A method and device for ionospheric environment information decision frequency planning analysis

Abstract

The application discloses an ionosphere environment information decision frequency planning analysis method and device, the method comprises the following steps: acquiring scattering sweep data information, oblique sweep data information and available frequency band resource information; processing the scattering sweep data information and the oblique sweep data information to obtain ionosphere three-dimensional distribution model information; processing the available frequency band resource information and the ionosphere three-dimensional distribution model information to obtain working frequency result information. It can be seen that, through the analysis of ionosphere environment information, the best frequency selection can be acquired, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and ensuring the stability and reliability of the communication system under different ionosphere environments, which is beneficial to improving the communication quality, especially in emergency communication and broadcast control occasions, ensuring the smooth and unobstructed information, and playing the maximum efficiency.

Description

Technical Field

[0001] This invention relates to the field of radio communication technology, and in particular to a frequency planning analysis method and apparatus for ionospheric environmental information decision-making. Background Technology

[0002] Shortwave communication, as an important wireless communication method, is widely used in emergency communication, radio broadcasting, military communication, and many other fields. Especially in emergency situations, shortwave communication plays a crucial role. In scenarios such as broadcast suppression and control, shortwave communication can overcome obstacles and traverse long distances, providing stable communication even without modern communication infrastructure. Since the propagation of shortwave signals depends on the ionosphere, its role in signal propagation is paramount. The ionosphere is a layer of charged particles in the Earth's atmosphere, mainly composed of electrons and ions. Changes in its ionization directly affect the propagation characteristics of radio waves. In shortwave communication, the ionosphere enables signals to propagate over long distances through reflection and refraction, allowing shortwave signals to travel thousands of kilometers, giving it a significant advantage, especially in long-distance communication. However, the ionization of the ionosphere is not constant; it fluctuates dramatically with the changing of day and night, seasonal variations, and solar activity cycles. Day and night, different ionospheric altitudes, and the intensity of solar activity all affect the electron density of the ionosphere, thus impacting the propagation quality of shortwave signals.

[0003] These varying characteristics of the ionosphere make frequency selection and propagation paths for shortwave communication highly complex. During the day, the electron density in the ionosphere is typically high, resulting in strong signal reflection. However, at night, the electron density is low, and signal propagation may be attenuated or even lost. Therefore, the selection of shortwave communication frequencies at different times is crucial. Seasonal changes also affect the state of the ionosphere; due to differences in solar radiation intensity, the ionosphere changes differently in winter and summer. Furthermore, the solar activity cycle has a profound impact on the ionosphere. In particular, when solar activity intensifies, the ionization degree of the ionosphere increases significantly, causing changes in the signal propagation capability of shortwave communication. In such cases, if the frequency is not selected properly, the signal may suffer severe attenuation or distortion, or even fail to propagate at all.

[0004] Therefore, to ensure the effectiveness of shortwave communication under different environmental conditions, real-time monitoring of the ionosphere and frequency selection based on ionospheric environmental information are crucial. Scientific frequency decisions can ensure that the communication system achieves optimal propagation under various ionospheric conditions, thereby improving the efficiency and reliability of shortwave communication. This is especially important in emergency communications and broadcast control, ensuring unimpeded information flow and maximizing effectiveness. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a frequency planning analysis method and apparatus for ionospheric environmental information decision-making. By analyzing ionospheric environmental information, the optimal frequency selection can be obtained, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and ensuring the stability and reliability of communication systems under different ionospheric environments. This is beneficial to improving communication quality, especially in emergency communication and broadcast control, ensuring unimpeded information flow and maximizing efficiency.

[0006] To address the aforementioned technical problems, a first aspect of this invention discloses a frequency planning analysis method for ionospheric environmental information decision-making, the method comprising:

[0007] S1, acquire scattering sweep frequency data information, oblique sweep frequency data information, and available frequency band resource information;

[0008] S2, The scattering frequency sweep data information and the oblique frequency sweep data information are processed to obtain the three-dimensional distribution model information of the ionosphere;

[0009] S3, process the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain the working frequency result information.

[0010] As an optional implementation, in the first aspect of the present invention, processing the scattering frequency sweep data information and the oblique frequency sweep data information to obtain the three-dimensional distribution model information of the ionosphere includes:

[0011] S21, Process the scattering frequency sweep data information to obtain the first ionospheric environment information;

[0012] S22, The oblique frequency sweep data information is processed to obtain the second ionospheric environment information;

[0013] S23, the first ionospheric environmental information and the second ionospheric environmental information are fused to obtain the three-dimensional distribution model information of the ionosphere.

[0014] As an optional implementation, in the first aspect of the present invention, processing the obliquely swept frequency data information to obtain second ionospheric environmental information includes:

[0015] S221, perform low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information; the lowest observable frequency information includes the lowest observable frequency information of the E layer, the lowest observable frequency information of the Es layer, the lowest observable frequency information of the F1 layer, and the lowest observable frequency information of the F2 layer.

[0016] S222, perform high-frequency band detection processing on the oblique frequency sweep data information to obtain the highest observable frequency information and group distance feature information;

[0017] S223, perform echo mode separation processing on the oblique sweep frequency data information to obtain trace feature information;

[0018] S224, Energy feature extraction processing is performed on the oblique frequency sweep data information to obtain echo energy feature information;

[0019] S225, the lowest observable frequency information, the highest observable frequency information, the group distance characteristic information, the tracking characteristic information and the echo energy characteristic information are analyzed and processed to obtain ionospheric parameter information;

[0020] S226, interpolate and reconstruct the ionospheric parameter information and perform region fitting processing to obtain the second ionospheric environment information.

[0021] As an optional implementation, in the first aspect of the present invention, the step of performing low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information includes:

[0022] S2211, Perform E-layer LOF extraction processing on the oblique sweep frequency data information to obtain the lowest observable frequency information of the E layer;

[0023] S2212, Perform Es layer LOF extraction processing on the oblique frequency sweep data information to obtain the Es layer lowest observable frequency information;

[0024] S2213, Perform F1 layer LOF extraction processing on the oblique sweep frequency data information to obtain the F1 layer lowest observable frequency information;

[0025] S2214, Perform F2 layer LOF extraction processing on the oblique sweep frequency data information to obtain the F2 layer lowest observable frequency information.

[0026] As an optional implementation, in a first aspect of the present invention, the analysis and processing of the lowest observable frequency information, the highest observable frequency information, the group distance feature information, the tracking feature information, and the echo energy feature information to obtain ionospheric parameter information includes:

[0027] S2251, using the link midpoint adjacent frequency calculation model, the lowest observable frequency information and the highest observable frequency information are processed to obtain the link midpoint adjacent frequency information;

[0028] The link midpoint adjacent frequency calculation model is as follows:

[0029]

[0030] In the formula, LP is the link midpoint adjacent frequency information, DP is the lowest observable frequency information, GP is the highest observable frequency information, SJ is the time adjustment factor, KJ is the spatial adjustment factor, θ1, θ2, θ3 and θ4 are the first adjustment factor, the second adjustment factor, the third adjustment factor and the fourth adjustment factor, respectively, and δ1 is the first weighting parameter. This is the ionospheric reference factor.

[0031] S2252, Process the group distance feature information and the trace feature information to obtain peak height information;

[0032] S2253, Process the link midpoint adjacent frequency information and the peak height information to obtain the maximum electron concentration parameter information;

[0033] S2254, The link midpoint adjacent frequency information, the peak height information and the maximum electron concentration parameter information are processed to obtain ionospheric parameter information.

[0034] As an optional implementation, in a first aspect of the present invention, the step of fusing the first ionospheric environmental information and the second ionospheric environmental information to obtain ionospheric three-dimensional distribution model information includes:

[0035] S231, Based on the first ionospheric environment information, the second ionospheric environment information is spatially transformed to obtain the third ionospheric environment information;

[0036] S232, Based on the first ionospheric environment information, the resolution conversion processing of the third ionospheric environment information is performed to obtain the fourth ionospheric environment information;

[0037] S233, using a three-dimensional electron concentration calculation model, the first ionospheric environment information and the fourth ionospheric environment information are processed to obtain three-dimensional electron concentration distribution information;

[0038] The three-dimensional electron concentration calculation model is as follows:

[0039]

[0040] 1≤i≤N;

[0041] 1≤j≤M;

[0042] In the formula, SW represents the three-dimensional electron concentration distribution information, E1 and E2 represent the first ionospheric environment information and the fourth ionospheric environment information, respectively, and A ij B ij and Cij These represent the first, second, and third nonlinear parameter information, respectively, and γ1, γ2, γ3, and γ4 represent the first, second, third, and fourth weighting factors, respectively. The parameters are the bias terms, where N and M are the first and second orders, respectively.

