Satellite amplification communication method, system and device for emergency scene and medium

Abstract

The invention discloses a satellite amplification communication method, system and device for an emergency scene and a medium, and relates to the technical field of satellite uplink signal amplification, and the method comprises the following steps: obtaining original reference data of a power amplifier, carrying out the preliminary screening processing, calculating cloud comprehensive features, cloud structure features and meteorological cooperation features, and carrying out the calculation of the cloud comprehensive features, the cloud structure features and the meteorological cooperation features; constructing second reference data; constructing an amplification feature vector, and constructing a power amplification processing model based on the second reference data; acquiring an amplification feature vector in real time, and dynamically adjusting the amplification power based on a power amplification calculation model; the satellite uplink signal amplification method is used for solving the problem that when an existing satellite uplink signal amplification technology carries out power amplification on a transmitting signal through a power amplifier, the relation among main meteorological factors, distance factors and signal loss cannot be fully considered, meanwhile, a relation model is established, the amplification power of the power amplifier is automatically and accurately adjusted according to the actual situation, and the reliability is high. And the problems of power waste and insufficient power are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite uplink signal amplification, and in particular to a satellite amplification communication method, system, equipment and medium for emergency scenarios. Background Art

[0002] Satellite uplink signal amplification technology refers to the technology of amplifying the power of uplink signals transmitted from ground stations to satellites in satellite communication systems. Its purpose is to enhance the strength of uplink signals to ensure that the signals can still be effectively received by satellites after long-distance transmission. During the transmission process from ground terminals to satellites, signals will be affected by various losses. Power amplification can effectively compensate for these losses to ensure that satellites receive signals of sufficient strength.

[0003] Existing satellite uplink signal amplification technologies often only consider the free space loss caused by the distance between the ground terminal and the satellite when amplifying the signal power, because the free space loss is proportional to the square of the signal transmission distance, and the distance that the signal propagates from the ground terminal to the satellite is very far, resulting in a very large free space loss; however, in the process of transmitting signals to the satellite, meteorological factors can also cause signal strength fluctuations; for example, cloud cover and rainfall can cause signal attenuation; therefore, the method of only considering free space loss for power amplification cannot adapt to meteorological changes, resulting in unstable signal strength received by the satellite under bad meteorological conditions; for example, in the patent application with publication number CN112533271A, a method and device for adaptively controlling the transmission power of a satellite communication module is disclosed, which adjusts the transmission power based on the distance between the ground terminal and the satellite to adjust the free space loss. Although power amplification is performed, the satellite may not receive the transmitted signal under bad weather conditions. Moreover, considering the distance factor, in order to ensure that the satellite can receive signals of sufficient strength under various weather conditions, the power may be over-amplified, resulting in energy waste. In addition, in the face of complex and changeable weather conditions, it is also very difficult to manually adjust the power of the power amplifier. Because there are many meteorological factors and their interactions are complex, it is difficult to determine the appropriate power adjustment strategy based on experience. Therefore, the existing satellite uplink signal amplification technology cannot fully consider the relationship between the main meteorological factors, distance factors and signal loss when amplifying the transmitted signal through the power amplifier, and at the same time establish a relationship model to automatically and accurately adjust the amplified power of the power amplifier according to the actual situation to avoid power waste and insufficient power. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent, by obtaining the original reference data for preliminary screening and processing, obtaining the first reference data, calculating the comprehensive characteristics of the cloud layer, the cloud layer structure characteristics and the meteorological coordination characteristics, and constructing the second reference data; constructing the original processing model and the amplification feature vector, and constructing the power amplification processing model; and dynamically adjusting the amplification power based on the power amplification calculation model; to solve the problem that the existing satellite uplink signal amplification technology cannot fully consider the relationship between the main meteorological factors, distance factors and signal loss when amplifying the transmission signal through the power amplifier, and at the same time establish a relationship model to automatically and accurately adjust the amplification power of the power amplifier according to the actual situation to avoid power waste and power shortage.

[0005] To achieve the above objectives, in a first aspect, the present application provides a satellite amplification communication method for emergency scenarios, comprising the following steps:

[0006] Acquire original reference data of the power amplifier when transmitting a signal, and perform preliminary screening and processing to obtain first reference data, wherein the original reference data includes: power information, loss information, signal strength information, and related meteorological information;

[0007] Calculating cloud layer comprehensive characteristics, cloud layer structural characteristics, and meteorological synergy characteristics based on the first reference data, and constructing second reference data;

[0008] constructing an original processing model and an amplified feature vector, and constructing a power amplification processing model based on the second reference data;

[0009] The amplification feature vector at the current moment is obtained in real time, and the amplification power is dynamically adjusted based on the power amplification calculation model.

[0010] Further, obtaining original reference data of the power amplifier when transmitting a signal and performing preliminary screening processing to obtain first reference data includes the following sub-steps:

[0011] When the ground terminal transmits a signal to the satellite through the power amplifier, the amplified power of the power amplifier and the transmission time are recorded and marked as power information;

[0012] and recording the distance between the satellite and the ground terminal when the signal is transmitted, recorded as the satellite distance, and the signal frequency of the transmitted signal, and calculating the transmission loss according to the signal frequency and the satellite distance, and marking the signal frequency of the transmitted signal, the satellite distance, and the transmission loss as loss information;

[0013] Obtain relevant meteorological information when transmitting signals, including cloud information and air information. Cloud information includes: cloud height and cloud thickness between the ground terminal and the satellite. Air information includes: air temperature, air humidity and unit rainfall at the location of the ground terminal.

