Unmanned aerial vehicle remote data transmission and monitoring system based on hydrogen energy power module

Abstract

The invention relates to the technical field of data transmission, in particular to an unmanned aerial vehicle remote data transmission and monitoring system based on a hydrogen energy power module, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: collecting signal-to-noise ratio data of a hydrogen power unmanned aerial vehicle during remote data transmission, forming a signal-to-noise ratio data sequence; for any signal-to-noise ratio data, acquiring the abnormal degree of any signal-to-noise ratio data; detecting whether abnormal signal-to-noise ratio data exists in the signal-to-noise ratio data sequence or not according to the abnormal degree of all the signal-to-noise ratio data; when abnormal signal-to-noise ratio data exists in the signal-to-noise ratio data sequence, acquiring a signal transmission quality abnormal value; according to the signal transmission quality abnormal value, the target data transmission frequency of the hydrogen-powered unmanned aerial vehicle is obtained, the target data transmission frequency is used for data transmission of the hydrogen-powered unmanned aerial vehicle, and the remote data transmission rate and anti-noise capability of the hydrogen-powered unmanned aerial vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a remote data transmission and monitoring system for unmanned aerial vehicles based on a hydrogen power module. Background Art

[0002] Hydrogen-powered drones use hydrogen fuel cells as their power system, combining the advantages of long endurance and battery power without vibration and noise. At present, China has issued national standards for hydrogen fuel cell power generation systems for drones. Hydrogen-powered drones can have a flight time of more than three hours and a coverage range of 200km, which is suitable for industrial applications, night emergency lighting, search and rescue, inspection, mapping and unmanned freight. Due to the high energy density of hydrogen fuel cells, compared with traditional electric drones, they can provide longer endurance and longer flight distance. This advantage enables hydrogen-powered drones to cover a wider area, especially for long-term or long-distance data collection and transmission tasks. However, as the distance between the drone and the transmission receiving end increases, the signal will gradually attenuate, which poses a challenge to real-time data transmission, especially in environments such as mountainous areas, forests or cities where radio remote transmission signals are susceptible to interference.

[0003] Usually, for long-distance data transmission, it is necessary to select a suitable data transmission frequency, which includes high-frequency transmission and low-frequency transmission. Low-frequency transmission signals are usually less subject to interference, especially in long-distance transmission, because the wavelength of low-frequency signals is longer and can bypass obstacles, but the transmission speed is slower. High-frequency transmission signals are more sensitive to interference, especially in complex environments, and are easily affected, but the transmission speed is faster. The existing technology usually uses the mean of the historical signal-to-noise ratio data of the hydrogen-powered drone channel to measure the channel quality for the frequency selection of long-distance data transmission of drones. If the mean of the signal-to-noise ratio data is low, low-frequency transmission is directly used, and if the mean of the signal-to-noise ratio data is high, high-frequency transmission is used. However, the dynamic adjustment ability of the data transmission frequency selected by the existing technology is poor, and the channel quality evaluated based on the mean of the signal-to-noise ratio data is easily affected by obstructions. For example, when a hydrogen-powered drone briefly passes through an obstruction such as a high-rise building, there is a large signal-to-noise ratio fluctuation, which causes the real quality of the channel to be lowered, so that the mean of the historical signal-to-noise ratio data cannot accurately represent the real quality of the recent hydrogen-powered drone channel, and thus makes it impossible to select a suitable data transmission frequency, reducing the efficiency of data transmission.

[0004] Therefore, how to accurately obtain the true quality of the drone channel and adjust the frequency of data transmission in time has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a UAV remote data transmission and monitoring system based on a hydrogen power module to solve the problem of how to accurately obtain the true quality of the UAV channel and timely adjust the frequency of data transmission.

[0006] In an embodiment of the present invention, a remote data transmission and monitoring system for a UAV based on a hydrogen power module is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following method is implemented:

[0007] Collect the signal-to-noise ratio data at each sampling moment when the hydrogen-powered UAV is performing long-distance data transmission, and compose the signal-to-noise ratio data within a preset time range into a signal-to-noise ratio data sequence;

[0008] For any signal-to-noise ratio data in the signal-to-noise ratio data sequence, a target window of a preset size is constructed with the any signal-to-noise ratio data as the center, and the abnormality degree of the any signal-to-noise ratio data is obtained according to the data difference between the any signal-to-noise ratio data and the signal-to-noise ratio data in the target window;

[0009] Acquiring the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence, and detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0010] When there is at least one abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, obtaining a signal transmission quality abnormal value within the preset time range according to distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0011] According to the signal transmission quality abnormal value within the preset time range, the target data transmission frequency of the hydrogen-powered UAV within the future preset time range is obtained, and the target data transmission frequency is used to perform data transmission of the hydrogen-powered UAV within the future preset time range.