[0043] S234, perform gridded modeling processing on the three-dimensional electron concentration analysis information to obtain the three-dimensional distribution model information of the ionosphere.

[0044] As an optional implementation, in the first aspect of the present invention, processing the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain operating frequency result information includes:

[0045] S31, perform time series prediction processing on the ionospheric three-dimensional distribution model information to obtain frequency prediction result information;

[0046] S32, the frequency prediction result information is matched with the available frequency band resource information to obtain the working frequency result information.

[0047] A second aspect of this invention discloses a frequency planning and analysis device for ionospheric environmental information decision-making, the device comprising:

[0048] The acquisition module is used to acquire scattering frequency sweep data information, oblique frequency sweep data information, and available frequency band resource information;

[0049] The first calculation model is used to process the scattering frequency sweep data information and the oblique frequency sweep data information to obtain the three-dimensional distribution model information of the ionosphere;

[0050] The second calculation module is used to process the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain the working frequency result information.

[0051] A third aspect of this invention discloses another frequency planning and analysis device for ionospheric environmental information decision-making, the device comprising:

[0052] processor;

[0053] A memory coupled to the processor stores executable program code;

[0054] The processor calls the executable program code stored in the memory to execute some or all of the steps of the frequency planning analysis method for ionospheric environmental information decision-making disclosed in the first aspect of the present invention.

[0055] The fourth aspect of this invention discloses a computer-readable storage medium storing computer instructions. When invoked, the computer instructions are used to execute some or all of the steps of the frequency planning analysis method for ionospheric environmental information decision-making disclosed in the first aspect of this invention.

[0056] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0057] In this embodiment of the invention, scattering sweep frequency data information, oblique sweep frequency data information, and available frequency band resource information are acquired; the scattering sweep frequency data information and the oblique sweep frequency data information are processed to obtain ionospheric three-dimensional distribution model information; the available frequency band resource information and the ionospheric three-dimensional distribution model information are processed to obtain operating frequency result information. It is evident that this embodiment, through the analysis of ionospheric environmental information, can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and ensuring the stability and reliability of the communication system under different ionospheric environments. This is beneficial for improving communication quality, especially in emergency communication and broadcast control situations, ensuring unimpeded information flow and maximizing efficiency. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating a frequency planning analysis method for ionospheric environmental information decision-making disclosed in an embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of the structure of a frequency planning and analysis device for ionospheric environmental information decision-making disclosed in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of another frequency planning and analysis device for ionospheric environmental information decision-making disclosed in an embodiment of the present invention. Detailed Implementation

[0062] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0064] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0065] This invention discloses a frequency planning analysis method and apparatus for ionospheric environmental information decision-making. By analyzing ionospheric environmental information, the optimal frequency selection can be obtained, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and ensuring the stability and reliability of communication systems under different ionospheric environments. This is beneficial for improving communication quality, especially in emergency communication and broadcast control situations, ensuring unimpeded information flow and maximizing effectiveness. Detailed descriptions follow.

[0066] Example 1

[0067] Please see Figure 1 , Figure 1 This is a flowchart illustrating a frequency planning analysis method for ionospheric environmental information decision-making disclosed in an embodiment of the present invention. Figure 1 The described frequency planning and analysis method for ionospheric environmental information decision-making is applied in a frequency planning and analysis device for ionospheric environmental information decision-making, such as a local server or cloud server for frequency planning and analysis optimization management of ionospheric environmental information decision-making. This invention is not limited to this specific embodiment. Figure 1 As shown, the frequency planning analysis method for ionospheric environmental information decision-making can include the following operations:

[0068] S1, acquire scattering sweep frequency data information, oblique sweep frequency data information, and available frequency band resource information;

[0069] It should be noted that backscattering frequency sweep data refers to the raw signal data collected by a return scattering radar (such as a ground-based or vehicle-mounted ionospheric radar) during vertical ionospheric exploration, which is then preprocessed. This data reflects the reflection characteristics of different ionospheric layers (such as the E layer, F1 layer, F2 layer, etc.) to radio waves of different frequencies, often manifested as echo waveforms, frequency-time delay sequences, etc.; oblique frequency sweep data refers to the raw signal data collected by an oblique frequency sweeping device (such as an oblique ionospheric radar or oblique sweeping instrument) during non-vertical ionospheric exploration (i.e., the transmitting and receiving points are not at the same location), which is then preprocessed. These data reflect the reflection and propagation characteristics of the ionosphere under different propagation paths and incident angles, often manifested as oblique echoes, link characteristics, and group distances. Available frequency band resource information refers to the resource information of all radio frequency bands available for service use within the current area, including available frequency bands, the interference coefficient, available bandwidth, and priority of each available frequency band. The interference coefficient is an indicator used to measure the degree of radio interference to a specific frequency band, usually represented by integers. A larger value indicates more severe interference, poorer communication quality, and less suitability for normal communication. Available bandwidth refers to the actual range of frequencies available for communication within the available frequency band, usually represented by positive integers or real numbers. A larger value indicates more available frequency resources, supporting higher data transmission rates or more communication services. Priority refers to the importance level of the available frequency band in resource allocation and scheduling, usually represented by positive integers. A larger value indicates higher priority, meaning it will be given priority consideration and protection during resource allocation.

[0070] It should be noted that the above preprocessing includes data cleaning, missing value handling, etc., and the specifics are not limited in the embodiments of the present invention.

[0071] S2, The scattering frequency sweep data information and the oblique frequency sweep data information are processed to obtain the three-dimensional distribution model information of the ionosphere;

[0072] S3, process the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain the working frequency result information.

[0073] It should be noted that the operating frequency result information is the optimal / most suitable operating frequency under the current region and current ionospheric environment, taking into account the available frequency bands and propagation conditions. It is used to guide communication equipment to select the best operating frequency in order to obtain the best communication quality and reliability.

[0074] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0075] In an optional embodiment, processing the scattering frequency sweep data information and the oblique frequency sweep data information to obtain the three-dimensional distribution model information of the ionosphere includes:

[0076] S21, Process the scattering frequency sweep data information to obtain the first ionospheric environment information;

[0077] S22, The oblique frequency sweep data information is processed to obtain the second ionospheric environment information;

[0078] S23, the first ionospheric environmental information and the second ionospheric environmental information are fused to obtain the three-dimensional distribution model information of the ionosphere.

[0079] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0080] In another optional embodiment, processing the scattering frequency sweep data information to obtain the first ionospheric environment information includes:

[0081] S211, Perform front-edge feature extraction processing on the scattering frequency sweep data information to obtain ionospheric front-edge feature information;

[0082] S212, perform trailing edge feature extraction processing on the scattering sweep frequency data information to obtain ionospheric trailing edge feature information;

[0083] S213, MOF feature extraction processing is performed on the scattering frequency sweep data information to obtain MOF feature information of the ionospheric environment;

[0084] S214, Perform signal-to-noise ratio feature extraction processing on the scattering frequency sweep data information to obtain ionospheric environment signal-to-noise ratio feature information;

[0085] It should be noted that the feature extraction processing of S211-S214 above can be achieved through various signal processing, statistical analysis, machine learning and other methods, such as envelope detection, threshold decision, time-frequency analysis, wavelet transform, deep learning feature extraction, etc. The specific implementation method is not limited in this invention.

