Ionospheric parameter prediction method and device based on sequence determination strategy

Abstract

The application discloses an ionosphere parameter prediction method and device based on a sequence determination strategy, relates to the technical field of ionosphere parameter prediction, and comprises the following steps: acquiring ionosphere sequence data to perform signal decomposition processing, and obtaining a sub-signal set; determining a non-error component subsequence and an error component subsequence based on the sub-signal set; performing determination processing on the non-error component subsequence to obtain a first prediction result; performing neural network processing on the error component subsequence to obtain a second prediction result; and adding the first prediction result and the second prediction result respectively to obtain a sequence prediction result. The method solves the problem of additional operation complexity caused by a signal decomposition algorithm in a traditional method, reduces the operation complexity of a model on the basis of accurately predicting an ionosphere sequence, and provides strong support for frequency selection of short-wave communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of short wave communication, and more particularly to an ionospheric parameter prediction method and device based on a sequence determination strategy. BACKGROUND

[0002] Short wave communication mainly relies on the reflection of short wave signals in the ionosphere to realize long-distance communication without relay. Short wave communication has the characteristics of simple network establishment and strong persistence. Short wave communication can complete communication under the condition of lack of communication infrastructure, and is widely used in military communication, civil aviation, marine transportation, meteorology, emergency rescue and other fields. Therefore, short wave communication is widely used in military communication, civil aviation, marine transportation, meteorology, emergency rescue and other fields.

[0003] However, short wave communication also has some shortcomings, such as difficulty in frequency selection, small communication capacity, congestion caused by a large number of users, and narrow available frequency band. These characteristics directly affect the performance of short wave communication. The ionosphere is affected by many factors such as solar and geomagnetic activity, and its change law often shows a nonlinear change law that is difficult to characterize. In order to make more accurate prediction, a prediction method with stronger fitting ability is needed. However, the current prediction method uses a deep learning method to increase the computational complexity of the model, and the computing power of the current short wave communication center operation node is limited. The current short wave communication ionospheric prediction method combining decomposition has the following shortcomings:

[0004] 1. The decomposition method has certain errors. The current signal decomposition method cannot fully extract the effective information in the ionospheric parameter sequence, resulting in a decrease in the prediction accuracy of the deep learning method combined with the decomposition method.

[0005] 2. The processing method has high complexity. The current deep learning method combined with signal decomposition brings an additional deep learning processing process, which requires high computing power for the short wave communication center operation node, and the computing power of the current short wave communication center operation node is limited. SUMMARY

[0006] In view of at least one defect or improvement demand of the prior art, the present application provides an ionospheric parameter prediction method and device based on a sequence determination strategy, which solves the problem of additional computational complexity caused by the signal decomposition algorithm in the traditional method. By completing the determination processing on the basis of the decomposed sub-signal, the sub-signal with low complexity generated in the decomposition process is replaced by a simple determination processing method instead of a neural network processing method, and only the error component with high complexity is processed by a neural network processing method to obtain a prediction result with high accuracy. The present application can reduce the computational complexity of the model on the basis of accurately predicting the ionospheric sequence, and provide support for short wave communication frequency selection.

[0007] To achieve the above object, according to a first aspect of the present application, a method for ionospheric parameter prediction based on sequence determination strategy is provided, comprising:

[0008] Obtaining ionospheric sequence data for signal decomposition processing to obtain a sub-signal set;

[0009] Determining a non-error component sub-sequence and an error component sub-sequence based on the sub-signal set;

[0010] Performing determination processing on the non-error component sub-sequence to obtain a first prediction result; and performing neural network processing on the error component sub-sequence to obtain a second prediction result;

[0011] Adding the first prediction result and the second prediction result respectively to obtain a sequence prediction result.

[0012] The method for ionospheric parameter prediction based on sequence determination strategy, the obtaining ionospheric sequence data for signal decomposition processing to obtain a sub-signal set, comprising:

[0013] Obtaining ionospheric sequence data;

[0014] Optimizing the variational mode decomposition parameters by using a beluga whale optimization algorithm to obtain a target variational mode decomposition model;

[0015] Inputting the ionospheric sequence data into the target variational mode decomposition model to output a sub-signal set.

