAV The flight duration adjustment at the next point in time; It is the preset flight time regression coefficient. The flight time is adjusted according to the degree of deviation. The negative sign indicates that when the flight time of the drone deviates greatly, its flight time should be reduced (if the deviation is positive) or increased (if the deviation is negative) to make its working state regress to the average level within the group. Its purpose is to dynamically adjust the working state of the drone so that its flight time tends to be balanced. In actual operation, the flight time can be controlled by adjusting the task allocation of the drone. For example, if the flight time of a drone deviates greatly, the corresponding adjustment amount can be calculated according to this formula. In the next task allocation, its flight task can be reduced or increased, so that its flight time gradually approaches the average level within the group, maintaining the working stability of the entire drone group, which can further optimize the data transmission.

[0033] Based on drone The flight duration adjustment at the next time point , predicting drones The deviation degree and failure risk rate at the next time point, and the drone Make dynamic adjustments.

[0034] Let d=d+1, iterate the drones, and perform overall management of all drones.

[0035] It should be noted that, through the analysis of the failure risk rate, individual drones with a higher degree of danger can be screened out. These screened drones are likely to affect the stability of data transmission during the collaborative process of the group, which is another key technical means of the present invention; further, in a large-scale remote sensing survey task, the drones in the drone group transmit remote sensing survey data through the transmitter and the receiver, and each drone has its own remote sensing survey area. When the failure risk rate of an individual drone is high, it means that the drone is likely to fail, and the data collection work in the area it is responsible for will be forced to be interrupted, which may cause blanks in the data in the area and affect the integrity of the overall data; and, there is usually a central collaborative drone in the drone group, which is responsible for collecting, aggregating and forwarding the data collected by each drone. If the drone fails, the data may not be transmitted to the ground control center or other storage devices in a timely manner.

[0036] A remote sensing survey data transmission system based on unmanned aerial vehicle, the system comprises: a position tracking and distance acquisition module, a group building module, a flight data statistics and deviation calculation module and a risk assessment and adjustment module.

[0037] The position tracking and distance acquisition module is used to track the position information of all drones dispatched for a remote sensing survey mission and obtain the distance between the drones.

[0038] The group building module is used to build a drone group based on the distance between the drones.

[0039] The flight data statistics and deviation calculation module calculates the individual flight mean of the drone and the group flight mean of the drone group, and calculates the deviation degree between the individual flight mean and the group flight mean.

[0040] The risk assessment and adjustment module calculates the failure risk rate of the drone based on the degree of deviation, presets a failure risk rate threshold, analyzes and transfers data transmission; calculates the adjustment amount of the drone's flight time, and manages all drones for the next remote sensing survey mission.

[0041] Furthermore, the position tracking and distance acquisition module includes a position tracking unit and a distance acquisition unit.

[0042] The position tracking unit tracks the position information of all drones dispatched for a remote sensing survey mission, forms dynamic position information of each drone, and obtains dynamic position information of any two drones.

[0043] The distance acquisition unit is configured to acquire the distance between the two drones based on the dynamic position information of the two drones.

[0044] Furthermore, the group construction module includes a group construction unit.

[0045] The group construction unit randomly selects a drone as a core drone, constructs a drone group based on the drone, presets a distance threshold between drones, and adds all drones whose distance to the drone is less than the distance threshold to the drone group of the drone; continues to randomly select core drones from the remaining drones until all drones are added to the drone group, and the remaining drones include all drones outside the drone group of the drone.

[0046] Furthermore, the flight data statistics and deviation calculation module includes a flight data statistics unit and a deviation calculation unit.

[0047] The flight data statistics unit: obtains flight records of drones dispatched to perform remote sensing survey tasks at past time points, wherein the flight records include the total number of flights of drones dispatched to perform remote sensing survey tasks at past time points and the flight duration of the drones each time they were dispatched to perform remote sensing survey tasks; calculates individual flight means of the drones; and calculates the group flight mean of the drone group based on the individual flight means.

[0048] The deviation calculation unit is used to calculate the deviation between the individual flight mean of the drone and the group flight mean of the drone group.

[0049] Furthermore, the risk assessment and dynamic adjustment module includes a risk assessment unit and a dynamic adjustment unit.