[0012] Preferably, obtaining the abnormality degree of any signal-to-noise ratio data according to the data difference between any signal-to-noise ratio data and the signal-to-noise ratio data in the target window includes:

[0013] Respectively obtaining the absolute values ​​of the differences between any one of the signal-to-noise ratio data and each of the signal-to-noise ratio data except the any one of the signal-to-noise ratio data in the target window, and correspondingly obtaining a first mean of the absolute values ​​of the differences, and performing normalization processing on the first mean of the absolute values ​​of the differences to obtain the overall deviation degree of the any one of the signal-to-noise ratio data;

[0014] Obtaining a first absolute value of a difference between any signal-to-noise ratio data and a previous signal-to-noise ratio data of any signal-to-noise ratio data, obtaining a second absolute value of a difference between any signal-to-noise ratio data and a next signal-to-noise ratio data of any signal-to-noise ratio data, calculating a first addition result of the first absolute value of the difference and the second absolute value of the difference, and performing normalization processing on the first addition result to obtain an instantaneous deviation degree of any signal-to-noise ratio data;

[0015] The abnormality degree of any signal-to-noise ratio data is obtained according to the sum of the overall deviation degree and the instantaneous deviation degree of any signal-to-noise ratio data.

[0016] Preferably, the detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence comprises:

[0017] If the abnormal degree of any signal-to-noise ratio data is greater than or equal to a preset signal-to-noise ratio threshold, the any signal-to-noise ratio data is confirmed to be abnormal signal-to-noise ratio data.

[0018] Preferably, obtaining the abnormal value of signal transmission quality within the preset time range according to the distribution characteristics of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence includes:

[0019] Acquire all abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence to form an abnormal signal-to-noise ratio data sequence, acquire the mean of the abnormal signal-to-noise ratio data sequence, and obtain the mean of the abnormal degree of the signal-to-noise ratio data sequence;

[0020] Respectively calculating the absolute values ​​of the differences between the sampling moments corresponding to every two adjacent abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, and obtaining a corresponding second difference absolute value mean, obtaining the inverse of the second difference absolute value mean, and obtaining the degree of aggregation of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0021] Obtaining the number of signal-to-noise ratio data in the signal-to-noise ratio data sequence, recorded as a first number, obtaining the number of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, recorded as a second number, calculating the ratio of the second number to the first number, and obtaining the proportion of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0022] Obtaining a sampling time corresponding to the first abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, recording it as the minimum abnormal sampling time; obtaining a sampling time corresponding to the last abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, recording it as the maximum abnormal sampling time; calculating the absolute value of the difference between the maximum abnormal sampling time and the minimum abnormal sampling time; calculating the ratio of the absolute value of the difference to the first number, and obtaining the continuity of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0023] The signal transmission quality abnormal value within the preset time range is obtained according to the product of the abnormal degree mean, the aggregation degree of the abnormal signal-to-noise ratio data, the proportion of the abnormal signal-to-noise ratio data, and the persistence degree of the abnormal signal-to-noise ratio data.

[0024] Preferably, the step of obtaining the target data transmission frequency of the hydrogen-powered UAV within a future preset time range according to the abnormal value of the signal transmission quality within the preset time range includes:

[0025] Setting the data transmission frequency of the hydrogen-powered drone to include a first frequency, a second frequency, and a third frequency, wherein the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency;

[0026] If the signal transmission quality abnormal value is greater than or equal to the preset signal transmission quality threshold, the target data transmission frequency of the hydrogen-powered UAV within the future preset time range is selected as the third frequency;

[0027] If the signal transmission quality threshold is less than the signal transmission quality threshold, the target data transmission frequency of the hydrogen-powered UAV within the future preset time range is selected as the second frequency.

[0028] Preferably, after detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence, the method further comprises:

[0029] When there is no abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the target data transmission frequency of the hydrogen-powered drone within the future preset time range is selected as the first frequency.

