A network security communication method, system and medium for unmanned equipment formation

Abstract

The present invention relates to the field of wireless communication technology, and in particular to a network security communication method, system and medium applied to unmanned equipment formations. The present invention first obtains the self-noise performance at the time to be analyzed based on the wind speed and wind direction, as well as the difference between the actual spatial position and the set spatial position of the unmanned equipment; adjusts the initial smoothing coefficient at the time to be analyzed based on the noise interference degree, and obtains the adjusted smoothing coefficient of the unmanned equipment at the time to be analyzed; filters the actual spatial position using the adjusted smoothing coefficient at each sampling moment, and obtains filtered actual spatial position data; and performs communication transmission based on the filtered actual spatial position data. The present invention performs exponential smoothing filtering on the actual spatial position data of the unmanned equipment by reasonably setting the smoothing coefficient at each sampling moment, so as to improve the filtering effect, improve the accuracy of the filtered data in wireless communication, and ensure the security of wireless communication.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a network security communication method, system and medium applied to an unmanned equipment formation. Background Art

[0002] Secure network communication for unmanned vehicle fleets is crucial, providing real-time, accurate data transmission to support real-time decision-making and coordinated operations within the fleet, improving mission execution efficiency and success rates. Currently, the actual spatial position of unmanned vehicles is monitored using GPS locators onboard. However, when GPS locators collect their actual spatial position, they are subject to interference from buildings, trees, and electronic devices, resulting in noisy data. To ensure data quality during wireless communication, real-time filtering of the actual spatial position data is necessary for secure network communication within unmanned vehicle fleets.

[0003] Exponential smoothing filtering is a common real-time data filtering algorithm. Existing technologies can use this algorithm to filter actual spatial location data in real time. However, the filtering effect is affected by the smoothing coefficient. Existing technologies typically determine this coefficient empirically, failing to fully consider the noise conditions experienced by unmanned equipment. This results in poor filtering effectiveness, inaccurate filtered data in wireless communications, and difficulties ensuring wireless security. Summary of the Invention

[0004] In order to solve the technical problem that the existing technology is difficult to ensure the filtering effect of actual spatial position data, the purpose of the present invention is to provide a network security communication method, system and medium for unmanned equipment formation. The technical solution adopted is as follows:

[0005] A network security communication method applied to an unmanned equipment formation, the method comprising:

[0006] In the UAV formation, obtain the set spatial position, wind speed, wind direction and actual spatial position of each UAV at each sampling moment;

[0007] Taking any of the sampling moments as the moment to be analyzed, and within a preset time interval of the moment to be analyzed, obtaining the flight stability at the moment to be analyzed based on the deviation between the actual spatial position of the unmanned aerial vehicle and the set spatial position; obtaining the self-noise performance at the moment to be analyzed based on the wind speed and wind direction, and the difference between the actual spatial position of the unmanned aerial vehicle and the set spatial position; and obtaining an initial smoothing coefficient of the unmanned aerial vehicle at the moment to be analyzed by combining the flight stability and the self-noise performance.

[0008] Obtaining reference surrounding devices of the unmanned device at the time to be analyzed; obtaining the noise interference degree of the unmanned device at the time to be analyzed based on the consistency of the set spatial position, wind speed, and wind direction of the unmanned device and the reference surrounding devices, and the deviation between the actual spatial position and the set spatial position; adjusting the initial smoothing coefficient at the time to be analyzed based on the noise interference degree, and obtaining the adjusted smoothing coefficient of the unmanned device at the time to be analyzed;

[0009] The actual spatial position is filtered using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and communication transmission is performed based on the filtered actual spatial position data.

[0010] Furthermore, the method for obtaining the flight stability includes:

[0011] In the preset time interval of the time to be analyzed, the Euclidean distance between the actual spatial position and the set spatial position corresponding to the sampling time is used as the offset distance of the sampling time;

[0012] The flight stability at the time to be analyzed is obtained based on the sizes and quantities of all the offset distances.

[0013] Furthermore, the method for obtaining the flight stability at the time to be analyzed based on the sizes and quantities of all the offset distances includes:

[0014] In the preset time interval of the moment to be analyzed, the sampling moment whose offset distance is greater than the preset offset value is marked as a deviation moment; and the time period in which the consecutive deviation moments are located is regarded as a deviation period;

[0015] The total number of the deviation periods, the total number of the deviation moments, and the mean of all the offset distances are forwardly fused to obtain the flight deviation at the moment to be analyzed, and the negative correlation mapping result of the flight deviation is used as the flight stability at the moment to be analyzed.

[0016] Furthermore, the method for obtaining the self-noise expression includes:

[0017] In the preset time interval of the time to be analyzed, every two adjacent sampling moments are regarded as a suspected yaw moment combination; the suspected yaw moment combination in which the offset distance of the previous sampling moment is 0 and the offset distance of the next sampling moment is not 0 is regarded as a real yaw moment combination;

[0018] Constructing an actual flight vector of the real yaw moment combination; the modulus of the actual flight vector is the Euclidean distance between the actual spatial positions corresponding to the two sampling moments corresponding to the real yaw moment combination; and the direction of the actual flight vector is the direction from the actual spatial position corresponding to the previous sampling moment to the next sampling moment of the real yaw moment combination;

[0019] Constructing a preset flight vector for the real yaw moment combination; the modulus of the preset flight vector is the Euclidean distance between the set spatial positions corresponding to the two sampling moments corresponding to the real yaw moment combination; and the direction of the preset flight vector is the direction from the set spatial position corresponding to the previous sampling moment to the next sampling moment of the real yaw moment combination;

[0020] Constructing a wind influence vector for the real yaw moment combination; the modulus of the wind influence vector is the wind speed at the previous sampling moment in the real yaw moment combination; the direction of the wind influence vector is the wind direction at the previous sampling moment corresponding to the real yaw moment combination;

[0021] The method further comprises calculating a difference between the actual flight vector and the preset flight vector corresponding to the real yaw moment combination to obtain the yaw vector of the real yaw moment combination; calculating a modulus of the difference between the yaw vector and the wind influence vector to obtain a local noise index of the real yaw moment combination; and calculating a cumulative value of the local noise index of all real yaw moment combinations in the preset time interval of the moment to be analyzed, normalizing the cumulative value, and obtaining a self-noise performance index at the moment to be analyzed.

