Short-wave frequency forecasting method and system based on external emission source data and medium

Abstract

The invention discloses a short-wave frequency forecasting method based on external emission source data, and relates to the technical field of short-term short-wave frequency forecasting, and the method comprises the steps: obtaining forecast short-wave link information and external emission source data, analyzing the content of the external emission source data, and extracting short-wave propagation data comprising effective content; setting a threshold condition according to the short-wave link information, and screening effective data meeting the threshold condition from the short-wave propagation data; based on a short-wave propagation loss model, calculating a pseudo sunspot number credible interval of all effective data to obtain a pseudo sunspot number credible interval sequence; and according to the pseudo-sunspot number credible interval sequence, calculating the optimal credible value of the pseudo-sunspot number so as to calculate the maximum short-wave passable frequency, and outputting a short-wave frequency forecasting result. According to the short-term short-wave forecasting method, the number of the pseudo sunspots is calculated by analyzing the data of the external emission source and utilizing the short-wave propagation model, and the short-wave MUF is forecasted on the basis, so that the short-term short-wave forecasting precision is effectively improved, and the method has universality.

Description

Technical Field

[0001] The present invention relates to the technical field of short-term shortwave frequency forecasting, and more specifically, to a shortwave frequency forecasting method and system based on external emission source data. Background Art

[0002] Since shortwave communication signals need to be reflected once or multiple times by the ionosphere, the transmission efficiency of shortwave signals is greatly affected by the ionosphere. The changes in the ionosphere are chaotic, so there is no way to ensure long-term stable predictions. Current shortwave predictions are generally based on statistical models and statistical predictions of the number of sunspots in the month, or high-precision ray trajectory calculation methods using real-time ionosphere models. The former is not real-time because it can only give the median monthly prediction, while the latter requires high-precision real-time ionosphere data, which places high demands on detection equipment.

[0003] Shortwave communication mainly relies on sky waves reflected by the ionosphere for transmission. It has the advantages of long distance, low cost, high security, and strong anti-interference ability. It is one of the main means of long-distance communication. In long-distance communication, the distribution of ionospheric refractive index on the propagation path is one of the main determinants of shortwave propagation, which is closely related to the ionosphere height, electron density distribution and shortwave signal frequency. Since the ionosphere has the characteristics of temporal and spatial variability, the height and electron density change dynamically with factors such as day and night, seasons, and space weather, and the corresponding propagation environment also changes dynamically. Therefore, how to scientifically select the frequency according to the changes in the ionosphere is the key to achieving stable and reliable shortwave communication. Traditional shortwave frequency selection is based on the prediction of the maximum passable frequency and propagation loss based on long-term forecast models such as ITU-R P.533. Since the long-term forecast model predicts the mid-month value, the real-time and short-term propagation environment of the ionosphere is not accurately grasped, and the support for long-distance shortwave communication is poor. On the other hand, due to the lack of controllable shortwave detection resources in most areas, it is impossible to obtain detection data in the target area to support shortwave frequency selection. Based on the real-time acquisition of ionospheric vertical measurement and oblique side data, the method of predicting muf by time and space extrapolation can perform quasi-real-time and short-term forecasts with high forecast accuracy, but this method requires the acquisition of short-term vertical measurement or oblique side data with a certain spatial correlation with the target link. Since the detection resources themselves are relatively scarce and the detection data themselves are not open enough, this method is not universal.

[0004] In addition to various business signals, there are also a large number of shortwave signals from amateur external transmitters in the shortwave signals propagating in the air. These signals are discretely distributed in the 1.8-30MHz frequency band, and the waveform is completely public. Through demodulation, the frequency, location and receiving signal-to-noise ratio of the transmitter can be obtained. WSPR is one of the most representative signals. WSPR (WeakSignal Propagation Reporter) was developed by Joe Taylor and uses a transmission mode called MEPT-JT. The WSPR signal is frequency shift keyed under fine-tuning and low rate, and the occupied bandwidth is only 6Hz, which allows many transmitting stations to work without interference within the range of 200Hz. Each MEPT-JT transmission lasts for 2 minutes, and the transmission information includes call sign, Maidenhead coordinates and power. The corresponding reception information includes time, signal-to-noise ratio, delay, frequency deviation, Maidenhead coordinates, etc. WSPR is mainly used to detect ionospheric propagation conditions. The reception data of users around the world can be uploaded to the official website of WSPRnet.org to form a global propagation map and open for download.

