UAV Remote Data Transmission and Monitoring System Based on Hydrogen Energy Power Module

Abstract

The present invention relates to the technical field of data transmission, and particularly to a remote data transmission and monitoring system for a hydrogen-powered unmanned aerial vehicle based on a hydrogen energy power module. The system includes a processor and a memory, and the processor executes a computer program in the memory to implement the following steps: collecting signal-to-noise ratio data during the remote data transmission of the hydrogen-powered unmanned aerial vehicle to form a signal-to-noise ratio data sequence; for any signal-to-noise ratio data, obtaining the degree of abnormality of any signal-to-noise ratio data; detecting whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence according to the degrees of abnormality of all signal-to-noise ratio data; when there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, obtaining an abnormal value of signal transmission quality; according to the abnormal value of signal transmission quality, obtaining the target data transmission frequency of the hydrogen-powered unmanned aerial vehicle, and using the target data transmission frequency for data transmission of the hydrogen-powered unmanned aerial vehicle, thereby improving the data transmission rate and anti-noise ability of the hydrogen-powered unmanned aerial vehicle during remote data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and particularly to a remote data transmission and monitoring system for an unmanned aerial vehicle (UAV) based on a hydrogen energy power module. Background Art

[0002] Hydrogen-powered UAVs use hydrogen fuel cells as the power system, and have the advantages of long endurance and no vibration and noise of battery power. At present, the national standard for hydrogen fuel cell power generation systems for UAVs has been issued in China. The endurance of hydrogen-powered UAVs can exceed three hours, and the coverage range can reach 200 km, which is suitable for industrial applications, night emergency lighting, search and rescue, inspection, mapping, and unmanned cargo transportation. Due to the high energy density of hydrogen fuel cells, compared with traditional electric UAVs, they can provide longer endurance and farther flight distance. This advantage enables hydrogen-powered UAVs to cover a wider area, especially suitable for long-term or long-distance data collection and transmission tasks. However, as the distance between the UAV and the transmission and reception end increases, the signal will gradually attenuate, which poses a challenge to real-time data transmission, especially in environments where radio remote transmission signals are easily interfered with, such as mountains, forests, or cities.

[0003] Generally, for remote data transmission, it is necessary to select an appropriate data transmission frequency. The data transmission frequency includes high-frequency transmission and low-frequency transmission. The signals of low-frequency transmission are usually less interfered with, especially during long-distance transmission, because the wavelength of low-frequency signals is longer and can bypass obstacles, but the transmission speed is slower. The signals of high-frequency transmission are more sensitive to interference, especially in complex environments and are easily affected, but the transmission speed is faster. The prior art usually uses the mean value of the historical signal-to-noise ratio (SNR) data of the hydrogen-powered UAV channel to measure the channel quality for the frequency selection of UAV remote data transmission. If the mean value of the SNR data is low, low-frequency transmission is directly used, and if the mean value of the SNR data is high, high-frequency transmission is used. However, the prior art has poor dynamic adjustment ability for the selected data transmission frequency, and the channel quality evaluated according to the mean value of the SNR data is easily interfered by obstacles. For example, when a hydrogen-powered UAV passes briefly through an obstacle such as a high-rise building, there is a large SNR fluctuation, resulting in the true quality of the channel being pulled down, so that the mean value of the historical SNR data cannot accurately represent the true quality of the hydrogen-powered UAV channel in the near future, thus making it impossible to select an appropriate data transmission frequency and reducing the data transmission efficiency.

[0004] Therefore, how to accurately obtain the true quality of the UAV channel and timely adjust the data transmission frequency 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 remote data transmission and monitoring system for an unmanned aerial vehicle (UAV) based on a hydrogen energy power module to solve the problem of how to accurately obtain the true quality of the UAV channel and timely adjust the data transmission frequency.