[0016] The method for ionospheric parameter prediction based on sequence determination strategy, the variational mode decomposition parameters comprising a penalty factor and a decomposition mode number parameter.

[0017] The method for ionospheric parameter prediction based on sequence determination strategy, the determining a non-error component sub-sequence and an error component sub-sequence based on the sub-signal set, comprising:

[0018] Performing oscillation frequency detection based on the sub-signal set to obtain an error component sub-sequence, the error component sub-sequence being a sub-signal with the highest oscillation frequency;

[0019] Determining a non-error component sub-sequence based on the ionospheric sequence data, the sub-signal set and the error component sub-sequence.

[0020] The method for ionospheric parameter prediction based on sequence determination strategy, the determining a non-error component sub-sequence based on the ionospheric sequence data, the sub-signal set and the error component sub-sequence, comprising:

[0021] Obtaining a difference component of the ionospheric sequence data and the sub-signal set to obtain an information loss component;

[0022] Obtaining the difference component of the sub-signal set and the error component sub-sequence to obtain a determined component through determination processing.

[0023] Obtaining the difference component of the non-error component sub-sequence and the determined component to obtain a determined component.

[0024] Obtaining the sum component of the information loss component, the determined component and the determined component to obtain a non-error component sub-sequence.

[0025] The ionospheric parameter prediction method based on the sequence determination strategy as described, the determination processing comprises:

[0026] The determination processing is performed by using a sine function fitting to obtain a determined sequence.

[0027] The ionospheric parameter prediction method based on the sequence determination strategy as described, after adding the first prediction result and the second prediction result respectively to obtain a sequence prediction result, further comprises:

[0028] Performing error analysis and operation processing complexity analysis on the sequence prediction result.

[0029] According to the second aspect of the present application, an ionospheric parameter prediction device based on a sequence determination strategy is also provided, which comprises: a decomposition unit configured to obtain ionospheric sequence data and perform signal decomposition processing to obtain a sub-signal set; a processing unit configured to determine a non-error component sub-sequence and an error component sub-sequence based on the sub-signal set; a first prediction unit configured to perform determination processing on the non-error component sub-sequence to obtain a first prediction result; a second prediction unit configured to perform neural network processing on the error component sub-sequence to obtain a second prediction result; and an output unit configured to add the first prediction result and the second prediction result respectively to obtain a sequence prediction result.

[0030] According to the third aspect of the present application, a computer readable storage medium is also provided, which stores a computer program, wherein the computer program is configured to execute the ionospheric parameter prediction method based on the sequence determination strategy when running.

[0031] According to the fourth aspect of the present application, an electronic device is also provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the ionospheric parameter prediction method based on the sequence determination strategy through the computer program.

[0032] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0033] (1) The application provides an ionospheric parameter prediction method based on a sequence determination strategy, signal decomposition processing is performed on acquired ionospheric sequence data to obtain a sub-signal set; based on the sub-signal set, a non-error component subsequence and an error component subsequence are determined; the non-error component subsequence is subjected to determination processing to obtain a first prediction result; the error component subsequence is subjected to neural network processing to obtain a second prediction result; the first prediction result and the second prediction result are added respectively to obtain a sequence prediction result. The method solves the problem of additional operation complexity caused by signal decomposition algorithms in traditional methods, reduces the operation complexity of the model on the basis of accurately predicting ionospheric sequences, and provides strong support for short-wave communication frequency selection.

[0034] (2) The application considers the problems of incomplete decomposition in current signal decomposition algorithms, and improves the feature extraction capability of the decomposition algorithm for ionospheric sequences by designing a beluga whale optimization algorithm combined with a variational mode decomposition method. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0036] Figure 1 A flowchart of an optional ionospheric parameter prediction method based on a sequence determination strategy is provided for the embodiments of the present application.

[0037] Figure 2 A flowchart of another optional ionospheric parameter prediction method based on a sequence determination strategy is provided for the embodiments of the present application.

[0038] Figure 3 A flowchart of an optional beluga whale optimization algorithm is provided for the embodiments of the present application.

[0039] Figure 4 A structural diagram of an optional ionospheric parameter prediction device based on a sequence determination strategy is provided for the embodiments of the present application.

