A method and system for predicting channel atmospheric attenuation

Abstract

The present invention discloses a method and system for predicting channel atmospheric attenuation, which belongs to the field of data information communication technology. The method includes: obtaining original atmospheric data between a ground receiving station and a communication satellite; extracting atmospheric characteristic parameters that characterize atmospheric characteristic attenuation from different meteorological phenomena, and layering the original atmospheric data according to the atmospheric characteristic parameters to obtain layered atmospheric data; calculating the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite; inputting the layered atmospheric data into a pre-constructed characteristic attenuation prediction model, and outputting the atmospheric characteristic attenuation of the layered atmospheric data; and obtaining the prediction result of the total atmospheric attenuation according to the atmospheric characteristic attenuation and atmospheric path attenuation of the layered atmospheric data. The present invention can accurately simulate the atmospheric attenuation in satellite communications, improve the stability and accuracy of signal transmission, and effectively improve the communication quality.

Description

Technical Field

[0001] The present invention relates to the field of data information communication technology, and in particular to a method and system for predicting channel atmospheric attenuation. Background Art

[0002] In satellite communications, wireless communications and other remote communication systems, the transmission quality of signals is affected by many factors, among which atmospheric attenuation is one of the key factors affecting signal quality. Atmospheric attenuation includes signal loss caused by the absorption, scattering and refraction of electromagnetic waves by gases, droplets and particles in the atmosphere. In complex meteorological environments, the composition, temperature, humidity, air pressure and other parameters of the atmosphere will fluctuate significantly with time and geographical location, making channel attenuation difficult to predict.

[0003] At present, the channel attenuation modeling technology based on atmospheric conditions can be roughly divided into two categories: one is the modeling method based on empirical formulas, and the other is the modeling method based on physical models. Modeling method based on empirical formulas: This method mainly relies on the statistical analysis of a large amount of measured data. According to the attenuation law of electromagnetic waves when propagating in the atmosphere, the attenuation value is calculated through empirical formulas. These models are simple and easy to use, but their accuracy is limited by the changes in meteorological parameters. Especially under dynamically changing meteorological conditions, the applicability and accuracy of empirical formulas are low. Modeling method based on physical models: Physical models mainly consider the absorption characteristics of gases such as water vapor, oxygen, and nitrogen in the atmosphere, as well as the scattering characteristics of aerosols in the atmosphere. This type of method can accurately predict the attenuation under different meteorological conditions, but its calculation amount is large and often requires more meteorological data support. In summary, although there are currently a variety of channel attenuation modeling methods, how to achieve high-precision, low-computation real-time attenuation prediction in complex meteorological environments is still a technical problem. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and system for predicting channel atmospheric attenuation, so as to achieve high-precision, low-computation real-time attenuation prediction, improve the stability and accuracy of signal transmission, and effectively improve the communication quality.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] On the one hand, the present invention provides a method for predicting channel atmospheric attenuation, comprising:

[0007] Obtaining raw atmospheric data between ground receiving stations and communication satellites;

[0008] Extracting atmospheric characteristic parameters representing atmospheric characteristic attenuation from different meteorological phenomena, and stratifying the original atmospheric data according to the atmospheric characteristic parameters to obtain stratified atmospheric data;

[0009] Calculating the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite;

[0010] Inputting the stratified atmospheric data into a pre-built characteristic attenuation prediction model, and outputting the atmospheric characteristic attenuation of the stratified atmospheric data;

[0011] Obtaining a prediction result of total atmospheric attenuation according to the atmospheric characteristic attenuation and atmospheric path attenuation of the layered atmospheric data;

[0012] The feature attenuation prediction model is constructed by adding a residual block between the convolutional layer and the fully connected layer of the original CRnet network model.