[0006] An embodiment of the present invention provides a remote data transmission and monitoring system for an unmanned aerial vehicle (UAV) based on a hydrogen energy power module, including 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 (SNR) data at each sampling moment when the hydrogen-powered UAV performs remote data transmission, and form an SNR data sequence with the SNR data within a preset time range;

[0008] For any SNR data in the SNR data sequence, with the any SNR data as the center, construct a target window of a preset size, and obtain the abnormality degree of the any SNR data according to the data difference between the any SNR data and the SNR data in the target window;

[0009] Obtain the abnormality degrees of all SNR data in the SNR data sequence, and detect whether there is abnormal SNR data in the SNR data sequence according to the abnormality degrees of all SNR data in the SNR data sequence;

[0010] When there is at least one abnormal SNR data in the SNR data sequence, obtain the signal transmission quality anomaly value within the preset time range according to the distribution characteristics of the abnormal SNR data in the SNR data sequence;

[0011] Obtain the target data transmission frequency of the hydrogen-powered UAV within a future preset time range according to the signal transmission quality anomaly value within the 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.

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

[0013] Respectively obtain the absolute value of the difference between the any SNR data and each SNR data other than the any SNR data in the target window, correspondingly obtain the average value of the first absolute value of the difference, and perform normalization processing on the average value of the first absolute value of the difference to obtain the overall deviation degree of the any SNR data;

[0014] Obtain the absolute value of the first difference between any one of the signal-to-noise ratio data and the previous signal-to-noise ratio data of any one of the signal-to-noise ratio data, obtain the absolute value of the second difference between any one of the signal-to-noise ratio data and the next signal-to-noise ratio data of any one of the signal-to-noise ratio data, calculate the first addition result of the absolute value of the first difference and the absolute value of the second difference, and perform normalization processing on the first addition result to obtain the instantaneous deviation degree of any one of the signal-to-noise ratio data;

[0015] According to the addition result of the overall deviation degree and the instantaneous deviation degree of any one of the signal-to-noise ratio data, obtain the abnormality degree of any one of the 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 degrees of all the signal-to-noise ratio data in the signal-to-noise ratio data sequence includes:

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

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

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

[0020] Calculate the absolute value of the difference between the sampling times corresponding to every two adjacent abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence respectively, correspondingly obtain the mean value of the absolute value of the second difference, and obtain the aggregation degree of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence by taking the reciprocal of the mean value of the absolute value of the second difference;

[0021] Obtain the number of the signal-to-noise ratio data in the signal-to-noise ratio data sequence, denoted as the first number, obtain the number of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, denoted as the second number, and calculate the ratio of the second number to the first number to obtain the proportion of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0022] Obtain the sampling time corresponding to the first abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, denoted as the minimum abnormal sampling time, obtain the sampling time corresponding to the last abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence, denoted as the maximum abnormal sampling time, calculate the absolute value of the difference between the maximum abnormal sampling time and the minimum abnormal sampling time, and calculate the ratio of the absolute value of the difference to the first number to obtain the duration degree of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0023] Obtain the signal transmission quality anomaly value within the preset time range according to the product among the average anomaly degree, the aggregation degree of the anomaly SNR data, the proportion of the anomaly SNR data, and the duration of the anomaly SNR data.

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

[0025] Set the data transmission frequency of the hydrogen-powered drone to include a first frequency, a second frequency, and a third frequency, where 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 anomaly value is greater than or equal to a preset signal transmission quality threshold, select the target data transmission frequency of the hydrogen-powered drone within the future preset time range as the third frequency;

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

[0028] Preferably, after detecting whether there is abnormal SNR data in the SNR data sequence according to the abnormal degree of all SNR data in the SNR data sequence, it further includes

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

[0030] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0031] The present invention collects the signal-to-noise ratio data at each sampling moment when a hydrogen-powered unmanned aerial vehicle (UAV) performs remote data transmission, and forms the signal-to-noise ratio data sequence from 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 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 the any signal-to-noise ratio data is obtained; the abnormality degrees of all the signal-to-noise ratio data in the signal-to-noise ratio data sequence are 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 degrees of all the 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, according to the distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, a signal transmission quality abnormality value within the preset time range is obtained; according to the signal transmission quality abnormality value within the preset time range, a target data transmission frequency of the hydrogen-powered UAV within a future preset time range is obtained, and the data transmission of the hydrogen-powered UAV within the future preset time range is performed using the target data transmission frequency. Among them, by obtaining the abnormality degree of the 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 there is abnormal signal-to-noise ratio data, according to the distribution characteristics of the abnormal signal-to-noise ratio data, a signal transmission quality abnormality value within the preset time range is obtained, so as to accurately evaluate the quality of remote data transmission within the preset time range. Furthermore, the target data transmission frequency of the hydrogen-powered UAV within a future preset time range is obtained, which improves the data transmission rate and anti-noise ability of the hydrogen-powered UAV during remote data transmission. Moreover, the present invention uses the historical signal-to-noise ratio data of the hydrogen-powered UAV during remote data transmission to obtain the data transmission frequency of the hydrogen-powered UAV within a future time range, which improves the dynamic adjustment ability of the hydrogen-powered UAV during remote data transmission. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

