A method for quickly identifying morse code

Abstract

The application discloses a kind of morse code fast identification method, comprising: with the signal-to-noise ratio of different stages of morse code audio message is divided into multiple audio message sections and is respectively cached;According to the average signal-to-noise ratio of each message section, determine the corresponding noise reduction threshold;Using noise reduction threshold eliminates each message section noise to obtain pure each target message section bit stream;From message database, find the message section bit stream matched with each target message section bit stream;If find the message section bit stream matched with each target message section bit stream, then according to the reference time interval of each type message section bit stream found, process each target message section bit stream;Otherwise, generate each type reference time interval, then utilize the generated each type reference time interval to process each target message section bit stream.

Description

Technical Field

[0001] This invention belongs to the field of radio technology, and particularly relates to a method for fast identification of Morse code. Background Technology

[0002] Morse code is a type of intermittent signal code that uses different sequences of letters, numbers, and punctuation marks to represent different characters. Unlike modern digital communication, Morse code uses only two states: 0 and 1. Its code includes five types: short dot signals "·", long signals "—" indicating pauses between dots and dashes, medium pauses between words, and long pauses between sentences. For variable-speed Morse code recognition, various reference time intervals need to be continuously generated based on the received Morse code data to determine the dot, dash, word, and character intervals in a received Morse signal, allowing for the lookup and identification of the corresponding characters. Currently, Morse code transcription and recognition still rely on experienced telegraph operators, which is prone to errors. Furthermore, the recognition, calculation, and decoding processes of Morse code are complex and tedious, resulting in relatively low efficiency and accuracy.

[0003] Due to differences in the proficiency and usage habits of telegraph operators, the code rate, point signal, and actual duration of each signal in manually transmitted Morse code vary greatly, and the proportions are not standardized, resulting in low accuracy in rapid Morse code identification.

[0004] In addition, noise can be introduced during message reception due to channel transmission, equipment, and the surrounding environment, which can reduce the accuracy of Morse code recognition.

[0005] Chinese patent document CN111404846B discloses a method for receiving and identifying Morse code radio frequency signals. The method involves edge-capturing processing of the 48kHz bitstream corresponding to the received Morse code radio frequency signal to identify the rising and falling edges of the corresponding Morse code message; timing each rising and falling edge using a timer and buffering the time of each rising and falling edge; establishing a buffered high-low level duration table based on the high-level duration of dot-dash codes and the low-level duration of code intervals, character intervals, or word intervals, where the high-level duration of dot-dash codes is the time difference between the falling edge and its next adjacent rising edge, and the low-level duration of code intervals, character intervals, or word intervals is the time difference between the rising edge and its next adjacent falling edge; calculating the dot-code time length, dash-code time length, code interval, character interval, and word interval time length based on the high-low level duration table; determining the decoding time window based on the dot-code time length, dash-code time length, code interval, character interval, and word interval time length; and retrieving the ASCII code of the corresponding character of the Morse code radio frequency signal within the decoding time window using a preset Morse code ASCII lookup table. This Morse code reception and identification method is only suitable for identifying ideal, noise-free Morse code. However, in real-world wireless environments, there are numerous noise sources, and the noise is random, which significantly affects the accuracy of Morse code identification. Furthermore, this patent does not consider the differences in code rate and point signal duration in manually transmitted Morse code, which also affects the accuracy of Morse code identification.

[0006] Chinese patent document CN111724800A discloses a Morse code audio noise reduction method. The method includes: sampling and converting an audio signal into a digital signal using an ADC; calculating the signal power of the digital signal using a sliding FFT algorithm; calculating the signal-to-energy ratio (SGR) of the digital signal using the power spectrum; determining a signal threshold by calculating an estimated average SGR of the useful signal; enhancing the useful signal and suppressing noise signals based on the threshold; finding the maximum signal energy point and updating the estimated average SGR; estimating the signal energy gain by judging the relationship between the maximum signal energy point and the average energy; synthesizing the digital signal to meet the data requirements of the DAC output; and performing amplitude limiting on the output digital signal before it enters the DAC unit for conversion. This Morse code audio noise reduction method calculates the estimated average SGR of the useful signal to determine the signal threshold, and enhances the useful signal and suppresses noise signals based on the threshold. However, noise is random. During message reception, the noise level fluctuates randomly. Therefore, calculating the estimated average SGR of the useful signal and determining the signal threshold without considering noise fluctuations cannot accurately identify the Morse code. Summary of the Invention

[0007] The purpose of this invention is to provide a method for fast Morse code recognition to solve the problems of the prior art.