[0040] Figure 5 A structural diagram of an optional electronic device is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0041] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0042] The terms "first", "second", "third" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0043] The ionosphere belongs to a part of the earth's atmosphere, located at an altitude of about 50 kilometers to 1000 kilometers from the earth's surface. In this layer, due to the influence of solar radiation, atmospheric molecules are ionized to form a large number of free electrons and ions, thereby forming a plasma state in the atmosphere. Due to the uneven distribution of the composition and density of different height parts of the atmosphere, the ionization radiation source frequency band and spectrum caused by solar radiation will also differ, resulting in uneven distribution of electron density at different altitudes of the ionosphere, and this uneven distribution of electron density at different altitudes directly affects short-wave communication transmission.

[0044] Generally, taking the ionospheric electron density distribution at different altitudes as the basic standard, the ionosphere can be divided into D, E, F1 and F2 layers from low to high, and the corresponding electron density also presents a change trend from low to high.

[0045] At the same time, the ionosphere time prediction process aims to fit the change characteristics in the ionosphere parameter sequence. The prediction method of ionosphere parameters is divided into long-term prediction method and short-term prediction method.

[0046] The long-term prediction method is used to predict the monthly statistical value of the statistical ionosphere parameter, and the time scale covered is larger and the predicted time range is larger, and the prediction time granularity is large. The expected goal is to provide change trend prediction for the long-term change of the ionosphere, and because the range covers a wide range and involves a long time, the accuracy is limited. The short-term prediction method of the ionosphere, also known as the near real-time prediction method, covers a shorter time range, has a small prediction time granularity, and the purpose is to predict the accurate ionosphere parameters in the next few days to a few hours.

[0047] In view of the above, the ionospheric parameter prediction method based on sequence determination strategy provided in the embodiments of the present application is based on ionospheric historical sequence, and a variational mode decomposition method optimized by designing beluga whale optimization algorithm is used to obtain signal subsequences generated after decomposition, and then a signal determination strategy is used to determine and predict the signal components with low complexity, and only the error components are predicted by using a neural network method, and the obtained prediction results are added to obtain the final signal prediction result.

[0048] According to an aspect of the embodiments of the present application, an ionospheric parameter prediction method based on sequence determination strategy is provided. The following will be described in combination with Figure 1 The ionospheric parameter prediction method based on sequence determination strategy provided in the embodiments of the present application is described.

[0049] Figure 1 is a flowchart of an optional ionospheric parameter prediction method based on sequence determination strategy provided in the embodiments of the present application, as shown in Figure 1 The flowchart of the method can include the following steps:

[0050] S102, ionospheric sequence data is acquired for signal decomposition processing to obtain a sub-signal set.

[0051] S104, based on the sub-signal set, a non-error component subsequence and an error component subsequence are determined.

[0052] S106, the non-error component subsequence is determined and processed to obtain a first prediction result;

[0053] S108, the error component subsequence is processed by using a neural network to obtain a second prediction result.

[0054] S110, the first prediction result and the second prediction result are added respectively to obtain a sequence prediction result.

[0055] The optional ionospheric parameter prediction method based on sequence determination strategy in the embodiments of the present application can be applied to short-term ionospheric prediction to provide fine ionospheric parameters within a few days.

[0056] Figure 2 is a flowchart of another optional ionospheric parameter prediction method based on sequence determination strategy provided in the embodiments of the present application, as shown in Figure 2As shown, firstly, ionospheric sequence data is subjected to signal decomposition processing, different signal decomposition methods are used to decompose the obtained ionospheric sequence data to obtain different sub-signals, and a sub-signal set is obtained. Secondly, the sub-signals are processed to determine non-error component sub-sequences and error component sub-sequences, and each sub-signal sequence including error components is obtained; finally, only the error component sub-sequences are processed through a neural network to obtain a second prediction result, and the remaining signal components are processed through a low-complexity determination processing method to obtain a first prediction result, and finally the first prediction result and the second prediction result are added respectively to obtain a final sequence prediction result and perform error analysis and operation complexity analysis.