[0014] After the signal is transmitted, the signal strength of the corresponding signal received by the satellite is obtained and recorded as signal strength information;

[0015] Repeatedly obtain power information, loss information, signal strength information and related meteorological information of multiple transmission signals, and store them according to the transmission time and mark them as original reference data;

[0016] Preliminary screening and processing: Set the signal strength threshold to DB, and mark the signal strength information in the original reference data that is greater than or equal to the signal strength threshold as valid strength information; filter out all valid strength information based on the original reference data, and filter out the power information, loss information and related meteorological information corresponding to the valid strength information, and classify and store all the filtered information according to the transmission time, and mark it as the first reference data.

[0017] Furthermore, calculating cloud layer comprehensive features, cloud layer structural features, and meteorological synergy features based on the first reference data and constructing the second reference data includes the following sub-steps:

[0018] Calculate the comprehensive characteristics of the cloud layer: Based on the cloud height, cloud thickness, air temperature, air humidity and unit rainfall, the comprehensive characteristics of the cloud layer are calculated by the cloud layer comprehensive impact formula. The cloud layer comprehensive impact formula is as follows: Where CI represents the comprehensive characteristics of the cloud layer, h represents the cloud layer height when the signal is transmitted, d represents the cloud layer thickness when the signal is transmitted, p represents the unit rainfall when the signal is transmitted, t represents the air temperature when the signal is transmitted, t0 represents the set reference air temperature, and r0 represents the set reference air humidity;

[0019] Calculate cloud structure characteristics: Based on cloud height, cloud thickness, air humidity and unit rainfall, the cloud structure characteristics are calculated using the cloud stability formula. The cloud stability formula is as follows: Where CS represents the cloud structure characteristics.

[0020] Furthermore, calculating cloud layer comprehensive features, cloud layer structural features, and meteorological synergy features based on the first reference data and constructing the second reference data also includes the following sub-steps:

[0021] Calculation of meteorological synergy characteristics: Based on cloud height, cloud thickness, air temperature, air humidity and unit rainfall, the meteorological synergy characteristics are calculated using the meteorological synergy formula. The meteorological synergy formula is as follows: Where MC represents the meteorological synergy characteristic, and p0 represents the set reference unit rainfall;

[0022] Based on the relevant meteorological information in the first reference data, the cloud comprehensive characteristics, cloud structure characteristics and meteorological coordination characteristics of each transmitted signal are repeatedly calculated, and are combined and stored according to the relevant meteorological information corresponding to the first reference data, and marked as second reference data.

[0023] Further, constructing the original processing model and the amplified feature vector, and constructing the power amplification processing model based on the second reference data includes the following sub-steps:

[0024] The original processing model is constructed based on the multi-layer perceptron. The original processing model includes: an input layer, a hidden processing layer and an output layer. The hidden processing layer includes a2 hidden layers. The number of neurons in the input layer is a1, the number of neurons in the hidden layer is a3, and the number of neurons in the output layer is a4.

[0025] Extract relevant meteorological information, cloud layer comprehensive characteristics, cloud layer structure characteristics, meteorological coordination characteristics, transmission loss, signal strength information and power information from the second reference data, and perform normalization processing according to the data type, normalize all data sizes to the [0, 1] interval, and obtain normalized reference data after completion;

[0026] All corresponding relevant meteorological information, cloud comprehensive characteristics, cloud structure characteristics, meteorological synergy characteristics, transmission loss, and signal strength information in the normalized reference data are combined into a vector, marked as an amplified feature vector, and recorded as M = {m1, m2, m3, m4, m5, m6, m7, m8, m9, m10}, where M represents the feature vector, and m1, m2, m3, m4, m5, m6, m7, m8, m9 and m10 represent cloud height, cloud thickness, air temperature, air humidity, unit rainfall, cloud comprehensive characteristics, cloud structure characteristics, meteorological synergy characteristics, transmission loss and signal strength information in order; all feature vectors in the normalized reference data are obtained and marked as the third reference data; the third reference data is divided into a reference training set and a reference test set in a ratio of 9:1.

[0027] Furthermore, constructing the original processing model and the amplified feature vector, and constructing the power amplification processing model based on the second reference data also includes the following sub-steps:

[0028] Set the learning rate of model training to b1, the number of training iterations to b2, and the batch size to b3; use the reference training set to train the original processing model, and after completion, obtain the power amplification processing model;

[0029] The power amplification processing model is tested using the reference test set, and the bias error of the power amplification processing model is calculated using the mean absolute error formula; the mean absolute error formula is as follows: Wherein AE is an error correction formula, Yk is the power information output by the power amplification processing model, Yk is the actual power information, and K is the number of eigenvectors input to the power amplification processing model.

[0030] Furthermore, obtaining the amplification feature vector at the current moment in real time and dynamically adjusting the amplification power based on the power amplification calculation model includes the following sub-steps:

[0031] Acquire cloud information and air information at a first time interval and record the acquisition time. Calculate cloud comprehensive features, cloud structure features and meteorological coordination features based on the acquired cloud information and air information, and combine them with the cloud information and air information to be recorded as the first amplification feature. The first time interval is t1.

[0032] The satellite distance is acquired in real time according to the satellite ephemeris of the satellite, and the transmission loss is calculated according to the signal frequency of the signal to be transmitted and the real-time satellite distance, which is recorded as the second amplification feature;

[0033] After setting the signal transmission, obtain the signal strength of the corresponding signal received by the satellite, record it as the required strength; and form an amplification feature vector according to the first amplification feature and the second amplification feature closest in time distance;

[0034] The amplified characteristic vector is input into the power amplification calculation model to obtain the amplified power corresponding to the required signal strength, which is recorded as the first estimated power. The first estimated power is error corrected using the error correction formula according to the bias error to obtain the corrected estimated power; the error correction formula is as follows: V2=V1+V1*AE, where V1 is the first estimated power and V2 is the corrected estimated power; the amplified power of the power amplifier is adjusted to the corrected estimated power.

