Signal intensity prediction method and device based on ionosphere reflection loss model

Abstract

The invention discloses a signal intensity prediction method based on an ionosphere reflection loss model. The method comprises the following steps: step 1, ionosphere parameter measurement; step 2, signal intensity prediction; and step 3, result display and output. According to the method disclosed by the invention, by introducing the ionosphere reflection loss model, the signal intensity can be accurately predicted, and the communication quality and network planning can be effectively optimized. Compared with a traditional method, the method can better consider the influence factors of the ionosphere, and improves the accuracy and reliability of signal prediction.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a signal strength prediction method and device based on an ionospheric reflection loss model in the field. Background Art

[0002] In long-distance communications, signal strength prediction is crucial for optimizing communication quality and network planning. Traditional signal strength prediction methods usually take into account the attenuation of the ground propagation path, but ignore the impact of the ionosphere on the signal. However, ionospheric reflection loss is an important factor in determining the quality of shortwave communications, and existing signal strength prediction devices cannot accurately consider the impact of the ionosphere.

[0003] The ionosphere is a layer of ionized gas in the Earth's atmosphere that has a great influence on the propagation of electromagnetic waves. In long-distance communications, especially for shortwave communications, the reflection and propagation loss of the ionosphere is one of the key factors that determine the quality of communication.

[0004] Traditional signal strength prediction methods usually only consider the attenuation of the ground propagation path, while ignoring the influence of the ionosphere. However, the ionosphere causes reflection and refraction of the signal, resulting in signal attenuation or enhancement. This reflection and propagation loss is related to the height, density and ionospheric parameters of the ionosphere. Therefore, in order to accurately predict the signal strength, the influence of the ionosphere on the signal must be taken into account.

[0005] Existing signal strength prediction devices cannot fully consider the influence of the ionosphere. This may lead to a certain gap between the prediction results and the actual situation, affecting the reliability and stability of communication. Therefore, it is very necessary to develop a device that can accurately predict signal strength. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a method and a device capable of accurately predicting signal strength, which not only considers the modeling of ionospheric effects, but also analyzes the reflection loss of signals.

[0007] The present invention adopts the following technical solution:

[0008] A signal strength prediction method based on an ionospheric reflection loss model, the improvement of which is that it comprises the following steps:

[0009] Step 1, ionospheric parameter measurement:

[0010] Step 11, receiving and acquiring ionospheric images;

[0011] Step 12, ionospheric parameter extraction;

[0012] Step 2, signal strength prediction:

[0013] Step 21, ionospheric parameter acquisition and analysis;

[0014] Step 22, ground propagation path modeling;

[0015] Step 23, signal strength prediction;

[0016] Step 3: Display and output the results.

[0017] Furthermore, in step 11, a receiver is used to receive the signal of the ionospheric reflection image, and the received signal is amplified. If the received signal is modulated, the signal needs to be demodulated to restore the original ionospheric reflection image signal, and the signal is converted into digital form using a data acquisition device, and filtered, enhanced, corrected and noise reduced.

[0018] Furthermore, in step 12, the received ionospheric reflection image is processed and analyzed, including background removal, noise suppression and edge detection, to extract ionospheric parameter information, including ionospheric height and ionospheric density:

[0019] Ionosphere height: H = FWHM / 2

[0020] Ionospheric density: N = ∫I(z)dz

[0021] In the above formula, H is the ionosphere height, FWHM is the full width at half maximum, N is the ionosphere density, and I(z) is the intensity value of the ionosphere image at height z;

[0022] Assume that the parameter information obtained through image analysis and feature extraction is P, the required ionospheric parameter information is I, the mathematical model is M, the statistical method is S, and the algorithm is A:

[0023] It can be expressed as: I=M(P)+S(P)+A(P).

[0024] Furthermore, in step 21, the collected ionospheric parameter data are analyzed, including data cleaning, processing and calibration; the ionospheric parameters are sorted and summarized, including averaging and interpolating the data.