[0086] It should be noted that ionospheric leading edge characteristic information refers to the characteristic parameters reflecting the leading edge (i.e., the part where the signal first rises significantly) in the ionospheric echo signal, such as the leading edge arrival time, leading edge slope, and leading edge amplitude; ionospheric trailing edge characteristic information refers to the characteristic parameters reflecting the trailing edge (i.e., the part where the signal falls back or disappears) in the ionospheric echo signal, such as the trailing edge termination time, trailing edge decay rate, and trailing edge amplitude; MOF (Maximum Observable Frequency) characteristic information refers to the maximum observable reflection frequency and its related characteristics in the scattering sweep data under the current ionospheric environment, such as the MOF value, the signal strength corresponding to the MOF, and the time of MOF appearance; ionospheric environmental signal-to-noise ratio characteristic information refers to the signal-to-noise ratio (SNR) and its variation characteristics extracted based on the scattering sweep data, such as the average signal-to-noise ratio, maximum / minimum signal-to-noise ratio, and signal-to-noise ratio distribution.

[0087] S215, the ionospheric leading edge feature information, the ionospheric trailing edge feature information, the ionospheric environment MOF feature information, and the ionospheric environment signal-to-noise ratio feature information are analyzed and processed to obtain single-point adjacent frequency data information, peak height distribution data information, and maximum electron concentration distribution data information;

[0088] It should be noted that the above analysis and processing can be carried out through statistical analysis, curve fitting, inversion algorithm, machine learning model, etc., and the specific implementation of this invention is not limited thereto.

[0089] It should be noted that single-point adjacent frequency data refers to the data on the critical frequencies of the ionosphere (such as foF2, foE, etc.) and their time-varying characteristics extracted from the leading edge, trailing edge, and MOF features of the ionospheric echo signal at a single observation point. This reflects the propagation limit of the ionosphere at that point to radio waves of different frequencies. It is calculated from the scattered frequency sweep data by combining the leading edge, trailing edge, and MOF features to obtain the critical frequencies of the ionosphere at that point and their variation sequence. Peak height distribution data refers to the height distribution information of the ionospheric reflector layer obtained through characteristic analysis of the ionospheric echo signal, such as the peak heights (hmF2, hmE, etc.) of the F and E layers and their spatial or temporal distribution. It is the peak height and distribution data of each layer of the ionosphere obtained by inversion based on the leading edge, trailing edge, and MOF features. Maximum electron concentration distribution data refers to the maximum electron density of the ionosphere (NmF2, etc.) and its spatial or temporal distribution obtained by inversion based on the characteristics of the ionospheric echo signal. It is the maximum electron concentration and its distribution data of the ionosphere calculated by combining MOF characteristics, signal-to-noise ratio characteristics, etc.

[0090] It should be noted that by using single-point adjacent frequency data, peak height distribution data, and maximum electron concentration distribution data, the propagation characteristics, spatial structure, and electron density distribution of the ionosphere in the current observation area can be comprehensively reflected, providing scientific basis and data support for applications such as radio communication, navigation, and space weather forecasting.

[0091] S216. Using the vertical profile model of ionospheric concentration, the single-point adjacent frequency data, the peak height distribution data, and the maximum electron concentration distribution data are processed to obtain the first ionospheric environmental information.

[0092] It should be noted that the above-mentioned vertical profile model of electron layer concentration can be the Chapman model, the hierarchical polynomial model, or the IRI model. Specifically, the embodiments of the present invention are not limited to this.

[0093] It should be noted that the ionospheric concentration vertical profile model is a mathematical model describing the relationship between ionospheric electron concentration and altitude. It can invert first ionospheric environmental information based on single-point adjacent frequency data, peak height distribution data, and maximum electron concentration distribution data. Processing the above data using this model not only yields ionospheric environmental information with stronger physical consistency but also improves the accuracy of parameter inversion and the precision of environmental modeling.

[0094] It should be noted that the first ionospheric environmental information is a profile of the ionospheric electron concentration distribution with altitude, as well as related key ionospheric parameters (such as peak height, maximum electron concentration, critical frequency, total electron content, etc.), which can comprehensively reflect the spatial structure and physical state of the ionosphere in the current region.

[0095] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0096] In another optional embodiment, the processing of the oblique frequency sweep data information to obtain the second ionospheric environment information includes:

[0097] S221, perform low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information; the lowest observable frequency information includes the lowest observable frequency information of the E layer, the lowest observable frequency information of the Es layer, the lowest observable frequency information of the F1 layer, and the lowest observable frequency information of the F2 layer.

[0098] S222, perform high-frequency band detection processing on the oblique frequency sweep data information to obtain the highest observable frequency information and group distance feature information; the highest observable frequency information includes the highest observable frequency information of the E layer, the highest observable frequency information of the Es layer, the highest observable frequency information of the F1 layer, and the highest observable frequency information of the F2 layer; the group distance feature information includes the group distance information of the E layer, the group distance information of the Es layer, the group distance information of the F1 layer, and the group distance information of the F2 layer.

[0099] S223, perform echo mode separation processing on the oblique frequency sweep data information to obtain tracking feature information; the tracking feature information includes E-layer echo tracking information, Es-layer echo tracking information, F1-layer echo tracking information, F2-layer low-angle echo tracking information, F2-layer high-angle O-wave echo tracking information and F2-layer high-angle X-wave echo tracking information;

[0100] S224, Energy feature extraction processing is performed on the oblique frequency sweep data information to obtain echo energy feature information; the echo energy feature information includes E-layer echo energy distribution information, Es-layer echo energy distribution information, F1-layer echo energy distribution information, F2-layer low-angle echo energy distribution information, F2-layer high-angle O-wave echo energy distribution information, and F2-layer high-angle X-wave echo energy distribution information;

[0101] S225, the lowest observable frequency information, the highest observable frequency information, the group distance characteristic information, the tracking characteristic information and the echo energy characteristic information are analyzed and processed to obtain ionospheric parameter information;

[0102] S226, interpolate and reconstruct the ionospheric parameter information and perform region fitting processing to obtain the second ionospheric environment information.

[0103] It should be noted that the above interpolation reconstruction process can use inverse distance weighted interpolation, nearest neighbor interpolation, linear interpolation, bilinear / trilinear interpolation, and region fitting process can use polynomial surface fitting, Gaussian process regression, etc. The specific implementation of this invention is not limited.

[0104] It should be noted that the second ionospheric environmental information is based on oblique frequency sweep data. By analyzing and processing various characteristic parameters such as the lowest / highest observable frequency, group distance, trace characteristics, and echo energy, and combining interpolation reconstruction and region fitting methods, the comprehensive environmental data obtained covering the target area, including the distribution of ionospheric electron concentration, the distribution of key parameters of each layer, and their spatial variation characteristics, can fully reflect the spatial structure and propagation environment of the ionosphere within the region.

[0105] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0106] In an optional embodiment, the step of performing low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information includes:

[0107] S2211, Perform E-layer LOF extraction processing on the oblique sweep frequency data information to obtain the lowest observable frequency information of the E layer;

[0108] S2212, Perform Es layer LOF extraction processing on the oblique frequency sweep data information to obtain the Es layer lowest observable frequency information;

[0109] S2213, Perform F1 layer LOF extraction processing on the oblique sweep frequency data information to obtain the F1 layer lowest observable frequency information;

[0110] S2214, Perform F2 layer LOF extraction processing on the oblique sweep frequency data information to obtain the F2 layer lowest observable frequency information.

[0111] It should be noted that the E, Es, F1, and F2 layers are different hierarchical structures within the ionosphere, located at different altitudes and exhibiting varying reflection characteristics for radio waves. The E and Es layers are primarily distributed at altitudes of 90–150 kilometers, while the F1 and F2 layers are located at even higher altitudes, with the F2 layer being the most critical for long-distance communication. LOF extraction refers to determining the lowest observable frequency (LOF) of each ionosphere (such as the E layer) by analyzing obliquely swept frequency data signals. This reflects the layer's ability to reflect low-frequency radio waves and is an important parameter in ionospheric detection and frequency planning.

[0112] It should be noted that the extraction processing of S2211-S2214 above can be performed through time-frequency analysis, signal envelope and slope analysis. Specifically, the embodiments of the present invention do not limit the specific processing.