[0057] Through the above steps S102 to S110, the sub-signals after decomposition are subjected to determination processing, the sub-signals with low complexity generated in the decomposition process are replaced by a simple determination processing method instead of neural network processing, and only the error components with high complexity are subjected to neural network processing to obtain a prediction result with high accuracy. On the basis of accurately predicting the ionospheric sequence, the operation complexity of the model is reduced, which provides strong support for short-wave communication frequency selection.

[0058] Based on the content of the above embodiment, as an optional embodiment, the ionospheric parameter prediction method based on sequence determination strategy provided by the application comprises the following steps of:

[0059] Obtaining ionospheric sequence data; using a beluga optimization algorithm to optimize variational mode decomposition parameters to obtain a target variational mode decomposition model; inputting the ionospheric sequence data into the target variational mode decomposition model to output a sub-signal set.

[0060] Specifically, the ionospheric sequence data of the ionospheric foF2 sequence is taken as an example for description.

[0061] In variational mode decomposition, the ionospheric sequence is decomposed into different signal components, and the specific decomposition process is as follows: firstly, the ionospheric sequence decomposition problem is converted into a variational problem, and the specific expression is as follows:

[0062]

[0063] Wherein, f(t) represents input data, u k represents a signal sub-component obtained by decomposition, w k represents the center frequency of different signal sub-components obtained in the decomposition process. * represents convolution processing, δ t represents an impulse function. In order to solve the above variational problem, a Lagrange operator λ and a penalty factor α are introduced, and the Lagrange algorithm strategy is as follows:

[0064]

[0065] wherein K represents the number of decomposed modes.

[0066] In order to solve the optimization problem existing in the above two formulas, the present application adopts the multiplier alternating iteration method to derive the variational modal decomposition model.

[0067] wherein u k and w k are updated as follows:

[0068]

[0069] wherein represents the Fourier transform result of .

[0070] Specifically, the specific process of the variational modal decomposition is summarized as follows:

[0071] 1) Let n = 1 and set the value of constant K, initialize the values of u k , w, λ in the first iteration cycle.

[0072] 2) Update the values of u k and w k according to the formula mentioned above.

[0073] 3) The value of Lagrange multiplier λ n (w) is gradually updated in the training process:

[0074]

[0075] wherein τ represents the noise tolerance size.

[0076] 4) Continue the iteration process until the calculated result of the decomposed component is less than the preset exact convergence tolerance criterion (i.e. ε), and then output the sub-signal set of the component generated after signal decomposition, which is specifically:

[0077]

[0078] Further, the variational modal decomposition parameters in the above variational modal decomposition model are optimized by using the white whale optimization algorithm to obtain the target variational modal decomposition model.

[0079] wherein the variational modal decomposition parameters include, for example, the penalty factor α and the decomposition mode number parameter K.

[0080] The white whale optimization algorithm is based on the population mechanism. It is assumed that each white whale is a candidate solution, and the candidate solution is constantly updated in the optimization process. The flowchart of the optimization algorithm is as follows:Figure 3 The algorithm flow can be divided into four stages, which are described as follows:

[0081] (1) Population initialization stage

[0082] First, let X represent the position population distribution matrix of the population, x1, x2...x n represent the individuals in the population, and we set the initial positions of different individuals in the population as follows:

[0083]

[0084] Where n represents the number of the population, and d represents the number of variable dimensions to be explored. Let F X represent the fitness value of the population, f(x 11 ,...x 1d ), and the fitness values corresponding to all individuals in the population are as follows:

[0085]

[0086] The present application adopts the envelope entropy of the foF2 sequence as the calculation method of the fitness value. A balance factor B f is set in the algorithm, and whether the individual in the population is suitable to continue exploration at the current position:

[0087] B f =B0(1-T / (2T max ))

[0088] Where T represents the current iteration number of the algorithm, T max represents the maximum iteration number of the limit. B0 is a random number between 0 and 1, and when B f is greater than 0.5, the individual corresponding to the current population will enter the exploration stage (i.e. stage (2)), and when B f is less than or equal to 0.5, the current individual will enter the development stage (i.e. stage (3)). With the increase of the iteration number, the fluctuation range of B f will gradually decrease, and the behavior of the individual tends to be stable.

[0089] (2) Exploration stage

[0090] In the exploration stage, the algorithm updates the position of the beluga whale population, and the update formula is as follows:

[0091] When j is even.