[0025] Further, in step 22, terrain and obstacle analysis:

[0026] Assume that the propagation path is P, the terrain data set is T, the digital elevation model is DEM, and the obstacle set is O, where the mountains are M and the buildings are B;

[0027] Terrain and obstacle analysis = obtaining terrain data (T) + building a digital elevation model (DEM) + identifying obstacles (O = {M, B, ...}) and related to the propagation path P;

[0028] Propagation model selection: including free space propagation model, two-line propagation model and Okumura-Hata model;

[0029] Propagation loss calculation, using the selected propagation model, calculates the propagation loss of the signal on the ground:

[0030] L=L0+10n*log 10 (d)+X f

[0031] In the above formula, L is the propagation loss, L0 is the free space loss, n is the propagation path loss exponent, d is the propagation distance, and X f is the additional loss;

[0032] The free space loss L0 is calculated according to the following formula:

[0033] L0=20log 10 (d)+20log 10 (f)-147.55

[0034] In the above formula, f is the frequency of the signal.

[0035] Further, in step 23, the parameters are integrated, the obtained ionospheric parameters and the ground propagation path model are integrated together, and the output of the ionospheric parameters and the propagation loss model are used as input for signal strength prediction:

[0036] Assume that the ionospheric parameter is I, the ground propagation path model is P, and the output of the propagation loss model is L. Then the signal strength prediction function is: f(I, P, L), where I and P are the integrated inputs, and L is the output of the propagation loss model;

[0037] Prediction model establishment, based on the integrated parameters, a signal strength prediction model is established. Assuming the integrated parameter is P, the signal strength is S, the machine learning method is M1, the regression analysis method is M2, and the artificial neural network method is M3, the prediction model is expressed as: S = f (P, M1, M2, M3), where f represents the function of predicting the change of signal strength;

[0038] Strength prediction: Use the established prediction model to predict the signal strength for given ionospheric parameters and ground propagation paths. Assuming that the ionospheric parameter is I, the ground propagation path is P, the prediction model is f, and the predicted signal strength value is S, it can be expressed as: S = f(I,P).

[0039] Furthermore, in step 3, result display includes data preparation, result processing and display; and output includes output type selection, output data processing and output.

[0040] A signal strength prediction device based on an ionospheric reflection loss model, the improvement of which comprises:

[0041] The ionospheric parameter measurement module obtains the ionospheric reflection image through the receiver and further extracts the ionospheric parameter information;

[0042] The signal strength prediction module predicts the signal strength based on the ionospheric reflection loss model, taking into account the ionospheric parameters and the ground propagation path;

[0043] The display and control module displays and outputs the signal strength prediction results and provides them to users for further analysis and decision-making.

[0044] The beneficial effects of the present invention are:

[0045] The method disclosed in the present invention can accurately predict signal strength and effectively optimize communication quality and network planning by introducing an ionospheric reflection loss model. Compared with traditional methods, it can better consider the influence of the ionosphere and improve the accuracy and reliability of signal prediction.

[0046] The method disclosed in the present invention establishes a suitable ionosphere model in order to accurately predict the signal strength. This model will take into account the influence of different ionosphere parameters on the propagation and attenuation of the signal. A large amount of ionosphere related data, including ionosphere height, density, ionosphere irregularity, etc., will be collected, and the ionosphere model will be established using these data. This model will take into account the influence of different frequencies, seasons, time and geographical locations on the ionosphere, and accurately predict signal propagation.

[0047] The method disclosed in the present invention analyzes the reflection loss of the signal. The ionosphere causes reflection and refraction of the signal, resulting in signal attenuation or enhancement. The signal reflection characteristics at different frequencies and antenna heights are studied, and the loss of the signal when it passes through the ionosphere during propagation is analyzed. Through this analysis, appropriate parameters are determined to measure the degree of signal loss in the ionosphere, thereby accurately predicting the signal strength.

[0048] The device disclosed in the present invention can more accurately consider the influencing factors of the ionosphere. By measuring the parameter information of the ionosphere, such as the ionosphere height, the ionosphere density, etc., these parameters are comprehensively considered with the attenuation factors of the ground propagation path. By using the ionosphere reflection loss model to predict the signal strength, the communication quality and network planning are optimized.