[0035] In a second aspect, the present application provides a satellite amplified communication system for emergency scenarios, including a sample acquisition module, a feature calculation module, a model construction module, and an amplified communication module;

[0036] The sample acquisition module includes an acquisition unit and a screening unit, the acquisition unit is used to acquire the original reference data of the power amplifier when transmitting the signal, the original reference data includes: power information, loss information, signal strength information and related meteorological information, and the screening unit is used to perform preliminary screening processing on the original reference data to obtain the first reference data;

[0037] The feature calculation module calculates cloud layer comprehensive features, cloud layer structural features and meteorological synergy features based on the first reference data, and constructs second reference data;

[0038] The model building module is used to build an original processing model and an amplified feature vector, and to build a power amplification processing model based on the second reference data;

[0039] The amplification communication module includes a collection unit and an amplification unit. The collection unit is used to obtain the amplification feature vector at the current moment in real time. The amplification unit dynamically adjusts the amplification power based on the power amplification calculation model.

[0040] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are performed.

[0041] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0042] Beneficial effects of the present invention: The present invention obtains the original reference data of the power amplifier when transmitting the signal, and performs preliminary screening and processing to obtain the first reference data, wherein the original reference data includes: power information, loss information, signal strength information and related meteorological information; based on the first reference data, the cloud layer comprehensive characteristics, cloud layer structure characteristics and meteorological coordination characteristics are calculated, and the second reference data is constructed; the original processing model and the amplification feature vector are constructed, and the power amplification processing model is constructed based on the second reference data; the amplification feature vector at the current moment is obtained in real time, and the amplification power is dynamically adjusted based on the power amplification calculation model; while comprehensively considering the relationship between meteorological factors, distance factors and signal loss, a relationship model is established, and in the face of complex and changeable meteorological conditions, the amplification power of the power amplifier can be automatically and accurately adjusted according to the actual situation to avoid power waste and power shortage;

[0043] The present invention calculates the comprehensive characteristics of the cloud layer, the characteristics of the cloud layer structure and the meteorological coordination characteristics, and can integrate the influence of the interaction of multiple meteorological factors on the signal strength and the transmission power; it can more comprehensively and accurately reflect the complex relationship between various factors in the actual environment, and improve the accuracy of the model; by comprehensively considering multiple meteorological factors and distance factors, it can more comprehensively depict the actual environment of satellite communication, improve the reliability and stability of the satellite communication system in the complex meteorological environment of some emergency scenarios, and improve the accuracy of amplified power adjustment, avoiding power waste and power shortage; the power amplification processing model established by the multi-layer perceptron has the advantage that the model can automatically learn these complex relationships, make reasonable power adjustment decisions in real time, and solve the problem of manual power adjustment difficulties under complex meteorological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a functional block diagram of the system of the present invention;

[0045] Figure 2 is a flow chart of the steps of the method of the present invention;

[0046] Figure 3 It is the structural diagram of the original processing model of the present invention;

[0047] Figure 4 It is a processing flow chart of the amplification communication module of the present invention;

[0048] Figure 5 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1, please refer to Figure 1 As shown, the present application provides a satellite amplified communication system for emergency scenarios, including a sample acquisition module, a feature calculation module, a model building module and an amplified communication module;

[0051] The sample acquisition module includes an acquisition unit and a screening unit. The acquisition unit is used to acquire the original reference data of the power amplifier when transmitting the signal. The original reference data includes: power information, loss information, signal strength information and related meteorological information. The screening unit is used to perform preliminary screening processing on the original reference data to obtain the first reference data.

[0052] The acquisition unit is configured with an information acquisition strategy, which includes: when the ground terminal transmits a signal to the satellite through the power amplifier, recording the amplified power of the power amplifier and the transmission time, and marking them as power information;

[0053] The distance between the satellite and the ground terminal when the signal is transmitted is recorded as the satellite distance, and the signal frequency of the transmitted signal is recorded. The transmission loss is calculated based on the signal frequency and the satellite distance. The signal frequency of the transmitted signal, the satellite distance and the transmission loss are marked as loss information. The transmission loss can be calculated using the free space propagation loss formula, which is as follows: Where L represents the transmission loss, d0 represents the distance between the satellite and the ground terminal, and f0 represents the frequency of the transmitted signal;

[0054] Obtain relevant meteorological information when transmitting signals, including cloud information and air information. Cloud information includes: cloud height and cloud thickness between the ground terminal and the satellite. Air information includes: air temperature, air humidity and unit rainfall at the location of the ground terminal.

[0055] After the signal is transmitted, the signal strength of the corresponding signal received by the satellite is obtained and recorded as signal strength information;

[0056] The power information, loss information, signal strength information and related meteorological information of multiple transmitted signals are repeatedly obtained, classified and stored according to the transmission time, and marked as original reference data.

[0057] The screening unit is configured with a screening processing strategy, which includes: setting the signal strength threshold as DB, marking the signal strength information in the original reference data that is greater than or equal to the signal strength threshold as effective strength information; screening out all effective strength information based on the original reference data, and screening out the power information, loss information and related meteorological information corresponding to the effective strength information, and storing all the screened information according to the transmission time, and marking it as the first reference data; in this embodiment, the strength threshold DB is -60dBm, and the original reference data is screened because the signal strength information corresponding to some of the power information is too weak, that is, when the signal is transmitted with these amplified powers, the signal strength received by the satellite is too weak, and this part of the data has no reference value for the establishment of the subsequent model, so it is necessary to screen and remove it;

[0058] In the specific implementation process, the impact of cloud height on signal strength is mainly reflected in the length of the signal path through the cloud. The lower the height, the more obvious the attenuation. The thickness of the cloud directly determines the length of the medium that the signal experiences when propagating inside the cloud. The thicker the cloud, the stronger the cumulative scattering and absorption of the signal, and the more attenuation. The scattering and absorption effects of raindrops on the signal are more significant. As the unit rainfall increases, raindrops intensify the scattering and absorption of the signal, and the intensity is greatly reduced. Temperature affects the atmospheric refractive index and the water vapor state of the cloud, causing changes in the signal propagation path and additional attenuation. Lower temperatures may cause water vapor in the cloud to condense into small water droplets or ice crystals more easily, thereby changing the scattering and absorption characteristics of the cloud for the signal. Humidity reflects the water vapor content in the atmosphere. A high humidity environment means that there are more water vapor molecules in the atmosphere. These water vapor molecules will absorb and scatter the signal. In addition, high humidity may also promote the formation and development of clouds, indirectly affecting the signal through the clouds.