[0092] When j is odd.

[0093] Where T represents the current iteration number, represents the updated position of the i-th beluga whale in the j-th dimension (relative to P j (j = 1, 2,..., d) represents a randomly selected dimension from the d-dimensional space. and represents the current position of the i-th and r-th beluga whale individual, r represents a randomly selected beluga whale, r1 and r2 represent random numbers between 0 and 1 to enhance the randomness of the exploration phase, sin(2πr2) and cos(2πr2) are used to simulate the synchronous mirror behavior of belugas when swimming and diving, while also serving as an average random number.

[0094] (3) Development phase

[0095] In the development phase of the beluga whale optimization algorithm, beluga whales can cooperate in foraging based on the positions of neighboring beluga whales. Different beluga whale individuals share position information with each other, and beluga whale individuals move based on this information. In terms of the algorithm, this is the process of finding the optimal solution. The update in the development phase is as follows:

[0096]

[0097] where T represents the current iteration number, represents the updated position of the beluga whale individual, represents a randomly selected beluga whale. represents the best position in the current phase. r3 and r4 are random numbers between 0 and 1, where C1 and L F are defined as follows to improve the convergence strategy of the algorithm (β is a constant set to 1.5):

[0098] C1 = 2r4(1 - T / T max )

[0099] L F = 0.05·u·δ / |v| 1 / β

[0100] where u and v are parameters with normal distribution characteristics, and δ is defined as:

[0101]

[0102] (4) Whale fall phase

[0103] During the exploration and development of the beluga whale population, the population will face the corresponding probability of extinction, which is called "whale fall" in the algorithm. This results in a small change in the population size. We set the step size of the beluga whale when it falls to X step , and the beluga whale position update is as follows:

[0104]

[0105] where, r5, r6 and r7 are random numbers between (0, 1), the step length X of the whale when falling step Determination is:

[0106]

[0107] Where u b And l b Respectively, the upper limit and the lower limit of the white whale falling step variable, C2 is defined as: C2=2W f ·n, where W f Is the probability of the whale falling in each step in the algorithm:

[0108] W f =0.1-0.05T / T max

[0109] The falling probability W f Gradually decreases during the iteration process, which means that the longer the survival time of the white whale, the more likely it is to find the location where the optimal parameters are located.

[0110] Therefore, in order to avoid the uncertainty of human selection of parameters, the white whale optimization algorithm is used to optimize the variational mode decomposition parameters in the variational mode decomposition, so that the variational mode decomposition result is more reliable.

[0111] The initial setting of the parameters to be optimized and the ionosphere sequence data to be decomposed are input into the white whale optimization algorithm as the initial position of the population, and the optimization result of the variational mode decomposition parameters to be optimized is output, so as to construct a target variational mode decomposition model. The present application considers the problems such as incomplete decomposition existing in the current signal decomposition algorithm, and improves the feature extraction capability of the decomposition algorithm for ionosphere foF2 sequence by designing white whale optimization algorithm combined with variational mode decomposition method.

[0112] Based on the content of the above embodiment, as an optional embodiment, the ionosphere sequence data is decomposed based on the sub-signal set, and the non-error component sub-sequence and the error component sub-sequence are determined, which comprises:

[0113] The oscillation frequency is detected based on the sub-signal set, and the error component sub-sequence is obtained. Based on the ionosphere sequence data, the sub-signal set and the error component sub-sequence, the non-error component sub-sequence is determined.

[0114] Considering the additional neural network processing complexity brought about by current deep learning combined with signal decomposition algorithms, the present invention considers classifying the components generated by signal decomposition, that is, the sub-signal set, to obtain non-error component subsequences and error component subsequences. The error component subsequence is the sub-signal with the highest oscillation frequency. The non-error component subsequence is the sub-signal set excluding the sub-signal with the highest oscillation frequency.

[0115] Optionally, after acquiring the ionospheric sequence data and performing signal decomposition processing on it, methods such as fast Fourier transform (FFT) or Hilbert-Huang transform can be used to find the sub-signal with the highest oscillation frequency to obtain the error component sub-sequence.