[0113] It should be noted that the lowest observable frequency information for the E layer refers to the minimum frequency at which the E layer echo signal is first observed, reflecting the E layer's minimum reflection capability of low-frequency radio waves. The lowest observable frequency information for the Es layer refers to the minimum frequency at which the intermittent E layer (Es layer) echo signal is first observed, reflecting the Es layer's minimum reflection capability. The lowest observable frequency information for the F1 layer refers to the minimum frequency at which the F1 layer echo signal is first observed, reflecting the F1 layer's minimum reflection capability. The lowest observable frequency information for the F2 layer refers to the minimum frequency at which the F2 layer echo signal is first observed, reflecting the F2 layer's minimum reflection capability. These information represent the lowest radio frequencies that each ionosphere can reflect under the current environment, and are important parameters for ionospheric propagation characteristics and frequency planning.

[0114] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0115] In an optional embodiment, the step of performing high-frequency band detection processing on the oblique frequency sweep data information to obtain the highest observable frequency information and group distance feature information includes:

[0116] S2221, Perform E-layer MOF and group distance extraction processing on the oblique frequency sweep data information to obtain the highest observable frequency information and E-layer group distance information of the E layer;

[0117] S2222, Perform Es layer MOF and group distance extraction processing on the oblique frequency sweep data information to obtain the highest observable frequency information of the Es layer and the group distance information of the Es layer;

[0118] S2223, perform F1 layer MOF and group distance extraction processing on the oblique sweep frequency data information to obtain the highest observable frequency information and F1 layer group distance information;

[0119] S2224, the oblique frequency sweep data information is processed to extract the MOF and group distance of the F2 layer, and the highest observable frequency information and group distance information of the F2 layer are obtained.

[0120] It should be noted that MOF extraction refers to determining the maximum observable reflection frequency of each ionosphere (such as the E layer) by analyzing obliquely swept frequency data signals. This reflects the maximum reflection capability of that layer for high-frequency radio waves and is an important parameter in ionospheric detection and frequency planning. Group distance extraction, on the other hand, refers to calculating the group distance of radio waves propagating through each ionosphere (such as the E layer, Es layer, F1 layer, and F2 layer) by analyzing obliquely swept frequency data signals. This is the path length or propagation delay of the signal from transmission to reception within the ionosphere. The group distance reflects the influence of the ionosphere on the propagation path and delay of radio waves.

[0121] It should be noted that the MOF and group distance extraction processing in S2221-S2224 above can be processed by time-frequency analysis, signal envelope and peak detection, etc. The specific implementation of this invention is not limited.

[0122] It should be noted that the highest observable frequency information for the E layer refers to the maximum observable reflection frequency in the E layer echo signal, reflecting the maximum reflection capability of the E layer to high-frequency radio waves. The highest observable frequency information for the Es layer refers to the maximum observable reflection frequency in the intermittent E layer (Es layer) echo signal, reflecting the maximum reflection capability of the Es layer. The highest observable frequency information for the F1 layer refers to the maximum observable reflection frequency in the F1 layer echo signal, reflecting the maximum reflection capability of the F1 layer. The highest observable frequency information for the F2 layer refers to the maximum observable reflection frequency in the F2 layer echo signal, reflecting the maximum reflection capability of the F2 layer. These information represent the highest radio frequencies that each ionosphere can reflect under the current environment, and are important parameters for ionospheric propagation characteristics and frequency planning.

[0123] It should be noted that E-layer group distance information refers to the group distance of the E-layer echo signal at the maximum observable frequency, reflecting the length of the signal's propagation path or propagation delay within the E-layer. The definitions of Es-layer, F1-layer, and F2-layer group distance information are similar, corresponding to the group distance of their respective ionospheres at the maximum observable frequency. This information reflects the influence of different ionospheres on the propagation path and delay of radio waves.

[0124] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0125] In an optional embodiment, the echo mode separation processing of the oblique frequency sweep data information to obtain trace feature information includes:

[0126] S2231, Perform E-layer trace extraction processing on the oblique frequency sweep data information to obtain E-layer echo trace information;

[0127] S2232, Perform Es layer trace extraction processing on the oblique frequency sweep data information to obtain Es layer echo trace information;

[0128] S2233, Perform F1 layer trace extraction processing on the oblique sweep frequency data information to obtain F1 layer echo trace information;

[0129] S2234, Perform F2 layer low-angle tracing extraction processing on the oblique frequency sweep data information to obtain F2 layer low-angle echo tracing information;

[0130] S2235, Perform F2 layer high-angle O-wave trace extraction processing on the oblique frequency sweep data information to obtain F2 layer high-angle O-wave echo trace information;

[0131] S2236, Perform F2 layer high-angle X-wave trace extraction processing on the oblique frequency sweep data information to obtain F2 layer high-angle X-wave echo trace information.

[0132] It should be noted that trace extraction refers to the process of analyzing obliquely swept frequency data signals to identify and extract the continuous trajectory (trace) of echo signals from each ionosphere (such as the E layer) on the frequency-time delay (or group distance) map, reflecting the propagation path and characteristics of radio waves in that layer. It is an important step in ionospheric detection and propagation characteristic analysis.

[0133] It should be noted that the trace extraction processing in S2231-S2236 above can be performed by methods such as time-frequency analysis, signal envelope and peak detection, edge detection, and connected component analysis. In particular, the embodiments of the present invention do not limit the specific processing.

[0134] It should be noted that E-layer echo tracing information refers to the continuous trajectory of E-layer echo signals in terms of frequency-time delay (or group distance), reflecting the reflection path and propagation characteristics of radio waves in the E-layer. The definitions of Es-layer, F1-layer, F2-layer low-angle echo tracing information, F2-layer high-angle O-wave echo tracing information, and F2-layer high-angle X-wave echo tracing information are similar, corresponding to the tracing characteristics of echo signals in their respective ionospheric or propagation modes. This information can intuitively reflect the propagation path, reflection characteristics, and spatial distribution of radio waves in different ionospheric and propagation modes.

[0135] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0136] In an optional embodiment, the step of performing energy feature extraction processing on the oblique frequency sweep data information to obtain echo energy feature information includes:

[0137] S2241, Perform E-layer echo energy extraction processing on the oblique frequency sweep data information to obtain E-layer echo energy distribution information;

[0138] S2242, Perform Es layer echo energy extraction processing on the oblique frequency sweep data information to obtain Es layer echo energy distribution information;

[0139] S2243, Perform F1 layer echo energy extraction processing on the oblique frequency sweep data information to obtain F1 layer echo energy distribution information;

[0140] S2244, Perform F2 layer low-angle echo energy extraction processing on the oblique frequency sweep data information to obtain F2 layer low-angle echo energy distribution information;

[0141] S2245, Perform F2 layer high-angle O-wave echo energy extraction processing on the oblique sweep frequency data information to obtain F2 layer high-angle O-wave echo energy distribution information;

[0142] S2246, Perform F2 layer high-angle X-wave echo energy extraction processing on the oblique frequency sweep data information to obtain F2 layer high-angle X-wave echo energy distribution information.

[0143] It should be noted that echo energy extraction refers to the process of analyzing obliquely swept frequency data signals to calculate the energy distribution of echo signals from each ionosphere (such as the E layer) at different frequencies and time delays (or group distances), reflecting the strength and spatial distribution characteristics of the layer's reflection of radio waves.

[0144] It should be noted that the echo energy extraction processing of S2241-S2246 above can be performed by methods such as time-frequency analysis, signal envelope detection, energy integration, peak detection, and image processing. In particular, the embodiments of the present invention do not limit the specific processing.

[0145] It should be noted that E-layer echo energy distribution information refers to the energy distribution of E-layer echo signals at different frequencies and time delays (or group distances), reflecting the intensity and spatial distribution characteristics of radio wave reflections from the E-layer. The definitions of Es-layer, F1-layer, F2-layer low-angle echo, F2-layer high-angle O-wave echo, and F2-layer high-angle X-wave echo energy distribution information are similar, corresponding to the energy distribution characteristics of echo signals under their respective ionospheric or propagation modes. This information can quantify the strength and distribution of radio wave reflections under different ionospheric and propagation modes.