[0049] The device disclosed in the present invention improves the performance and reliability of the communication system, and can help operators and network planners better optimize signal coverage and signal quality. At the same time, in key scenarios such as emergency communication and remote communication, accurate signal strength prediction is also crucial to ensure the stability and reliability of communication.

[0050] During the development of the device, advanced computing technology and data analysis methods were used to process a large amount of ionospheric data and signal data. A high-precision model was established and the algorithm was optimized to improve the accuracy and real-time performance of the prediction results. At the same time, the portability and ease of use of the device were also considered to make it suitable for different scenarios and application requirements.

[0051] With this device that can accurately predict signal strength, communication system operators and network planners will be able to better plan and optimize signal coverage areas and improve communication reliability and stability. In addition, in critical scenarios such as emergency communications and remote communications, accurate signal strength prediction will become an important tool to ensure communications, helping to respond to emergencies in a timely manner and provide effective communication support. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0053] Figure 2 It is a schematic flow diagram of step 1 of the method of the present invention;

[0054] Figure 3 It is a schematic flow diagram of step 2 of the method of the present invention;

[0055] Figure 4 It is a schematic flow diagram of step 3 of the method of the present invention;

[0056] Figure 5 It is a block diagram of the composition of the device of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] Embodiment 1, this embodiment discloses a signal strength prediction method based on ionospheric reflection loss model, such as Figure 1 As shown, the following steps are included:

[0059] Step 1, ionospheric parameter measurement, such as Figure 2 As shown, the following steps are included:

[0060] Step 11, ionospheric image reception and acquisition:

[0061] Specifically, in step 11, in the ionospheric parameter measurement step, it is necessary to receive and acquire the ionospheric image.

[0062] 1. Use of appropriate receivers: The device should be equipped with appropriate receivers such as antennas or receiving antenna arrays to receive signals of images reflected from the ionosphere. These receivers should be able to receive and process radio signals reflected from the ionosphere.

[0063] a) Antenna selection: Selecting the right antenna is an important factor to ensure the correct reception of the image signal reflected from the ionosphere. For radio communications, the antenna is a key component that captures the signal reflected from the ionosphere and transmits it to the receiver. According to the application requirements and the communication frequency range, select the appropriate type of antenna, such as monopole antenna, dipole antenna, array antenna, etc.

[0064] b) Antenna layout: The layout of the antenna is also a key factor in determining the signal reception effect. When selecting the antenna layout, it is necessary to consider the characteristics of the signal propagation path and the location of the ionospheric reflection. Sometimes it may be necessary to use multiple antennas or antenna arrays to achieve directional reception, reduce interference, and improve reception sensitivity.

[0065] c) Signal processing: The receiver should be able to receive and process radio signals reflected from the ionosphere. This includes signal demodulation, amplification, filtering and other processing steps to extract useful information and exclude noise and interference.

[0066] d) Frequency selection: According to the frequency range of the signal to be received, select the working frequency band of the receiver to ensure that the frequency range of the image signal reflected by the ionosphere can be received. Different propagation paths and weather conditions may cause changes in the signal spectrum, so it is very important to select the appropriate frequency range.

[0067] e) Sensitivity and dynamic range: The sensitivity and dynamic range of the receiver are also considerations. Sensitivity determines how well the receiver perceives weak signals, while dynamic range determines whether the receiver can maintain a low noise level in the presence of strong signals.

[0068] 2. Signal reception and amplification: The receiver amplifies the received signal to enhance the signal strength and improve the accuracy of subsequent processing. This is achieved by using an amplifier or signal processing circuit.

[0069] Signal reception and amplification is a vital step in wireless communication systems. The main task of the receiver is to receive the signal from the transmitter and amplify it to a sufficient strength for subsequent processing and decoding.

[0070] a) Amplifier: An amplifier is an electronic device that increases the amplitude or strength of a signal. It selects the appropriate amplifier type based on the characteristics of the signal, such as a low noise amplifier (LNA) for boosting weak signals and a power amplifier (PA) for boosting the power of a signal. Amplifiers typically use active devices such as transistors or field effect transistors to provide amplification, and are cascaded as needed to achieve higher amplification.