[0008] The present invention provides a method for fast Morse code recognition, comprising:

[0009] Receive and save complete Morse code audio messages, and calculate the signal-to-noise ratio of the Morse code audio messages in real time;

[0010] The Morse code audio message is divided into multiple Morse code audio message segments according to the real-time calculated signal-to-noise ratio.

[0011] Calculate the average signal-to-noise ratio for each Morse code audio segment;

[0012] The average signal-to-noise ratio of each Morse code audio segment is determined as the noise reduction threshold for each Morse code audio segment;

[0013] Noise in each Morse code audio segment is eliminated by using the noise reduction threshold of each Morse code audio segment, and jitter removal is performed to obtain a clean bitstream of each target segment.

[0014] Search the message database for the segment bitstream that matches each target segment bitstream;

[0015] If a segment bitstream that matches each target segment bitstream is found, then each target segment bitstream is processed according to various reference time intervals of the found segment bitstream in order to identify Morse code audio messages.

[0016] If no matching segment bitstream is found for each target segment bitstream, various reference time intervals are generated, and each target segment bitstream is processed using these generated reference time intervals to identify Morse code audio messages.

[0017] Preferably, processing the target segment bitstream according to various reference time intervals of the found segment bitstream includes:

[0018] The dot code, dash code, and various time intervals of each target segment bit stream are determined according to the various reference time intervals of the found segment bit stream to form a dot and dash coding sequence;

[0019] The generated dot and dash coding sequence is matched and decoded with a preset lookup table, and then the complete message is output.

[0020] Preferably, the various reference time intervals of the segment bit stream stored in the message database are based on the dot code, scratch code, and various time intervals of the target segment bit stream that has been received and processed previously.

[0021] Preferably, generating various reference time intervals includes: capturing the bit stream edge of the target message segment and recording the time, calculating the duration of high and low levels to establish a time data table; and generating reference time intervals for identifying Morse code by performing statistical analysis on the time data table.

[0022] Preferably, processing each target segment bitstream using the generated reference time intervals includes: determining the dot code, dash code, and various time intervals of each target segment bitstream using the generated reference time intervals to form a dot and dash coding sequence; matching and decoding the dot and dash coding sequence with a preset lookup table, and splicing it to output a complete message.

[0023] Preferably, the message database stores the processed target message segment bit stream and the corresponding generated reference time intervals in real time, and uses them as the message segment bit stream and its various time intervals stored in the message database.

[0024] Preferably, when dividing Morse code audio message segments according to signal-to-noise ratio (SNR), the SNR is calculated by sampling the Morse code audio message frame by frame, and the segmentation point is the position where the SNR change exceeds a preset threshold.

[0025] Preferably, the noise reduction threshold T is positively correlated with the average signal-to-noise ratio, satisfying T=k×SNR+b, where k is the proportionality coefficient, SNR is the average signal-to-noise ratio, and b is the basic threshold coefficient.

[0026] Preferably, when generating various reference time intervals, samples of high and low level durations are extracted and outliers are removed. The reference durations of dotted codes, scratch codes, and various intervals are all set based on the dotted code reference duration.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] Adaptive segmented noise reduction to accommodate random noise fluctuations: This invention segments the Morse code audio message according to the signal-to-noise ratio at different stages, independently determines the noise reduction threshold for each message segment and performs noise reduction, solving the problems of existing noise reduction methods that do not consider random fluctuations in noise levels and have poor noise reduction effects. It effectively filters out various noises introduced by channel transmission, equipment and environment, and improves the foundation for signal recognition in noisy environments.

[0029] It can quickly process each segment bitstream based on various reference time intervals of the known segment bitstream stored in the message database, so as to splice and output a complete Morse code message.

[0030] Generates and dynamically generates various reference time intervals to adapt to manual variable-speed transmission: Through real-time statistical analysis of high and low level duration data, it dynamically generates reference time intervals for dot codes, dash codes, and various intervals. It can adaptively match the problems of code speed changes and non-standard dot-dash duration ratios in manually transmitted Morse code, solving the problem of low recognition accuracy caused by differences in code speed and duration in existing technologies. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the Morse code fast recognition method of the present invention. Detailed Implementation