[0050] The risk assessment unit calculates the failure risk rate of the drone based on the degree of deviation of the drone from the drone group; presets a failure risk rate threshold, and if the failure risk rate of the drone is greater than the failure risk rate threshold, it is determined that the drone has a fault, and the remote sensing survey data of the drone is preferentially sent to the drones in the drone group that do not have faults for transmission of the remote sensing survey data.

[0051] The dynamic adjustment unit: calculates the flight duration adjustment amount of the drone at the next time point based on the deviation degree of the drone from the drone group; predicts the deviation degree and failure risk rate of the drone at the next time point based on the flight duration adjustment amount of the drone at the next time point, and dynamically adjusts the drone; iterates the drones and performs overall management of all drones.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a remote sensing survey data transmission method and system based on drones provided by the present invention, by tracking the dynamic position information of drones in a single dispatch mission and obtaining the distance between drones, the real-time and accuracy of the position information are ensured; then, a group is constructed based on the distance between the drones, and a core drone grouping method is adopted to effectively optimize the allocation of drone resources and enhance the group coordination capability; then, by calculating the individual flight mean and the group flight mean, the degree of deviation between the individual and the group is analyzed, providing a reliable basis for subsequent risk assessment; further, the fault risk rate is calculated based on the degree of deviation, and the real-time identification and data transmission transfer of the faulty drone is realized in combination with a preset threshold, and the flight time is adjusted according to the degree of deviation to dynamically optimize the task allocation of the drone; through these steps, the method effectively solves the problem of low efficiency of drone management and data transmission in remote sensing survey tasks, and significantly improves the reliability of survey data and the service life of drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0054] Figure 1 It is a schematic diagram of the steps of a remote sensing survey data transmission method based on an unmanned aerial vehicle of the present invention;

[0055] Figure 2 It is a structural schematic diagram of a remote sensing survey data transmission system based on an unmanned aerial vehicle of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] See also Figure 1 In the first embodiment, a method for transmitting remote sensing data based on an unmanned aerial vehicle is provided, and the method comprises the following steps:

[0058] Step S1: Track the location information of all UAVs dispatched for a single remote sensing survey mission and obtain the distance between the UAVs.

[0059] Step S11: Specifically, the position information of all drones dispatched for a single remote sensing survey mission is tracked to form the dynamic position information of each drone, and the dynamic position information of any two drones is obtained, which is recorded as and ,in, represents the ath time point, Indicates the i-th drone at time point Dynamic location information, Indicates the jth UAV at time point Dynamic location information.

[0060] Step S12: Further, based on the i-th UAV at time point Dynamic location information and the jth UAV at time point Dynamic location information , get the i-th UAV and the j-th UAV at time point The distance between .

[0061] Step S2: constructing a drone group based on the distance between the drones.

[0062] Step S21: Specifically, based on the distance between the drones , build a drone group, as follows:

[0063] Step S21.1: Randomly select the i-th UAV as the core UAV, build a UAV group based on the i-th UAV, and preset the distance threshold between UAVs , the distance between the i-th UAV Less than distance threshold All drones of join the drone group of the i-th drone.

[0064] Step S21.2: Continue to randomly select core drones from the remaining drones and repeat the above step S21.1 until all drones are added to the drone group, and the remaining drones include all drones outside the drone group of the i-th drone.

[0065] Step S22: Further, the drone group of the i-th drone is recorded as ,in, Represents a drone group The dth drone in the , D represents the drone group The total number of drones in the

[0066] Step S3: Calculate the individual flight mean of the drone and the group flight mean of the drone group, and calculate the degree of deviation between the individual flight mean and the group flight mean.

[0067] Step S31: Specifically, obtain the drone Flight records of remote sensing survey missions dispatched at the past N time points, including drones The total number of flights and drones dispatched for remote sensing missions at the past N time points The duration of each remote sensing mission.

[0068] Step S32: Further, calculate the drone The individual flight mean of the drone The average flight time of remote sensing missions dispatched at the past N time points is calculated as follows:

[0069] ;

[0070] in, Indicates drone The individual flight mean, B represents the drone The total number of flights dispatched for remote sensing missions at the past N time points, Indicates drone The flight duration of the bth remote sensing survey mission.

[0071] For example, assuming the total number of flights B is 4, , , , , substitute the formula to calculate the drone The individual flight mean .