[0013] Optionally, the atmospheric characteristic parameters include atmospheric pressure, atmospheric temperature and water vapor density, expressed as:

[0014] ;

[0015] ;

[0016] ;

[0017] in, Indicates atmospheric pressure; represents the atmospheric temperature; represents water vapor density; Indicates the atmospheric pressure corresponding to the standard sea level altitude; Indicates the atmospheric temperature corresponding to the standard sea level; represents the temperature gradient; Indicates altitude; Indicates the water vapor density corresponding to the standard sea level height; Indicates the altitude at standard water vapor density.

[0018] Optionally, the layered atmosphere data is expressed as:

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] in, Represents layered atmospheric data; represents the atmospheric characteristic data at the i-th geographical location; represents the atmospheric path data at the i-th geographical location; represents the altitude of the ground receiving station at the i-th geographical location; Indicates the height step length; Indicates Atmospheric pressure in layered atmospheric data; Indicates Water vapor density in layered atmospheric data; Indicates Atmospheric temperature in layered atmospheric data; represents the transmission distance between the ground receiving station and the communication satellite at the i-th geographical location; Indicates the transmission power of the communication satellite; Indicates the gain of the transmitting and receiving antennas; It represents the elevation angle of the ray between the ground receiving station and the communication satellite; Indicates the orbital altitude of the communications satellite.

[0024] Optionally, the number of layers of the layered atmospheric data is expressed as:

[0025] ;

[0026] in, The number of layers representing layered atmospheric data; Indicates the height of different geographical locations from the top of the stratosphere; Indicates the height step.

[0027] Optionally, calculating the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and the ray elevation angle between the ground receiving station and the communication satellite includes:

[0028] ;

[0029] ;

[0030] in, represents the atmospheric path attenuation; Indicates the transmission power of the communication satellite; Indicates the gain of the transmitting and receiving antennas; Indicates the transmitting frequency of the ground receiving station; represents the speed of light; represents the propagation path of the emission ray in the atmosphere; Represents the product of the effective areas of the transmitting and receiving antennas; Indicates the transmission distance between the ground receiving station and the communication satellite; Represents the elevation angle of the ray between the ground receiving station and the communication satellite.

[0031] Optionally, the training step of the feature prediction model includes:

[0032] Calculate the actual result of historical total atmospheric attenuation based on the receiving power of the ground receiving station and the transmitting power of the communication satellite;

[0033] According to the actual result of historical total atmospheric attenuation and the calculated historical atmospheric path attenuation, the actual result of historical atmospheric characteristic attenuation is obtained;

[0034] Input the acquired historical stratified atmospheric data into a pre-built characteristic attenuation prediction model, and output the prediction results of the historical atmospheric characteristic attenuation;

[0035] According to the actual results of the historical atmospheric characteristic attenuation and the predicted results of the historical atmospheric characteristic attenuation, the characteristic attenuation prediction model is trained using a mean square error loss function to obtain a trained characteristic attenuation prediction model.

[0036] Optionally, the actual result of the historical atmospheric characteristic attenuation is expressed as:

[0037] ;

[0038] ;

[0039] in, Represents the actual result of the attenuation of historical atmospheric characteristics; represents the historical atmospheric path attenuation; Represents the actual result of the total historical atmospheric attenuation; Indicates the receiving power of the ground receiving station; Indicates the transmission power of the communication satellite.

[0040] Optionally, the mean square error loss function is expressed as:

[0041] ;

[0042] in, represents the mean square error loss function; Represents the number of historical stratified atmospheric data used for training; represents the prediction result of the historical atmospheric characteristic attenuation corresponding to the u-th historical layered atmospheric data; Represents the actual result of the historical atmospheric characteristic attenuation corresponding to the u-th historical stratified atmospheric data.

[0043] Optionally, obtaining a prediction result of total atmospheric attenuation according to the atmospheric characteristic attenuation and the atmospheric path attenuation of the layered atmospheric data includes:

[0044] ;

[0045] in, It represents the prediction result of total atmospheric attenuation; Indicates the attenuation of atmospheric characteristics; represents the atmospheric path attenuation; represents the atmospheric characteristic attenuation weight coefficient; Represents the atmospheric path attenuation weight coefficient.