[0022] Furthermore, the method for obtaining the initial smoothing coefficient includes:

[0023] The flight stability and the self-noise performance are reversely fused to obtain an initial smoothing coefficient of the unmanned equipment at the time to be analyzed.

[0024] Furthermore, the method for obtaining the noise interference degree includes:

[0025] Obtaining, in the preset time interval of the time to be analyzed, influence similarity weights of the unmanned device and its reference surrounding devices based on differences in set spatial positions, wind speeds, and wind directions between the unmanned device and its reference surrounding devices based on a DTW algorithm;

[0026] Obtaining an offset difference index between the unmanned device and its reference surrounding devices based on a DTW algorithm according to a degree of difference in the offset distance between the unmanned device and its reference surrounding devices;

[0027] The offset difference index is weightedly summed using the influence similarity weights of the unmanned device and all of its reference surrounding devices to obtain the noise interference degree of the unmanned device at the time to be analyzed.

[0028] Furthermore, the method for obtaining the affected similarity weight includes:

[0029] The spatial position, wind speed, and wind direction are set as the influencing parameters of the unmanned equipment; in the preset time interval of the time to be analyzed, the influencing parameters of all sampling moments are counted in chronological order to obtain the influencing parameter sequence of the unmanned equipment;

[0030] The DTW distances of each influencing parameter sequence between the unmanned device and its reference surrounding devices are reversely fused to obtain the influence similarity weights of the unmanned device and its reference surrounding devices.

[0031] Furthermore, the method for obtaining the adjusted smoothing coefficient includes:

[0032] The negative correlation mapping result of the noise interference degree at the time to be analyzed is used as the adjustment parameter; the adjustment parameter at the time to be analyzed and the initial smoothing coefficient are forwardly integrated to obtain the adjusted smoothing coefficient at the time to be analyzed.

[0033] A network security communication system for an unmanned equipment formation, the system comprising:

[0034] The data acquisition module is used to obtain the set spatial position, wind speed, wind direction and actual spatial position of each unmanned device in the unmanned device formation at each sampling moment;

[0035] An initial smoothing coefficient analysis module is configured to use any of the sampling moments as a time to be analyzed, and within a preset time interval of the time to be analyzed, obtain the flight stability at the time to be analyzed based on the deviation between the actual spatial position of the unmanned aerial vehicle and the set spatial position; obtain the self-noise performance at the time to be analyzed based on the wind speed and wind direction, as well as the difference between the actual spatial position of the unmanned aerial vehicle and the set spatial position; and obtain the initial smoothing coefficient of the unmanned aerial vehicle at the time to be analyzed based on the flight stability and the self-noise performance.

[0036] The adjusted smoothing coefficient analysis module is configured to obtain reference surrounding devices of the unmanned device at the time to be analyzed; obtain the noise interference level of the unmanned device at the time to be analyzed based on the consistency of the set spatial position, wind speed, and wind direction of the unmanned device and its reference surrounding devices, as well as the deviation between the actual spatial position and the set spatial position; and adjust the initial smoothing coefficient at the time to be analyzed based on the noise interference level to obtain the adjusted smoothing coefficient of the unmanned device at the time to be analyzed;

[0037] The communication module is used to filter the actual spatial position using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and perform communication transmission based on the filtered actual spatial position data.

[0038] A network security communication medium applied to an unmanned equipment formation includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a network security communication method applied to an unmanned equipment formation as described in any one of the above items are implemented.

[0039] The present invention proposes a network security communication system applied to an unmanned equipment formation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the network security communication method applied to an unmanned equipment formation are implemented.

[0040] The present invention has the following beneficial effects:

[0041] Any sampling moment is used as the moment to be analyzed. To analyze the flight conditions at that moment, a preset time interval is constructed. The present invention first analyzes the flight conditions of a single unmanned aerial vehicle (UAV) and preliminarily determines the UAV's initial smoothing coefficient at the moment to be analyzed. The deviation between the UAV's actual spatial position and its set spatial position is first compared to assess the UAV's flight stability. The greater the flight stability, the more likely the UAV is to be in its set ideal position at the moment to be analyzed, i.e., the more stable its flight at that moment. To assess the likelihood of noise in the actual spatial position data corresponding to the moment to be analyzed, the self-noise performance index is obtained for the moment to be analyzed. The greater the self-noise performance index, the greater the likelihood of noise in the actual spatial position data corresponding to the moment to be analyzed. Considering that the more unstable the flight at the moment to be analyzed, the larger the smoothing coefficient required, i.e., the more consideration should be given to the UAV's actual spatial position at that moment, the algorithm can quickly respond to the UAV's yaw trajectory, adjusting the flight trajectory to help the UAV quickly return to the correct course. Considering that a greater self-noise performance indicates a greater likelihood of noise-induced yaw shifts at the time to be analyzed, a smaller smoothing coefficient is required to reduce the impact of noise on data filtering. Combining flight stability and self-noise performance, a preliminary and reasonable initial smoothing coefficient for the UAV at the time to be analyzed is established. To more accurately determine the smoothing coefficient, the flight conditions of the UAV's surrounding UAVs are analyzed. The noise interference coefficient of the UAV at the time to be analyzed is constructed to reflect the degree of noise interference with the actual spatial position data at the time to be analyzed. A greater noise interference coefficient indicates a greater difference in yaw between the UAV and its reference surroundings, even when the flight environment at the time to be analyzed is similar to the set flight trajectory. This indicates a greater degree of noise interference with the actual spatial position data at the time to be analyzed. Based on the noise interference coefficient, the initial smoothing coefficient at the time to be analyzed is adjusted to more accurately determine the adjusted smoothing coefficient for the UAV at the time to be analyzed. By appropriately setting the smoothing coefficient at each sampling moment, exponential smoothing filtering is performed on the UAV's actual spatial position data, improving the filtering effect, enhancing the accuracy of filtered data in wireless communications, and ensuring wireless communication security. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A flowchart of a network security communication method for an unmanned equipment formation provided by one embodiment of the present invention;

[0044] Figure 2 A flow chart of a method for obtaining flight stability provided by one embodiment of the present invention;

[0045] Figure 3 A flow chart of a method for obtaining noise interference level provided by one embodiment of the present invention;

[0046] Figure 4 A structural diagram of a network security communication system for an unmanned equipment formation provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0047] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a network security communication method, system, and medium for unmanned equipment fleets proposed in accordance with the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The following describes in detail a specific solution of a network security communication method, system and medium for unmanned equipment formation provided by the present invention with reference to the accompanying drawings.