[0072] It should be noted that in long-term remote sensing survey missions, the flight time of a drone is an important indicator reflecting its work intensity and potential failure risks. By calculating the average flight time, the working status of the drone can be comprehensively evaluated. For example, if the average flight time of a drone is long, this indicates that it is under high work intensity, and its internal mechanical parts such as motors and rotors will experience more wear, and electronic components will also accumulate heat after long-term operation. These factors may increase the probability of failure. Therefore, by monitoring the average flight time, maintenance work for such high-intensity drones can be predicted and arranged in advance to avoid mission interruptions due to mechanical failures or failures of electronic components during mission execution.

[0073] Step S32.1: Calculate the drone group based on the individual flight mean The group flight mean is the group flight mean of the drone group The average of the individual flight means of the drones is calculated as follows:

[0074] ;

[0075] in, Represents a drone group The group flight mean.

[0076] Step S32.2: Calculate the drone The individual flight mean Swarming with drones The group flight mean The degree of deviation between them is as follows:

[0077] ;

[0078] in, Indicates the degree of deviation.

[0079] For example, suppose D is 3, , , known , put it into the formula to calculate the drone group The group flight mean , the degree of deviation .

[0080] It should be noted that this formula intuitively calculates the difference between the working time of the drone and the average level within the group by taking the difference; the longer the flight time, the higher the loss of the drone, and the greater the probability of failure caused by the loss risk. At the same time, the failure probability of the individual drone is indirectly fed back through the individual flight mean. The group risk caused by the failure probability of the individual drone is the key technical means of the present invention. Therefore, the failure risk rate is quantified by the degree of deviation between the individual flight mean and the group flight mean, which comprehensively reflects the severity of the failure rate and the consequences of the failure. The larger the failure risk rate, the greater the flight risk of the group caused by the individual drone; for example, if a drone's If the value is large and positive, it means that its individual flight mean is long, then the higher the loss of the drone, the greater the probability of failure caused by loss risk; conversely, if the loss of a drone is The smaller it is, the smaller its individual flight mean is. The data transmission task of the drone with a longer individual flight mean can be assigned to this drone.

[0081] Step S4: Calculate the failure risk rate of the drone based on the degree of deviation, preset the failure risk rate threshold, analyze and transfer the data transmission; calculate the flight time adjustment of the drone, and manage all drones for the next remote sensing survey mission.

[0082] Step S41: Specifically, based on the drone For drone swarms The degree of deviation , computing drones The failure risk rate is calculated as follows:

[0083] ;

[0084] in, Indicates drone The failure risk rate, represents the preset basic failure risk rate, Indicates the rate at which the preset failure risk rate changes with the degree of deviation.

[0085] For example, assuming the base failure risk rate is 0.5, the rate of change is 1.2, and the UAV is calculated by substituting it into the formula Failure risk rate .

[0086] It should be noted that in remote sensing survey missions, the failure of UAVs will lead to interruption or loss of data collection, affecting the quality of mission completion. By calculating the failure risk rate of UAVs, measures can be taken in advance, such as transferring remote sensing survey data in advance for UAVs with higher failure risk rates. For example, if the failure risk rate of a certain UAV is calculated to be higher according to the formula, its important data collection tasks can be transferred to other UAVs with lower failure risk rates. Task allocation and maintenance plans can be optimized to reduce losses caused by UAV failures and improve the reliability of the entire remote sensing survey mission.

[0087] Step S42: Further, a fault risk rate threshold is preset. If the drone Failure risk rate If the fault risk rate is greater than the threshold, the drone is judged to be If there is a fault, the drone The remote sensing survey data is sent to the drone group first There is no fault in the UAV for transmission of remote sensing survey data.

[0088] For example, assuming the failure risk rate threshold is 0.8, the drone Failure risk rate If the fault risk rate is greater than the threshold, the drone is judged to be If there is a fault, the drone The remote sensing survey data is sent to the drone group first There is no fault in the UAV for transmission of remote sensing survey data.

[0089] Step S43: Further, based on drone For drone swarms The degree of deviation , computing drones The flight duration adjustment at the next time point is calculated as follows:

[0090] ;

[0091] in, Indicates drone The flight duration adjustment at the next time point, Represents the preset flight duration regression coefficient.

[0092] For example, suppose the regression coefficient for flight time is is 0.2, it is known , put it into the formula to get the drone The flight duration adjustment at the next time point .