[0116] For the signal component with the highest oscillation frequency, a neural network is used for prediction;

[0117] For the remaining signal components, deterministic processing is adopted. Deterministic processing can obtain fitting expressions of the sinusoidal functions of the components. These expressions use the prediction time as the independent variable. Substituting the prediction time into the expression can generate the prediction results of each component and obtain the first prediction result. The first prediction result of the deterministic processing is added to the second prediction result of the neural network to obtain the sequence prediction result under the deterministic strategy.

[0118] Optionally, the deterministic processing is performed by using sine function fitting to obtain a deterministic sequence.

[0119] In one exemplary embodiment, considering the advantage of the sine fitting algorithm in accurately fitting periodic series data, a deterministic strategy is employed to employ a sine function fitting algorithm to deterministically fit the sub-signals generated after signal decomposition, thereby fitting the different sub-signals into deterministic signals. Considering the varying non-stationarity of the sub-signals obtained after, for example, time series decomposition, when determinizing the sequence decomposition results, a sine fitting deterministic processing is performed on the remaining sub-signals, excluding the sub-signal with the strongest non-stationarity (i.e., the error component sub-sequence), to obtain deterministic sub-signals. For the sub-signal with the strongest non-stationarity, given its complex variation characteristics, a deep learning approach is still employed for prediction. This operation requires only a single deep learning model to complete the prediction, thereby reducing implementation complexity.

[0120] Specifically, for the sub-signals generated by decomposition, a sine function fitting algorithm is used to decompose them into a combination of multiple sine functions with different amplitudes and phases. The fitting result sequence of the determined sub-signals is represented by g(x), and its combination form can be summarized as:

[0121]

[0122] Where x represents the changing time a and b of the independent variable fitting sequence, and c represents the combination coefficient.

[0123] In an exemplary embodiment, a method for predicting ionospheric foF2 parameters by combining deep learning with signal decomposition is, for example:

[0124] Input: original foF2 sequence S

[0125] Output: Predicted foF2 sequence S′

[0126] Decompose the original foF2 sequence S and obtain the signal subcomponent set f N

[0127] For the signal subcomponent set f N Each signal subcomponent f in x , x=1,2...,n-1, except f n :

[0128] The various sub-signal components f are obtained by deterministic strategy x The deterministic prediction result f′ x

[0129] Signal loss r = Sf′ x

[0130] Determined component C = C + f′ x

[0131] End the loop

[0132] The error component f generated by decomposition n =f n +r

[0133] The error component f is obtained by deep learning method n The prediction result f′ n

[0134] The predicted foF2 sequence result S′=C+f′ n

[0135] Returns the predicted foF2 sequence S′

[0136] Among them, f′ n represents the second prediction result, and C represents the first prediction result

[0137] In summary, the present invention has the following characteristics:

[0138] 1. The additional computational complexity brought about by deep learning combined with signal decomposition methods is considered, and the complexity of sequence processing is reduced through a sequence deterministic strategy.

[0139] 2. Considering the problems of incomplete decomposition in current signal decomposition algorithms, a white whale optimization algorithm combined with variational mode decomposition method is designed to improve the feature extraction capability of the decomposition algorithm for ionospheric foF2 sequence.

[0140] According to another aspect of the embodiments of the present application, an ionospheric parameter prediction device for implementing the ionospheric parameter prediction method based on the sequence determination strategy is also provided. Figure 4 is a structural schematic diagram of an optional ionospheric parameter prediction device based on the sequence determination strategy according to the embodiments of the present application, as Figure 4 shown, the device can include:

[0141] The decomposition unit 402 is configured to acquire ionospheric sequence data for signal decomposition processing to obtain a sub-signal set.

[0142] The processing unit 404 is configured to determine a non-error component sub-sequence and an error component sub-sequence based on the sub-signal set.

[0143] The first prediction unit 406 is configured to perform determination processing on the non-error component sub-sequence to obtain a first prediction result.

[0144] The second prediction unit 408 is configured to perform neural network processing on the error component sub-sequence to obtain a second prediction result.

[0145] The output unit 410 is configured to add the first prediction result and the second prediction result respectively to obtain a sequence prediction result.