[0146] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0147] In an optional embodiment, the analysis and processing of the lowest observable frequency information, the highest observable frequency information, the group distance feature information, the tracking feature information, and the echo energy feature information to obtain ionospheric parameter information includes:

[0148] S2251, using the link midpoint adjacent frequency calculation model, the lowest observable frequency information and the highest observable frequency information are processed to obtain the link midpoint adjacent frequency information;

[0149] The link midpoint adjacent frequency calculation model is as follows:

[0150]

[0151] In the formula, LP is the link midpoint adjacent frequency information, DP is the lowest observable frequency information, GP is the highest observable frequency information, SJ is the time adjustment factor, KJ is the spatial adjustment factor, θ1, θ2, θ3 and θ4 are the first adjustment factor, the second adjustment factor, the third adjustment factor and the fourth adjustment factor, respectively, and δ1 is the first weighting parameter. This is the ionospheric reference factor.

[0152] It should be noted that the value ranges of the first adjustment factor, the second adjustment factor, the third adjustment factor, the fourth adjustment factor, the first weight parameter, and the ionospheric reference factor can be set by the user or obtained based on historical data. Specifically, the embodiments of the present invention do not limit this.

[0153] It should be noted that the time adjustment factor is a parameter used to reflect the impact of ionospheric parameters changing over time (such as day and night, seasons, solar activity cycles, etc.) on the adjacent frequency of the link midpoint. It can be obtained through time series analysis and statistical modeling of historical ionospheric observation data, or calculated based on a physical model (such as an IRI model). Specifically, this embodiment of the invention does not limit the specific method. The value range of the time adjustment factor is usually a real number, and can be set according to the temporal variation of the actual observation data, with a value range of [-10, 10] to accommodate ionospheric changes at different time scales.

[0154] It should be noted that the spatial adjustment factor reflects the impact of spatial variations in ionospheric parameters at different geographical locations (such as latitude, longitude, altitude, and magnetic latitude) on the adjacent frequency of the link midpoint. It can be obtained through spatial interpolation of multi-point observation data, regional fitting, or calculation using a physical model based on geographic information. The spatial adjustment factor is typically a real number, ranging from [-8, 8], to accommodate the differences in ionospheric environments across different geographical regions.

[0155] It should be noted that the first, second, third, and fourth adjustment factors are the weight parameters of the lowest observable frequency, highest observable frequency, time adjustment factor, and spatial adjustment factor in the model, respectively. Their values ​​all range from [0,1], and the sum of the four is greater than 0. By flexibly adjusting these weights, the impact of each parameter on the adjacent frequency of the link midpoint can be optimized according to actual application needs, achieving model adaptation and optimal fitting, and improving the model's generalization ability and applicability to different ionospheric environments and business scenarios.

[0156] It should be noted that the first weight parameter is an overall weighting coefficient, with a value range of [0,1]. It is used to globally scale or normalize the final result, facilitating integration with other model outputs or subsequent processing, and improving the model's flexibility and controllability. The ionospheric reference factor is used to normalize or standardize the highest observable frequency (GP), eliminating scale differences under different observation conditions and ensuring the comparability and universality of the model output. Its value is a positive real number. Specifically, the settings can be based on historical data statistics, physical model parameters, or empirical values, such as taking the mean, median, or standard value of GP.

[0157] It should be noted that the lowest observable frequency (DP) and highest observable frequency (GP) reflect the minimum and maximum reflection capabilities of the ionosphere for radio waves, respectively, accurately capturing the propagation limits of the link under different ionospheric conditions. The time adjustment factor (SJ) and spatial adjustment factor (KJ) incorporate the effects of ionospheric variations over time (e.g., day / night, season, solar activity) and space (e.g., different geographical locations, magnetic latitude), respectively, enabling the model to adapt to the dynamic changes in the ionospheric environment. Each adjustment factor (θ)

[0158] By flexibly allocating weights (θ1, θ2, θ3, θ4), the influence of different parameters can be finely controlled, enabling optimization of model output based on actual observations and operational needs, thus improving the model's generalization ability and applicability. The first weight parameter (δ1) performs global scaling or normalization of the overall results, facilitating seamless integration of the model with other components and subsequent processing. Ionospheric reference factor. The highest observable frequency is standardized to eliminate scale differences under different observation conditions, ensuring the comparability and stability of the model output. Overall, by integrating multi-source information such as the lowest and highest observable frequencies, time adjustment factors, and spatial adjustment factors, the model can dynamically and accurately reflect the impact of the ionospheric propagation environment on the critical frequency of the link midpoint. This model not only improves the accuracy and robustness of adjacent frequency estimation but also flexibly adjusts parameters according to the spatiotemporal changes of the actual ionospheric environment, significantly enhancing the scientific nature and adaptability of frequency planning. This provides a solid data foundation and decision support for optimal frequency selection and efficient use of spectrum resources in radio communication links, effectively avoiding frequency interference and improving communication quality.

[0159] S2252, Process the group distance feature information and the trace feature information to obtain peak height information;

[0160] It should be noted that the above processing can be carried out by means of maximum value detection method, inflection point detection and fitting method, polynomial or Gaussian curve fitting, signal envelope analysis, machine learning modeling, etc., or by means of peak height calculation model. Specifically, the embodiments of the present invention are not limited.

[0161] The peak height calculation model is as follows:

[0162]

[0163] In the formula, FZ is the peak height information, JL is the group distance feature information, MJ is the trace feature information, α1 and α2 are the normalization factor and the difference adjustment factor, respectively, and δ2 and δ3 are the second weight parameter and the third weight parameter, respectively.

[0164] It should be noted that the normalization factor, difference adjustment factor, second weight parameter, and third weight parameter can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specifics.

[0165] It should be noted that the peak height calculation model, by fusing group distance and trace feature information, can accurately invert the peak heights of each layer of the ionosphere (such as hmF2 and hmE of the F and E layers), reflecting the spatial distribution and structural characteristics of the ionospheric reflector layers. This model not only improves the accuracy and physical consistency of peak height estimation but also allows for flexible adjustment based on the weights of different feature information, adapting to diverse ionospheric environments and observation conditions. It provides crucial parameter support for ionospheric 3D modeling, spatial structure analysis, and communication link design, effectively improving spectrum resource utilization efficiency and communication quality.

[0166] It should be noted that the normalization factor is used to normalize the sum of squares term in the model to prevent distortion of the model output due to excessively large units or values, and its value range is [1, 10]. The difference adjustment factor is used to adjust the influence of the difference term between the group distance feature and the trace feature on the peak height, and its value range is [0, 1]. By adjusting the difference adjustment factor, the sensitivity of the model to feature differences can be flexibly controlled, thereby improving the model's adaptability and robustness.

[0167] It should be noted that the second and third weighting parameters are used to adjust the weights of group distance and trace features in the model, respectively, with values ​​ranging from [0.2, 0.8]. By flexibly adjusting these two weighting parameters, the contribution of different features to peak height estimation can be optimized, achieving model adaptation and optimal fitting, and improving the accuracy and rationality of peak height inversion.

[0168] S2253, Process the link midpoint adjacent frequency information and the peak height information to obtain the maximum electron concentration parameter information;

[0169] It should be noted that the above processing can be carried out by physical formula inversion method (such as NmF2=1.24×1010×(foF2)2), empirical model / parameterized model, multiple regression algorithm or machine learning modeling, etc., or it can be carried out by the maximum electron concentration parameter calculation model. In particular, the embodiments of the present invention do not limit the specific processing.

[0170] The calculation model for the maximum electron concentration parameter is as follows:

[0171]

[0172] δ4+δ5+δ6=1;

[0173] 0≤δ4,δ5,δ6≤1;

[0174] In the formula, ZD is the maximum electron concentration parameter information, LP is the link midpoint adjacent frequency information, FZ is the peak height information, and δ4, δ5 and δ6 are the fourth weight parameter, the fifth weight parameter and the sixth weight parameter, respectively.

[0175] It should be noted that the fourth, fifth, and sixth weight parameters can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specific parameters.

[0176] It should be noted that the fourth, fifth, and sixth weighting parameters are used to adjust the weights of the LP squared term, logarithmic term, and exponential term in the model, respectively. By flexibly adjusting these three weighting parameters, the contribution of different mathematical features to the estimation of maximum electron concentration can be optimized according to the actual ionospheric environment and observational data, achieving model adaptation and optimal fitting, and further improving the accuracy and rationality of maximum electron concentration inversion.