[0093] It should be noted that this formula is used to calculate the UAV The flight duration adjustment at the next point in time; It is the preset flight time regression coefficient. The flight time is adjusted according to the degree of deviation. The negative sign indicates that when the flight time of the drone deviates greatly, its flight time should be reduced (if the deviation is positive) or increased (if the deviation is negative) to make its working state regress to the average level within the group. Its purpose is to dynamically adjust the working state of the drone so that its flight time tends to be balanced. In actual operation, the flight time can be controlled by adjusting the task allocation of the drone. For example, if the flight time of a drone deviates greatly, the corresponding adjustment amount can be calculated according to this formula. In the next task allocation, its flight task can be reduced or increased, so that its flight time gradually approaches the average level within the group, maintaining the working stability of the entire drone group, which can further optimize the data transmission.

[0094] Step S43.1: Based on drone The flight duration adjustment at the next time point , predicting drones The deviation degree and failure risk rate at the next time point, and the drone Make dynamic adjustments.

[0095] Step S43.2: Let d=d+1, return to step S3, iterate the drones, and perform overall management of all drones.

[0096] It should be noted that, through the analysis of the failure risk rate, individual drones with a higher degree of danger can be screened out. These screened drones are likely to affect the stability of data transmission during the collaborative process of the group, which is another key technical means of the present invention; further, in a large-scale remote sensing survey task, the drones in the drone group transmit remote sensing survey data through the transmitter and the receiver, and each drone has its own remote sensing survey area. When the failure risk rate of an individual drone is high, it means that the drone is likely to fail, and the data collection work in the area it is responsible for will be forced to be interrupted, which may cause blanks in the data in the area and affect the integrity of the overall data; and, there is usually a central collaborative drone in the drone group, which is responsible for collecting, aggregating and forwarding the data collected by each drone. If the drone fails, the data may not be transmitted to the ground control center or other storage devices in a timely manner.

[0097] See also Figure 2 In the second embodiment of the present invention, a remote sensing survey data transmission system based on an unmanned aerial vehicle is provided, the system comprising: a position tracking and distance acquisition module, a group building module, a flight data statistics and deviation calculation module and a risk assessment and adjustment module.

[0098] The position tracking and distance acquisition module is used to track the position information of all drones dispatched for a remote sensing survey mission and obtain the distance between the drones.

[0099] The group building module is used to build a drone group based on the distance between the drones.

[0100] The flight data statistics and deviation calculation module calculates the individual flight mean of the drone and the group flight mean of the drone group, and calculates the deviation degree between the individual flight mean and the group flight mean.

[0101] The risk assessment and adjustment module calculates the failure risk rate of the drone based on the degree of deviation, presets a failure risk rate threshold, analyzes and transfers data transmission; calculates the adjustment amount of the drone's flight time, and manages all drones for the next remote sensing survey mission.

[0102] Furthermore, the position tracking and distance acquisition module includes a position tracking unit and a distance acquisition unit.

[0103] The position tracking unit tracks the position information of all drones dispatched for a remote sensing survey mission, forms dynamic position information of each drone, and obtains dynamic position information of any two drones.

[0104] The distance acquisition unit is configured to acquire the distance between the two drones based on the dynamic position information of the two drones.

[0105] Furthermore, the group construction module includes a group construction unit.

[0106] The group construction unit randomly selects a drone as a core drone, constructs a drone group based on the drone, presets a distance threshold between drones, and adds all drones whose distance to the drone is less than the distance threshold to the drone group of the drone; continues to randomly select core drones from the remaining drones until all drones are added to the drone group, and the remaining drones include all drones outside the drone group of the drone.

[0107] Furthermore, the flight data statistics and deviation calculation module includes a flight data statistics unit and a deviation calculation unit.

[0108] The flight data statistics unit: obtains flight records of drones dispatched to perform remote sensing survey tasks at past time points, wherein the flight records include the total number of flights of drones dispatched to perform remote sensing survey tasks at past time points and the flight duration of the drones each time they were dispatched to perform remote sensing survey tasks; calculates individual flight means of the drones; and calculates the group flight mean of the drone group based on the individual flight means.

[0109] The deviation calculation unit is used to calculate the deviation between the individual flight mean of the drone and the group flight mean of the drone group.