[0005] Therefore, how to effectively use the public external emission source data, especially the WSPR data with a wide propagation range and high real-time performance, to predict the short-term shortwave maximum usable frequency (MUF) is an important issue. Summary of the invention

[0006] The purpose of the present invention is to provide a shortwave frequency forecasting method, system and medium based on external emission source data, so as to effectively utilize the public external emission source data, especially the WSPR data with a wide propagation range and high real-time performance, to forecast the short-term shortwave maximum usable frequency (MUF), thereby improving the universality, accuracy and real-time performance of the short-term shortwave frequency forecast.

[0007] To achieve the above object, the technical solution of the present invention is as follows:

[0008] The present invention provides a shortwave frequency prediction method based on external emission source data, comprising the following steps:

[0009] Acquire the shortwave link information and external transmission source data that need to be predicted, parse the data content of the external transmission source data, and extract the shortwave propagation data including effective content;

[0010] Setting a threshold condition according to the shortwave link information, and filtering valid data satisfying the threshold condition from the shortwave propagation data;

[0011] Based on the shortwave propagation loss model, the pseudo sunspot number credible intervals of all the valid data are calculated in reverse order to obtain a pseudo sunspot number credible interval sequence;

[0012] Calculating the best credible value of the pseudo sunspot number according to the pseudo sunspot number credible interval sequence;

[0013] The shortwave maximum passable frequency is calculated using the pseudo sunspot number best credible value, and the shortwave frequency prediction result is output.

[0014] Preferably, the shortwave link information includes the sending position coordinates P t 、Accept position coordinates P r and computation time T.

[0015] The valid content includes the transmitting station coordinates p t , receive object coordinates p r , receiving signal-to-noise ratio snr, frequency f and receiving time t.

[0016] Preferably, the threshold condition specifically includes:

[0017] dis(P t ,p r )<Thresh dis

[0018] snr>Thresh snr

[0019] abs(HOUR(t)-HOUR(T))<Thresh hour

[0020] abs(tT) <Thresh time

[0021] Among them, dis() is the distance calculation function between two coordinate points, abs() means taking the absolute value, HOUR() means taking the hour value in time, Thresh() means taking the hour value in time. dis is the distance threshold, Thresh snr is the signal-to-noise ratio threshold, Thrsh hour is the hour difference threshold, Thresh time is the time difference threshold.

[0022] Preferably, the shortwave propagation loss model includes an ITU-R P.533 statistical model or a VOPCAP statistical model.

[0023] Preferably, the pseudo sunspot number credible intervals of all the valid data are inversely calculated based on the shortwave propagation loss model to obtain a pseudo sunspot number credible interval sequence, specifically:

[0024] The calculation expression of the shortwave maximum passable frequency muf of the shortwave propagation loss model is:

[0025] muf = prop(p t , p r , t, ssn)

[0026] Wherein, ssn is the sunspot number, prop() is the calculation function of the shortwave propagation loss model;

[0027] When the sunspot number ssn takes a value interval of [0,200], prop() is a monotonic function, so according to its inverse function prop -1 The pseudo sunspot number can be calculated by inverse calculation using the maximum shortwave passable frequency muf:

[0028] fssn=prop -1 (p t , p r , t, muf)

[0029] Among them, the pseudo sunspot number fssn∈[0,200] in the inverse result;

[0030] The passable shortwave frequency range is a certain proportion range of the shortwave maximum passable frequency muf:

[0031] [α s *muf,α e *muf]

[0032] Among them, α s ∈(0,1],α e ∈(0,1] and α s <α e ;

[0033] Then for a piece of valid data, the credible interval of the pseudo sunspot number is:

[0034] [fssn s ,fssn e ]=[prop -1 (p t ,p r ,t,f / α e ),prop -1 (p t ,p r ,t,f / α s )]

[0035] All the valid data are traversed to calculate the pseudo sunspot number credible interval sequence.

[0036] Preferably, the step of calculating the best credible value of the pseudo sunspot number according to the credible interval sequence of the pseudo sunspot number is as follows:

[0037] According to the calculated pseudo sunspot number credible interval sequence, the pseudo sunspot number is calculated so that the pseudo sunspot number satisfies as many pseudo sunspot number credible interval sequences as possible:

[0038]

[0039] It is known that the total number of time points for screening valid data is N, recorded as The credible interval of the calculated pseudo sunspot number is Then the best credible value of pseudo sunspot number is:

[0040]

[0041] Among them, ave{} is the averaging function.