[0050] The embodiment of the present invention provides a network security communication method, system and medium for unmanned equipment formation. Figure 1 , which shows a flow chart of a network security communication method for an unmanned device formation provided by one embodiment of the present invention, the method comprising the following steps:

[0051] Step S1: In the unmanned equipment formation, obtain the set spatial position, wind speed, wind direction and actual spatial position of each unmanned equipment at each sampling moment.

[0052] In order to ensure the subsequent data filtering effect, it is first necessary to obtain the actual spatial position, set spatial position, wind speed and wind direction of each unmanned device at each sampling moment to provide data support for subsequent data analysis.

[0053] The present invention uses the scenario of a drone formation performing a swarm performance as an example. During the swarm performance, the position of each drone at each moment is pre-designed. Wireless communication is required within the drone formation and between the drone formation and the command center. This allows the flight trajectory to be adjusted when a drone yaws, ensuring that the drones fly according to the set flight trajectory and form the desired performance pattern in the air. Each drone represents an unmanned device.

[0054] Using the unmanned device management system, sampling is performed at a preset frequency to obtain the corresponding set spatial position of each unmanned device at each sampling moment. For example, based on a three-dimensional spatial coordinate system, the X-axis of the three-dimensional spatial coordinate system represents the longitude of the unmanned device; the Y-axis represents the latitude of the unmanned device; and the Z-axis represents the altitude of the unmanned device. At any sampling moment, the longitude of the unmanned device is x1, the latitude is y1, and the altitude is z1. When the unmanned device is mapped to the three-dimensional spatial coordinate system, the corresponding set spatial position of the unmanned device at that sampling moment is (x1, y1, z1).

[0055] Using the unmanned vehicle monitoring system, the actual spatial position, wind speed, and wind direction of each unmanned vehicle in the fleet are obtained at each sampling moment. The specific process involves synchronous sampling at a preset frequency, taking into account that each unmanned vehicle is equipped with a GPS locator, a wind speed sensor, and a wind direction sensor. For example, if the actual spatial position of an unmanned vehicle at any sampling moment is x² in longitude, y² in latitude, and z² in altitude, the corresponding actual spatial position of the unmanned vehicle at that sampling moment is (x², y², z²).

[0056] It should be noted that, in one embodiment of the present invention, synchronous sampling is performed according to a preset frequency, each sampling is regarded as a sampling moment, the preset frequency is 1 time / second, and the last sampling moment is the current moment. It should be noted that, in order to facilitate calculations, all indicator data involved in the calculations in the embodiment of the present invention are subjected to data preprocessing to eliminate the dimension effect. The specific means of removing the dimension effect are technical means well known to those skilled in the art and are not limited here.

[0057] Considering that the wind speed and wind direction of the unmanned equipment at each sampling moment are directly measured by the sensors on the drone, the noise interference is small, and the moving average filtering algorithm can be directly used for denoising; because the GPS locator carried by the unmanned equipment is interfered by various factors such as buildings, trees, electronic equipment, and multipath effects when collecting the actual spatial position, resulting in the actual spatial position data being subject to greater noise interference, in order to ensure the data quality in wireless communication, it is necessary to perform exponential smoothing filtering on the actual spatial position data. In order to better perform exponential smoothing filtering on the actual spatial position data, the present invention performs exponential smoothing filtering on the actual spatial position data of the unmanned equipment by reasonably setting the smoothing coefficient at each sampling moment to improve the filtering effect. Considering that the actual spatial position data of each unmanned equipment in the unmanned equipment formation needs to be filtered, and the filtering process of each unmanned equipment in the unmanned equipment formation is the same, the present invention is explained with the filtering process of the actual spatial position data of any unmanned equipment.

[0058] Step S2: Take any sampling moment as the moment to be analyzed. In the preset time interval of the moment to be analyzed, obtain the flight stability at the moment to be analyzed based on the deviation between the actual spatial position of the unmanned equipment and the set spatial position; obtain the self-noise performance at the moment to be analyzed based on the wind speed and wind direction, as well as the deviation between the actual spatial position of the unmanned equipment and the set spatial position; and combine the flight stability and self-noise performance to obtain the initial smoothing coefficient of the unmanned equipment at the moment to be analyzed.

[0059] Taking any sampling moment as the moment to be analyzed, a preset time interval is constructed to analyze the flight conditions at that moment. The present invention first analyzes the flight conditions of a single unmanned aerial vehicle (UAV) at that moment and preliminarily determines the UAV's initial smoothing coefficient at that moment. The deviation between the UAV's actual spatial position and its set spatial position is first compared to assess the UAV's flight stability. The greater the flight stability, the more likely the UAV is to be in its set ideal position at the moment to be analyzed, i.e., the more stable its flight at that moment. To assess the likelihood of noise in the actual spatial position data corresponding to the moment to be analyzed, the self-noise performance index is obtained at the moment to be analyzed. The greater the self-noise performance index, the greater the likelihood of noise in the actual spatial position data corresponding to the moment to be analyzed. Considering that the more unstable the flight at the moment to be analyzed, the larger the smoothing coefficient required, i.e., the more consideration should be given to the UAV's actual spatial position at that moment, the algorithm can quickly respond to the UAV's yaw trajectory, adjusting the flight trajectory to help the UAV quickly return to the correct course. Considering that the greater the self-noise performance, the greater the possibility of yaw change caused by noise at the time to be analyzed, the smaller the smoothing coefficient is required to reduce the impact of noise on data filtering. Combined with flight stability and self-noise performance, the initial smoothing coefficient of the unmanned equipment at the time to be analyzed is preliminarily and reasonably set.

[0060] In one embodiment of the present invention, in order to analyze the flight situation at the time to be analyzed, a preset time interval of the time to be analyzed is constructed, and a method for obtaining the preset time interval includes:

[0061] The time interval that includes the time to be analyzed and a preset number of sampling moments preceding the time to be analyzed is used as the preset time interval for the time to be analyzed. In one embodiment of the present invention, the preset number is 30, which can be set by the implementer based on the implementation scenario. It should be noted that, considering that the preset time interval cannot be constructed from the first 30 sampling moments and the subsequent adjusted smoothing coefficient cannot be calculated, the present invention sets the adjusted smoothing coefficient for the first 30 sampling moments to 0.5 based on experience, which can be set by the implementer based on the implementation scenario.