[0030] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0031] The present invention collects signal-to-noise ratio data at each sampling moment when a hydrogen-powered unmanned aerial vehicle performs remote data transmission, and forms a signal-to-noise ratio data sequence with the signal-to-noise ratio data within a preset time range; for any signal-to-noise ratio data in the signal-to-noise ratio data sequence, a target window of a preset size is constructed with the any signal-to-noise ratio data as the center, and the abnormality degree of the any signal-to-noise ratio data is obtained according to the data difference between the any signal-to-noise ratio data and the signal-to-noise ratio data in the target window; the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence is obtained, and whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence is detected according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence; when there is at least one abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the signal transmission quality abnormal value within the preset time range is obtained according to the distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; according to the signal transmission quality abnormal value within the preset time range, the target data transmission frequency of the hydrogen-powered unmanned aerial vehicle within the future preset time range is obtained, and the data transmission of the hydrogen-powered unmanned aerial vehicle within the future preset time range is performed using the target data transmission frequency. Among them, by obtaining the abnormal degree of signal-to-noise ratio data, it is detected whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence. When abnormal signal-to-noise ratio data exists, the abnormal value of signal transmission quality within a preset time range is obtained according to the distribution characteristics of the abnormal signal-to-noise ratio data, and the quality of remote data transmission within the preset time range is accurately evaluated, thereby obtaining the target data transmission frequency of the hydrogen-powered UAV within the future preset time range, thereby improving the rate and anti-noise ability of the hydrogen-powered UAV in remote data transmission. In addition, the present invention uses the historical signal-to-noise ratio data of the hydrogen-powered UAV when performing remote data transmission to obtain the frequency of remote data transmission by the hydrogen-powered UAV within the future time range, thereby improving the dynamic adjustment ability of the hydrogen-powered UAV during remote data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0033] Figure 1 This is a flow chart of a method for remote data transmission and monitoring of a UAV based on a hydrogen power module provided in Embodiment 1 of the present invention;

[0034] Figure 2 This is an example diagram of signal-to-noise ratio data fluctuation when a hydrogen-powered UAV briefly passes through an obstruction, provided in the first embodiment of the present invention;

[0035] Figure 3This is an example diagram of signal-to-noise ratio data fluctuations of a hydrogen-powered UAV under interference from weather conditions or dense buildings, provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0036] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0037] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0038] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0039] An embodiment of the present invention provides a remote data transmission and monitoring system for a UAV based on a hydrogen power module, comprising a processor and a memory, wherein the processor executes a computer program of the memory to implement a remote data transmission and monitoring method for a UAV based on a hydrogen power module, such as Figure 1 As shown, the method comprises the following steps:

[0040] Step S101, collecting signal-to-noise ratio data at each sampling moment when the hydrogen-powered UAV performs remote data transmission, and composing the signal-to-noise ratio data within a preset time range into a signal-to-noise ratio data sequence.

[0041] Hydrogen-powered drones use hydrogen fuel cells as their power system, which have the advantages of long endurance and no vibration and noise of battery power, and are suitable for long-term or long-distance data collection and transmission tasks. However, during the data transmission process, as the distance between the hydrogen-powered drone and the transmission receiving end increases, the signal will gradually attenuate, especially in environments such as mountainous areas, forests or cities where radio long-distance transmission signals are susceptible to interference, data transmission will be interfered, affecting the efficiency of data transmission.

[0042] Usually, for long-distance transmission of data, it is necessary to select a suitable data transmission frequency. The existing technology usually uses the mean of the historical signal-to-noise ratio data of the hydrogen-powered drone channel to measure the channel quality for the frequency selection of long-distance transmission of drone data. If the mean of the signal-to-noise ratio data is low, low-frequency transmission is directly used, and if the mean of the signal-to-noise ratio data is high, high-frequency transmission is used. However, the dynamic adjustment capability of the data transmission frequency selected by the existing technology is poor, and the channel quality evaluated based on the mean of the signal-to-noise ratio data is easily affected by obstructions. For example, when a hydrogen-powered drone briefly passes through an obstruction such as a high-rise building, there is a large signal-to-noise ratio fluctuation, which causes the real quality of the channel to be lowered, so that the mean of the historical signal-to-noise ratio data cannot accurately represent the real quality of the recent hydrogen-powered drone channel, and thus makes it impossible to select a suitable data transmission frequency, reducing the efficiency of data transmission.

[0043] Therefore, this embodiment monitors the signal-to-noise ratio data of the hydrogen-powered UAV for remote data transmission, detects abnormal signal-to-noise ratio data within a preset time range, and obtains the true quality of the channel in which the hydrogen-powered UAV is performing remote data transmission by analyzing the distribution characteristics of the abnormal signal-to-noise ratio data, thereby selecting the transmission frequency that best matches the current true state of the hydrogen-powered UAV, and improving the rate and noise resistance of the hydrogen-powered UAV's remote data transmission.

[0044] When a hydrogen-powered drone performs a long-distance mission, it transmits the collected data to a signal receiving end, which monitors the signal-to-noise ratio data during the long-distance data transmission in real time. In this embodiment, the monitoring and collection frequency of the signal-to-noise ratio data is 10 Hz, and the preset time range is 1 minute. The signal-to-noise ratio data collected within 1 minute constitute a signal-to-noise ratio data sequence. There is no limitation here, and it can be set according to the specific implementation scenario.