[0110] Furthermore, the risk assessment and dynamic adjustment module includes a risk assessment unit and a dynamic adjustment unit.

[0111] The risk assessment unit calculates the failure risk rate of the drone based on the degree of deviation of the drone from the drone group; presets a failure risk rate threshold, and if the failure risk rate of the drone is greater than the failure risk rate threshold, it is determined that the drone has a fault, and the remote sensing survey data of the drone is preferentially sent to the drones in the drone group that do not have faults for transmission of the remote sensing survey data.

[0112] The dynamic adjustment unit: calculates the flight duration adjustment amount of the drone at the next time point based on the deviation degree of the drone from the drone group; predicts the deviation degree and failure risk rate of the drone at the next time point based on the flight duration adjustment amount of the drone at the next time point, and dynamically adjusts the drone; iterates the drones and performs overall management of all drones.

[0113] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0114] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A remote sensing survey data transmission method based on an unmanned aerial vehicle, characterized in that: The method comprises the following steps: Step S1: Track the location information of all drones dispatched for a single remote sensing survey mission and obtain the distance between the drones; Step S2: constructing a drone group based on the distances between the drones; Step S3: calculating the individual flight mean of the drone and the group flight mean of the drone group, and calculating the degree of deviation between the individual flight mean and the group flight mean; Step S4: Calculate the failure risk rate of the drone based on the degree of deviation, preset the failure risk rate threshold, analyze and transfer the data transmission; calculate the flight time adjustment of the drone, and manage all drones for the next remote sensing survey mission.

2. The method for transmitting remote sensing data based on an unmanned aerial vehicle according to claim 1, characterized in that: The specific implementation process of step S1 includes: Step S11: Track the position information of all drones dispatched for a single remote sensing survey mission to form the dynamic position information of each drone, and obtain the dynamic position information of any two drones, which are recorded as and ,in, represents the ath time point, Indicates the i-th drone at time point Dynamic location information, Indicates the jth UAV at time point Dynamic location information; Step S12: Based on the i-th UAV at time point Dynamic location information and the jth UAV at time point Dynamic location information , get the i-th UAV and the j-th UAV at time point The distance between .

3. The method for transmitting remote sensing data based on an unmanned aerial vehicle according to claim 2, characterized in that: The specific implementation process of step S2 includes: Step S21: Based on the distance between drones , build a drone group, as follows: Step S21.1: Randomly select the i-th UAV as the core UAV, build a UAV group based on the i-th UAV, and preset the distance threshold between UAVs , the distance between the i-th UAV Less than distance threshold All drones of join the drone group of the i-th drone; Step S21.2: Continue to randomly select core drones from the remaining drones, and repeat the above step S21.1 until all drones are added to the drone group, and the remaining drones include all drones outside the drone group of the i-th drone; Step S22: Record the drone group of the i-th drone as ,in, Represents a drone group The dth drone in the , D represents the drone group The total number of drones in the 4. The method for transmitting remote sensing data based on an unmanned aerial vehicle according to claim 3, characterized in that: The specific implementation process of step S3 includes: Step S31: Get the drone Flight records of remote sensing survey missions dispatched at the past N time points, including drones The total number of flights and drones dispatched for remote sensing missions at the past N time points The duration of each remote sensing mission; Step S32: Calculate the drone The individual flight mean of the drone The average flight time of remote sensing missions dispatched at the past N time points is calculated as follows: ； in, Indicates drone The individual flight mean, B represents the drone The total number of flights dispatched for remote sensing missions at the past N time points, Indicates drone The flight duration of the remote sensing survey mission sent out for the bth time; Step S32.1: Calculate the drone group based on the individual flight mean The group flight mean is the group flight mean of the drone group The average of the individual flight means of the drones is calculated as follows: ； in, Represents a drone group The group flight mean; Step S32.2: Calculate the drone The mean of individual flights Swarming with drones The group flight mean The degree of deviation between them is as follows: ； in, Indicates the degree of deviation.