[0146] It should be noted that the decomposition unit 402 in this embodiment can be configured to perform the above step S102, the processing unit 404 in this embodiment can be configured to perform the above step S104, the first prediction unit 406 in this embodiment can be configured to perform the above step S106, the second prediction unit 408 in this embodiment can be configured to perform the above step S108, and the output unit 410 in this embodiment can be configured to perform the above step S110.

[0147] Through the above modules, the ionospheric sequence data is acquired for signal decomposition processing to obtain a sub-signal set; the non-error component sub-sequence and the error component sub-sequence are determined based on the sub-signal set; the first prediction result is obtained by performing determination processing on the non-error component sub-sequence; the second prediction result is obtained by performing neural network processing on the error component sub-sequence; and the first prediction result and the second prediction result are added respectively to obtain a sequence prediction result. The problem of additional computational complexity caused by the signal decomposition algorithm in the traditional method is solved, the computational complexity of the model is reduced on the basis of accurately predicting the ionospheric sequence, and strong support is provided for short-wave communication frequency selection.

[0148] It should be noted that the above modules and the examples and scenarios realized by the corresponding steps are the same as the above embodiments, but are not limited to the above disclosed content. It should be noted that the above modules can be run in a hardware environment as part of the device, can be implemented by software, or can be implemented by hardware, wherein the hardware environment includes a network environment.

[0149] According to another aspect of the embodiments of the present application, a storage medium is also provided. Optionally, in the present embodiment, the above storage medium can be used to execute the program code of any of the above ionospheric parameter prediction methods based on sequence determination strategy in the embodiments of the present application.

[0150] Optionally, in the present embodiment, the storage medium is configured to store program code for executing the following steps:

[0151] S1, obtaining ionospheric sequence data for signal decomposition processing to obtain a sub-signal set;

[0152] S2, determining a non-error component sub-sequence and an error component sub-sequence based on the sub-signal set;

[0153] S3, performing determination processing on the non-error component sub-sequence to obtain a first prediction result;

[0154] S4, performing neural network processing on the error component sub-sequence to obtain a second prediction result;

[0155] S5, adding the first prediction result and the second prediction result respectively to obtain a sequence prediction result.

[0156] Optionally, the specific examples in the present embodiment can refer to the examples described in the above embodiments, which will not be described herein.

[0157] The computer readable storage medium can include, but is not limited to, any type of disk including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic or optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0158] According to another aspect of the embodiments of the present application, an electronic device for implementing the above ionospheric parameter prediction method based on sequence determination strategy is also provided. The electronic device can be a server, a terminal, or a combination thereof.

[0159] Figure 5 is a structural schematic diagram of an optional electronic device according to the embodiments of the present application, as Figure 5As shown, the electronic device includes a processor 502, a communication interface 504, a memory 506 and a communication bus 508, wherein the processor 502, the communication interface 504 and the memory 506 communicate with each other through the communication bus 508, and

[0160] The memory 506 is configured to store a computer program.

[0161] The processor 502 is configured to execute the computer program stored in the memory 506 to implement the following steps:

[0162] S1, obtaining ionospheric sequence data for signal decomposition processing to obtain a sub-signal set;

[0163] S2, determining a non-error component sub-sequence and an error component sub-sequence based on the sub-signal set;

[0164] S3, performing determination processing on the non-error component sub-sequence to obtain a first prediction result;

[0165] S4, performing neural network processing on the error component sub-sequence to obtain a second prediction result;

[0166] S5, adding the first prediction result and the second prediction result respectively to obtain a sequence prediction result.

[0167] Optionally, the communication bus can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus or the like. The communication bus can be divided into an address bus, a data bus, a control bus and the like. For the convenience of representation, Figure 5 In the figure, only one thick line is used to represent the communication bus, but it does not mean that there is only one bus or only one type of bus. The communication interface is used for communication between the electronic device and other devices.

[0168] The memory can include a RAM and can also include a non-volatile memory, for example, at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0169] As an example, the memory 506 can include, but is not limited to, the receiving unit 402, the first verification unit 404, the second verification unit 406 and the adding unit 408 in the ionospheric parameter prediction device based on the sequence determination strategy. In addition, other module units in the ionospheric parameter prediction device based on the sequence determination strategy can also be included, but not limited to, which will not be described in detail in this example.