[0045] Step S102, for any signal-to-noise ratio data in the signal-to-noise ratio data sequence, a target window of a preset size is constructed with the any signal-to-noise ratio data as the center, and the abnormality degree of the any signal-to-noise ratio data is obtained according to the data difference between the any signal-to-noise ratio data and the signal-to-noise ratio data in the target window.

[0046] During the long-distance data transmission process of hydrogen-powered UAVs, there may be interference from factors such as weather environment and building obstructions in the data transmission path. When the long-distance data transmission of the hydrogen-powered UAV is interfered with, it will affect the signal-to-noise ratio data detected by the receiving end, causing the signal-to-noise ratio data to be abnormal. Therefore, it is possible to determine whether the channel where the hydrogen-powered UAV is performing long-distance data transmission is interfered with by detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, and then select the transmission frequency of the hydrogen-powered UAV for long-distance data transmission based on the interference of the channel where the long-distance data transmission is located.

[0047] When the hydrogen-powered drone is disturbed during long-distance data transmission, the signal-to-noise ratio data monitored by the receiving end will be abnormal, that is, the signal-to-noise ratio data will change to a large extent in a short period of time, especially a drastic change that drops rapidly in a short period of time. Therefore, for any signal-to-noise ratio data in the signal-to-noise ratio data sequence, it is recorded as the target data, and a target window with a preset size of 9 (i.e., including 9 signal-to-noise ratio data) is established with the target data as the center. There is no restriction here, and it can be set according to the specific implementation scenario. The abnormality of the target data can be obtained according to the data difference between the target data and other data in the target window. According to the abnormality of the target data, it is judged whether the target data is disturbed, and then according to the abnormality of all signal-to-noise ratio data in the signal-to-noise ratio data sequence, the interference of the channel where the hydrogen-powered drone is located when performing long-distance data transmission is judged.

[0048] Among them, according to the data difference between the target data and other data in the target window, the method of obtaining the abnormality degree of the target data is as follows:

[0049] Respectively obtaining the absolute values ​​of the differences between any one of the signal-to-noise ratio data and each of the signal-to-noise ratio data except the any one of the signal-to-noise ratio data in the target window, and correspondingly obtaining a first mean of the absolute values ​​of the differences, and performing normalization processing on the first mean of the absolute values ​​of the differences to obtain the overall deviation degree of the any one of the signal-to-noise ratio data;

[0050] Obtaining a first absolute value of a difference between any signal-to-noise ratio data and a previous signal-to-noise ratio data of any signal-to-noise ratio data, obtaining a second absolute value of a difference between any signal-to-noise ratio data and a next signal-to-noise ratio data of any signal-to-noise ratio data, calculating a first addition result of the first absolute value of the difference and the second absolute value of the difference, and performing normalization processing on the first addition result to obtain an instantaneous deviation degree of any signal-to-noise ratio data;

[0051] The abnormality degree of any signal-to-noise ratio data is obtained according to the sum of the overall deviation degree and the instantaneous deviation degree of any signal-to-noise ratio data.

[0052] In one implementation, taking the qth signal-to-noise ratio data in the signal-to-noise ratio data sequence as an example, the qth signal-to-noise ratio data is recorded as the target data, and the abnormality degree of the target data is calculated:

[0053]

[0054] in, is the abnormality degree of the target data; q is the sequence number of the signal-to-noise ratio data in the signal-to-noise ratio data sequence; is the target data (i.e., the qth signal-to-noise ratio data in the signal-to-noise ratio data sequence); is the i-th signal-to-noise ratio data excluding the target data in the target window; i is the sequence number of the signal-to-noise ratio data excluding the target data in the target window; m is the number of signal-to-noise ratio data excluding the target data in the target window; is the previous signal-to-noise ratio data of the target data in the target window; is the next signal-to-noise ratio data of the target data in the target window; norm() is the normalization function; | | is the absolute value symbol.

[0055] It should be noted that is the overall deviation degree of the target data, indicating the overall deviation degree of the target data from other signal-to-noise ratio data in the target window. The larger it is, the more the target data deviates from other signal-to-noise ratio data in the target window, the greater the overall difference between the target data and other signal-to-noise ratio data in the target window, the more likely the target data is abnormal, and the greater the degree of abnormality of the target data; is the instantaneous deviation degree of the target data, indicating the difference degree between the target data and its adjacent data. The larger it is, the greater the difference between the target data and its adjacent signal-to-noise ratio data, the more drastic the change of the target data, the more likely the target data is abnormal, and the greater the degree of abnormality of the target data.

[0056] At this point, the abnormality degree of the target data is obtained.

[0057] Step S103, obtaining the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence, and detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence.