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

[0034] Figure 2 is an example diagram of the signal-to-noise ratio data fluctuation when a hydrogen-powered UAV briefly passes through an obstacle;

[0035] Figure 3It is an example diagram of the signal-to-noise ratio data fluctuation of a hydrogen-powered drone provided in the first embodiment of the present invention under the interference of weather environment or dense buildings. Detailed implementation manners

[0036] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation to the present disclosure.

[0037] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above accompanying drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are only 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, it will be described below through specific embodiments.

[0039] The embodiment of the present invention provides a remote data transmission and monitoring system for a drone based on a hydrogen energy power module, including a processor and a memory. The processor executes the computer program in the memory to implement a method for remote data transmission and monitoring of a drone based on a hydrogen energy power module, as Figure 1 shown. This method includes the following steps:

[0040] Step S101, collect the signal-to-noise ratio data at each sampling moment when the hydrogen-powered drone is performing remote data transmission, and form a signal-to-noise ratio data sequence with the signal-to-noise ratio data within a preset time range.

[0041] The hydrogen-powered drone uses a hydrogen fuel cell as the power system, and has the advantages of long endurance and no vibration and noise of the battery power, and is suitable for long-time 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 where radio remote transmission signals are easily interfered with, such as mountains, forests or cities, the data transmission will be interfered with, affecting the efficiency of data transmission.

[0042] Generally, it is necessary to select an appropriate data transmission frequency for remote data transmission. In the prior art, the mean value of the historical signal-to-noise ratio (SNR) data of the hydrogen-powered drone channel is usually used to measure the channel quality when selecting the data transmission frequency for the hydrogen-powered drone. If the mean value of the SNR data is low, low-frequency transmission is directly used; if the mean value of the SNR data is high, high-frequency transmission is used. However, the prior art has poor dynamic adjustment ability for the selected data transmission frequency, and the channel quality evaluated based on the mean value of the SNR data is easily affected by obstacles. For example, when the hydrogen-powered drone briefly passes by obstacles such as high-rise buildings, there are large SNR fluctuations, resulting in a decrease in the true quality of the channel, so that the mean value of the historical SNR data cannot accurately represent the true quality of the hydrogen-powered drone channel in the near future, and thus it is impossible to select an appropriate data transmission frequency, reducing the data transmission efficiency.

[0043] Therefore, in this embodiment, by monitoring the SNR data of the hydrogen-powered drone for remote data transmission, detecting the abnormal SNR data within a preset time range, and analyzing the distribution characteristics of the abnormal SNR data, the true quality of the channel where the hydrogen-powered drone is located during remote data transmission is obtained, so as to select the transmission frequency that best conforms to the current true state of the hydrogen-powered drone, improving the data transmission rate and anti-noise ability of the hydrogen-powered drone for remote data transmission.

[0044] During the process of the hydrogen-powered drone performing a remote mission, the collected data will be transmitted to the signal receiving end. The signal receiving end monitors the SNR data in real time during remote data transmission. In this embodiment, the monitoring and acquisition frequency of the SNR data is 10 Hz, the preset time range is 1 minute, and the SNR data within 1 minute is collected to form an SNR data sequence. There is no limitation here and it can be set according to the specific implementation scenario.

[0045] Step S102, for any SNR data in the SNR data sequence, with the any SNR data as the center, construct a target window of a preset size, and obtain the abnormal degree of the any SNR data according to the data difference between the any SNR data and the SNR data in the target window.