[0071] b) Signal processing circuits: In addition to pure amplifiers, signal processing circuits are also widely used for signal enhancement and optimization. These circuits perform functions such as filtering, equalization or frequency conversion. Filters can be used to remove noise or select signals within a specific frequency range to improve signal quality. Equalizers can be used to offset signal distortion during transmission to make the received signal closer to the original signal. Frequency converters can be used to convert signals from one frequency range to another to meet different application requirements.

[0072] c) Automatic Gain Control (AGC): AGC is a technology that automatically adjusts the receiver's gain to accommodate changes in signal strength. When the received signal strength is weak, AGC increases the gain to improve the receiving sensitivity. When the signal strength is strong, AGC decreases the gain to avoid signal overload.

[0073] 3. Signal demodulation: If the received signal is modulated (through frequency modulation or other modulation techniques), the signal needs to be demodulated to restore the original ionospheric reflection image signal.

[0074] Signal demodulation is used to process modulated signals in order to extract the original information from them. When the received signal is modulated by frequency modulation or other modulation techniques, its characteristics are embedded in the carrier signal, making the signal impossible to interpret directly. Therefore, through the demodulation process, these characteristics are restored to obtain useful information.

[0075] The goal of demodulation is to convert the modulated signal back to the original ionospheric reflection image signal. To achieve this goal, a variety of demodulation techniques and algorithms are used. Among them, frequency demodulation technology is often used to process FM signals by detecting changes in the carrier frequency and converting it into the audio signal of the original signal. Phase demodulation technology is applicable to phase modulation or phase shift keying signals by measuring changes in the signal phase and converting it into digital data. AM demodulation technology is often used to process AM signals by detecting changes in the carrier amplitude and converting it into the amplitude of the original signal.

[0076] In actual applications, other complex demodulation techniques and algorithms may be used according to different modulation schemes and requirements. The purpose of these demodulation processes is to ensure that the receiver can accurately restore the original ionospheric reflection image signal. This is critical for signal reception in wireless communication systems, radars and other applications to ensure that the signal can be correctly decoded and understood.

[0077] 4. Data acquisition and processing: By connecting the receiver to the data acquisition equipment, the demodulated signal is converted into digital data, and the data is sampled and processed. These data are represented as pixel values ​​or other relevant parameters of the ionospheric reflection image.

[0078] Data acquisition and processing are important steps to convert the demodulated signal into digital data. By connecting the receiver with the data acquisition device, the demodulated signal can be obtained and converted into a processable digital form. These digital data are represented as pixel values ​​or other related parameters of the ionospheric reflection image, providing a basis for subsequent analysis and application.

[0079] During the data acquisition process, the demodulated signal is sampled using a data acquisition device. Sampling means discretizing a continuous signal, that is, taking a snapshot of the signal at a certain time interval. By setting an appropriate sampling frequency, enough samples are captured to accurately represent the characteristics of the original signal.

[0080] The collected data also needs to be processed to meet specific requirements. Processing steps may include data filtering, enhancement, correction, and noise reduction. Filtering removes irrelevant noise or interference to highlight the characteristics of the signal. Enhancement techniques improve the quality or significance of the image, making it easier to analyze and interpret the data. Correction steps correct errors caused by equipment or transmission, making the data more accurate and reliable. Noise reduction techniques further optimize data quality and improve the signal-to-noise ratio.

[0081] Step 12, ionospheric parameter extraction:

[0082] Specifically, in step 12, after obtaining the ionospheric reflection image data, the device needs to further extract ionospheric parameter information.

[0083] 1. Image analysis: The received ionospheric reflection images are processed and analyzed. This may involve image processing techniques such as background removal, noise suppression, and edge detection to accurately extract the characteristics of the ionosphere.

[0084] Assume that the received ionospheric reflection image is I, and the set of image processing and analysis operations is O, which includes background removal operation B, noise suppression operation N, edge detection operation E, etc.

[0085] It can be expressed as: O(I) = B(I) ∩ N(I) ∩ E(I) ..., to extract the characteristics of the ionosphere.