[0046] On the other hand, the present invention also provides a prediction system for channel atmospheric attenuation, comprising:

[0047] The data acquisition module is used to: acquire the original atmospheric data between the ground receiving station and the communication satellite;

[0048] A data stratification module is used to: extract atmospheric characteristic parameters characterizing atmospheric characteristic attenuation from different meteorological phenomena, and stratify the original atmospheric data according to the atmospheric characteristic parameters to obtain stratified atmospheric data;

[0049] A path attenuation calculation module is used to calculate the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite;

[0050] A characteristic attenuation calculation module is used to: input the stratified atmospheric data into a pre-built characteristic attenuation prediction model, and output the atmospheric characteristic attenuation of the stratified atmospheric data;

[0051] A total attenuation calculation module is used to obtain a prediction result of the total atmospheric attenuation according to the atmospheric characteristic attenuation and the atmospheric path attenuation of the layered atmospheric data;

[0052] The feature attenuation prediction model is constructed by adding a residual block between the convolutional layer and the fully connected layer of the original CRnet network model.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention calculates the attenuation values ​​of different layers in the atmosphere through atmospheric characteristic parameters, the transmission distance between the ground receiving station and satellite communication, the ray elevation parameters, and the characteristic attenuation prediction model, thereby achieving high-precision and low-computation atmospheric attenuation prediction under different environmental conditions. It can accurately simulate the atmospheric attenuation in satellite communication and improve the stability and accuracy of signal transmission. In particular, it can effectively improve the communication quality under high altitude and complex meteorological conditions. It has high practical value and innovation, and provides an effective attenuation calculation solution for satellite-based communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 FIG. 1 is a flow chart of a method for predicting channel atmospheric attenuation in an embodiment of the present invention;

[0056] Figure 2 Shown is a schematic diagram of the structure of a characteristic attenuation prediction model of the present invention in one embodiment;

[0057] Figure 3 Shown is a comparison diagram of the actual result of the total atmospheric attenuation of the present invention and the predicted result of the total atmospheric attenuation in an embodiment. DETAILED DESCRIPTION

[0058] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0059] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0060] Example 1

[0061] like Figure 1 As shown, this embodiment introduces a method for predicting channel atmospheric attenuation, which can effectively deal with the signal attenuation problem under different geographical environments and meteorological conditions. It uses a prediction model and a layered atmospheric calculation method to achieve high-precision, low-computation atmospheric attenuation prediction under different environmental conditions. It can accurately simulate the atmospheric attenuation in satellite communications, significantly improve the performance of the communication system in complex meteorological environments, and provide theoretical support and practical guidance for applications such as satellite communications and ground receiving station communications.

[0062] The method specifically comprises the following steps:

[0063] Step 1: Obtain the original atmospheric data between the ground receiving station and the communication satellite.

[0064] Step 2: stripping out atmospheric characteristic parameters, and stratifying the original atmospheric data according to the atmospheric characteristic parameters: stripping out atmospheric characteristic parameters characterizing atmospheric characteristic attenuation from different meteorological phenomena, and stratifying the original atmospheric data according to the atmospheric characteristic parameters to obtain stratified atmospheric data, specifically:

[0065] Atmospheric characteristic attenuation is caused by different meteorological phenomena, such as rain, snow, haze, hail, etc. The same meteorological phenomenon can also have a great difference in the electromagnetic propagation environment: the signal transmission conditions in light rain and heavy rain weather will be very different; the coexistence of multiple meteorological phenomena such as rain and fog weather will also interfere with the analysis; this embodiment uses atmospheric characteristic parameters at different altitudes as the intrinsic representation to describe the meteorological phenomenon. The same atmospheric characteristic parameters will lead to the same meteorological phenomenon and electromagnetic transmission environment; the atmospheric characteristic parameters considered mainly include atmospheric pressure. , atmospheric temperature and water vapor density , the atmospheric characteristic parameters vary with the height, expressed as:

[0066] ;

[0067] ;

[0068] ;

[0069] in, Indicates atmospheric pressure; represents the atmospheric temperature; represents water vapor density; Indicates the atmospheric pressure corresponding to the standard sea level altitude; Indicates the atmospheric temperature corresponding to the standard sea level; represents the temperature gradient; Indicates altitude; Indicates the water vapor density corresponding to the standard sea level height; represents the height under standard water vapor density. In this embodiment, The value of is 1013hPa, The value of is 15℃, The value is 7.5g / m 3 , The value is -6.5K / km, The value is 2km.

[0070] According to the change law of atmospheric characteristic parameters with altitude, the atmosphere is layered according to altitude; the communication between satellite and ground receiving station needs to pass through the non-homogeneous layer and the homogeneous layer. Meteorological parameters are different in different atmospheric layers, so the altitude step size is set to , in height step The atmospheric data in the layer is regarded as layered atmospheric data, and the changes in meteorological parameters are ignored; when calculating the total atmospheric attenuation, the atmospheric characteristic attenuation and atmospheric path attenuation in each layer are accumulated. The specific number of layers of layered atmospheric data The formula is:

[0071] ;

[0072] in, Indicates the height of different geographical locations from the top of the stratosphere; Indicates the height step; in this embodiment .

[0073] The layered atmospheric data It is expressed as:

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] in, represents the atmospheric characteristic data at the i-th geographical location; represents the atmospheric path data at the i-th geographical location; represents the altitude of the ground receiving station at the i-th geographical location; Indicates the height step length; Indicates Atmospheric pressure in layered atmospheric data; Indicates Water vapor density in layered atmospheric data; Indicates Atmospheric temperature in layered atmospheric data; represents the transmission distance between the ground receiving station and the communication satellite at the i-th geographical location; Indicates the transmission power of the communication satellite; Indicates the gain of the transmitting and receiving antennas; It represents the elevation angle of the ray between the ground receiving station and the communication satellite; Indicates the orbital altitude of the communications satellite.

[0079] Step 3: Calculate the atmospheric characteristic attenuation of the layered atmospheric data: Calculate the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite, specifically:

[0080] Atmospheric path attenuation is caused by the loss of electromagnetic waves when they pass through the atmosphere. It is not affected by complex terrain factors and is described using a direct path. It mainly considers the transmission distance between the communication satellite and the ground receiving station and the influence of the ray elevation angle on the atmospheric path attenuation. , ray elevation , the transmission power of different communication satellites and the gain of the transmitting and receiving antennas , the atmospheric path attenuation at different elevation angles and transmission distances can be obtained, including:

[0081] ;

[0082] ;

[0083] in, Indicates the transmitting frequency of the ground receiving station; represents the speed of light; represents the propagation path of the emission ray in the atmosphere; Represents the product of the effective areas of the transmitting and receiving antennas.

[0084] Step 4: Predicting the atmospheric characteristic attenuation of the layered atmospheric data: Inputting the layered atmospheric data into a pre-built characteristic attenuation prediction model, and outputting the atmospheric characteristic attenuation of the layered atmospheric data, specifically:

[0085] like Figure 2 As shown in FIG, the feature attenuation prediction model is based on the original convolutional recurrent network (CRnet) model structure. The specific structure mainly includes an input layer, a feature extraction layer and an output layer, wherein the input layer contains channels, each channel is a 2*4 grayscale image, the feature extraction layer is composed of a convolutional neural network, a residual network and a fully connected layer, a residual block is added between the convolutional layer and the fully connected layer of the original CRnet network model to obtain the feature attenuation prediction model, the output layer outputs 1*1 data representing the atmospheric feature attenuation, and the layered atmospheric data corresponds to different geographical locations. After the stratification, the uniform atmosphere has a total of 2*4 Component layer atmospheric data.