[0062] To analyze the flight stability at the time to be analyzed, refer to Figure 2 , which shows a flow chart of a method for obtaining flight stability in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining flight stability includes:

[0063] Step S201: In a preset time interval of the time to be analyzed, the Euclidean distance between the actual spatial position corresponding to the sampling time and the set spatial position is used as the offset distance of the sampling time.

[0064] The offset distance reflects the degree of offset between the actual spatial position and the set spatial position corresponding to the sampling moment. It should be noted that the Euclidean distance is an existing technology well known to those skilled in the art and will not be described in detail here.

[0065] Step S202: Based on the size and quantity of all offset distances, the flight stability at the time to be analyzed is obtained.

[0066] Flight stability reflects the possibility of the unmanned equipment being in the ideal position. The greater the flight stability, the more likely the unmanned equipment is to be in the set ideal position, that is, the more stable the flight at the time to be analyzed.

[0067] Preferably, in one embodiment of the present invention, the method for obtaining flight stability includes:

[0068] Within a preset time interval of the moment to be analyzed, sampling moments with an offset greater than a preset offset value are marked as deviation moments. The time period containing consecutive deviation moments is considered a deviation period. The total number of deviation periods, the total number of deviation moments, and the average of all offset distances are forward-fused to obtain the flight deviation degree at the moment to be analyzed. The negative correlation mapping result of the flight deviation degree is used as the flight stability at the moment to be analyzed. In one embodiment of the present invention, the preset offset value is 0; implementers can set it based on the implementation scenario.

[0069] Specifically, in the preset time interval of the moment to be analyzed, the sampling moment with an offset distance greater than the preset offset value is marked as the deviation moment; the time period of all consecutive deviation moments is regarded as the deviation period; the product of the total number of deviation periods, the total number of deviation moments and the mean of all offset distances is calculated to obtain the flight deviation degree of the moment to be analyzed, and the negative correlation mapping result of the flight deviation degree is used as the flight stability of the moment to be analyzed.

[0070] In the above steps, sampling moments with offset distances greater than a preset offset value are first identified and marked as deviation moments. The preset offset value can be adjusted based on actual conditions. In this embodiment, it is set to 0, indicating that any deviation is considered a deviation. The deviation period reflects the period of time over which the UAV deviates from the set ideal position. To quantify the degree of flight deviation from the ideal position, a greater number of deviation periods indicates frequent deviations during flight, which in turn indicates a greater degree of flight deviation and lower flight stability. A greater number of deviation moments indicates frequent positional deviations during flight, which in turn indicates a greater degree of flight deviation and lower flight stability. The offset distance is the Euclidean distance between the UAV's actual position and its set position. It quantifies the degree to which the UAV deviates from the ideal flight trajectory. By calculating the mean of all offset distances, we can obtain an indicator reflecting the overall degree of deviation of the UAV. A larger mean value indicates a greater degree of overall deviation during flight and lower flight stability. The flight deviation reflects the overall deviation of the UAV during flight, and the flight stability reflects the possibility of the UAV being in the ideal position. The greater the flight stability, the more likely the UAV is to be in the ideal position, that is, the more stable the flight at the time to be analyzed.

[0071] In other embodiments of the present invention, the method for obtaining flight stability further includes: within a preset time interval of the time to be analyzed, marking sampling moments with an offset distance greater than 0 as deviation moments; calculating the total number of all deviation moments as the deviation frequency; calculating the mean of the deviation distances corresponding to all deviation moments as the degree of deviation; calculating the product of the deviation frequency and the degree of deviation to obtain the flight deviation at the time to be analyzed, and using the negative correlation mapping result of the flight deviation as the flight stability at the time to be analyzed. It should be noted that negative correlation mapping is a technical means well known to those skilled in the art, and negative correlation mapping can be in the form of inverse proportion or negative exponential power. In one embodiment of the present invention, the negative correlation mapping result is obtained by raising the inverse of the flight deviation to the power of an exponential function with a natural constant as the base.

[0072] Considering that random noise data may cause data that is not actually yawed to appear as yawed data, in order to assess the possibility of noise in the actual spatial position data corresponding to the time to be analyzed, a self-noise representation index is constructed for the time to be analyzed. The larger the self-noise representation index, the greater the possibility of noise in the actual spatial position data corresponding to the time to be analyzed. Preferably, in one embodiment of the present invention, the method for obtaining the self-noise representation index includes:

[0073] In the preset time interval of the time to be analyzed, every two adjacent sampling moments are regarded as a suspected yaw moment combination; the suspected yaw moment combination in which the offset distance of the previous sampling moment is 0 and the offset distance of the next sampling moment is not 0 is regarded as the real yaw moment combination;

[0074] Construct an actual flight vector for the real yaw moment combination; the modulus of the actual flight vector is the Euclidean distance between the actual spatial positions corresponding to two sampling moments in the real yaw moment combination; the direction of the actual flight vector is the direction from the actual spatial position corresponding to the previous sampling moment to the next sampling moment of the real yaw moment combination;

[0075] Construct a preset flight vector for the real yaw moment combination; the modulus of the preset flight vector is the Euclidean distance between the set spatial positions corresponding to the two sampling moments in the real yaw moment combination; the direction of the preset flight vector is the direction of the set spatial position from the previous sampling moment to the next sampling moment in the real yaw moment combination;

[0076] Construct a wind influence vector of the real yaw moment combination; the modulus of the wind influence vector is the wind speed at the previous sampling moment in the real yaw moment combination; the direction of the wind influence vector is the wind direction at the previous sampling moment in the real yaw moment combination;

[0077] The difference between the actual flight vector and the preset flight vector corresponding to the actual yaw moment combination is calculated to obtain the yaw vector of the actual yaw moment combination. The modulus of the difference between the yaw vector and the wind influence vector is calculated to obtain the local noise index of the actual yaw moment combination. The local noise index of all actual yaw moment combinations within the preset time interval of the time to be analyzed is accumulated and normalized to obtain the self-noise performance index at the time to be analyzed. It should be noted that normalization is a technical means well known to those skilled in the art. Normalization can be performed using linear normalization or other methods, and is not limited here.