[0058] The greater the abnormality of the target data, the more likely it is that the target data is caused by interference. Further, according to the method for obtaining the abnormality of the target data, the abnormality of all the signal-to-noise ratio data in the signal-to-noise ratio data sequence is obtained, and according to the abnormality of all the signal-to-noise ratio data in the signal-to-noise ratio data sequence, it is detected whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, and then the interference of the channel where the hydrogen-powered UAV is located during long-distance data transmission is determined.

[0059] Specifically, the signal-to-noise ratio threshold is set to 0.6, which is not limited here and can be set according to the specific implementation scenario. If the abnormality degree of any signal-to-noise ratio data is greater than or equal to 0.6, then the any signal-to-noise ratio data is confirmed to be abnormal signal-to-noise ratio data, and according to the abnormal signal-to-noise ratio data judgment method, it is judged whether all the signal-to-noise ratio data in the signal-to-noise ratio data sequence are abnormal signal-to-noise ratio data.

[0060] When there is no abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, it is confirmed that the channel where the hydrogen-powered drone performs long-distance data transmission is not interfered with or the interference degree is extremely low. At this time, the target data transmission frequency of the hydrogen-powered drone in the next 1 minute is set to the first frequency, i.e., 5.8 GHz. There is no restriction here and it can be set according to the specific implementation scenario. The data transmission speed at this frequency is faster. Although it is easily blocked by obstacles and has poor penetration ability, it can obtain more stable high-speed transmission under the premise of accurate channel quality evaluation results.

[0061] When there is at least one abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, it is confirmed that the channel where the hydrogen-powered UAV performs long-distance data transmission is interfered with, and it is necessary to further analyze the degree of interference with the channel where the long-distance data transmission is located, and judge the true quality of the channel according to the degree of interference with the channel where the long-distance data transmission is located, and then select the transmission frequency of the hydrogen-powered UAV for long-distance data transmission according to the true quality of the channel.

[0062] Step S104: when there is at least one abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, obtaining a signal transmission quality abnormal value within the preset time range according to distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence.

[0063] Since the actual quality of the channel is affected by different factors in the data transmission path, when a hydrogen-powered drone passes through an obstruction such as a building at a short distance, the signal will be strongly blocked during the short time it passes, resulting in a large signal-to-noise ratio fluctuation, such as Figure 2 The following is an example of signal-to-noise ratio fluctuations when a hydrogen-powered drone briefly passes through an obstruction. Figure 2 The horizontal axis is the sampling time T when the signal receiving end monitors the remote data transmission, and the vertical axis is the signal-to-noise ratio data db monitored by the signal receiving end. Although it only affects the signal-to-noise ratio data at a few sampling times when passing through the obstruction, in the overall calculation of the signal-to-noise ratio mean, a lower signal-to-noise ratio mean will be obtained, and then a lower data transmission frequency will be obtained in the frequency selection. The lower the data transmission frequency, the lower the transmission rate, resulting in normal channel quality but low transmission rate during most transmission periods. The assessment of channel quality is incomplete and inaccurate, wasting the channel transmission rate resources. When the transmission path between the drone and the receiving end is affected by the environment such as rain, snow, dust and other weather conditions, rain, dust and other particles will refract and scatter the signal in the transmission path, causing the signal-to-noise ratio to fluctuate abnormally; on the other hand, if the flight area of ​​the drone is between densely populated buildings, it is easily affected by continuous building obstruction, and the signal-to-noise ratio will also show continuous fluctuation characteristics, such as Figure 3 The following is an example of signal-to-noise ratio fluctuations of hydrogen-powered drones under the interference of weather conditions or dense buildings. Figure 3The horizontal axis is the sampling time T when the signal receiving end monitors the remote data transmission, and the vertical axis is the signal-to-noise ratio data db monitored by the signal receiving end. The fluctuation of the signal-to-noise ratio data caused by weather environment or dense buildings is moderate or low, but in the process of hydrogen-powered drones transmitting signals remotely, it occupies a large proportion of the time. In the process of calculating the overall mean signal-to-noise ratio, if only the mean signal-to-noise ratio is used for calculation, a higher mean signal-to-noise ratio may be obtained, and then a higher data transmission frequency is obtained in the frequency selection. The higher the data transmission frequency, the lower the transmission quality, resulting in more obstruction and interference to the signal during most of the transmission time.

[0064] Therefore, when at least one abnormal signal-to-noise ratio data is detected in the signal-to-noise ratio data sequence, it is necessary to further analyze whether the interference to the long-distance data transmission is caused by briefly passing through an obstruction or by environmental influences or passing through a building complex, and then judge the true quality of the channel where the long-distance data transmission is located. According to the true quality of the channel, select the transmission frequency that best matches the current true state of the hydrogen-powered drone, and improve the rate and noise resistance of the long-distance data transmission of the hydrogen-powered drone.