[0042] Preferably, the forecast shortwave frequency is calculated based on the best credible value of the pseudo sunspot number to obtain a forecast result by the following calculation expression:

[0043] fmuf = prop(P t ,P r ,T,fssn 0 )

[0044] Among them, fmuf is the predicted shortwave frequency.

[0045] On one hand, the present invention also provides a shortwave frequency prediction system based on external emission source data, applying the shortwave frequency prediction method based on external emission source data described in the above technical solution, the system comprises:

[0046] A data acquisition module, used to acquire the shortwave link information and external transmission source data that need to be predicted, and parse the data content of the external transmission source data to extract the shortwave propagation data including effective content;

[0047] A data screening module, used for setting a threshold condition according to the shortwave link information, and screening valid data satisfying the threshold condition from the shortwave propagation data;

[0048] A pseudo sunspot number credible interval calculation module is used to reversely calculate the pseudo sunspot number credible intervals of all the valid data based on a shortwave propagation loss model to obtain a pseudo sunspot number credible interval sequence;

[0049] A pseudo sunspot number best credible value calculation module, used to calculate the pseudo sunspot number best credible value according to the pseudo sunspot number credible interval sequence;

[0050] The forecast result output module is used to calculate the maximum passable shortwave frequency using the pseudo sunspot number best credible value and output the shortwave frequency forecast result.

[0051] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a shortwave frequency prediction method based on external emission source data described in the above technical solution are implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention analyzes external emission source data, sets threshold conditions to extract valid data, and uses a shortwave propagation loss model to reversely calculate a credible interval sequence of pseudo sunspot numbers, thereby calculating the best credible value of the pseudo sunspot number with short-term characteristics, and based on this, predicts the shortwave frequency, effectively utilizes the public shortwave external emission source data and improves the accuracy and real-time performance of short-term shortwave forecasts, and has strong universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the steps of a shortwave frequency prediction method based on external emission source data of the present application;

[0055] Figure 2 This is a graph of pseudo sunspot number calculation results for the Guangzhou-Beijing link from November 1 to 5, provided in Example 1 of the present application, based on WSPR data per hour within one day using the method of the present invention;

[0056] Figure 3 This is a result comparison chart of the muf calculated based on the ITU-RP.533 model and the muf calculated by the method of the present invention for the Guangzhou-Beijing link from November 1 to 5 provided in Example 1 of the present application;

[0057] Figure 4 This is a comparison chart of the results of the Guangzhou-Beijing link from November 1 to 5, provided in Example 1 of the present application, of the muf within one day calculated based on the ITU-RP.533 model, the muf within one day calculated by the method of the present invention, and the muf within one day measured by the shortwave measurement equipment. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0060] Example 1

[0061] See also Figure 1 The embodiment of the present invention provides a shortwave frequency prediction method based on external emission source data, comprising the following steps:

[0062] S1: Acquire the shortwave link information and external transmission source data that need to be predicted, parse the data content of the external transmission source data, and extract the shortwave propagation data including effective content.

[0063] The external emission source data in Embodiment 1 of the present invention adopts the WSPR (Weak Signal Propagation Reporter) data described in the background art.

[0064] Obtain shortwave link information and WSPR data within a period of time or in the near future, and parse the data content in the WSPR data, where the shortwave link information includes the sending position coordinates P t 、Accept position coordinates P r and calculation time T (time to be predicted), the valid content must at least include the transmitting station coordinates p t , receive object coordinates p r , receiving signal-to-noise ratio snr, frequency f and receiving time t.

[0065] S2: Setting a threshold condition according to the shortwave link information, and filtering valid data satisfying the threshold condition from the shortwave propagation data.

[0066] The threshold condition is set according to the shortwave link information in step S1 as follows:

[0067] dis(P t , p r )<Thresh dis

[0068] snr>Thresh snr

[0069] abs(HOUR(T)-HOUR(T))<Thresh hour

[0070] abs(tT) <Thresh time

[0071] Among them, dis() is the distance calculation function between two coordinate points, abs() means taking the absolute value, HOUR() means taking the hour value in time, Thresh() means taking the hour value in time. dis is the distance threshold, Thresh snr is the signal-to-noise ratio threshold, Thresh hour is the hour difference threshold, Thresh time is the time difference threshold.

[0072] According to the set threshold conditions, the valid data that meets the threshold conditions are screened out from the acquired WSPR shortwave propagation data.