[0059] The feature calculation module calculates cloud layer comprehensive features, cloud layer structural features and meteorological synergy features based on the first reference data, and constructs second reference data;

[0060] The feature calculation module is configured with a feature calculation strategy, which includes: Calculating cloud layer comprehensive features: Based on cloud layer height, cloud layer thickness, air temperature, air humidity and unit rainfall, the cloud layer comprehensive features are calculated through the cloud layer comprehensive impact formula. The cloud layer comprehensive impact formula is as follows: Wherein CI represents the comprehensive characteristics of the cloud layer, h represents the cloud layer height when the signal is transmitted, d represents the cloud layer thickness when the signal is transmitted, p represents the unit rainfall when the signal is transmitted, t represents the air temperature when the signal is transmitted, t0 represents the set reference air temperature, which can be the comfortable temperature under the typical climatic conditions of the region, r0 represents the set reference air humidity, which can be the comfortable humidity under the typical climatic conditions of the region, in this embodiment, t0 = 20 degrees Celsius, r0 = 60%;

[0061] The comprehensive characteristics of the cloud layer are an indicator that comprehensively measures the degree of influence of the cloud layer and related meteorological factors on signal propagation; the square root of h*d can reflect the overall size of the cloud layer to a certain extent, and multiplying it by the unit rainfall takes into account the enhanced effect of precipitation on signal attenuation; the adjustment items of temperature and humidity relative to the reference value can capture the additional impact on signal propagation when meteorological conditions deviate from normal conditions; for example, if the temperature is higher than the average temperature, the convection and water vapor state of the air may change, thereby affecting the propagation of the signal in the cloud layer; the change of humidity is similar, and higher humidity may cause more water vapor to condense in the cloud layer, increasing the scattering and absorption of the signal;

[0062] Calculate cloud structure characteristics: Based on cloud height, cloud thickness, air humidity and unit rainfall, the cloud structure characteristics are calculated using the cloud stability formula. The cloud stability formula is as follows: Where CS represents the cloud structure characteristics;

[0063] Cloud structure characteristics are mainly used to describe the stability of the internal structure of clouds, which can help the model understand the potential impact of the stability of the internal structure of clouds on signal strength; h / d reflects the aspect ratio of clouds. A higher aspect ratio may mean that the cloud structure is relatively unstable, and the signal may be affected by more complex scattering and refraction when passing through such clouds; and 1+(p+r) in the denominator takes into account the combined effect of unit rainfall and humidity. When the unit rainfall and humidity are large, the water vapor content in the clouds is rich, which may make the cloud structure more unstable, thereby causing greater interference to the signal;

[0064] Calculation of meteorological synergy characteristics: Based on cloud height, cloud thickness, air temperature, air humidity and unit rainfall, the meteorological synergy characteristics are calculated using the meteorological synergy formula. The meteorological synergy formula is as follows: Wherein MC represents the meteorological synergy feature, p0 represents the set reference unit rainfall, and p0 can be set to the annual maximum unit rainfall according to the local historical unit rainfall data. In this embodiment, p0=20 mm / h;

[0065] Meteorological synergy characteristics are used to describe the comprehensive characteristics of mutual coordination and interaction between different meteorological factors. (h+d) represents the overall size of the cloud layer. Multiplying it by sin[(π / 2)*(p / p0)] makes the contribution of rainfall to the characteristics the largest when it reaches p0, simulating the nonlinear effect of rainfall on signal attenuation, because signal attenuation usually increases sharply when rainfall is large. The following two exponential terms consider the effects of temperature and humidity on the signal, centered at 20 degrees Celsius and 60%. When temperature and humidity deviate from these values, the exponential terms decrease, reflecting the changes in the impact on the signal when it deviates from ideal meteorological conditions. It can capture the comprehensive impact of the synergy between different meteorological factors on signal strength, so that subsequent models can better adapt to complex and changeable meteorological environments.

[0066] Based on the relevant meteorological information in the first reference data, the cloud layer comprehensive characteristics, cloud layer structure characteristics and meteorological coordination characteristics of each transmitted signal are repeatedly calculated, and the relevant meteorological information corresponding to the first reference data is combined and stored, and marked as the second reference data;

[0067] In the specific implementation process, the comprehensive characteristics of the cloud layer, the structural characteristics of the cloud layer, and the meteorological synergy characteristics combine the cloud height, thickness, unit rainfall, temperature, and humidity from different angles, providing a more comprehensive perspective to evaluate the impact of meteorological conditions on signal strength. It is no longer limited to the analysis of a single meteorological factor, but takes into account the interrelationships and synergies between them, so as to more accurately describe the complex relationship between meteorological environment and signal strength. It includes meteorological factors closely related to signal attenuation and their combination methods, which can be used as model input or separate evaluation indicators to better fit the law of signal strength changes with meteorological conditions. It helps to improve the accuracy of subsequent model processing.