[0177] It should be noted that the maximum electron concentration parameter calculation model is obtained by further calculation based on the link midpoint adjacent frequency information and peak height information. By using the outputs of the first two steps as inputs, the organic correlation and progressive nature of key ionospheric physical parameters are achieved. The LP parameter in the model... 2The / FZ term reflects the direct physical relationship between the midpoint adjacent frequency and the peak height, mainly reflecting the influence of the ionosphere on the radio wave propagation limit; the log(1+FZ / LP) term describes the logarithmic relationship between the peak height and the adjacent frequency, enhancing the model's adaptability to variations at different height levels; the exp(-FZ / LP) term reflects the exponential decay effect of the peak height-adjacent frequency ratio on electron concentration, improving the model's robustness to extreme environments. By flexibly allocating the weights of these three terms, the model can dynamically adapt to different ionospheric environments and observation conditions, improving the accuracy and stability of maximum electron concentration estimation. This model not only achieves organic connection and progressive derivation between parameters, but also provides a scientific and quantitative basis for ionospheric three-dimensional distribution modeling, spatial structure analysis, and communication link optimization, significantly enhancing the overall effectiveness of this scheme in frequency planning and communication quality improvement.

[0178] S2254, The link midpoint adjacent frequency information, the peak height information and the maximum electron concentration parameter information are processed to obtain ionospheric parameter information.

[0179] It should be noted that the above processing can be achieved through statistical analysis, curve fitting, inversion algorithms, neural networks, support vector machines, Kalman filtering, Bayesian fusion, etc., and the specific implementation of this invention is not limited thereto. These methods can fully integrate feature information from different sources and types, extract key physical parameters of the ionosphere, and achieve a comprehensive characterization and dynamic modeling of the ionospheric environment.

[0180] It should be noted that the ionospheric parameter information obtained through the above algorithm can comprehensively reflect the key physical states of the ionosphere in the current region, such as propagation characteristics, spatial structure, and electron density distribution. This parameter information not only provides a solid data foundation for subsequent ionospheric three-dimensional distribution modeling, frequency prediction, and optimal frequency selection, but also effectively improves spectrum resource utilization efficiency, avoids frequency interference, and enhances the quality and reliability of communication links. The acquisition of ionospheric parameter information enables refined, dynamic, and intelligent modeling of the ionospheric environment, and is a core component of this scheme for achieving scientific frequency planning and efficient communication assurance.

[0181] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0182] In an optional embodiment, the step of fusing the first ionospheric environmental information and the second ionospheric environmental information to obtain the three-dimensional distribution model information of the ionosphere includes:

[0183] S231, Based on the first ionospheric environment information, the second ionospheric environment information is spatially transformed to obtain the third ionospheric environment information;

[0184] It should be noted that the spatial transformation processing described above can typically be performed using spatial interpolation, coordinate transformation, spatial registration, and geographic projection transformation. Specific methods are not limited in the embodiments of this invention. For example, inverse distance weighted interpolation, kriging interpolation, nearest neighbor interpolation, bilinear / trilinear interpolation, and other methods can be used to unify ionospheric environmental information from different observation points or at different resolutions under the same spatial reference frame. Alternatively, spatial alignment and fusion of data can be achieved through spatial transformation methods such as geographic coordinates and magnetic coordinates. For multi-source data, Bayesian fusion, principal component analysis, and other methods can also be used to improve the consistency and accuracy of spatial information.

[0185] It should be noted that spatial transformation processing can spatially unify the first ionospheric environmental information (such as electron concentration profiles obtained from vertical detection) with the second ionospheric environmental information (such as regional distributions obtained from oblique detection), resulting in third ionospheric environmental information with a more complete spatial distribution and higher resolution. This processing not only improves the spatial accuracy and physical consistency of ionospheric environment modeling but also provides a solid data foundation for subsequent resolution transformation, three-dimensional electron concentration distribution modeling, and frequency planning analysis. The acquisition of third ionospheric environmental information realizes the spatial integration and information complementarity of multi-source observation data.

[0186] S232, Based on the first ionospheric environment information, the resolution conversion processing of the third ionospheric environment information is performed to obtain the fourth ionospheric environment information;

[0187] It should be noted that the above-mentioned resolution conversion processing can typically employ various resolution conversion algorithms or methods, including upsampling, downsampling, multi-scale fusion, wavelet transform, super-resolution reconstruction, etc. Specific methods are not limited in the embodiments of this invention. These methods can unify data with different spatial resolutions or observation accuracies, ensuring that ionospheric environmental information achieves consistent resolution requirements in spatial, temporal, or parametric dimensions, facilitating subsequent 3D modeling and analysis.

[0188] It should be noted that resolution conversion further refines and optimizes the data structure based on spatial conversion. By enhancing or matching the resolution of the third ionospheric environmental information, it maintains consistency with the first ionospheric environmental information in terms of spatial scale and accuracy, thereby obtaining higher resolution and more refined fourth ionospheric environmental information. This process not only improves the spatial detail and physical consistency of ionospheric environmental modeling but also provides more accurate and richer data support for three-dimensional electron concentration distribution modeling and frequency planning analysis. The acquisition of fourth ionospheric environmental information achieves resolution unification and information fusion of multi-source, multi-scale observation data.

[0189] S233, using a three-dimensional electron concentration calculation model, the first ionospheric environment information and the fourth ionospheric environment information are processed to obtain three-dimensional electron concentration distribution information;

[0190] The three-dimensional electron concentration calculation model is as follows:

[0191]

[0192] 1≤i≤N;

[0193] 1≤j≤M;

[0194] In the formula, SW represents the three-dimensional electron concentration distribution information, E1 and E2 represent the first ionospheric environment information and the fourth ionospheric environment information, respectively, and A ij B ij and C ij These represent the first, second, and third nonlinear parameter information, respectively, and γ1, γ2, γ3, and γ4 represent the first, second, third, and fourth weighting factors, respectively. The parameters are the bias terms, where N and M are the first and second orders, respectively.

[0195] It should be noted that the first weight factor, the second weight factor, the third weight factor, the fourth weight factor, the bias term parameter, the first order, and the second order can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specifics.

[0196] It should be noted that the three-dimensional electron concentration calculation model is the result of further fusion calculations based on the environmental information of the first and fourth ionospheric environments. Through multiple nonlinear combinations and multi-parameter adjustments, it achieves high-precision modeling of the electron concentration distribution in the three-dimensional space of the ionosphere. This model can not only comprehensively reflect the characteristics of the ionospheric environment under different observation sources and resolutions, but also adapt to the complex and ever-changing spatial structure of the ionosphere through parameter adjustment. A in the model... ij B ijand C ij These parameters, acting as nonlinear parameters, can flexibly fit changes in electron concentration at different spatial points and features. They can be obtained through data fitting or machine learning methods such as least squares, gradient descent, genetic algorithms, and Bayesian optimization, by training and optimizing with historical observation data or simulation data.

[0197] It should be noted that N and M are the order of the model, which are set according to the actual spatial resolution and modeling accuracy requirements, and the value range is between [10, 30]. The higher the order, the stronger the model's expressive power, but the computational complexity also increases accordingly.

[0198] It should be noted that the values ​​of the first, second, third, and fourth weighting factors are all within the range of [0,1], and can be determined based on the actual ionospheric environment, observation data characteristics, and modeling requirements. These weighting factors can be adaptively adjusted through data-driven methods (such as cross-validation, Bayesian optimization, and genetic algorithms) according to different regions, time periods, or ionospheric activity intensities, to achieve dynamic allocation of the contribution of each feature. For example, in regions with intense ionospheric activity or complex spatial structures, the weights corresponding to nonlinear terms (such as tanh and exp terms) can be appropriately increased to enhance the model's ability to fit complex changes; while in situations where the ionospheric environment is relatively stable or the data noise is high, the weights of logarithmic or bias terms can be increased to improve the model's robustness and anti-interference ability. Through this weight design, the model can better adapt to different ionospheric environments and business scenarios, improve the accuracy, flexibility, and physical consistency of three-dimensional electron concentration distribution modeling, and provide strong support for this scheme to achieve high-precision frequency planning and communication quality assurance.