[0086] Assume that the background removal function is f B (I), the noise suppression function is f N (I), the edge detection function is f E (I).

[0087] Then it can be expressed in more detail as: O(I) = f B (I)×f N (I)×f E (I)...

[0088] 2. Feature extraction: Based on the reflection image of the ionosphere, the ionosphere parameter information is extracted by using image processing algorithms and related technologies. These parameters include ionosphere height, ionosphere density, etc.

[0089] In the process of ionospheric image feature extraction, key ionospheric parameter information is extracted from the ionospheric reflection image through image processing algorithms and related technologies. The ionospheric altitude refers to the altitude position where the peak concentration of free electrons in the ionosphere is located. By analyzing the brightness and reflection intensity distribution of the ionospheric reflection image, methods such as the Full Width at Half Maximum (FWHM) method or signal processing technology are used to accurately estimate the ionospheric altitude.

[0090] Ionospheric density refers to the number of free electrons per unit volume. By analyzing the intensity changes and density distribution of the ionospheric image, the ionospheric density can be inferred using the radiation transfer model or the inverse problem solving method. The ionospheric density can be estimated using the Boltzmann equation or the inverse problem solving method based on statistical methods (such as the least squares method).

[0091] The accurate extraction of these two parameters is of great significance for the study of the ionosphere and the application of various wireless communication systems. It is used to predict the propagation characteristics of electromagnetic signals in the ionosphere, evaluate the quality of communication links, and optimize the design and deployment of wireless communication systems. The mathematical expression of feature extraction is as follows:

[0092] Ionosphere height: H = FWHM / 2

[0093] Ionospheric density: N = ∫I(z)dz

[0094] Among them, H is the ionosphere height, FWHM is the full width at half maximum, N is the ionosphere density, and I(z) is the intensity value of the ionosphere image at height z. Through these formulas and corresponding image processing algorithms, important ionosphere parameter information can be extracted from the ionosphere reflection image.

[0095] 3. Data processing and calculation: The parameter information obtained through image analysis and feature extraction is further processed and calculated. This may involve mathematical models, statistical methods and algorithms to derive the required ionospheric parameter information.

[0096] Suppose the parameter information obtained through image analysis and feature extraction is P, the required ionospheric parameter information is I, the mathematical model is M, the statistical method is S, and the algorithm is A.

[0097] It can be expressed as: I = M (P) + S (P) + A (P)

[0098] In the data processing and calculation stage, the ionospheric parameter information obtained through image analysis and feature extraction is further processed and calculated to obtain the required ionospheric parameters. This process involves the application of mathematical models, statistical methods and algorithms.

[0099] The relationship between ionospheric parameters and image features is established using existing mathematical models. These mathematical models are based on ionospheric physics principles, such as radiation transfer models or ionospheric density distribution models. By matching image features with mathematical models, a preliminary estimate of ionospheric parameters is obtained.

[0100] Statistical methods are applied to further optimize the estimation of ionospheric parameters. The least squares method is used to fit the error between the actual observation data and the model prediction value, thus obtaining more accurate ionospheric parameter estimation results.

[0101] Calculation is also indispensable. For complex ionospheric parameter calculation problems, computer algorithms are used for numerical calculation and optimization. Iterative algorithms or optimization algorithms are used to maximize the likelihood function or minimize the error function to obtain the optimal ionospheric parameter estimate.

[0102] 4. Parameter output or display: The extracted ionospheric parameter information is output or displayed for use by users or other systems. This is achieved by displaying it on the device screen, sending it to other devices, or storing it in memory.

[0103] Step 2, signal strength prediction, such as Figure 3 As shown, the following steps are included:

[0104] Step 21, ionospheric parameter acquisition and analysis.

[0105] Specifically, in step 21, in the process of signal strength prediction, it is first necessary to obtain and analyze ionospheric parameters to consider the impact of the ionosphere on signal propagation.

[0106] 1. Data acquisition: Obtain ionospheric parameter data such as ionospheric height, ionospheric density, etc. through receivers or other equipment. These data can be obtained from ionospheric detectors or related equipment.