5. The method for transmitting remote sensing data based on an unmanned aerial vehicle according to claim 4, characterized in that: The specific implementation process of step S4 includes: Step S41: Based on drone For drone swarms The degree of deviation , computing drones The failure risk rate is calculated as follows: ； in, Indicates drone The failure risk rate, represents the preset basic failure risk rate, Indicates the rate of change of the preset failure risk rate with the degree of deviation; Step S42: Preset the fault risk rate threshold. If the drone Failure risk rate If the fault risk rate is greater than the threshold, the drone is judged to be If there is a fault, the drone The remote sensing survey data is sent to the drone group first The transmission of remote sensing survey data by drones without any faults; Step S43: Based on drone For drone swarms The degree of deviation , computing drones The flight duration adjustment at the next time point is calculated as follows: ； in, Indicates drone The flight duration adjustment at the next time point, Represents the preset flight duration regression coefficient; Step S43.1: Based on drone The flight duration adjustment at the next time point , predicting drones The deviation degree and failure risk rate at the next time point, and the drone Make dynamic adjustments; Step S43.2: Let d=d+1, return to step S3, iterate the drones, and perform overall management of all drones.

6. A remote sensing survey data transmission system based on an unmanned aerial vehicle, executing a remote sensing survey data transmission method based on an unmanned aerial vehicle as claimed in any one of claims 1 to 5, characterized in that: The system includes: a position tracking and distance acquisition module, a group building module, a flight data statistics and deviation calculation module, and a risk assessment and adjustment module: The position tracking and distance acquisition module is used to track the position information of all drones dispatched for a remote sensing survey mission and obtain the distance between the drones; The group building module is used to build a drone group based on the distance between the drones. The flight data statistics and deviation calculation module calculates the individual flight mean of the drone and the group flight mean of the drone group, and calculates the deviation between the individual flight mean and the group flight mean; The risk assessment and adjustment module calculates the failure risk rate of the drone based on the degree of deviation, presets a failure risk rate threshold, analyzes and transfers data transmission; calculates the adjustment amount of the drone's flight time, and manages all drones for the next remote sensing survey mission.

7. The remote sensing survey data transmission system based on an unmanned aerial vehicle according to claim 6, characterized in that: The position tracking and distance acquisition module includes a position tracking unit and a distance acquisition unit; The position tracking unit: tracks the position information of all drones dispatched for a single remote sensing survey mission, forms dynamic position information of each drone, and obtains dynamic position information of any two drones; The distance acquisition unit is configured to acquire the distance between the two drones based on the dynamic position information of the two drones.

8. The remote sensing survey data transmission system based on an unmanned aerial vehicle according to claim 7, characterized in that: The group construction module includes a group construction unit; The group construction unit randomly selects a drone as a core drone, constructs a drone group based on the drone, presets a distance threshold between drones, and adds all drones whose distance to the drone is less than the distance threshold to the drone group of the drone; continues to randomly select core drones from the remaining drones until all drones are added to the drone group, and the remaining drones include all drones outside the drone group of the drone.

9. The remote sensing survey data transmission system based on unmanned aerial vehicle according to claim 8, characterized in that: The flight data statistics and deviation calculation module includes a flight data statistics unit and a deviation calculation unit; The flight data statistics unit: obtains flight records of drones dispatched to perform remote sensing survey tasks at past time points, wherein the flight records include the total number of flights of drones dispatched to perform remote sensing survey tasks at past time points and the flight duration of the drones at each time of being dispatched to perform remote sensing survey tasks; calculates individual flight averages of the drones; and calculates group flight averages of the drone group based on the individual flight averages; The deviation calculation unit is used to calculate the deviation between the individual flight mean of the drone and the group flight mean of the drone group.

10. The remote sensing survey data transmission system based on unmanned aerial vehicle according to claim 9, characterized in that: The risk assessment and dynamic adjustment module includes a risk assessment unit and a dynamic adjustment unit; The risk assessment unit calculates the failure risk rate of the drone based on the degree of deviation of the drone from the drone group; A fault risk rate threshold is preset. If the fault risk rate of the drone is greater than the fault risk rate threshold, it is determined that the drone has a fault, and the remote sensing survey data of the drone is preferentially sent to drones in the drone group that do not have a fault to transmit the remote sensing survey data; The dynamic adjustment unit: calculates the flight duration adjustment amount of the drone at the next time point based on the deviation degree of the drone from the drone group; predicts the deviation degree and failure risk rate of the drone at the next time point based on the flight duration adjustment amount of the drone at the next time point, and dynamically adjusts the drone; iterates the drones and performs overall management of all drones.

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