[0073] S3: Based on the shortwave propagation loss model, reversely calculate the pseudo sunspot number credible intervals of all the valid data to obtain a pseudo sunspot number credible interval sequence.

[0074] The sunspot magnitude can range from 0 (a very quiet Sun) to over 200 (a very active Sun). The solar activity cycle is about 11 years, during which the number of sunspots goes through a periodic change from peak to trough. During the solar maximum, the sunspot magnitude can increase significantly, while it decreases significantly during the trough. Changes in the sunspot magnitude have important effects on the Earth's space weather, including effects on the Earth's magnetic field, ionosphere, and possible increases in auroral electron events. Therefore, monitoring the sunspot number is very important for shortwave signal propagation research.

[0075] In this embodiment 1, the shortwave propagation loss model adopts the ITU-R P.533 statistical model (the relevant information can be downloaded from the website of the International Telecommunication Union). In other specific embodiments, other relevant shortwave propagation loss statistical models such as VOPCAP (issued by: USIA Voice of America, version number 16.1207W) can also be used.

[0076] The maximum passable frequency muf calculation of the shortwave propagation loss model is related to the longitude and latitude of the transmitting and receiving points (WGS-84 coordinate system), the frequency of shortwave propagation, the time of shortwave propagation, and the number of sunspots. The maximum passable frequency muf calculation can be expressed as:

[0077] muf=prop(p t , p r , t, ssn) where ssn is the sunspot number, and prop() is the calculation function of the shortwave propagation loss model (for detailed calculation formula, please refer to the reference document "Prediction Method for HF Circuit Performance in Recommendation ITU-R P.533-14" on the website of the International Telecommunication Union).

[0078] When the sunspot number ssn takes a value interval of [0,200], prop() is a monotonic function, so according to its inverse function prop -1 And the maximum shortwave passable frequency muf is used to inversely calculate the number of pseudo sunspots:

[0079] fssn=prop -1 (p t , p r , t, muf)

[0080] Among them, the pseudo sunspot number fssn∈[0,200] in the inverse result;

[0081] The passable shortwave frequency range is a certain proportion range of the shortwave maximum passable frequency muf:

[0082] [α s *muf,α e *muf]

[0083] Among them, α s ∈(0,1],α e ∈(0,1] and α s <α e ;

[0084] Then for a piece of valid data, the credible interval of the pseudo sunspot number is:

[0085] [fssn s ,fssn e ]=[prop -1 (p t , p r , t, f / α e ), prop -1 (p t , p r , t, f / α s )]

[0086] All the valid data are traversed to calculate the pseudo sunspot number credible interval sequence.

[0087] S4: Calculate the best credible value of the pseudo sunspot number according to the credible interval sequence of the pseudo sunspot number.

[0088] According to the calculated pseudo sunspot number credible interval sequence, the pseudo sunspot number is calculated so that the pseudo sunspot number satisfies as many pseudo sunspot number credible interval sequences as possible:

[0089]

[0090] It is known that the total number of time points for screening valid data is N, recorded as The credible interval of the calculated pseudo sunspot number is Then the best credible value of pseudo sunspot number is:

[0091]

[0092] Among them, ave{} is the averaging function.

[0093] S5: Calculate the maximum passable shortwave frequency using the pseudo sunspot number best credible value, and output the shortwave frequency prediction result.

[0094] The calculation expression is as follows:

[0095] fmuf = prop(P t , P r ,T,fssn 0 )

[0096] Among them, fmuf is the predicted maximum shortwave passable frequency.

[0097] The predicted shortwave maximum passable frequency fmuf is output as the shortwave frequency forecast result.

[0098] In order to verify the effectiveness of the method of the present invention, a test experiment was conducted based on the WSPR data of the Guangzhou-Beijing link from November 1 to 5 of a certain year, as follows:

[0099] See also Figure 2 , Figure 2 This is a graph showing the calculation results of the pseudo sunspot number (the best credible value of the pseudo sunspot number) per hour based on WSPR data for the Guangzhou-Beijing link in the short term from November 1 to 5, using the method of the present invention. The horizontal axis in the figure represents hours, and the vertical axis represents the number of sunspots in the best credible value of the pseudo sunspot number.