[0199] S234, perform gridded modeling processing on the three-dimensional electron concentration analysis information to obtain the three-dimensional distribution model information of the ionosphere.

[0200] It should be noted that the above-mentioned mesh modeling processing can be performed using three-dimensional spatial interpolation (such as trilinear interpolation, inverse distance weighted interpolation), three-dimensional resampling, three-dimensional mesh partitioning (such as octree partitioning), finite element mesh generation, three-dimensional convolution, etc. Specifically, this invention does not limit the methods. These methods can map discrete electron concentration data points or continuous distribution functions onto regular or adaptive three-dimensional spatial meshes, achieving spatial discretization and structured representation of ionospheric electron concentration.

[0201] It should be noted that the ionospheric 3D distribution model information refers to a structured data model that reflects the electron concentration distribution characteristics of the ionosphere in three-dimensional space, obtained through gridded modeling based on the 3D electron concentration distribution results. This model not only includes electron concentration values ​​at different altitudes and geographical locations but also reflects the continuity, hierarchy, and dynamic changes of the ionospheric spatial structure. Its purpose is to provide a high-precision and operable spatial data foundation for subsequent applications such as frequency prediction, optimal frequency selection, communication link simulation, and space weather forecasting. Through gridded modeling, the ionospheric 3D distribution model enables unified expression and efficient retrieval of multi-source observation data, supports complex spatial analysis and visualization, and greatly enhances the scientific rigor and practicality of this solution in areas such as spectrum resource optimization, interference avoidance, and communication quality assurance.

[0202] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0203] In an optional embodiment, processing the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain operating frequency result information includes:

[0204] S31, perform time series prediction processing on the ionospheric three-dimensional distribution model information to obtain frequency prediction result information;

[0205] It should be noted that time series forecasting can be performed using autoregressive moving average models, seasonal ARIMA, exponential smoothing, long short-term memory neural networks (LSTM), gated recurrent units (GRU), etc. The specific implementation of this invention is not limited to any particular method. These methods can extract patterns from historical ionospheric three-dimensional distribution data over time and predict the future evolution trend of the ionospheric environment.

[0206] S32, the frequency prediction result information is matched with the available frequency band resource information to obtain the working frequency result information;

[0207] It should be noted that the above processing can be carried out by interval matching algorithm, priority sorting and optimal selection, greedy algorithm, multi-objective optimization algorithm, etc., or by working frequency calculation model. Specifically, the embodiments of the present invention do not limit the specific processing.

[0208] The operating frequency calculation model is as follows:

[0209]

[0210] 1≤j≤L;

[0211] ω1+ω2+ω3+ω4=1;

[0212] 0≤ω1,ω2,ω3,ω4≤1;

[0213] In the formula, ZZ represents the operating frequency result information, and YC... j KY represents the j-th frequency prediction value in the frequency prediction result information, and KY represents the available frequency band resource information. j For the j-th available frequency band in the available frequency band resource information, GR j DK represents the interference coefficient corresponding to the j-th available frequency band in the available frequency band resource information. j YX represents the available bandwidth corresponding to the j-th available frequency band in the available frequency band resource information. j Let φ be the priority corresponding to the j-th available frequency band in the available frequency band resource information, Φ(·) be the global gating, RELU(·) be the ReLU activation function, L be the number of available frequency bands in the available frequency band resource information, and ω1, ω2, ω3 and ω4 be the first parameter factor, the second parameter factor, the third parameter factor and the fourth parameter factor, respectively.

[0214] It should be noted that the first parameter factor, the second parameter factor, the third parameter factor, and the fourth parameter factor can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specific parameters.

[0215] It should be noted that this model comprehensively considers multiple dimensions such as frequency prediction, available frequency bands, interference coefficients, available bandwidth, and priority, and uses parameter factors ω1, ω2, ω3, and ω4 to weight and adjust the influence of each factor. Φ(·) is a global gating function used to dynamically adjust the matching degree between frequency prediction and available frequency bands, and ReLU(·) is a commonly used nonlinear activation function used to enhance the nonlinear expressive power of the model. The model calculates a comprehensive score for each available frequency band and finally selects the frequency band with the highest score as the optimal operating frequency (ZZ).

[0216] It should be noted that the operating frequency calculation model selects the optimal operating frequency by comprehensively matching the frequency prediction results with available frequency band resource information through multiple factors. This process can be achieved using methods such as heuristic search, weighted scoring and ranking, linear or nonlinear programming, and genetic algorithms. For complex scenarios, machine learning models (such as neural networks and ensemble learning) can be combined to adaptively optimize the weights of each parameter factor. The global gating Φ(·) and ReLU activation function in the model can be implemented using gating mechanisms and nonlinear activation in deep learning to enhance the model's ability to express multi-source heterogeneous features. The final operating frequency result refers to the optimal or most suitable operating frequency calculated under the current ionospheric environment and available spectrum resource conditions, considering multiple factors such as frequency prediction, interference, bandwidth, and priority. This result can provide dynamic and intelligent frequency selection suggestions for radio communication equipment, maximizing the quality and reliability of communication links, avoiding frequency conflicts and interference, and optimizing spectrum resource utilization efficiency. It is the final decision output for achieving scientific frequency planning, intelligent spectrum scheduling, and efficient communication assurance, and is also the core of the entire ionospheric environment information decision-making link.

[0217] It is evident that the frequency planning and analysis method for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0218] Example 2

[0219] Please see Figure 2 , Figure 2 This is a schematic diagram of a frequency planning and analysis device for ionospheric environmental information decision-making disclosed in an embodiment of the present invention. Figure 2 The described frequency planning and analysis device for ionospheric environmental information decision-making is applied to a frequency planning and analysis optimization system for ionospheric environmental information decision-making, such as a local server or cloud server used for frequency planning and analysis of ionospheric environmental information decision-making. This invention does not limit the application of such devices. Figure 2 As shown, the frequency planning and analysis device for ionospheric environmental information decision-making includes:

[0220] The acquisition module 201 is used to acquire scattering frequency sweep data information, oblique frequency sweep data information, and available frequency band resource information;

[0221] The first calculation model 202 is used to process the scattering frequency sweep data information and the oblique frequency sweep data information to obtain the three-dimensional distribution model information of the ionosphere;

[0222] The second calculation module 203 is used to process the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain the working frequency result information.

[0223] As can be seen, the frequency planning and analysis device for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0224] Example 3

[0225] Please see Figure 3 , Figure 3 This is a schematic diagram of another frequency planning analysis device for ionospheric environmental information decision-making disclosed in an embodiment of the present invention. Figure 3 The described frequency planning and analysis device for ionospheric environmental information decision-making is applied to a frequency planning and analysis optimization system for ionospheric environmental information decision-making, such as a local server or cloud server used for frequency planning and analysis of ionospheric environmental information decision-making. This invention does not limit the application of such devices. Figure 3 As shown, the frequency planning and analysis device for ionospheric environmental information decision-making includes:

[0226] Processor 301;

[0227] A memory 302 containing executable program code is coupled to the processor 301;

[0228] The processor 301 calls the executable program code stored in the memory 302 to execute some or all of the steps of the frequency planning analysis method for ionospheric environmental information decision-making in Embodiment 1.

[0229] As can be seen, the frequency planning and analysis device for ionospheric environmental information decision-making described in the embodiments of the present invention can obtain the optimal frequency selection, thereby effectively improving the utilization efficiency of spectrum resources, avoiding frequency interference, and improving communication quality.

[0230] Example 4

[0231] This invention discloses a computer-readable storage medium storing computer instructions. When the computer instructions are invoked, they are used to execute some or all of the steps of the frequency planning analysis method for ionospheric environmental information decision-making in Embodiment 1.

[0232] Example 5

[0233] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps in the frequency planning analysis method for ionospheric environmental information decision-making described in Embodiment 1.