[0065] Due to the different factors, the fluctuation characteristics of the signal-to-noise ratio data in the signal-to-noise ratio data sequence are different. For example, when the hydrogen-powered drone passes through buildings and other obstructions at a short distance, the signal-to-noise ratio data will fluctuate greatly in a short time, and when it is affected by the environment or passes through a group of buildings, the signal-to-noise ratio data will fluctuate continuously. Therefore, according to the distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the abnormal value of the signal transmission quality during long-distance data transmission within 1 minute can be obtained, and then the real quality of the channel where the long-distance data transmission is carried out within 1 minute can be judged according to the abnormal value of the signal transmission quality, so as to obtain the target transmission frequency of the hydrogen-powered drone in the next 1 minute according to the real quality of the channel within 1 minute.

[0066] Among them, according to the distribution characteristics of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the method for obtaining the abnormal value of signal transmission quality during remote data transmission within a preset time range of 1 minute is as follows:

[0067] Acquire all abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence to form an abnormal signal-to-noise ratio data sequence, acquire the mean of the abnormal signal-to-noise ratio data sequence, and obtain the mean of the abnormal degree of the signal-to-noise ratio data sequence;

[0068] Respectively calculating the absolute values ​​of the differences between the sampling moments corresponding to every two adjacent abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, and obtaining a corresponding second difference absolute value mean, obtaining the inverse of the second difference absolute value mean, and obtaining the degree of aggregation of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0069] Obtaining the number of signal-to-noise ratio data in the signal-to-noise ratio data sequence, recorded as a first number, obtaining the number of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, recorded as a second number, calculating the ratio of the second number to the first number, and obtaining the proportion of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0070] Obtaining a sampling time corresponding to the first abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, recording it as the minimum abnormal sampling time; obtaining a sampling time corresponding to the last abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, recording it as the maximum abnormal sampling time; calculating the absolute value of the difference between the maximum abnormal sampling time and the minimum abnormal sampling time; calculating the ratio of the absolute value of the difference to the first number, and obtaining the continuity of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0071] The signal transmission quality abnormal value within the preset time range is obtained according to the product of the abnormal degree mean, the aggregation degree of the abnormal signal-to-noise ratio data, the proportion of the abnormal signal-to-noise ratio data, and the persistence degree of the abnormal signal-to-noise ratio data.

[0072] In one implementation, the calculation formula for the signal transmission quality abnormal value is:

[0073]

[0074] Where M is the abnormal value of signal transmission quality; is the mean value of the abnormal degree of the signal-to-noise ratio data sequence (that is, the mean value of the abnormal signal-to-noise ratio data sequence); n is the number of abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence; is the sampling time corresponding to the kth abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence; is the sampling time corresponding to the k+1th abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence; N is the number of signal-to-noise ratio data in the signal-to-noise ratio data sequence; is the sampling time corresponding to the first abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; is the sampling time corresponding to the last abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; k is the sequence number of the abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence; || is the absolute value symbol; 1 is a constant.

[0075] It should be noted that is the mean value of the abnormality of the signal-to-noise ratio data sequence, The larger the value is, the greater the overall abnormality of the signal-to-noise ratio data in the signal-to-noise ratio data sequence is, and the greater the abnormal value of the signal transmission quality within the preset time range corresponding to the signal-to-noise ratio data sequence is; is the degree of aggregation of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, It represents the difference in sampling time corresponding to adjacent abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence. The smaller it is, the smaller the time interval between adjacent abnormal SNR data is, the denser the distribution of abnormal SNR data in the SNR data sequence is, and the larger the abnormal value of signal transmission quality within the preset time range corresponding to the SNR data sequence is; is the proportion of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, The larger the value is, the more abnormal signal-to-noise ratio data there are in the signal-to-noise ratio data sequence, and the larger the abnormal value of signal transmission quality within the preset time range corresponding to the signal-to-noise ratio data sequence is; is the persistence degree of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, The larger it is, the longer the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence lasts, the stronger the persistence of the abnormal signal-to-noise ratio data is, and the more it conforms to the distribution characteristics of the abnormal signal-to-noise ratio data in the weather environment or dense buildings during long-distance data transmission. The larger the signal transmission quality abnormal value within the preset time range corresponding to the signal-to-noise ratio data sequence is.

[0076] At this point, the signal transmission quality abnormal value within the preset time range, ie, 1 minute, corresponding to the signal-to-noise ratio data sequence is obtained.