[0107] 2. Data analysis: Analyze the collected ionospheric parameter data. This may involve data cleaning, processing and calibration to ensure data quality and accuracy.

[0108] 3. Parameter sorting: sorting and summarizing the ionospheric parameters, including averaging, interpolation or other statistical processing of the data to obtain more accurate and usable parameters.

[0109] Step 22, ground propagation path modeling:

[0110] Specifically, in step 22, when considering the ground propagation path, a propagation model is established to simulate the propagation and attenuation of the signal on the ground.

[0111] 1. Terrain and obstacle analysis: Analyze the terrain and obstacles on the propagation path. This includes obtaining terrain data, building digital elevation models, and identifying obstacles that affect propagation, such as mountains, buildings, etc.

[0112] Assume that the propagation path is P, the terrain data set is T, the digital elevation model is DEM, and the obstacle set is O (where mountains are M, buildings are B, etc.).

[0113] Terrain and obstacle analysis = obtaining terrain data (T) + establishing a digital elevation model (DEM) + identifying obstacles (O = {M, B, ...}) and related to the propagation path P.

[0114] 2. Propagation model selection: Select an appropriate propagation model based on the propagation environment and prediction requirements. This may involve free space propagation model, two-line propagation model, Okumura-Hata model, etc. Select the most appropriate model based on the specific situation.

[0115] 3. Propagation loss calculation: Use the selected propagation model to calculate the propagation loss of the signal on the ground. This will take into account factors such as terrain, obstacles, attenuation and reflection of the signal, and thus predict the change in signal strength.

[0116] Propagation loss calculation estimates the propagation loss of the signal on the ground by selecting an appropriate propagation model. The calculation takes into account factors such as terrain, obstacles, attenuation and reflection of the signal to predict changes in signal strength.

[0117] The propagation loss is expressed by the following formula:

[0118] L=L0+10n*log 10 (d)+X f

[0119] Where L is the propagation loss (expressed in decibels), L0 is the free space loss (attenuation per unit distance dB / m), n is the propagation path loss exponent (usually between 2 and 4), d is the propagation distance (in meters), and X f is the additional loss (taking into account the effects of terrain and obstacles).

[0120] The free space loss L0 is calculated according to the following formula:

[0121] L0=20log 10 (d)+20log 10 (f)-147.55

[0122] Where f is the frequency of the signal (in Hertz Hz).

[0123] The additional loss Xf is calculated by taking into account the effects of terrain and obstacles. This uses a terrain and obstacle database, as well as specialized algorithms to evaluate the attenuation and reflection of the signal in terrain and obstacles. The specific calculation method varies depending on the model and data used.

[0124] Step 23, signal strength prediction:

[0125] Specifically, in step 23, the signal strength is predicted using ionospheric parameters and a ground propagation path model.

[0126] 1. Parameter integration: Integrate the obtained ionospheric parameters and the ground propagation path model. This uses the ionospheric parameters and the output of the propagation loss model as input for signal strength prediction.

[0127] Assume that the ionospheric parameters are I, the ground propagation path model is P, and the output of the propagation loss model is L.

[0128] It can be expressed as: signal strength prediction function f(I,P,L)

[0129] Where I and P are the inputs of the integration, and L is the output of the propagation loss model.

[0130] Assuming that the signal strength prediction is obtained by simple addition operation,

[0131] Then the formula is: S=f(I,P,L)=I+P+L, where S represents the predicted signal strength.

[0132] Assume that the signal strength prediction is obtained by multiplication,

[0133] Then the formula is: S = f(I, P, L) = I × P × L, where S represents the predicted signal strength.

[0134] 2. Prediction model establishment: Based on the integrated parameters, a signal strength prediction model is established. The model is established using machine learning, regression analysis, artificial neural network and other methods to predict changes in signal strength.

[0135] Assume that the integrated parameter is P, the signal strength is S, the machine learning method is M1, the regression analysis method is M2, and the artificial neural network method is M3.

[0136] The prediction model can be expressed as: S = f(P, M1, M2, M3), where f represents a functional relationship established by a corresponding method, which is used to predict changes in signal strength.