[0234] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0235] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0236] Finally, it should be noted that the frequency planning analysis method and apparatus for ionospheric environmental information decision-making disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A frequency planning analysis method for ionospheric environmental information decision-making, characterized in that, The method includes: S1, acquire scattering sweep frequency data information, oblique sweep frequency data information, and available frequency band resource information; S2, The scattering frequency sweep data information and the oblique frequency sweep data information are processed to obtain the three-dimensional distribution model information of the ionosphere; S3, process the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain the working frequency result information; S2 includes: S21, Process the scattering frequency sweep data information to obtain the first ionospheric environment information; S22, The oblique frequency sweep data information is processed to obtain the second ionospheric environment information; S23, the first ionospheric environmental information and the second ionospheric environmental information are fused to obtain the three-dimensional distribution model information of the ionosphere; S22 includes: S221, perform low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information; the lowest observable frequency information includes the lowest observable frequency information of the E layer, the lowest observable frequency information of the Es layer, the lowest observable frequency information of the F1 layer, and the lowest observable frequency information of the F2 layer. S222, perform high-frequency band detection processing on the oblique frequency sweep data information to obtain the highest observable frequency information and group distance feature information; S223, perform echo mode separation processing on the oblique sweep frequency data information to obtain trace feature information; S224, Energy feature extraction processing is performed on the oblique frequency sweep data information to obtain echo energy feature information; S225, the lowest observable frequency information, the highest observable frequency information, the group distance characteristic information, the tracking characteristic information and the echo energy characteristic information are analyzed and processed to obtain ionospheric parameter information; S226, interpolate and reconstruct the ionospheric parameter information and perform region fitting processing to obtain the second ionospheric environment information; S23 includes: S231, Based on the first ionospheric environment information, the second ionospheric environment information is spatially transformed to obtain the third ionospheric environment information; S232, Based on the first ionospheric environment information, the resolution conversion processing of the third ionospheric environment information is performed to obtain the fourth ionospheric environment information; S233, using a three-dimensional electron concentration calculation model, the first ionospheric environment information and the fourth ionospheric environment information are processed to obtain three-dimensional electron concentration distribution information; The three-dimensional electron concentration calculation model is as follows: ； ； ； In the formula, The three-dimensional electron concentration distribution information, and These are the first ionospheric environmental information and the fourth ionospheric environmental information, respectively. and These are the first nonlinear parameter information, the second nonlinear parameter information, and the third nonlinear parameter information, respectively. , , and These are the first weighting factor, the second weighting factor, the third weighting factor, and the fourth weighting factor, respectively. The parameters are the bias terms, where N and M are the first and second orders, respectively. S234, perform gridded modeling processing on the three-dimensional electron concentration analysis information to obtain the three-dimensional distribution model information of the ionosphere.

2. The frequency planning analysis method for ionospheric environmental information decision-making according to claim 1, characterized in that, The step of performing low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information includes: S2211, Perform E-layer LOF extraction processing on the oblique sweep frequency data information to obtain the lowest observable frequency information of the E layer; S2212, Perform Es layer LOF extraction processing on the oblique frequency sweep data information to obtain the Es layer lowest observable frequency information; S2213, Perform F1 layer LOF extraction processing on the oblique sweep frequency data information to obtain the F1 layer lowest observable frequency information; S2214, Perform F2 layer LOF extraction processing on the oblique sweep frequency data information to obtain the F2 layer lowest observable frequency information.

3. The frequency planning analysis method for ionospheric environmental information decision-making according to claim 1, characterized in that, The analysis and processing of the lowest observable frequency information, the highest observable frequency information, the group distance feature information, the tracking feature information, and the echo energy feature information yields ionospheric parameter information, including: S2251, using the link midpoint adjacent frequency calculation model, the lowest observable frequency information and the highest observable frequency information are processed to obtain the link midpoint adjacent frequency information; The link midpoint adjacent frequency calculation model is as follows: ； In the formula, This refers to the adjacent frequency information of the midpoint of the link. This refers to the lowest observable frequency information. The highest observable frequency information, For time adjustment factor, Spatial adjustment factor, , and These are the first adjustment factor, the second adjustment factor, the third adjustment factor, and the fourth adjustment factor, respectively. As the first weight parameter, This is the ionospheric reference factor. S2252, Process the group distance feature information and the trace feature information to obtain peak height information; S2253, Process the link midpoint adjacent frequency information and the peak height information to obtain the maximum electron concentration parameter information; S2254, The link midpoint adjacent frequency information, the peak height information and the maximum electron concentration parameter information are processed to obtain ionospheric parameter information.

4. The frequency planning analysis method for ionospheric environmental information decision-making according to claim 1, characterized in that, The process of processing the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain operating frequency result information includes: S31, perform time series prediction processing on the ionospheric three-dimensional distribution model information to obtain frequency prediction result information; S32, the frequency prediction result information is matched with the available frequency band resource information to obtain the working frequency result information.

5. A frequency planning and analysis device for ionospheric environmental information decision-making, applied to the frequency planning and analysis method for ionospheric environmental information decision-making as described in claim 1, characterized in that, The device includes: The acquisition module is used to acquire scattering frequency sweep data information, oblique frequency sweep data information, and available frequency band resource information; The first calculation model is used to process the scattering frequency sweep data information and the oblique frequency sweep data information to obtain the three-dimensional distribution model information of the ionosphere; The second calculation module is used to process the available frequency band resource information and the ionospheric three-dimensional distribution model information to obtain the working frequency result information; The first calculation module includes: The first calculation submodule is used to process the scattering frequency sweep data information to obtain the first ionospheric environment information; The first calculation second submodule is used to process the oblique frequency sweep data information to obtain the second ionospheric environment information; The first calculation third submodule is used to fuse the first ionospheric environmental information and the second ionospheric environmental information to obtain ionospheric three-dimensional distribution model information; The first calculation second submodule includes: The first processing unit is used to perform low-frequency band detection processing on the oblique frequency sweep data information to obtain the lowest observable frequency information; the lowest observable frequency information includes the lowest observable frequency information of the E layer, the lowest observable frequency information of the Es layer, the lowest observable frequency information of the F1 layer, and the lowest observable frequency information of the F2 layer. The second processing unit is used to perform high-frequency band detection processing on the oblique frequency sweep data information to obtain the highest observable frequency information and group distance feature information; The third processing unit is used to perform echo mode separation processing on the oblique frequency sweep data information to obtain trace feature information; The fourth processing unit is used to perform energy feature extraction processing on the oblique frequency sweep data information to obtain echo energy feature information; The fifth processing unit is used to analyze and process the lowest observable frequency information, the highest observable frequency information, the group distance feature information, the tracking feature information and the echo energy feature information to obtain ionospheric parameter information; The sixth processing unit is used to perform interpolation reconstruction and region fitting processing on the ionospheric parameter information to obtain the second ionospheric environment information; The first calculation third submodule includes: The seventh processing unit is used to perform spatial transformation processing on the second ionospheric environment information based on the first ionospheric environment information to obtain the third ionospheric environment information; The eighth processing unit is used to perform resolution conversion processing on the third ionospheric environment information based on the first ionospheric environment information to obtain the fourth ionospheric environment information; The ninth processing unit is used to process the first ionospheric environment information and the fourth ionospheric environment information using a three-dimensional electron concentration calculation model to obtain three-dimensional electron concentration distribution information. The three-dimensional electron concentration calculation model is as follows: ； ； ； In the formula, The three-dimensional electron concentration distribution information, and These are the first ionospheric environmental information and the fourth ionospheric environmental information, respectively. and These are the first nonlinear parameter information, the second nonlinear parameter information, and the third nonlinear parameter information, respectively. , , and These are the first weighting factor, the second weighting factor, the third weighting factor, and the fourth weighting factor, respectively. The parameters are the bias terms, where N and M are the first and second orders, respectively. The tenth processing unit is used to perform gridded modeling processing on the three-dimensional electron concentration analysis information to obtain the three-dimensional distribution model information of the ionosphere.

6. A frequency planning and analysis device for ionospheric environmental information decision-making, characterized in that, The device includes: processor; A memory coupled to the processor stores executable program code; The processor calls the executable program code stored in the memory to execute the frequency planning analysis method for ionospheric environmental information decision-making as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the frequency planning analysis method for ionospheric environmental information decision-making as described in any one of claims 1-4.

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