[0100] See also Figure 3 , Figure 3 Example 1 of the present application provides a comparison chart of the results of the muf calculated based on the ITU-RP.533 model and the muf calculated by the method of the present invention for the Guangzhou-Beijing link from November 1 to 5; Figure 3 In the figure, the horizontal axis represents the hour time in a day, the vertical axis represents the frequency, the broken line with an asterisk is the muf curve calculated directly based on the ITU-R P.533 model without using the method of the present invention, and the red broken line is the muf curve calculated using Figure 2 The muf curve of the sunspot number calculated by the method of the present invention is shown in the figure. The blue data point is the equivalent communication frequency of the Guangzhou-Beijing link after the WSPR data transformation, that is, the actual muf. Figure 3It can be seen that the method of the present invention is closer to the actual muf and is significantly higher than the ITU-R P.533 model, especially after sunset (after 19:00) every day, which is basically consistent with the actual muf.

[0101] In order to further illustrate the effectiveness and accuracy of the method of the present invention, in this embodiment 1, shortwave measurement equipment is also used to measure the muf of the Guangzhou-Beijing link multiple times from November 1 to 5, see Figure 4 , Figure 4 This is a comparison chart of the results of the Guangzhou-Beijing link from November 1 to 5, provided in Example 1 of the present application, of the muf calculated based on the ITU-R P.533 model within one day, the muf calculated by the method of the present invention within one day, and the muf measured by the shortwave measurement equipment within one day, Figure 4 In the figure, the horizontal axis represents the hour of the day and the vertical axis represents the frequency. Figure 4 compared to Figure 3 Four broken lines with + signs are added, which are the muf data measured by multiple sweeps of shortwave measurement equipment (due to the upper limit of the detection frequency of the shortwave measurement equipment, muf above 25MHz cannot be measured). Figure 4 It can be seen that the effectiveness and accuracy of the method of the present invention are higher, and the change trend of the MUF after sunset (after 19:00) every day is consistent with the actual MUF data.

[0102] In summary, the present invention proposes a shortwave MUF forecasting method based on WSPR data. By statistically analyzing the shortwave propagation data with a wide range of public propagation areas, the pseudo sunspot number with short-term characteristics is calculated using the shortwave propagation model, and the shortwave MUF is forecasted based on this, which effectively improves the accuracy of shortwave short-term forecasting and has good feasibility.

[0103] Example 2

[0104] Based on Example 1, this Example 2 proposes a shortwave frequency prediction system based on external emission source data, and applies the shortwave frequency prediction method based on external emission source data described in Example 1. The system includes:

[0105] A data acquisition module, used to acquire the shortwave link information and external transmission source data that need to be predicted, and parse the data content of the external transmission source data to extract the shortwave propagation data including effective content;

[0106] A data screening module, used for setting a threshold condition according to the shortwave link information, and screening valid data satisfying the threshold condition from the shortwave propagation data;

[0107] A pseudo sunspot number credible interval calculation module is used to reversely calculate the pseudo sunspot number credible intervals of all the valid data based on a shortwave propagation loss model to obtain a pseudo sunspot number credible interval sequence;

[0108] A pseudo sunspot number best credible value calculation module, used to calculate the pseudo sunspot number best credible value according to the pseudo sunspot number credible interval sequence;

[0109] The forecast result output module is used to calculate the maximum passable shortwave frequency using the pseudo sunspot number best credible value and output the shortwave frequency forecast result.

[0110] The other technical features or steps of this embodiment 2 are consistent with those of embodiment 1 and will not be repeated here.

[0111] Example 3

[0112] Based on Example 1, this Example 3 also provides a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of a shortwave frequency prediction method based on external transmission source data described in Example 1 are implemented.

[0113] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store used or received data, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0114] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A shortwave frequency prediction method based on external emission source data, characterized in that: The following steps are involved: Acquire the shortwave link information and external transmission source data that need to be predicted, parse the data content of the external transmission source data, and extract the shortwave propagation data including effective content; Setting a threshold condition according to the shortwave link information, and filtering valid data satisfying the threshold condition from the shortwave propagation data; Based on the shortwave propagation loss model, the pseudo sunspot number credible intervals of all the valid data are calculated in reverse order to obtain a pseudo sunspot number credible interval sequence; Calculating the best credible value of the pseudo sunspot number according to the pseudo sunspot number credible interval sequence; The shortwave maximum passable frequency is calculated using the pseudo sunspot number best credible value, and the shortwave frequency prediction result is output.

2. A shortwave frequency prediction method based on external emission source data according to claim 1, characterized in that: The shortwave link information includes the sending position coordinates P t , accept the position coordinates P r and computation time T.