[0068] The model building module is used to build an original processing model and an amplified feature vector, and to build a power amplification processing model based on the second reference data;

[0069] The model building module is configured with a model building strategy, which includes: building an original processing model based on a multi-layer perceptron, see Figure 3As shown, the original processing model includes: an input layer, a hidden processing layer and an output layer, the hidden processing layer includes a2 hidden layers, the number of neurons in the input layer is a1, the number of neurons in the hidden layer is a3, and the number of neurons in the output layer is a4; in this embodiment, a1=10, because there are 10 features in the subsequent amplified feature vector, a2=2, a3=16, a4=1, because only one amplified power is output;

[0070] Extract relevant meteorological information, cloud layer comprehensive features, cloud layer structure features, meteorological coordination features, transmission loss, signal strength information, and power information from the second reference data, and perform normalization processing according to the data type, normalize all data sizes to the [0, 1] interval, and obtain normalized reference data after completion; normalization can map all features to a similar value range, thereby speeding up model training and improving model accuracy and stability;

[0071] Combine all corresponding relevant meteorological information, cloud comprehensive characteristics, cloud structure characteristics, meteorological coordination characteristics, transmission loss, and signal strength information in the normalized reference data into a vector, marked as an amplified feature vector, denoted as M = {m1, m2, m3, m4, m5, m6, m7, m8, m9, m10}, where M represents a feature vector, and m1, m2, m3, m4, m5, m6, m7, m8, m9, and m10 represent cloud height, cloud thickness, air temperature, air humidity, rainfall, cloud comprehensive characteristics, cloud structure characteristics, meteorological coordination characteristics, transmission loss, and signal strength information in order; obtain all feature vectors in the normalized reference data, marked as the third reference data; divide the third reference data into a reference training set and a reference test set in a ratio of 9:1;

[0072] The learning rate of model training is set to b1, the number of training iterations is set to b2, and the batch size is set to b3; the original processing model is trained using the reference training set, and a power amplification processing model is obtained after completion; in this embodiment, b1=0.001, b2=200, i.e., a total of 200 rounds of training are performed, and b3=16, i.e., 16 amplification feature vectors are input into the model each time;

[0073] The power amplification processing model is tested using the reference test set, and the bias error of the power amplification processing model is calculated using the mean absolute error formula; the mean absolute error formula is as follows: Wherein AE is an error correction formula, Yk is the power information output by the power amplification processing model, Yk is the actual power information, and K is the number of eigenvectors input to the power amplification processing model; if the bias error of the tested power amplification processing model is too large, it means that the model training is insufficient, and the reference training set can be used to continue training. For example, in this embodiment, if the bias error is greater than 0.15, it is determined that the model training is insufficient, and the reference training set is used to continue training until the bias error is no greater than 0.15. The smaller the bias error, the higher the accuracy of the model.

[0074] In the specific implementation process, for the setting of the number of hidden layers, for relatively simple data sets and relationships, one hidden layer may be sufficient. When there are more complex hierarchical structures or interactive relationships between data, two hidden layers may be more appropriate. When the data is very complex and there are multiple levels of feature abstraction and complex long-term dependencies, three or more hidden layers may be required; but too many hidden layers will increase the complexity of the model and extend the training time; in this embodiment, 2-3 layers are sufficient and can be set according to the actual application scenario; and the number of neurons in each layer is generally 10-20; it can also be adjusted according to the actual application scenario, and it does not have to be the same for each layer. If the amount of data is large and the relationship between the input features and the transmission power is more complex, the number of neurons can be appropriately increased. If there is a strong correlation between the input features or some features have little effect on the transmission power, the number of neurons can be appropriately reduced.

[0075] The amplification communication module includes a collection unit and an amplification unit. The collection unit is used to obtain the amplification feature vector at the current moment in real time. The amplification unit dynamically adjusts the amplification power based on the power amplification calculation model. Figure 4 As shown;

[0076] The acquisition unit is configured with a data acquisition strategy, which includes: acquiring cloud information and air information at a first time interval, and recording the acquisition time, and calculating cloud comprehensive characteristics, cloud structure characteristics and meteorological coordination characteristics according to the acquired cloud information and air information, and merging them with the cloud information and air information, and recording them as the first amplification characteristics, the first time interval is t1; in this embodiment, the first time interval is 5 seconds; the cloud information can be acquired using a laser ceilometer;

[0077] The satellite distance is acquired in real time according to the satellite ephemeris of the satellite, and the transmission loss is calculated according to the signal frequency of the signal to be transmitted and the real-time satellite distance, which is recorded as the second amplification feature;

[0078] After setting the signal transmission, obtain the signal strength of the corresponding signal received by the satellite, which is recorded as the required strength; the required strength can be set according to actual needs; and form an amplified feature vector based on the first amplified feature and the second amplified feature closest in time distance. The amplified feature vector is exactly the same as the amplified feature vector during model training, including the position of each feature in the amplified feature vector;

[0079] The amplification unit is configured with an amplification adjustment strategy, which includes: inputting the amplification characteristic vector into the power amplification calculation model to obtain the amplified power corresponding to the required signal strength, recorded as the first estimated power, and performing error correction on the first estimated power using the error correction formula according to the bias error to obtain the corrected estimated power; the error correction formula is as follows: V2=V1+V1*AE, wherein V1 is the first estimated power and V2 is the corrected estimated power; adjusting the amplified power of the power amplifier to the corrected estimated power, the bias error can intuitively reflect the average deviation between the amplified power output by the model and the actual amplified power corresponding to the required strength; the average deviation may be too large or too small, and the reason for performing error correction according to the small deviation is that the actual signal strength received by the satellite can meet the required strength, that is, greater than the required strength, to ensure that in an emergency scenario, there will be no special situation where the satellite cannot receive the signal;

[0080] In the specific implementation process, the relationship between the amplified power and many related features is often nonlinear; for example, the impact of cloud height, thickness and rainfall on signal attenuation is not a simple linear superposition, but is intertwined, complex and changeable; the power amplification calculation model can capture this nonlinear relationship well through training; it can learn the complex interactions between different input features and more accurately predict the corresponding amplified power based on the input characteristics; compared with traditional linear regression, it has obvious advantages in dealing with such complex relationships.