[0170] The processor can be a general processor, which can include but is not limited to: a CPU (Central Processing Unit), a NP (Network Processor), etc.; and can also be a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0171] Optionally, the specific examples in the embodiment can refer to the examples described in the above embodiments, and the embodiment will not be described here.

[0172] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, some steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0173] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can refer to the related description of other embodiments.

[0174] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented by other means. For example, the device embodiments described above are only schematic, and the division of the units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some service interface, device or unit, and can be electrical or other forms.

[0175] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0176] In addition, each of the function units in the embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0177] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned memory includes: a U disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0178] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0179] The above is only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0180] Each of the technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present disclosure.

[0181] Those skilled in the art can easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting ionospheric parameters based on a sequence determination strategy, characterized in that: include: Acquire ionospheric sequence data, perform signal decomposition processing, and obtain a sub-signal set; The acquiring of ionospheric sequence data for signal decomposition processing to obtain a sub-signal set comprises: acquiring ionospheric sequence data; optimizing variational modal decomposition parameters using a Beluga optimization algorithm to obtain a target variational modal decomposition model, wherein the variational modal decomposition parameters include a penalty factor and a decomposition mode quantity parameter; and inputting the ionospheric sequence data into the target variational modal decomposition model to output a sub-signal set. Determining a non-error component subsequence and an error component subsequence based on the sub-signal set; Performing deterministic processing on the non-error component subsequence to obtain a first prediction result; performing neural network processing on the error component subsequence to obtain a second prediction result; The first prediction result and the second prediction result are added together to obtain a sequence prediction result.

2. The ionospheric parameter prediction method based on the sequence determination strategy according to claim 1, characterized in that: The determining, based on the sub-signal set, a non-error component subsequence and an error component subsequence comprises: Performing oscillation frequency detection based on the sub-signal set to obtain an error component sub-sequence, wherein the error component sub-sequence is the sub-signal with the highest oscillation frequency; A non-error component subsequence is determined based on the ionospheric sequence data, the sub-signal set and the error component subsequence.

3. The ionospheric parameter prediction method based on the sequence determination strategy according to claim 2, characterized in that: The determining of the non-error component subsequence based on the ionospheric sequence data, the sub-signal set, and the error component subsequence comprises: Obtaining a difference component between the ionospheric sequence data and the sub-signal set to obtain an information loss component; Obtaining a difference component between the sub-signal set and the error component subsequence and performing deterministic processing to obtain a deterministic component; Obtaining a difference component between the non-error component subsequence and the deterministic component to obtain the deterministic component; The information loss component, the deterministic component and the sum component of the deterministic component are obtained to obtain a non-error component subsequence.

4. The ionospheric parameter prediction method based on the sequence determination strategy according to claim 1 or 3, characterized in that The deterministic processing includes: Sine function fitting is used for deterministic processing to obtain a deterministic sequence.

5. The ionospheric parameter prediction method based on the sequence determination strategy according to any one of claims 1 to 4, characterized in that: After respectively adding the first prediction result and the second prediction result to obtain a sequence prediction result, the method further includes: Error analysis and computational complexity analysis are performed on the sequence prediction results.

6. An ionospheric parameter prediction device based on a sequence determination strategy, characterized in that: include: A decomposition unit is used to obtain ionospheric sequence data, perform signal decomposition processing, and obtain a sub-signal set; The acquiring of ionospheric sequence data for signal decomposition processing to obtain a sub-signal set comprises: acquiring ionospheric sequence data; optimizing variational modal decomposition parameters using a Beluga optimization algorithm to obtain a target variational modal decomposition model, wherein the variational modal decomposition parameters include a penalty factor and a decomposition mode quantity parameter; and inputting the ionospheric sequence data into the target variational modal decomposition model to output a sub-signal set. a processing unit, configured to determine a non-error component subsequence and an error component subsequence based on the sub-signal set; A first prediction unit, configured to perform deterministic processing on the non-error component subsequence to obtain a first prediction result; A second prediction unit is configured to perform neural network processing on the error component subsequence to obtain a second prediction result; An output unit is used to add the first prediction result and the second prediction result respectively to obtain a sequence prediction result.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 5 when executed.

8. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 5 through the computer program.

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