[0046] During the process of the hydrogen-powered drone performing remote data transmission, there may be interference factors such as weather conditions and building blocks in the data transmission path. When the hydrogen-powered drone is interfered during remote data transmission, it will affect the SNR data detected by the receiving end, causing the SNR data to be abnormal. Therefore, it is possible to judge whether the channel where the hydrogen-powered drone is located during remote data transmission is interfered by detecting whether there is abnormal SNR data in the SNR data sequence, and then select the transmission frequency for the hydrogen-powered drone to perform remote data transmission according to the interference situation of the channel where the remote data transmission is located.

[0047] When the hydrogen-powered drone is interfered during remote data transmission, the signal-to-noise ratio data monitored at the receiving end will show anomalies, that is, the signal-to-noise ratio data will change significantly within a short period of time, especially the drastic change of rapid decline within 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. Taking the target data as the center, a target window with a preset size of 9 (that is, including 9 signal-to-noise ratio data) is established. There is no limit here and it can be set according to the specific implementation scenario. According to the data difference between the target data and other data in the target window, the anomaly degree of the target data can be obtained. According to the anomaly degree of the target data, it can be judged whether the target data is interfered, and then according to the anomaly degrees of all the signal-to-noise ratio data in the signal-to-noise ratio data sequence, the interference situation of the channel where the hydrogen-powered drone is located during remote data transmission can be judged.

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

[0049] The absolute value of the difference between any signal-to-noise ratio data and each signal-to-noise ratio data other than the any signal-to-noise ratio data in the target window is obtained respectively, and the average value of the first absolute value of the difference is correspondingly obtained. The average value of the first absolute value of the difference is normalized to obtain the overall deviation degree of the any signal-to-noise ratio data;

[0050] The first absolute value of the difference between any signal-to-noise ratio data and the previous signal-to-noise ratio data of the any signal-to-noise ratio data is obtained, and the second absolute value of the difference between any signal-to-noise ratio data and the next signal-to-noise ratio data of the any signal-to-noise ratio data is obtained. The first addition result of the first absolute value of the difference and the second absolute value of the difference is calculated, and the first addition result is normalized to obtain the instantaneous deviation degree of the any signal-to-noise ratio data;

[0051] According to the addition result of the overall deviation degree and the instantaneous deviation degree of the any signal-to-noise ratio data, the anomaly degree of the any signal-to-noise ratio data is obtained.

[0052] In an embodiment, taking the q-th signal-to-noise ratio data in the signal-to-noise ratio data sequence as an example, the q-th signal-to-noise ratio data is recorded as the target data, and the anomaly degree of the target data is calculated:

[0053]

[0054] Among them, is the anomaly degree of the target data; q is the serial number of the signal-to-noise ratio data in the signal-to-noise ratio data sequence; is the target data (that is, the q-th 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 remote data transmission is not interfered or is very slightly interfered. At this time, set the target data transmission frequency of the hydrogen-powered drone within the next 1 minute to the first frequency, i.e., 5.8 GHz. There is no limitation here and it can be set according to specific implementation scenarios. The data transmission speed is faster at this frequency. Although it is easily blocked by obstacles and has poor penetration ability, on the premise of an accurate channel quality assessment result, a more stable high-speed transmission can be obtained.

[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 drone performs remote data transmission is interfered. It is necessary to further analyze the degree of interference of the channel where the remote data transmission is located, judge the true quality of the channel according to the degree of interference of the channel where the remote data transmission is located, and then select the transmission frequency for the hydrogen-powered drone to perform remote 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, obtain the signal transmission quality anomaly value within the preset time range according to the distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence.