[0081] Example 2, please refer to Figure 2 As shown, the present application provides a satellite amplification communication method for emergency scenarios, comprising the following steps:

[0082] Step S1, obtaining the original reference data of the power amplifier when transmitting the signal, and performing preliminary screening processing to obtain the first reference data, the original reference data includes: power information, loss information, signal strength information and related meteorological information; Step S1 includes the following sub-steps:

[0083] Step S101, when the ground terminal transmits a signal to the satellite through the power amplifier, the amplified power of the power amplifier and the transmission time are recorded and marked as power information;

[0084] Step S102, and record the distance between the satellite and the ground terminal when the signal is transmitted, recorded as the satellite distance, and the signal frequency of the transmitted signal, and calculate the transmission loss according to the signal frequency and the satellite distance, and mark the signal frequency of the transmitted signal, the satellite distance and the transmission loss as loss information;

[0085] Step S103, obtaining relevant meteorological information when transmitting the signal, the relevant meteorological information includes cloud information and air information, the cloud information includes: cloud height and cloud thickness between the ground terminal and the satellite, the air information includes: air temperature, air humidity and unit rainfall at the location of the ground terminal;

[0086] Step S104, after the signal is transmitted, the signal strength of the corresponding signal received by the satellite is obtained and recorded as signal strength information;

[0087] Step S105, repeatedly acquiring power information, loss information, signal strength information and related meteorological information of multiple transmission signals, and storing them according to transmission time, and marking them as original reference data;

[0088] Step S106, preliminary screening process: set the signal strength threshold to DB, and mark the signal strength information in the original reference data that is greater than or equal to the signal strength threshold as valid strength information; filter out all valid strength information based on the original reference data, and filter out the power information, loss information and related meteorological information corresponding to the valid strength information, and classify and store all the filtered information according to the transmission time, and mark it as the first reference data.

[0089] Step S2, calculating cloud layer comprehensive characteristics, cloud layer structural characteristics and meteorological synergy characteristics based on the first reference data, and constructing second reference data; Step S2 includes the following sub-steps:

[0090] Step S201, calculating the comprehensive characteristics of the cloud layer: based on the cloud layer height, cloud layer thickness, air temperature, air humidity and unit rainfall, the comprehensive characteristics of the cloud layer are calculated by the cloud layer comprehensive influence formula, and the cloud layer comprehensive influence formula is as follows: Where CI represents the comprehensive characteristics of the cloud layer, h represents the cloud layer height when the signal is transmitted, d represents the cloud layer thickness when the signal is transmitted, p represents the unit rainfall when the signal is transmitted, t represents the air temperature when the signal is transmitted, t0 represents the set reference air temperature, and r0 represents the set reference air humidity;

[0091] Step S202, calculating cloud structure characteristics: based on cloud height, cloud thickness, air humidity and unit rainfall, the cloud structure characteristics are calculated using a cloud stability formula, which is as follows: Where CS represents the cloud structure characteristics;

[0092] Step S203, calculating meteorological synergy characteristics: based on cloud height, cloud thickness, air temperature, air humidity and unit rainfall, the meteorological synergy characteristics are calculated by a meteorological synergy formula, and the meteorological synergy formula is as follows: Where MC represents the meteorological synergy characteristic, and p0 represents the set reference unit rainfall;

[0093] Step S204, based on the relevant meteorological information in the first reference data, repeatedly calculate the cloud comprehensive characteristics, cloud structure characteristics and meteorological coordination characteristics of each transmitted signal, and merge and store them according to the relevant meteorological information corresponding to the first reference data, and mark them as second reference data.

[0094] Step S3, constructing an original processing model and an amplified feature vector, and constructing a power amplification processing model based on the second reference data; Step S3 includes the following sub-steps:

[0095] Step S301, constructing an original processing model based on a multi-layer perceptron, the original processing model includes: an input layer, a hidden processing layer and an output layer, the hidden processing layer includes a2 hidden layers, the number of neurons in the input layer is a1, the number of neurons in the hidden layer is a3, and the number of neurons in the output layer is a4;

[0096] Step S302, extracting relevant meteorological information, cloud layer comprehensive characteristics, cloud layer structure characteristics, meteorological coordination characteristics, transmission loss, signal strength information, and power information from the second reference data, and performing normalization processing according to the data type, normalizing all data sizes to the [0, 1] interval, and obtaining normalized reference data after completion;

[0097] Step S303, combining all corresponding relevant meteorological information, cloud layer comprehensive characteristics, cloud layer structure characteristics, meteorological coordination characteristics, transmission loss, and signal strength information in the normalized reference data into a vector, marked as an amplified feature vector, denoted as M = {m1, m2, m3, m4, m5, m6, m7, m8, m9, m10}, where M represents a feature vector, and m1, m2, m3, m4, m5, m6, m7, m8, m9, and m10 represent cloud layer height, cloud layer thickness, air temperature, air humidity, unit rainfall, cloud layer comprehensive characteristics, cloud layer structure characteristics, meteorological coordination characteristics, transmission loss, and signal strength information in order, respectively;

[0098] Step S304, obtaining all feature vectors in the normalized reference data and marking them as third reference data; dividing the third reference data into a reference training set and a reference test set in a ratio of 9:1;

[0099] Step S305, setting the learning rate of model training to b1, the number of training iterations to b2, and the batch size to b3; using the reference training set to perform model training on the original processing model, and after completion, obtaining a power amplification processing model;

[0100] Step S306, using the reference test set to test the power amplification processing model, and calculating the bias error of the power amplification processing model by using the mean absolute error formula; the mean absolute error formula is as follows: Wherein AE is an error correction formula, Yk is the power information output by the power amplification processing model, Yk is the actual power information, and K is the number of eigenvectors input to the power amplification processing model.