[0086] That is, after normalizing all layered atmospheric data, the input data of the characteristic attenuation prediction model, i.e., a 2*4 grayscale image, has a total of channels; the feature extraction layer embeds a residual network on the basis of the convolutional neural network to reduce the disappearance of features. The convolution kernel size of the first layer of the convolutional neural network is 1*1, the stride is 1, the expansion is 0, and the number of channels is , the convolution output enters the next layer of convolutional neural network after maximum pooling. The convolution kernel size of the second layer of convolutional neural network is 1*1, the stride is 1, the expansion is 0, and the number of channels is After the convolution output is max-pooled, it is superimposed with the original input data layered atmospheric data at the corresponding position through the batch normalization (BN) layer to obtain a 2*4 The new atmospheric data of channels is then passed through three fully connected layers. The first fully connected layer contains The second fully connected layer contains 32 neurons, the third fully connected layer contains 8 neurons; finally, a scalar data with an output length of 1 is obtained, which represents the atmospheric characteristic attenuation calculated according to the geographical location.

[0087] Collect historical altitude, temperature, atmospheric humidity and other environmental factors, and then calculate the historical atmospheric path attenuation of each layer of stratified atmospheric data under these conditions and the actual result of the historical total atmospheric attenuation , the actual result of the historical total atmospheric attenuation The transmission power of the communication satellite and the receiving power of the ground receiving station The actual result of the historical total atmospheric attenuation is obtained by subtracting It is expressed as:

[0088] ;

[0089] According to the historical atmospheric path attenuation and the actual result of the historical total atmospheric attenuation , calculate the actual result of the historical atmospheric characteristic attenuation , expressed as:

[0090] ;

[0091] The predicted results of historical atmospheric characteristic attenuation predicted by the characteristic attenuation prediction model and the actual results of historical atmospheric characteristic attenuation obtained above are used to calculate the loss using the mean square error loss function, and then back propagation is used to optimize the parameters of the network to obtain a trained characteristic attenuation prediction model with a smaller error. The mean square error loss function It is expressed as:

[0092] ;

[0093] in, Represents the number of historical stratified atmospheric data used for training; represents the prediction result of the historical atmospheric characteristic attenuation corresponding to the u-th historical layered atmospheric data; Represents the actual result of the historical atmospheric characteristic attenuation corresponding to the u-th historical stratified atmospheric data.

[0094] Step 5: Calculate the prediction result of total atmospheric attenuation: According to the atmospheric characteristic attenuation and atmospheric path attenuation of the layered atmospheric data, the prediction result of total atmospheric attenuation is obtained, which is specifically:

[0095] Total atmospheric attenuation caused by complex meteorological environment between ground receiving station and communication satellite In this embodiment Represents the prediction result of total atmospheric attenuation, divided into atmospheric characteristic attenuation and atmospheric path attenuation , according to the different longitude and latitude of the geographical location of the ground receiving station, the characteristic attenuation and path attenuation of the signal received by each ground receiving station in the atmosphere account for different weights of the total attenuation, specifically:

[0096] ;

[0097] in, ; and are the weight coefficients of atmospheric characteristic attenuation and atmospheric path attenuation respectively.

[0098] The weight coefficient is related to the local longitude and latitude and is obtained when training the model. The specific process can be: by back-propagating the error value between the actual result of historical atmospheric characteristic attenuation and the predicted result of historical atmospheric characteristic attenuation calculated by the neural network through feature extraction, the weight of atmospheric characteristic attenuation in the total atmospheric attenuation under different environmental conditions is continuously optimized. The weight of atmospheric path attenuation in the total atmospheric attenuation The optimization goal is to minimize the error between the predicted result of historical atmospheric attenuation and the actual result of historical atmospheric attenuation.

[0099] Using the formula Calculate the predicted total attenuation for each layer of the atmosphere , accumulate the predicted results of the total attenuation of each layer of the atmosphere.