[0063] Since the true quality of the channel is affected by different factors in the data transmission path, when the hydrogen-powered drone passes by obstacles such as buildings in a short time and at a short distance, the signal will be strongly blocked during the short passing time, resulting in a large signal-to-noise ratio fluctuation. For example, Figure 2 As shown, it is an example diagram of the signal-to-noise ratio data fluctuation when the hydrogen-powered drone briefly passes by an obstacle. Figure 2 In the figure, 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 several sampling times when passing by the obstacle, in the process of overall calculating the signal-to-noise ratio mean value, a lower signal-to-noise ratio mean value 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 a situation where the quality of the channel is normal but the transmission rate is low during most transmission periods, and the evaluation of the channel quality is not comprehensive and accurate, wasting the transmission rate resources of the channel. On the other hand, when the transmission path between the drone and the receiving end is affected by the environment such as rainfall, snowfall, sand and dust, etc., particles such as rain and sand will refract and scatter the signals in the transmission path, resulting in continuous abnormal fluctuations in the signal-to-noise ratio; on the other hand, if the flight area where the drone is located is between relatively dense building clusters, it is easily affected by continuous building blockages, and the signal-to-noise ratio will also show continuous fluctuation characteristics. For example, Figure 3 As shown, it is an example diagram of the signal-to-noise ratio data fluctuation of the hydrogen-powered drone under the interference of weather environment or dense buildings. Figure 3The horizontal axis is the sampling time T during the remote data transmission monitored by the signal receiving end, and the vertical axis is the signal-to-noise ratio data db monitored by the signal receiving end. The fluctuation degree of the signal-to-noise ratio data caused by weather conditions or dense buildings is moderate or low. However, during the remote transmission of signals by the hydrogen-powered drone, it occupies a relatively large proportion of the time. In the process of calculating the overall signal-to-noise ratio mean, if only the signal-to-noise ratio mean is used for calculation, a relatively high signal-to-noise ratio mean may be obtained, and then a relatively high data transmission frequency may be obtained in frequency selection. The higher the data transmission frequency, the lower the transmission quality, resulting in more signal blockage and interference 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 suffered by the remote data transmission is caused by a temporary passage through an obstacle or by environmental influence or passing through a building complex, and then judge the true quality of the channel where the remote 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 to improve the data transmission rate and anti-noise ability of the hydrogen-powered drone for remote data transmission.

[0065] Due to the different fluctuation characteristics of the signal-to-noise ratio data in the signal-to-noise ratio data sequence under the influence of different factors. For example, when the hydrogen-powered drone passes by an obstacle such as a building in a short time and at a short distance, the signal-to-noise ratio data will fluctuate greatly in a short time, while when affected by the environment or passing through a building complex, the signal-to-noise ratio data will show continuous fluctuations. Therefore, according to the distribution characteristics of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence, the signal transmission quality abnormal value during the remote data transmission within 1 minute can be obtained, and then the true quality of the channel where the remote data transmission is located within 1 minute can be judged according to the signal transmission quality abnormal value. Thus, according to the true quality of the channel within 1 minute, the target transmission frequency of the hydrogen-powered drone within the next 1 minute can be obtained.

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

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

[0068] Calculate the absolute value of the difference between the sampling times corresponding to each two adjacent abnormal signal-to-noise ratio data in the abnormal signal-to-noise ratio data sequence respectively, and correspondingly obtain the mean of the second absolute value of the difference. Obtain the reciprocal of the mean of the second absolute value of the difference to obtain the aggregation degree of the abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence;

[0069] Obtain the number of SNR data in the SNR data sequence, denoted as the first quantity, obtain the number of abnormal SNR data in the SNR data sequence, denoted as the second quantity, calculate the ratio of the second quantity to the first quantity to obtain the proportion of abnormal SNR data in the SNR data sequence;

[0070] Obtain the sampling moment corresponding to the first abnormal SNR data in the abnormal SNR data sequence, denoted as the minimum abnormal sampling moment, obtain the sampling moment corresponding to the last abnormal SNR data in the abnormal SNR data sequence, denoted as the maximum abnormal sampling moment, calculate the absolute value of the difference between the maximum abnormal sampling moment and the minimum abnormal sampling moment, and calculate the ratio of the absolute value of the difference to the first quantity to obtain the duration of the abnormal SNR data in the SNR data sequence;

[0071] According to the product of the average abnormal degree, the aggregation degree of the abnormal SNR data, the proportion of the abnormal SNR data, and the duration of the abnormal SNR data, obtain the signal transmission quality anomaly value within the preset time range.

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

[0073]

[0074] where M is the signal transmission quality anomaly value; is the average abnormal degree of the SNR data sequence (i.e., the average of the abnormal SNR data sequence); n is the number of abnormal SNR data in the abnormal SNR data sequence; is the sampling moment corresponding to the k-th abnormal SNR data in the abnormal SNR data sequence; is the sampling moment corresponding to the (k + 1)-th abnormal SNR data in the abnormal SNR data sequence; N is the number of SNR data in the SNR data sequence; is the sampling moment corresponding to the first abnormal SNR data in the SNR data sequence; is the sampling moment corresponding to the last abnormal SNR data in the SNR data sequence; k is the serial number of the abnormal SNR data in the abnormal SNR data sequence; || is the absolute value symbol; 1 is a constant.