[0137] If regression analysis is used, S = aP + b linear function,

[0138] Where a and b are coefficients obtained through data analysis. Using artificial neural network methods, its functional form may be very complex, involving connections and weight calculations between multiple layers of neurons.

[0139] 3. Strength prediction: Use the established prediction model to predict the signal strength for given ionospheric parameters and ground propagation path. This results in predicted signal strength values ​​for subsequent applications and decision making.

[0140] Assume that the ionospheric parameter is I, the ground propagation path is P, the prediction model is f, and the predicted signal strength value is S.

[0141] It can be expressed as: S=f(I,P).

[0142] Step 3, display and control, such as Figure 4 As shown, the following steps are included:

[0143] Step 31, the result shows:

[0144] Specifically, in step 31, in the display and control module of signal strength prediction, the predicted signal strength result needs to be displayed first for user observation and analysis.

[0145] 1. Data preparation: Get the prediction result data from the signal strength prediction module. These data are digital signal strength values ​​or other related information.

[0146] 2. Result processing: Process and prepare the obtained prediction result data. This may include data format conversion, unit calibration, screening and sorting, etc., to facilitate subsequent display operations.

[0147] 3. Result display: Use a suitable display device or interface to visualize the signal strength prediction results. This is in the form of a display screen, chart, graph, etc., so that the user can clearly observe the prediction results.

[0148] Step 32, output function:

[0149] Specifically, in step 32, in addition to displaying the signal strength prediction result, the result is also output for the user to perform further analysis and decision making.

[0150] 1. Output type selection: According to user needs and application scenarios, select the appropriate output type. This is the form of saving as a file, transferring to other devices, printing out, etc.

[0151] 2. Output data processing: Perform appropriate data processing on the signal strength prediction results. This may include data format conversion, encryption, compression, packaging, etc. to ensure data integrity and security.

[0152] 3. Output operation: According to the selected output type, the corresponding output operation is performed. This may involve file saving, data transmission, printing instructions, etc., to output the prediction results to the target location or device required by the user.

[0153] Step 33, user analysis and decision making:

[0154] Specifically, in step 33, users analyze the prediction results and make decisions based on their needs and goals.

[0155] 1. Result analysis: By observing the displayed signal strength prediction results, the user conducts analysis and evaluation. This may include observation and judgment of trends, fluctuations, outliers, etc. to obtain information about signal strength.

[0156] 2. Data processing and decision-making: Based on the results of the analysis, users conduct further data processing and decision-making. This may involve operations such as data screening, integration, and correlation analysis to draw conclusions or make corresponding decisions.

[0157] 3. Application and feedback: Users apply the results of the analysis to actual scenarios and provide feedback. This may be changing device configurations, adjusting communication parameters, recording observed problems, etc. to improve communication quality or solve problems.

[0158] In summary, the method of the present invention can accurately predict signal strength, can provide more accurate signal strength prediction, provide more stable and reliable communication quality for long-distance communication, and provide an important reference basis for network planning and optimization, and has important application and promotion value.

[0159] This embodiment also discloses a signal strength prediction device based on an ionospheric reflection loss model. Figure 5 As shown, including:

[0160] The ionospheric parameter measurement module obtains the ionospheric reflection image through the receiver and further extracts the ionospheric parameter information, including ionospheric height, ionospheric density, etc.

[0161] The signal strength prediction module predicts the signal strength based on the ionospheric reflection loss model, taking into account the ionospheric parameters and the ground propagation path; this model takes into account the reflection and propagation loss of shortwave signals in the ionosphere and can predict the signal strength more accurately.

[0162] The display and control module displays and outputs the signal strength prediction results and provides them to users for further analysis and decision-making.

Claims

1. A signal strength prediction method based on an ionospheric reflection loss model, characterized in that: The steps include: Step 1, ionospheric parameter measurement: Step 11, receiving and acquiring ionospheric images; Step 12, ionospheric parameter extraction; Step 2, signal strength prediction: Step 21, ionospheric parameter acquisition and analysis; Step 22, ground propagation path modeling; Step 23, signal strength prediction; Step 3: Display and output the results.