[0077] Step S105, according to the signal transmission quality abnormal value within the preset time range, obtain the target data transmission frequency of the hydrogen-powered UAV within the future preset time range, and use the target data transmission frequency to perform data transmission of the hydrogen-powered UAV within the future preset time range.

[0078] Furthermore, after obtaining the signal transmission quality abnormal value within 1 minute, the true quality of the channel where remote data transmission is performed within 1 minute is judged based on the signal transmission quality abnormal value within 1 minute, thereby obtaining the target transmission frequency of the hydrogen-powered drone within the next 1 minute based on the true quality of the channel within 1 minute.

[0079] Specifically, the signal transmission quality abnormality threshold is set to 0.5, which is not limited here and can be set according to the specific implementation scenario. If the signal transmission quality abnormality value within 1 minute is less than 0.5, it is confirmed that the channel where the hydrogen-powered drone performs remote data transmission is subject to low interference, and there may be a short-term signal-to-noise ratio abnormality caused by the obstruction of some obstacles such as buildings. At this time, the target data transmission frequency of the hydrogen-powered drone in the next 1 minute is set to the second frequency, i.e., 2.4 GHz. The data transmission speed at this frequency is relatively fast and has certain penetration, and can be stably transmitted while ensuring a certain rate; if the signal transmission quality abnormality value within 1 minute is greater than or equal to 0.5, it is confirmed that the channel where the hydrogen-powered drone performs remote data transmission is subject to high interference, and the hydrogen-powered drone flight environment has abnormal weather environment or long-term dense building obstruction. At this time, the target data transmission frequency of the hydrogen-powered drone in the next 1 minute is set to the third frequency, i.e., 1.3 GHz. The data transmission speed at this frequency is low, but it has strong signal penetration ability, and can be transmitted relatively stably over long distances with interference and obstacles.

[0080] After obtaining the target transmission frequency of the hydrogen-powered drone in the next 1 minute, the target transmission frequency is used as the data transmission frequency between the hydrogen-powered drone and the receiving end in the next 1 minute to transmit data of the hydrogen-powered drone. The target data transmission frequency thus obtained best matches the current real state of the hydrogen-powered drone and is more compatible with the actual environment of the hydrogen-powered drone. It not only improves the rate and anti-noise ability of the hydrogen-powered drone's remote data transmission, but also improves the dynamic adjustment ability of the hydrogen-powered drone during the remote data transmission process.

[0081] The embodiment of the present invention collects signal-to-noise ratio data at each sampling moment when the hydrogen-powered unmanned aerial vehicle performs remote data transmission, and the signal-to-noise ratio data within a preset time range are combined into a signal-to-noise ratio data sequence; for any signal-to-noise ratio data in the signal-to-noise ratio data sequence, a target window of a preset size is constructed with the any signal-to-noise ratio data as the center, and the abnormality degree of the any signal-to-noise ratio data is obtained according to the data difference between the any signal-to-noise ratio data and the signal-to-noise ratio data in the target window; the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence is obtained, and whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence is detected according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence; when there is at least one abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the signal transmission quality abnormal value within the preset time range is obtained according to the distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; according to the signal transmission quality abnormal value within the preset time range, the target data transmission frequency of the hydrogen-powered unmanned aerial vehicle within the future preset time range is obtained, and the data transmission of the hydrogen-powered unmanned aerial vehicle within the future preset time range is performed using the target data transmission frequency. Among them, by obtaining the abnormal degree of signal-to-noise ratio data, it is detected whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence. When abnormal signal-to-noise ratio data exists, the abnormal value of signal transmission quality within a preset time range is obtained according to the distribution characteristics of the abnormal signal-to-noise ratio data, and the quality of remote data transmission within the preset time range is accurately evaluated, thereby obtaining the target data transmission frequency of the hydrogen-powered UAV within the future preset time range, thereby improving the rate and anti-noise ability of the hydrogen-powered UAV in remote data transmission. In addition, the present invention uses the historical signal-to-noise ratio data of the hydrogen-powered UAV when performing remote data transmission to obtain the frequency of remote data transmission by the hydrogen-powered UAV within the future time range, thereby improving the dynamic adjustment ability of the hydrogen-powered UAV during remote data transmission.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A remote data transmission and monitoring system for unmanned aerial vehicles based on a hydrogen power module, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the following method is implemented: Collect the signal-to-noise ratio data at each sampling moment when the hydrogen-powered UAV is performing long-distance data transmission, and compose the signal-to-noise ratio data within a preset time range into a signal-to-noise ratio data sequence; For any signal-to-noise ratio data in the signal-to-noise ratio data sequence, a target window of a preset size is constructed with the any signal-to-noise ratio data as the center, and the abnormality degree of the any signal-to-noise ratio data is obtained according to the data difference between the any signal-to-noise ratio data and the signal-to-noise ratio data in the target window; Acquiring the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence, and detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence; When there is at least one abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, obtaining a signal transmission quality abnormal value within the preset time range according to distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; According to the signal transmission quality abnormal value within the preset time range, the target data transmission frequency of the hydrogen-powered UAV within the future preset time range is obtained, and the target data transmission frequency is used to perform data transmission of the hydrogen-powered UAV within the future preset time range.