[0078] To analyze each UAV yaw event, each pair of adjacent sampling moments is first considered a suspected yaw moment combination. This suspected yaw moment combination provides data support for analyzing the corresponding yaw moment pairs. The actual yaw moment combination reflects the corresponding moment pairs when the UAV yawed, that is, the moment pairs when the UAV shifted from a set position to a flight path that deviated from the set position. The actual flight vector reflects the UAV's actual flight conditions at the time of yaw. The preset flight vector in the actual yaw moment combination reflects the preset flight conditions at the time of yaw. The wind effect vector in the actual yaw moment combination reflects the wind effect on the UAV during yaw. The yaw vector represents the deviation between the actual flight path and the preset flight path. The modulus length of the difference between the yaw vector and the wind effect vector is calculated to obtain the local noise index of the actual yaw moment combination. The local noise index measures the difference between the yaw vector and the wind effect vector. A larger local noise index indicates that the yaw represented by the actual yaw moment combination is less likely to be caused by wind and more likely to be caused by noise. The local noise index for all combinations of actual yaw moments within a preset time interval is accumulated and normalized to obtain the self-noise performance index at the time to be analyzed. The self-noise performance index reflects the likelihood of yaw shift caused by noise in the preset time interval. A higher self-noise performance index indicates a greater likelihood of noise in the actual spatial position data at the time to be analyzed.

[0079] Preferably, in one embodiment of the present invention, considering that the lower the flight stability, the more unstable the flight at the time to be analyzed, the larger the smoothing coefficient required at the time to be analyzed, that is, the actual spatial position at the time to be analyzed should be considered more, so that the algorithm can quickly respond to the yaw trajectory of the drone to adjust the flight trajectory to help the drone quickly return to the correct route. Considering that the greater the self-noise performance, the greater the possibility of yaw change caused by noise at the time to be analyzed, and the smaller the smoothing coefficient is required to reduce the impact of noise on data filtering, the initial smoothing coefficient of the unmanned equipment at the time to be analyzed can be preliminarily and reasonably set in combination with the flight stability and self-noise performance. The method for obtaining the initial smoothing coefficient includes:

[0080] The flight stability and self-noise performance are reversely integrated to obtain the initial smoothing coefficient of the unmanned equipment at the time to be analyzed.

[0081] In one embodiment of the present invention, the mean of the flight stability and self-noise performance is calculated, and the difference between 1 and the mean is calculated to obtain the initial smoothing coefficient of the unmanned device at the time to be analyzed. In other embodiments of the present invention, the sum of the flight stability and self-noise performance can also be calculated, and the sum is inversely normalized to obtain the initial smoothing coefficient of the unmanned device at the time to be analyzed. In one embodiment of the present invention, the inverse normalization method is to raise the inverse of the sum to the power of an exponential function with a natural constant as the base to obtain a negative correlation mapping result.

[0082] At this point, the initial smoothing coefficient is obtained.

[0083] Step S3: Obtain the reference surrounding devices of the unmanned device at the time to be analyzed; obtain the noise interference degree of the unmanned device at the time to be analyzed based on the consistency of the set spatial position, wind speed and wind direction of the unmanned device and its reference surrounding devices, as well as the deviation between the actual spatial position and the set spatial position; adjust the initial smoothing coefficient at the time to be analyzed based on the noise interference degree, and obtain the adjusted smoothing coefficient of the unmanned device at the time to be analyzed.

[0084] The above steps preliminarily determine the initial smoothing coefficient of a UAV at the time to be analyzed based on the flight performance of a single UAV. To more accurately determine the smoothing coefficient, it is necessary to analyze the flight performance of the UAV's surrounding UAVs. Considering that when performing a clustered UAV formation, adjacent UAVs have similar flight trajectories to achieve coordination and synchronization between the UAVs, and that clustered UAV formations often operate in open areas, the wind speed and direction of adjacent UAVs are generally consistent. Under similar flight environments and with similarly set flight trajectories, the yaw behavior of adjacent UAVs is normally similar. Due to the random nature of noise interference, random noise interference can cause differences in the yaw behavior of an UAV and its surrounding UAVs. First, determine the UAV's reference surroundings at the time to be analyzed. These reference surroundings reflect the UAV's neighboring UAVs at that time. The noise interference index is constructed to reflect the degree of noise interference with the UAV's actual spatial position data at the time of analysis. A higher noise interference index indicates a greater difference in yaw between the UAV and its reference surrounding devices, even when the flight environment at the time of analysis is similar to the set flight trajectory. This indicates a greater degree of noise interference with the UAV's actual spatial position data at the time of analysis. Based on the noise interference index, the initial smoothing coefficient at the time of analysis is adjusted to more accurately determine the adjusted smoothing coefficient for the UAV at the time of analysis.

[0085] First, the reference surrounding devices of the unmanned device at the time to be analyzed are determined. The reference surrounding devices of the unmanned device at the time to be analyzed reflect the adjacent unmanned devices of the unmanned device at the time to be analyzed. Preferably, in one embodiment of the present invention, the method for obtaining the reference surrounding devices of the unmanned device at the time to be analyzed includes:

[0086] At the time to be analyzed, any unmanned device is used as the target device. The Euclidean distance between each unmanned device and the target device is calculated, and the Euclidean distances are sorted in ascending order. The first preset number of Euclidean distances corresponding to the unmanned device are used as the reference surrounding devices of the target device at the time to be analyzed. In one embodiment of the present invention, the preset number of surrounding devices is 3, which can be set by the implementer according to the implementation scenario.

[0087] In order to analyze the degree to which the actual spatial position data corresponding to the time to be analyzed is affected by noise, please refer to Figure 3 , which shows a flow chart of a method for obtaining noise interference level in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining noise interference level includes:

[0088] Step S301: In a preset time interval of the time to be analyzed, based on the DTW algorithm, the influence similarity weights of the unmanned device and its reference surrounding devices are obtained according to the differences in the set spatial positions, wind speeds, and wind directions of the unmanned device and its reference surrounding devices.

[0089] Considering that adjacent UAVs normally exhibit similar yaw behavior under similar flight environments and similarly set flight trajectories, the influence similarity weights of the UAVs and their reference surroundings are determined based on the differences in their set spatial positions, wind speeds, and wind directions. These influence similarity weights reflect the differences in the flight environment and set flight trajectories between the UAVs and their reference surroundings at the time of analysis. A larger influence similarity weight indicates a smaller difference in the flight environment and set flight trajectories between the UAVs and their reference surroundings, and a greater similarity.