[0100] In this embodiment, by collecting meteorological parameters such as different altitudes, temperatures, humidity and pressures, as well as parameters such as communication frequency, ray elevation angle and transmission distance of satellite communications, the attenuation values ​​of different layers in the atmosphere are calculated, and the parameters in the attenuation prediction model are continuously adjusted through back propagation technology to achieve atmospheric attenuation prediction under different environmental conditions. It can accurately simulate the atmospheric attenuation in satellite communications and improve the stability and accuracy of signal transmission.

[0101] Example 2

[0102] like Figure 3 As shown, this embodiment introduces a specific experimental example of a method for predicting channel atmospheric attenuation, including the following steps:

[0103] The experimental data collection was carried out in different geographical locations in my country, including plains, basins, plateaus, etc. First, the data of the altitude of each place, the number of altitude steps contained in the distance between the altitude of each place and the top of the stratosphere, the altitude difference between the altitude of each place and the bottom of the outer atmosphere, the transmission power of the communication satellite, the communication frequency and the transmission elevation angle were collected as shown in Table 1.

[0104] Table 1 Atmospheric parameters in different geographical locations

[0105]

[0106] The original atmospheric data at position i is taken for detailed description. The atmospheric pressure, atmospheric temperature, water vapor density data and corresponding parameters affecting the attenuation of atmospheric characteristics in each uniform atmospheric layer from position i to the top of the stratosphere are collected with a height step of 1 km to obtain complete original atmospheric data. The original atmospheric data at the i-th geographical location are as follows:

[0107] ;

[0108] After normalizing all layered atmospheric data, the input data of the feature attenuation prediction model is obtained, that is, a 2*4 grayscale image with a total of n channels; the feature extraction layer embeds a residual network on the basis of the convolutional neural network to reduce the disappearance of features. The convolution kernel size of the first layer of the convolutional neural network is 1*1, the stride is 1, the expansion is 0, and the number of channels is 2n. The convolution output enters the next layer of the convolutional neural network after maximum pooling. The convolution kernel size of the second layer of the convolutional neural network is 1*1, the stride is 1, the expansion is 0, and the number of channels is n. After the convolution output passes through the maximum pooling, it is superimposed with the original input data layered atmospheric data at the corresponding position through the batch normalization (BN) layer to obtain 2*4 new atmospheric data with n channels, and then passes through three layers of fully connected layers. The first layer of fully connected layers contains The second fully connected layer contains 32 neurons, the third fully connected layer contains 8 neurons; finally, a scalar data with an output length of 1 is obtained, which represents the atmospheric characteristic attenuation calculated according to the geographical location. Calculate the predicted total attenuation for each layer of the atmosphere , accumulate the predicted results of the total attenuation of each layer of the atmosphere, such as Figure 3 As shown in the figure, it is a comparison chart of the actual and predicted results of the total atmospheric attenuation at a specific geographical location. The corresponding data in the figure are shown in Table 2.

[0109] Table 2 Comparison of predicted and actual results of total atmospheric attenuation

[0110]

[0111] In Table 2, experimental group number 1 is a rainy day in summer, experimental group number 2 is a cloudy day in autumn, experimental group number 3 is a dry day in winter, experimental group number 4 is a rainy day in spring, experimental group number 5 is a sunny day in autumn, and experimental group number 6 is a rainy day in winter.

[0112] According to Table 2, it can be clearly seen that there are significant differences in the total atmospheric attenuation under different meteorological conditions, indicating that the total atmospheric attenuation is closely related to meteorological conditions. By comparing the predicted value in the second row with the actual value in the third row, the results show that this embodiment can accurately predict the difference in the total atmospheric attenuation under different meteorological conditions, and the effect is excellent.