[0101] Step S4, obtaining the amplification feature vector at the current moment in real time, and dynamically adjusting the amplification power based on the power amplification calculation model; Step S4 includes the following sub-steps:

[0102] Step S401, obtaining cloud information and air information at a first time interval, and recording the acquisition time, and calculating cloud comprehensive features, cloud structure features, and meteorological synergy features based on the obtained cloud information and air information, and merging them with the cloud information and air information to be recorded as a first amplification feature, and the first time interval is t1;

[0103] Step S402, obtaining the satellite distance in real time according to the satellite ephemeris of the satellite, and calculating the transmission loss according to the signal frequency of the signal to be transmitted and the real-time satellite distance, which is recorded as the second amplification feature;

[0104] Step S403, after setting the signal transmission, obtain the signal strength of the corresponding signal received by the satellite, record it as the required strength; and form an amplification feature vector according to the first amplification feature and the second amplification feature which are closest in time distance;

[0105] Step S404, inputting the amplified feature vector into a power amplification calculation model to obtain an amplified power corresponding to the required signal strength, which is recorded as a first estimated power;

[0106] Step S405, perform error correction on the first estimated power using an error correction formula according to the bias error to obtain a corrected estimated power; the error correction formula is as follows: V2=V1+V1*AE, where V1 is the first estimated power and V2 is the corrected estimated power; adjust the amplified power of the power amplifier to the corrected estimated power.

[0107] Example 3, please refer to Figure 5 As shown, Figure 5The schematic diagram of the structure of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor may call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a satellite amplification communication method for an emergency scenario are executed to achieve the following functions: obtaining the original reference data of the power amplifier when transmitting the signal, and performing preliminary screening processing to obtain the first reference data, the original reference data including: power information, loss information, signal strength information and related meteorological information; calculating the cloud layer comprehensive characteristics, cloud layer structure characteristics and meteorological synergy characteristics based on the first reference data, and constructing the second reference data; constructing the original processing model and the amplification feature vector, and constructing the power amplification processing model based on the second reference data; obtaining the amplification feature vector at the current moment in real time, and dynamically adjusting the amplification power based on the power amplification calculation model.

[0108] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0109] Embodiment 4, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the satellite amplification communication method for the above emergency scenario are executed to achieve the following functions: obtaining the original reference data of the power amplifier when transmitting the signal, and performing preliminary screening and processing to obtain the first reference data, the original reference data including: power information, loss information, signal strength information and related meteorological information; calculating the comprehensive characteristics of the cloud layer, the structural characteristics of the cloud layer and the meteorological coordination characteristics based on the first reference data, and constructing the second reference data; constructing the original processing model and the amplification feature vector, and constructing the power amplification processing model based on the second reference data; obtaining the amplification feature vector at the current moment in real time, and dynamically adjusting the amplification power based on the power amplification calculation model.

[0110] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0111] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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 application.

Claims

1. A satellite amplification communication method for emergency scenarios, characterized in that: The steps include: Acquire original reference data of the power amplifier when transmitting a signal, and perform preliminary screening and processing to obtain first reference data, wherein the original reference data includes: power information, loss information, signal strength information, and related meteorological information; Calculating cloud layer comprehensive characteristics, cloud layer structural characteristics, and meteorological synergy characteristics based on the first reference data, and constructing second reference data; constructing an original processing model and an amplified feature vector, and constructing a power amplification processing model based on the second reference data; The amplification feature vector at the current moment is obtained in real time, and the amplification power is dynamically adjusted based on the power amplification calculation model.

2. The satellite amplification communication method for emergency scenarios according to claim 1, characterized in that: Acquiring original reference data of the power amplifier when transmitting a signal and performing preliminary screening processing to obtain first reference data includes the following sub-steps: When the ground terminal transmits a signal to the satellite through the power amplifier, the amplified power of the power amplifier and the transmission time are recorded and marked as power information; and recording the distance between the satellite and the ground terminal when the signal is transmitted, recorded as the satellite distance, and the signal frequency of the transmitted signal, and calculating the transmission loss according to the signal frequency and the satellite distance, and marking the signal frequency of the transmitted signal, the satellite distance, and the transmission loss as loss information; Obtain relevant meteorological information when transmitting signals, including cloud information and air information. Cloud information includes: cloud height and cloud thickness between the ground terminal and the satellite. Air information includes: air temperature, air humidity and unit rainfall at the location of the ground terminal. After the signal is transmitted, the signal strength of the corresponding signal received by the satellite is obtained and recorded as signal strength information; Repeatedly obtain power information, loss information, signal strength information and related meteorological information of multiple transmission signals, and store them according to the transmission time and mark them as original reference data; Preliminary screening and processing: Set the signal strength threshold to DB, and mark the signal strength information in the original reference data that is greater than or equal to the signal strength threshold as valid strength information; filter out all valid strength information based on the original reference data, and filter out the power information, loss information and related meteorological information corresponding to the valid strength information, and classify and store all the filtered information according to the transmission time, and mark it as the first reference data.

3. The satellite amplification communication method for emergency scenarios according to claim 2, characterized in that: Calculating cloud layer comprehensive features, cloud layer structural features, and meteorological synergy features based on the first reference data and constructing the second reference data includes the following sub-steps: Calculate the comprehensive characteristics of the cloud layer: Based on the cloud height, cloud thickness, air temperature, air humidity and unit rainfall, the comprehensive characteristics of the cloud layer are calculated by the cloud layer comprehensive impact formula. The cloud layer comprehensive impact formula is as follows: Where CI represents the comprehensive characteristics of the cloud layer, h represents the cloud layer height when the signal is transmitted, d represents the cloud layer thickness when the signal is transmitted, p represents the unit rainfall when the signal is transmitted, t represents the air temperature when the signal is transmitted, t0 represents the set reference air temperature, and r0 represents the set reference air humidity; Calculate cloud structure characteristics: Based on cloud height, cloud thickness, air humidity and unit rainfall, the cloud structure characteristics are calculated using the cloud stability formula. The cloud stability formula is as follows: Where CS represents the cloud structure characteristics.