[0090] In one embodiment of the present invention, a method for obtaining the affected similarity weight includes:

[0091] The spatial location, wind speed, and wind direction are set as the influencing parameters of the unmanned device. Within a preset time interval of the time to be analyzed, the influencing parameters of all sampling moments are sequentially counted in chronological order to obtain an influencing parameter sequence for the unmanned device. The DTW distances between the unmanned device and its reference surrounding devices corresponding to each influencing parameter sequence are reversely fused to obtain the influencing similarity weights between the unmanned device and its reference surrounding devices. The mean of the DTW distances between the unmanned device and its reference surrounding devices corresponding to all influencing parameter sequences is calculated, and the negative correlation mapping result of the mean is used as the influencing similarity weight between the unmanned device and its reference surrounding devices. It should be noted that using the DTW (Dynamic Time Warping) algorithm to calculate DTW distances between sequences is well known to those skilled in the art and will not be described in detail here. Here, a brief description is given of how the DTW algorithm is used to calculate the influencing parameter sequence corresponding to the wind direction of the unmanned device and its reference surrounding devices: After DTW matching, the angle between the two wind directions corresponding to each matching pair is used as the matching distance of the matching pair, and the sum of the matching distances of all matching pairs is used as the DTW distance between the unmanned device and its reference surrounding devices corresponding to the wind direction of the influencing parameter sequence.

[0092] In the above steps, considering that the set spatial position, wind speed, and wind direction are the main factors affecting the actual flight of the unmanned device, the set spatial position, wind speed, and wind direction are used as the influencing parameters of the unmanned device. The DTW distance between the unmanned device and its reference surrounding devices for each influencing parameter sequence can reflect the difference in influencing parameters. The smaller the DTW distance, the greater the similarity of the influencing factors. The DTW distance between the unmanned device and its reference surrounding devices for each influencing parameter sequence is reversely fused to obtain the influence similarity weight of the unmanned device and its reference surrounding devices. The influence similarity weight reflects the similarity between the flight environment and the set flight trajectory of the unmanned device and its reference surrounding devices at the time to be analyzed. The larger the influence similarity weight, the greater the similarity.

[0093] In other embodiments of the present invention, the method for obtaining the affected similarity weight further includes: based on a three-dimensional spatial coordinate system, taking the angle between the wind direction and the positive direction of the X-axis as the wind direction characteristic angle of each wind direction; in a preset time interval of the time to be analyzed, sequentially counting the set spatial position, wind speed, and wind direction characteristic angle of the unmanned equipment at all sampling moments in chronological order to obtain a set spatial position sequence, wind speed sequence, and wind direction characteristic angle sequence of the unmanned equipment; and taking the set spatial position sequence, wind speed sequence, and wind direction characteristic angle sequence as an influence parameter sequence, respectively;

[0094] Using the DTW algorithm, the DTW distances between the unmanned device and its reference surrounding devices corresponding to the influencing parameter sequences are obtained. The mean of the DTW distances between the unmanned device and its reference surrounding devices corresponding to all influencing parameter sequences is calculated, and the negative correlation mapping result of the mean is used as the influence similarity weight between the unmanned device and its reference surrounding devices. In one embodiment of the present invention, the negative correlation mapping result is obtained by raising the inverse of the mean to the power of an exponential function with a natural constant as the base. The X-axis of the three-dimensional spatial coordinate system represents the longitude of the unmanned device, and the positive direction of the X-axis is due north.

[0095] Step S302: Obtain an offset difference index between the unmanned device and its reference surrounding devices based on the DTW algorithm according to the degree of difference in offset distance between the unmanned device and its reference surrounding devices.

[0096] The offset difference index reflects the degree of difference in the offset distance between the unmanned equipment and its reference surrounding equipment. The larger the value of the offset difference index, the greater the degree of difference.

[0097] In one embodiment of the present invention, the offset distances of all sampling moments of the unmanned equipment are counted in chronological order to obtain an offset distance sequence of the unmanned equipment; the DTW distances of the offset distance sequences corresponding to the unmanned equipment and its reference surrounding equipment are calculated based on the DTW algorithm to obtain an offset difference index between the unmanned equipment and its reference surrounding equipment.

[0098] Step S303: performing weighted summation on the offset difference index using the influence similarity weights of the unmanned device and all of its reference surrounding devices to obtain the noise interference degree of the unmanned device at the time to be analyzed.

[0099] The noise interference level of the UAV at the time of analysis is calculated by combining the influence similarity weights and offset difference indicators between the UAV and all its reference surrounding devices. A higher noise interference level indicates a greater difference in yaw between the UAV and its reference surrounding devices, even when the flight environment at the time of analysis is similar to the set flight trajectory. This indicates a greater degree of noise interference with the actual spatial position data at the time of analysis.

[0100] Considering that the initial smoothing coefficient is obtained by analyzing the flight conditions of a single unmanned device at the time to be analyzed, it preliminarily reflects the degree of demand for the smoothing coefficient of the unmanned device. The noise interference degree further reflects the degree of noise interference to the actual spatial position data corresponding to the time to be analyzed by analyzing the flight conditions of the unmanned devices around the unmanned device. The greater the noise interference degree, the greater the degree of noise interference at the time to be analyzed, and the smaller the smoothing coefficient is required to reduce the impact of noise on data filtering. In combination with the noise interference degree, the initial smoothing coefficient at the time to be analyzed is adjusted to more accurately determine the adjusted smoothing coefficient of the unmanned device at the time to be analyzed. In one embodiment of the present invention, the method for obtaining the adjusted smoothing coefficient includes:

[0101] The negative correlation mapping result of the noise interference degree at the time to be analyzed is used as the adjustment parameter; the adjustment parameter at the time to be analyzed and the initial smoothing coefficient are forward fused to obtain the adjusted smoothing coefficient at the time to be analyzed. Specifically, in one embodiment of the present invention, the negative correlation mapping result of the noise interference degree at the time to be analyzed is used as the adjustment parameter; the negative correlation mapping result of the noise interference degree at the time to be analyzed is used as the adjustment parameter; the product of the adjustment parameter at the time to be analyzed, the initial smoothing coefficient, and the preset adjustment value is calculated, and the sum of the preset reference value and the product is calculated to obtain the adjusted smoothing coefficient at the time to be analyzed. The specific formula for obtaining the adjusted smoothing coefficient is:

[0102] ;in, is the preset benchmark value; is the preset adjustment value; is the initial smoothing coefficient at the time to be analyzed; is the adjusted smoothing coefficient at the time to be analyzed; is the noise interference degree; To adjust the parameters; In one embodiment of the present invention, the preset reference value is set to 0.3 and the preset adjustment value is set to 0.4 based on experience. The implementer can set the values ​​according to the implementation scenario.