[0113] Example 3

[0114] Based on the same inventive concept as Example 1, this embodiment introduces a channel atmospheric attenuation prediction system, which specifically includes:

[0115] The data acquisition module is used to: acquire the original atmospheric data between the ground receiving station and the communication satellite;

[0116] A data stratification module is used to: extract atmospheric characteristic parameters characterizing atmospheric characteristic attenuation from different meteorological phenomena, and stratify the original atmospheric data according to the atmospheric characteristic parameters to obtain stratified atmospheric data;

[0117] A path attenuation calculation module is used to calculate the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite;

[0118] A characteristic attenuation calculation module is used to: input the stratified atmospheric data into a pre-built characteristic attenuation prediction model, and output the atmospheric characteristic attenuation of the stratified atmospheric data;

[0119] A total attenuation calculation module is used to obtain a prediction result of the total atmospheric attenuation according to the atmospheric characteristic attenuation and the atmospheric path attenuation of the layered atmospheric data;

[0120] Wherein, the construction of the feature attenuation prediction model includes:

[0121] Get the original CRnet network model;

[0122] A residual block is added between the convolutional layer and the fully connected layer of the original CRnet network model to obtain the feature attenuation prediction model.

[0123] The specific functional implementation of each of the above modules can be found in the relevant contents of the method in Example 1 and will not be elaborated here.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for predicting channel atmospheric attenuation, characterized in that: include: Obtaining raw atmospheric data between ground receiving stations and communication satellites; Extracting atmospheric characteristic parameters characterizing atmospheric characteristic attenuation from different meteorological phenomena, and stratifying the original atmospheric data according to the atmospheric characteristic parameters to obtain stratified atmospheric data; the atmospheric characteristic parameters include atmospheric pressure, atmospheric temperature and water vapor density; Calculating the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite; Inputting the stratified atmospheric data into a pre-built characteristic attenuation prediction model, and outputting the atmospheric characteristic attenuation of the stratified atmospheric data; Obtaining a prediction result of total atmospheric attenuation according to the atmospheric characteristic attenuation and atmospheric path attenuation of the layered atmospheric data; The feature attenuation prediction model is constructed by adding a residual block between the convolutional layer and the fully connected layer of the original CRnet network model; Calculating the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and the ray elevation angle between the ground receiving station and the communication satellite, including: ； ； in, represents the atmospheric path attenuation; Indicates the transmission power of the communication satellite; Indicates the gain of the transmitting and receiving antennas; Indicates the transmitting frequency of the ground receiving station; represents the speed of light; represents the propagation path of the emission ray in the atmosphere; Represents the product of the effective areas of the transmitting and receiving antennas; Indicates the transmission distance between the ground receiving station and the communication satellite; Represents the elevation angle of the ray between the ground receiving station and the communication satellite.

2. The method for predicting channel atmospheric attenuation according to claim 1, characterized in that: The atmospheric pressure, atmospheric temperature and water vapor density are expressed as: ； ； ； in, Indicates atmospheric pressure; represents the atmospheric temperature; represents water vapor density; Indicates the atmospheric pressure corresponding to the standard sea level altitude; Indicates the atmospheric temperature corresponding to the standard sea level; represents the temperature gradient; Indicates altitude; Indicates the water vapor density corresponding to the standard sea level height; Indicates the altitude at standard water vapor density.

3. The method for predicting channel atmospheric attenuation according to claim 1, characterized in that: The layered atmospheric data is expressed as: ； ； ； ； in, Represents layered atmospheric data; Indicates i Atmospheric characteristics data at each geographical location; Indicates i Atmospheric path data at geographical locations; Indicates i The altitude of the ground receiving station at each geographical location; Indicates the height step length; Indicates Atmospheric pressure in layered atmospheric data; Indicates Water vapor density in layered atmospheric data; Indicates Atmospheric temperature in layered atmospheric data; Indicates i The transmission distance between the ground receiving station and the communication satellite at each geographical location; Indicates the transmission power of the communication satellite; Indicates the gain of the transmitting and receiving antennas; It represents the elevation angle of the ray between the ground receiving station and the communication satellite; Indicates the orbital altitude of the communications satellite.