4. The satellite amplification communication method for emergency scenarios according to claim 3, characterized in that: Calculating cloud layer comprehensive features, cloud layer structural features, and meteorological synergy features based on the first reference data and constructing the second reference data also includes the following sub-steps: Calculation of meteorological synergy characteristics: Based on cloud height, cloud thickness, air temperature, air humidity and unit rainfall, the meteorological synergy characteristics are calculated using the meteorological synergy formula. The meteorological synergy formula is as follows: Where MC represents the meteorological synergy characteristic, and p0 represents the set reference unit rainfall; Based on the relevant meteorological information in the first reference data, the cloud comprehensive characteristics, cloud structure characteristics and meteorological coordination characteristics of each transmitted signal are repeatedly calculated, and are combined and stored according to the relevant meteorological information corresponding to the first reference data, and marked as second reference data.

5. The satellite amplification communication method for emergency scenarios according to claim 4, characterized in that: Constructing the original processing model and the amplified feature vector, and constructing the power amplification processing model based on the second reference data includes the following sub-steps: The original processing model is constructed based on the multi-layer perceptron. The original processing model includes: an input layer, a hidden processing layer and an output layer. The hidden processing layer includes a2 hidden layers. The number of neurons in the input layer is a1, the number of neurons in the hidden layer is a3, and the number of neurons in the output layer is a4. Extract relevant meteorological information, cloud layer comprehensive characteristics, cloud layer structure characteristics, meteorological coordination characteristics, transmission loss, signal strength information and power information from the second reference data, and perform normalization processing according to the data type, normalize all data sizes to the [0, 1] interval, and obtain normalized reference data after completion; All corresponding relevant meteorological information, cloud comprehensive characteristics, cloud structure characteristics, meteorological synergy characteristics, transmission loss, and signal strength information in the normalized reference data are combined into a vector, marked as an amplified feature vector, and recorded as M = {m1, m2, m3, m4, m5, m6, m7, m8, m9, m10}, where M represents the feature vector, and m1, m2, m3, m4, m5, m6, m7, m8, m9 and m10 represent cloud height, cloud thickness, air temperature, air humidity, unit rainfall, cloud comprehensive characteristics, cloud structure characteristics, meteorological synergy characteristics, transmission loss and signal strength information in order; all feature vectors in the normalized reference data are obtained and marked as the third reference data; the third reference data is divided into a reference training set and a reference test set in a ratio of 9:

1.

6. The satellite amplification communication method for emergency scenarios according to claim 5, characterized in that: Constructing the original processing model and the amplified feature vector, and constructing the power amplification processing model based on the second reference data also includes the following sub-steps: Set the learning rate of model training to b1, the number of training iterations to b2, and the batch size to b3; use the reference training set to train the original processing model, and after completion, obtain the power amplification processing model; The power amplification processing model is tested using the reference test set, and the bias error of the power amplification processing model is calculated using the mean absolute error formula; the mean absolute error formula is as follows: Wherein AE is an error correction formula, Yk is the power information output by the power amplification processing model, Yk is the actual power information, and K is the number of eigenvectors input to the power amplification processing model.

7. The satellite amplification communication method for emergency scenarios according to claim 6, characterized in that: Acquiring the amplification feature vector at the current moment in real time and dynamically adjusting the amplification power based on the power amplification calculation model includes the following sub-steps: Acquire cloud information and air information at a first time interval and record the acquisition time. Calculate cloud comprehensive features, cloud structure features and meteorological coordination features based on the acquired cloud information and air information, and combine them with the cloud information and air information to be recorded as the first amplification feature. The first time interval is t1. The satellite distance is acquired in real time according to the satellite ephemeris of the satellite, and the transmission loss is calculated according to the signal frequency of the signal to be transmitted and the real-time satellite distance, which is recorded as the second amplification feature; After setting the signal transmission, obtain the signal strength of the corresponding signal received by the satellite, record it as the required strength; and form an amplification feature vector according to the first amplification feature and the second amplification feature closest in time distance; The amplified characteristic vector is input into the power amplification calculation model to obtain the amplified power corresponding to the required signal strength, which is recorded as the first estimated power. The first estimated power is error corrected using the error correction formula according to the bias error to obtain the corrected estimated power; the error correction formula is as follows: V2=V1+V1*AE, where V1 is the first estimated power and V2 is the corrected estimated power; the amplified power of the power amplifier is adjusted to the corrected estimated power.

8. A satellite amplification communication system for emergency scenarios, applicable to a satellite amplification communication method for emergency scenarios as claimed in any one of claims 1 to 7, characterized in that: It includes a sample acquisition module, a feature calculation module, a model building module and an amplification communication module; The sample acquisition module includes an acquisition unit and a screening unit, the acquisition unit is used to acquire the original reference data of the power amplifier when transmitting the signal, the original reference data includes: power information, loss information, signal strength information and related meteorological information, and the screening unit is used to perform preliminary screening processing on the original reference data to obtain the first reference data; The feature calculation module calculates cloud layer comprehensive features, cloud layer structural features and meteorological synergy features based on the first reference data, and constructs second reference data; The model building module is used to build an original processing model and an amplified feature vector, and to build a power amplification processing model based on the second reference data; The amplification communication module includes a collection unit and an amplification unit. The collection unit is used to obtain the amplification feature vector at the current moment in real time. The amplification unit dynamically adjusts the amplification power based on the power amplification calculation model.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are executed.

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