[0103] Step S4: Filtering the actual spatial position using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and performing communication transmission based on the filtered actual spatial position data.

[0104] The above steps perform exponential smoothing filtering on the actual spatial position data of the unmanned equipment by reasonably setting the smoothing coefficient at each sampling moment, so as to improve the filtering effect, increase the accuracy of the filtered data in wireless communication, and ensure the security of wireless communication.

[0105] In one embodiment of the present invention, an exponential smoothing filtering algorithm is used to filter the actual spatial position at each sampling moment using an adjusted smoothing coefficient at each sampling moment to obtain filtered data. It should be noted that exponential smoothing filtering is a well-known prior art technique for those skilled in the art and will not be described in detail here. For example, if the actual spatial position of an unmanned device at any sampling moment has longitude x1, latitude y1, and altitude z1, the corresponding actual spatial position of the unmanned device is (x1, y1, z1). The three position components of longitude, latitude, and altitude at each sampling moment are sequentially counted in chronological order to obtain a longitude sequence, a latitude sequence, and an altitude sequence, respectively. The longitude sequence, latitude sequence, and altitude sequence are then subjected to exponential smoothing filtering using the adjusted smoothing coefficient at each sampling moment to obtain a filtered longitude sequence, a filtered latitude sequence, and a filtered altitude sequence. The filtered longitude sequence, latitude sequence, and altitude sequence are combined to obtain filtered actual spatial position data.

[0106] Specifically, the filtered actual spatial position data is used to perform wireless communication within the unmanned equipment formation and between the unmanned equipment formation and the command center.

[0107] The present invention also proposes a network security communication system for unmanned equipment formation, see Figure 4 , which shows a structural diagram of a network security communication system for an unmanned equipment formation provided by an embodiment of the present invention. The system includes: a data acquisition module 101, an initial smoothing coefficient analysis module 102, an adjusted smoothing coefficient analysis module 103 and a communication module 104.

[0108] The data acquisition module 101 is used to obtain the set spatial position, wind speed, wind direction and actual spatial position of each unmanned device in the unmanned device formation at each sampling time;

[0109] The initial smoothing coefficient analysis module 102 is configured to use any sampling moment as the moment to be analyzed. Within a preset time interval of the moment to be analyzed, the module determines the flight stability at that moment based on the deviation between the actual spatial position of the UAV and the set spatial position. The module also determines the self-noise performance at that moment based on the wind speed and direction, as well as the difference between the actual spatial position of the UAV and the set spatial position. The module then combines the flight stability and self-noise performance to determine the initial smoothing coefficient of the UAV at that moment.

[0110] The adjusted smoothing coefficient analysis module 103 is configured to obtain reference surrounding devices of the unmanned device at the time to be analyzed; obtain the noise interference level of the unmanned device at the time to be analyzed based on the consistency of the set spatial position, wind speed, and wind direction between the unmanned device and its reference surrounding devices, as well as the deviation between the actual spatial position and the set spatial position; and adjust the initial smoothing coefficient at the time to be analyzed based on the noise interference level to obtain the adjusted smoothing coefficient of the unmanned device at the time to be analyzed.

[0111] The communication module 104 is configured to filter the actual spatial position using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and perform communication transmission based on the filtered actual spatial position data.

[0112] It should be noted that the system provided in the above embodiment is merely an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the network security communication system for unmanned equipment formations provided in the above embodiment and the network security communication method embodiment for unmanned equipment formations are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0113] A network security communication medium applied to an unmanned equipment formation, characterized in that it includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of the above-mentioned network security communication method applied to an unmanned equipment formation are implemented.

[0114] In summary, the embodiments of the present invention provide a network security communication method, system, and medium for unmanned equipment formations. The present invention first obtains the self-noise performance at the time to be analyzed based on the wind speed and wind direction, as well as the difference between the actual spatial position and the set spatial position of the unmanned equipment; adjusts the initial smoothing coefficient at the time to be analyzed based on the noise interference degree to obtain the adjusted smoothing coefficient of the unmanned equipment at the time to be analyzed; uses the adjusted smoothing coefficient at each sampling moment to filter the actual spatial position to obtain filtered actual spatial position data; and performs communication transmission based on the filtered actual spatial position data. The present invention performs exponential smoothing filtering on the actual spatial position data of the unmanned equipment by reasonably setting the smoothing coefficient at each sampling moment to improve the filtering effect, improve the accuracy of the filtered data in wireless communication, and ensure the security of wireless communication.

[0115] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A network security communication method applied to an unmanned equipment formation, characterized in that: The method comprises: In the UAV formation, obtain the set spatial position, wind speed, wind direction and actual spatial position of each UAV at each sampling moment; Taking any of the sampling moments as the moment to be analyzed, and within a preset time interval of the moment to be analyzed, obtaining the flight stability at the moment to be analyzed based on the deviation between the actual spatial position of the unmanned aerial vehicle and the set spatial position; obtaining the self-noise performance at the moment to be analyzed based on the wind speed and wind direction, and the difference between the actual spatial position of the unmanned aerial vehicle and the set spatial position; and obtaining an initial smoothing coefficient of the unmanned aerial vehicle at the moment to be analyzed by combining the flight stability and the self-noise performance. Obtaining reference surrounding devices of the unmanned device at the time to be analyzed; obtaining the noise interference degree of the unmanned device at the time to be analyzed based on the consistency of the set spatial position, wind speed, and wind direction of the unmanned device and the reference surrounding devices, and the deviation between the actual spatial position and the set spatial position; adjusting the initial smoothing coefficient at the time to be analyzed based on the noise interference degree, and obtaining the adjusted smoothing coefficient of the unmanned device at the time to be analyzed; The actual spatial position is filtered using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and communication transmission is performed based on the filtered actual spatial position data.