3. A shortwave frequency prediction method based on external emission source data according to claim 1, characterized in that: The valid content includes the transmitting station coordinates p t , receive object coordinates p r , receiving signal-to-noise ratio snr, frequency f and receiving time t.

4. A shortwave frequency prediction method based on external emission source data according to any one of claims 1 to 3, characterized in that: The threshold conditions specifically include: dis(P t ,p r )<Thresh dis snr>Thresh snr abs(HOUR(t)-HOUR(T))<Thresh hour abs(t-T)<Thresh time Among them, dis() is the distance calculation function between two coordinate points, abs() means taking the absolute value, HOUR() means taking the hour value in time, Thresh() means taking the hour value in time. dis is the distance threshold, Thresh snr is the signal-to-noise ratio threshold, Thresh hour is the hour difference threshold, Thresh time is the time difference threshold.

5. A shortwave frequency prediction method based on external emission source data according to claim 4, characterized in that: The shortwave propagation loss model includes an ITU-R P.533 statistical model or a VOPCAP statistical model.

6. A shortwave frequency prediction method based on external emission source data according to claim 5, characterized in that: Based on the shortwave propagation loss model, the pseudo sunspot number credible intervals of all the valid data are calculated in reverse order to obtain a pseudo sunspot number credible interval sequence, which is specifically: The calculation expression of the shortwave maximum passable frequency muf of the shortwave propagation loss model is: muf=prop(p t ,p r ,t,ssn) Wherein, ssn is the sunspot number, prop() is the calculation function of the shortwave propagation loss model; When the sunspot number ssn takes a value interval of [0,200], prop() is a monotonic function, so according to its inverse function prop -1 And the maximum shortwave passable frequency muf is used to inversely calculate the number of pseudo sunspots: fssn=prop -1 (p t ,pr,t,muf) Among them, the pseudo sunspot number fssn∈[0,200] in the inverse result; The passable shortwave frequency range is a certain proportion range of the shortwave maximum passable frequency muf: [a s *muf, a e *muf] where α s ∈(0, 1], α e ∈(0, 1] and α s <α e ; Then for a piece of valid data, the credible interval of the pseudo sunspot number is: [fssn s ,fssn e ]=[prop -1 (p t ,p r ,t,f / α e ),prop -1 (p t ,p r ,t,f / α s )] All the valid data are traversed to calculate the pseudo sunspot number credible interval sequence.

7. A shortwave frequency prediction method based on external emission source data according to claim 6, characterized in that: The step of calculating the best credible value of the pseudo sunspot number according to the credible interval sequence of the pseudo sunspot number is as follows: According to the calculated pseudo sunspot number credible interval sequence, the pseudo sunspot number is calculated so that the pseudo sunspot number satisfies as many pseudo sunspot number credible interval sequences as possible: It is known that the total number of time points for screening valid data is N, recorded as The credible interval of the calculated pseudo sunspot number is Then the best credible value of pseudo sunspot number is: Among them, ave{} is the averaging function.

8. A shortwave frequency prediction method based on external emission source data according to claim 7, characterized in that: The predicted shortwave frequency is calculated based on the best credible value of the pseudo sunspot number, and the calculation expression for the prediction result is as follows: fmuf=prop(P t ,P r ,T,fssn0) Among them, fmuf is the predicted maximum shortwave passable frequency.

9. A shortwave frequency prediction system based on external emission source data, using a shortwave frequency prediction method based on external emission source data as claimed in any one of claims 1 to 9, characterized in that: The system comprises: A data acquisition module, used to acquire the shortwave link information and external transmission source data that need to be predicted, and parse the data content of the external transmission source data to extract the shortwave propagation data including effective content; A data screening module, used for setting a threshold condition according to the shortwave link information, and screening valid data satisfying the threshold condition from the shortwave propagation data; A pseudo sunspot number credible interval calculation module is used to reversely calculate the pseudo sunspot number credible intervals of all the valid data based on a shortwave propagation loss model to obtain a pseudo sunspot number credible interval sequence; A pseudo sunspot number best credible value calculation module, used to calculate the pseudo sunspot number best credible value according to the pseudo sunspot number credible interval sequence; The forecast result output module is used to calculate the maximum passable shortwave frequency using the pseudo sunspot number best credible value and output the shortwave frequency forecast result.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a shortwave frequency prediction method based on external emission source data as described in any one of claims 1 to 8 are implemented.

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