4. The method for predicting channel atmospheric attenuation according to claim 1, characterized in that: The number of layers of the layered atmospheric data is expressed as: ； in, The number of layers representing layered atmospheric data; Indicates the height of different geographical locations from the top of the stratosphere; Indicates the height step length; 5. The method for predicting channel atmospheric attenuation according to claim 1, characterized in that: The training steps of the feature prediction model include: Calculate the actual result of historical total atmospheric attenuation based on the receiving power of the ground receiving station and the transmitting power of the communication satellite; According to the actual result of historical total atmospheric attenuation and the calculated historical atmospheric path attenuation, the actual result of historical atmospheric characteristic attenuation is obtained; Input the acquired historical stratified atmospheric data into a pre-built characteristic attenuation prediction model, and output the prediction results of the historical atmospheric characteristic attenuation; According to the actual results of the historical atmospheric characteristic attenuation and the predicted results of the historical atmospheric characteristic attenuation, the characteristic attenuation prediction model is trained using a mean square error loss function to obtain a trained characteristic attenuation prediction model.

6. The method for predicting channel atmospheric attenuation according to claim 5, characterized in that: The actual result of the historical atmospheric characteristic attenuation is expressed as: ； ； in, Represents the actual result of the attenuation of historical atmospheric characteristics; represents the historical atmospheric path attenuation; Represents the actual result of the total historical atmospheric attenuation; Indicates the receiving power of the ground receiving station; Indicates the transmission power of the communication satellite.

7. The method for predicting channel atmospheric attenuation according to claim 5, characterized in that: The mean square error loss function is expressed as: ； in, represents the mean square error loss function; Represents the number of historical stratified atmospheric data used for training; Indicates u The prediction results of historical atmospheric characteristic attenuation corresponding to historical stratified atmospheric data; Indicates u The actual result of the historical atmospheric characteristic attenuation corresponding to the historical stratified atmospheric data.

8. The method for predicting channel atmospheric attenuation according to claim 1, characterized in that: According to the atmospheric characteristic attenuation and atmospheric path attenuation of the layered atmospheric data, a prediction result of the total atmospheric attenuation is obtained, including: ； in, It represents the prediction result of total atmospheric attenuation; Indicates the attenuation of atmospheric characteristics; represents the atmospheric path attenuation; represents the atmospheric characteristic attenuation weight coefficient; Represents the atmospheric path attenuation weight coefficient.

9. A channel atmospheric attenuation prediction system, characterized in that: include: The data acquisition module is used to: acquire the original atmospheric data between the ground receiving station and the communication satellite; A data stratification module is used to: extract atmospheric characteristic parameters characterizing atmospheric characteristic attenuation from different meteorological phenomena, and stratify the original atmospheric data according to the atmospheric characteristic parameters to obtain stratified atmospheric data; the atmospheric characteristic parameters include atmospheric pressure, atmospheric temperature and water vapor density; A path attenuation calculation module is used to calculate the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and ray elevation angle between the ground receiving station and the communication satellite; A characteristic attenuation calculation module is used to: input the stratified atmospheric data into a pre-built characteristic attenuation prediction model, and output the atmospheric characteristic attenuation of the stratified atmospheric data; A total attenuation calculation module is used to obtain a prediction result of the total atmospheric attenuation according to the atmospheric characteristic attenuation and the atmospheric path attenuation of the layered atmospheric data; The feature attenuation prediction model is constructed by adding a residual block between the convolutional layer and the fully connected layer of the original CRnet network model; Calculating the atmospheric path attenuation of the layered atmospheric data according to the transmission distance and the ray elevation angle between the ground receiving station and the communication satellite, including: ； ； in, represents the atmospheric path attenuation; Indicates the transmission power of the communication satellite; Indicates the gain of the transmitting and receiving antennas; Indicates the transmitting frequency of the ground receiving station; represents the speed of light; represents the propagation path of the emission ray in the atmosphere; Represents the product of the effective areas of the transmitting and receiving antennas; Indicates the transmission distance between the ground receiving station and the communication satellite; Represents the elevation angle of the ray between the ground receiving station and the communication satellite.

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