2. The UAV remote data transmission and monitoring system based on hydrogen power module according to claim 1 is characterized in that: The obtaining the abnormality degree of any signal-to-noise ratio data according to the data difference between any signal-to-noise ratio data and the signal-to-noise ratio data in the target window comprises: Respectively obtaining the absolute values ​​of the differences between any one of the signal-to-noise ratio data and each of the signal-to-noise ratio data except the any one of the signal-to-noise ratio data in the target window, and correspondingly obtaining a first mean of the absolute values ​​of the differences, and performing normalization processing on the first mean of the absolute values ​​of the differences to obtain the overall deviation degree of the any one of the signal-to-noise ratio data; Obtaining a first absolute value of a difference between any signal-to-noise ratio data and a previous signal-to-noise ratio data of any signal-to-noise ratio data, obtaining a second absolute value of a difference between any signal-to-noise ratio data and a next signal-to-noise ratio data of any signal-to-noise ratio data, calculating a first addition result of the first absolute value of the difference and the second absolute value of the difference, and performing normalization processing on the first addition result to obtain an instantaneous deviation degree of any signal-to-noise ratio data; The abnormality degree of any signal-to-noise ratio data is obtained according to the sum of the overall deviation degree and the instantaneous deviation degree of any signal-to-noise ratio data.

3. The UAV remote data transmission and monitoring system based on hydrogen power module according to claim 1 is characterized in that: The detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence comprises: If the abnormal degree of any signal-to-noise ratio data is greater than or equal to a preset signal-to-noise ratio threshold, the any signal-to-noise ratio data is confirmed to be abnormal signal-to-noise ratio data.

4. The UAV remote data transmission and monitoring system based on hydrogen power module according to claim 3 is characterized in that: The obtaining, according to the distribution characteristics of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, abnormal values ​​of signal transmission quality within the preset time range comprises: Acquire all abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence to form an abnormal signal-to-noise ratio data sequence, acquire the mean of the abnormal signal-to-noise ratio data sequence, and obtain the mean of the abnormal degree of the signal-to-noise ratio data sequence; Respectively calculating the absolute values ​​of the differences between the sampling moments corresponding to every two adjacent abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, and obtaining a corresponding second difference absolute value mean, obtaining the inverse of the second difference absolute value mean, and obtaining the degree of aggregation of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; Obtaining the number of signal-to-noise ratio data in the signal-to-noise ratio data sequence, recorded as a first number, obtaining the number of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, recorded as a second number, calculating the ratio of the second number to the first number, and obtaining the proportion of abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; Obtaining a sampling time corresponding to the first abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, recording it as the minimum abnormal sampling time; obtaining a sampling time corresponding to the last abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, recording it as the maximum abnormal sampling time; calculating the absolute value of the difference between the maximum abnormal sampling time and the minimum abnormal sampling time; calculating the ratio of the absolute value of the difference to the first number, and obtaining the continuity of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence; The signal transmission quality abnormal value within the preset time range is obtained according to the product of the abnormal degree mean, the aggregation degree of the abnormal signal-to-noise ratio data, the proportion of the abnormal signal-to-noise ratio data, and the persistence degree of the abnormal signal-to-noise ratio data.

5. The UAV remote data transmission and monitoring system based on hydrogen power module according to claim 1 is characterized in that: The step of obtaining the target data transmission frequency of the hydrogen-powered UAV within a future preset time range according to the abnormal value of the signal transmission quality within the preset time range includes: Setting the data transmission frequency of the hydrogen-powered drone to include a first frequency, a second frequency, and a third frequency, wherein the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency; If the signal transmission quality abnormal value is greater than or equal to the preset signal transmission quality threshold, the target data transmission frequency of the hydrogen-powered UAV within the future preset time range is selected as the third frequency; If the signal transmission quality threshold is less than the signal transmission quality threshold, the target data transmission frequency of the hydrogen-powered UAV within the future preset time range is selected as the second frequency.

6. The UAV remote data transmission and monitoring system based on hydrogen power module according to claim 5 is characterized in that: After detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the abnormality degree of all signal-to-noise ratio data in the signal-to-noise ratio data sequence, the method further includes: When there is no abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the target data transmission frequency of the hydrogen-powered drone within the future preset time range is selected as the first frequency.

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