2. The signal strength prediction method based on the ionospheric reflection loss model according to claim 1, characterized in that: In step 11, a receiver is used to receive the signal of the ionospheric reflection image, and the received signal is amplified. If the received signal is modulated, the signal needs to be demodulated to restore the original ionospheric reflection image signal. The signal is converted into digital form using a data acquisition device, and filtered, enhanced, corrected and noise reduced.

3. The signal strength prediction method based on the ionospheric reflection loss model according to claim 1, characterized in that: In step 12, the received ionospheric reflection image is processed and analyzed, including background removal, noise suppression and edge detection, to extract ionospheric parameter information, including ionospheric height and ionospheric density: Ionosphere height: H = FWHM / 2 Ionospheric density: N = ∫I(z)dz In the above formula, H is the ionosphere height, FWHM is the full width at half maximum, N is the ionosphere density, and I(z) is the intensity value of the ionosphere image at height z; Assume that the parameter information obtained through image analysis and feature extraction is P, the required ionospheric parameter information is I, the mathematical model is M, the statistical method is S, and the algorithm is A: It can be expressed as: I=M(P)+S(P)+A(P).

4. The signal strength prediction method based on the ionospheric reflection loss model according to claim 1, characterized in that: In step 21, the collected ionospheric parameter data are analyzed, including data cleaning, processing and calibration; the ionospheric parameters are sorted and summarized, including averaging and interpolating the data.

5. The signal strength prediction method based on the ionospheric reflection loss model according to claim 1, characterized in that: In step 22, terrain and obstacle analysis: Assume that the propagation path is P, the terrain data set is T, the digital elevation model is DEM, and the obstacle set is O, where the mountains are M and the buildings are B; Terrain and obstacle analysis = obtaining terrain data (T) + building a digital elevation model (DEM) + identifying obstacles (O = {M, B, ...}) and related to the propagation path P; Propagation model selection: including free space propagation model, two-line propagation model and Okumura-Hata model; Propagation loss calculation, using the selected propagation model, calculates the propagation loss of the signal on the ground: L=L0+10n*log 10 (d)+X f In the above formula, L is the propagation loss, L0 is the free space loss, n is the propagation path loss exponent, d is the propagation distance, and X f is the additional loss; The free space loss L0 is calculated according to the following formula: L0=20log 10 (d)+20log 10 (f)-147.55 In the above formula, f is the frequency of the signal.

6. The signal strength prediction method based on the ionospheric reflection loss model according to claim 1, characterized in that: In step 23, the parameters are integrated to integrate the obtained ionospheric parameters and the ground propagation path model, and the output of the ionospheric parameters and the propagation loss model are used as input for signal strength prediction: Assume that the ionospheric parameter is I, the ground propagation path model is P, and the output of the propagation loss model is L. Then the signal strength prediction function is: f(I, P, L), where I and P are the integrated inputs, and L is the output of the propagation loss model; Prediction model establishment, based on the integrated parameters, a signal strength prediction model is established. Assuming the integrated parameter is P, the signal strength is S, the machine learning method is M1, the regression analysis method is M2, and the artificial neural network method is M3, the prediction model is expressed as: S = f (P, M1, M2, M3), where f represents the function of predicting the change of signal strength; Strength prediction: Use the established prediction model to predict the signal strength for given ionospheric parameters and ground propagation paths. Assuming that the ionospheric parameter is I, the ground propagation path is P, the prediction model is f, and the predicted signal strength value is S, it can be expressed as: S = f(I,P).

7. The signal strength prediction method based on the ionospheric reflection loss model according to claim 1, characterized in that: In step 3, result display includes data preparation, result processing and display; output includes output type selection, output data processing and output.

8. A signal strength prediction device based on an ionospheric reflection loss model, characterized in that: include: The ionospheric parameter measurement module obtains the ionospheric reflection image through the receiver and further extracts the ionospheric parameter information; The signal strength prediction module predicts the signal strength based on the ionospheric reflection loss model, taking into account the ionospheric parameters and the ground propagation path; The display and control module displays and outputs the signal strength prediction results and provides them to users for further analysis and decision-making.

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