[0075] It should be noted that is the average abnormal degree of the SNR data sequence, the larger it is, the greater the overall abnormal degree of the SNR data in the SNR data sequence, and the larger the signal transmission quality anomaly value corresponding to the SNR data sequence within the preset time range; is the aggregation degree of the abnormal SNR data in the SNR data sequence, It represents the difference in sampling times corresponding to adjacent abnormal SNR data in the abnormal SNR data sequence. The smaller it is, the smaller the time interval between adjacent abnormal SNR data, the denser the distribution of abnormal SNR data in the SNR data sequence, and the greater the abnormal value of the signal transmission quality within the preset time range corresponding to the SNR data sequence. It is the proportion of abnormal SNR data in the SNR data sequence. The larger it is, the more abnormal SNR data in the SNR data sequence, and the greater the abnormal value of the signal transmission quality within the preset time range corresponding to the SNR data sequence. It is the duration of abnormal SNR data in the SNR data sequence. The larger it is, the longer the duration of abnormal SNR data in the SNR data sequence, the stronger the duration of abnormal SNR data, the more in line with the distribution characteristics of current abnormal SNR data in weather conditions or dense building clusters during remote data transmission, and the greater the abnormal value of the signal transmission quality within the preset time range corresponding to the SNR data sequence.

[0076] Thus, the abnormal value of the signal transmission quality within the preset time range corresponding to the SNR data sequence, that is, within 1 minute, is obtained.

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

[0078] Furthermore, after obtaining the abnormal value of the signal transmission quality within 1 minute, judge the true quality of the channel where remote data transmission is performed within 1 minute according to the abnormal value of the signal transmission quality within 1 minute, and thus obtain the target transmission frequency of the hydrogen-powered drone within the next 1 minute according to the true quality of the channel within 1 minute.

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

[0080] After obtaining the target transmission frequency of the hydrogen-powered UAV within the next 1 minute, use the target transmission frequency as the data transmission frequency between the hydrogen-powered UAV and the receiving end within the next 1 minute to perform data transmission of the hydrogen-powered UAV. The target data transmission frequency obtained in this way is most in line with the current real state of the hydrogen-powered UAV and is more matched with the actual environment where the hydrogen-powered UAV is located. It not only improves the data transmission rate and anti-noise ability of the hydrogen-powered UAV for remote transmission, but also improves the dynamic adjustment ability of the hydrogen-powered UAV during the remote data transmission process.

[0081] In an embodiment of the present invention, the signal-to-noise ratio data at each sampling moment when a hydrogen-powered unmanned aerial vehicle (UAV) performs remote data transmission is collected, and the signal-to-noise ratio data within a preset time range is formed 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 degree of abnormality 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 degrees of abnormality of all the signal-to-noise ratio data in the signal-to-noise ratio data sequence are obtained, and whether there is abnormal signal-to-noise ratio data in the signal-to-noise ratio data sequence is detected according to the degrees of abnormality of all the 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, a signal transmission quality abnormality 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 abnormality value within the preset time range, a target data transmission frequency of the hydrogen-powered UAV within a future preset time range is obtained, and the data transmission of the hydrogen-powered UAV within the future preset time range is performed using the target data transmission frequency. Among them, by obtaining the degree of abnormality of the 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 there is abnormal signal-to-noise ratio data, according to the distribution characteristics of the abnormal signal-to-noise ratio data, a signal transmission quality abnormality value within the preset time range is obtained, and the quality of remote data transmission within the preset time range is accurately evaluated. Furthermore, a target data transmission frequency of the hydrogen-powered UAV within a future preset time range is obtained, which improves the rate and anti-noise ability of the hydrogen-powered UAV for remote data transmission. Moreover, the present invention uses the historical signal-to-noise ratio data when the hydrogen-powered UAV performs remote data transmission to obtain the data transmission frequency of the hydrogen-powered UAV within a future time range, which improves 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 and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all 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 abnormal value 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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