2. A network security communication method for unmanned equipment formation according to claim 1, characterized in that: The method for obtaining the flight stability includes: In the preset time interval of the time to be analyzed, the Euclidean distance between the actual spatial position and the set spatial position corresponding to the sampling time is used as the offset distance of the sampling time; The flight stability at the time to be analyzed is obtained based on the sizes and quantities of all the offset distances.

3. A network security communication method for unmanned equipment formation according to claim 2, characterized in that: The method for obtaining the flight stability at the time to be analyzed based on the sizes and quantities of all the offset distances includes: In the preset time interval of the moment to be analyzed, the sampling moment whose offset distance is greater than the preset offset value is marked as a deviation moment; and the time period in which the consecutive deviation moments are located is regarded as a deviation period; The total number of the deviation periods, the total number of the deviation moments, and the mean of all the offset distances are forwardly fused to obtain the flight deviation at the moment to be analyzed, and the negative correlation mapping result of the flight deviation is used as the flight stability at the moment to be analyzed.

4. A network security communication method for unmanned equipment formation according to claim 2, characterized in that: The method for obtaining the self-noise expression comprises: In the preset time interval of the time to be analyzed, every two adjacent sampling moments are regarded as a suspected yaw moment combination; the suspected yaw moment combination in which the offset distance of the previous sampling moment is 0 and the offset distance of the next sampling moment is not 0 is regarded as a real yaw moment combination; Constructing an actual flight vector of the real yaw moment combination; the modulus of the actual flight vector is the Euclidean distance between the actual spatial positions corresponding to two sampling moments in the real yaw moment combination; and the direction of the actual flight vector is the direction from the actual spatial position corresponding to the previous sampling moment to the next sampling moment of the real yaw moment combination; Constructing a preset flight vector for the real yaw moment combination; the modulus of the preset flight vector is the Euclidean distance between the set spatial positions corresponding to two sampling moments in the real yaw moment combination; and the direction of the preset flight vector is the direction of the set spatial position from the previous sampling moment to the next sampling moment of the real yaw moment combination; Constructing a wind influence vector of a real yaw moment combination; the modulus of the wind influence vector is the wind speed at the previous sampling moment in the real yaw moment combination; and the direction of the wind influence vector is the wind direction at the previous sampling moment in the real yaw moment combination; The method further comprises calculating a difference between the actual flight vector and the preset flight vector corresponding to the real yaw moment combination to obtain the yaw vector of the real yaw moment combination; calculating a modulus of the difference between the yaw vector and the wind influence vector to obtain a local noise index of the real yaw moment combination; and calculating a cumulative value of the local noise index of all real yaw moment combinations in the preset time interval of the moment to be analyzed, normalizing the cumulative value, and obtaining a self-noise performance index at the moment to be analyzed.

5. The network security communication method for unmanned equipment formation according to claim 1, characterized in that: The method for obtaining the initial smoothing coefficient includes: The flight stability and the self-noise performance are reversely fused to obtain an initial smoothing coefficient of the unmanned equipment at the time to be analyzed.

6. A network security communication method for unmanned equipment formation according to claim 2, characterized in that: The method for obtaining the noise interference degree includes: Obtaining, in the preset time interval of the time to be analyzed, influence similarity weights of the unmanned device and its reference surrounding devices based on differences in set spatial positions, wind speeds, and wind directions between the unmanned device and its reference surrounding devices based on a DTW algorithm; Obtaining an offset difference index between the unmanned device and its reference surrounding devices based on a DTW algorithm according to a degree of difference in the offset distance between the unmanned device and its reference surrounding devices; The offset difference index is weightedly summed using the influence similarity weights of the unmanned device and all of its reference surrounding devices to obtain the noise interference degree of the unmanned device at the time to be analyzed.

7. A network security communication method for unmanned equipment formation according to claim 6, characterized in that: The methods for obtaining the affected similarity weights include: The spatial position, wind speed, and wind direction are set as the influencing parameters of the unmanned equipment; in the preset time interval of the time to be analyzed, the influencing parameters of all sampling moments are counted in chronological order to obtain the influencing parameter sequence of the unmanned equipment; The DTW distances of each influencing parameter sequence between the unmanned device and its reference surrounding devices are reversely fused to obtain the influence similarity weights of the unmanned device and its reference surrounding devices.

8. The network security communication method for unmanned equipment formation according to claim 1, characterized in that: Methods for obtaining the adjusted smoothing coefficient include: The negative correlation mapping result of the noise interference degree at the time to be analyzed is used as the adjustment parameter; the adjustment parameter at the time to be analyzed and the initial smoothing coefficient are forwardly integrated to obtain the adjusted smoothing coefficient at the time to be analyzed.

9. A network security communication system for unmanned equipment formation, characterized in that: The system comprises: The data acquisition module is used to obtain the set spatial position, wind speed, wind direction and actual spatial position of each unmanned device in the unmanned device formation at each sampling moment; An initial smoothing coefficient analysis module is configured to use any of the sampling moments as a time to be analyzed, and within a preset time interval of the time to be analyzed, obtain the flight stability at the time to be analyzed based on the deviation between the actual spatial position of the unmanned aerial vehicle and the set spatial position; obtain the self-noise performance at the time to be analyzed based on the wind speed and wind direction, as well as the difference between the actual spatial position of the unmanned aerial vehicle and the set spatial position; and obtain the initial smoothing coefficient of the unmanned aerial vehicle at the time to be analyzed based on the flight stability and the self-noise performance. The adjusted smoothing coefficient analysis module is configured to obtain reference surrounding devices of the unmanned device at the time to be analyzed; obtain the noise interference level of the unmanned device at the time to be analyzed based on the consistency of the set spatial position, wind speed, and wind direction of the unmanned device and its reference surrounding devices, as well as the deviation between the actual spatial position and the set spatial position; and adjust the initial smoothing coefficient at the time to be analyzed based on the noise interference level to obtain the adjusted smoothing coefficient of the unmanned device at the time to be analyzed; The communication module is used to filter the actual spatial position using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and perform communication transmission based on the filtered actual spatial position data.

10. A network security communication medium for unmanned equipment formation, characterized in that: The invention comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a network security communication method applied to an unmanned equipment formation as described in any one of claims 1 to 8 are implemented.

Images