[0032] Figure 1 This invention illustrates a fast Morse code identification method, comprising: receiving and storing a complete Morse code audio message, and calculating the signal-to-noise ratio (SNR) of the Morse code audio message in real time; dividing the Morse code audio message into multiple Morse code audio message segments according to the real-time calculated SNR; calculating the average SNR of each Morse code audio message segment; determining the average SNR of each Morse code audio message segment as a noise reduction threshold for each Morse code audio message segment; using the noise reduction threshold for each Morse code audio message segment to eliminate noise in each Morse code audio message segment, and performing jitter removal processing to obtain a clean target image. The process involves several steps: first, identifying the segment bitstream (i.e., the segment bitstream to be processed); second, searching the segment database for a segment bitstream that matches each target segment bitstream; third, if a matching segment bitstream is found, processing each target segment bitstream according to various reference time intervals of the found segment bitstream to identify Morse code audio messages; and fourth, if no matching segment bitstream is found, generating various reference time intervals and then processing each target segment bitstream using these generated reference time intervals to identify Morse code audio messages.

[0033] In the above method of the present invention, processing each target segment bitstream according to various reference time intervals of the found segment bitstream includes: determining the dot code, dash code and various time intervals of each target segment bitstream according to various reference time intervals of the found segment bitstream to form a dot and dash coding sequence; matching and decoding the formed dot and dash coding sequence with a preset lookup table, and splicing and outputting a complete message.

[0034] In the above method of the present invention, the various reference time intervals of the segment bit stream stored in the message database are based on the dotted code and scratch code time intervals of the target segment bit stream that has been received and processed previously.

[0035] In the above method of the present invention, generating various reference time intervals includes: capturing the edge of the target bit stream and recording the time, calculating the duration of high and low levels to establish a time data table; and generating reference time intervals for identifying Morse code by performing statistical analysis on the time data table.

[0036] In the above method of the present invention, processing each segment bit stream using the generated reference time intervals includes: determining the dot code, dash code and various time intervals of each target segment bit stream using the generated reference time intervals to form a dot and dash coding sequence; matching and decoding the formed dot and dash coding sequence with a preset lookup table, and splicing and outputting a complete message.

[0037] In the above method of the present invention, the message database saves the processed target message segment bit stream and the corresponding generated reference time intervals in real time, and uses them as the message segment bit stream and the various time intervals saved in the message database.

[0038] In the above method of the present invention, when dividing audio message segments according to signal-to-noise ratio, the signal-to-noise ratio is calculated by sampling the audio message frame by frame, and the position where the signal-to-noise ratio changes beyond a preset threshold is taken as the segmentation node, and the signal-to-noise ratio fluctuation within each message segment is within a preset range.

[0039] In the above method of the present invention, the noise reduction threshold T is positively correlated with the average signal-to-noise ratio, satisfying T=k×SNR+b, where k is the proportionality coefficient, SNR is the average signal-to-noise ratio, and b is the basic threshold coefficient.

[0040] In the above method of the present invention, when generating various reference time intervals, high and low level duration samples are extracted and outliers are removed. The reference durations of dot codes, scratch codes and various intervals are all set based on the dot code reference duration.

[0041] In one specific embodiment, a complete Morse code audio message is received and stored; the Morse code audio message is divided into multiple Morse code audio message segments based on the signal-to-noise ratio (SNR) of the stored Morse code audio message at different stages, and these multiple Morse code audio message segments are cached in multiple buffers respectively; a noise reduction threshold is determined for each Morse code audio message segment based on its average SNR; noise in each Morse code audio message segment is eliminated using the noise reduction threshold to obtain a clean Morse code audio signal; the clean Morse code audio signal is converted into a corresponding bitstream, and the bitstream is simultaneously de-jittered to filter out glitches and interference; edge capture processing is performed on the de-jittered bitstream to identify rising and falling edges in the signal, and the occurrence time of each rising and falling edge is recorded using a timer; based on the recorded rising and falling edge times, the high-level duration and low-level duration in the bitstream are calculated. A high-level duration data table is established, where the high-level duration is the time difference between the falling edge and the next adjacent rising edge, and the low-level duration is the time difference between the rising edge and the next adjacent falling edge. Statistical analysis is performed on the data in the high-level duration data table to dynamically generate reference time intervals for Morse code recognition. These reference time intervals include dot code reference duration, dash code reference duration, code interval reference duration, character interval reference duration, and word interval reference duration. Based on the dynamically generated reference time intervals, high-level data in the high-level duration data table is determined to be either dot code or dash code, and low-level data is determined to be either code interval, character interval, or word interval, forming a dot-dash code sequence for Morse code. This dot-dash code sequence is matched against a preset Morse code lookup table to obtain the corresponding characters, numbers, or punctuation marks, completing the Morse code recognition and decoding. The decoded characters, numbers, or punctuation marks are then concatenated according to the message order to output the complete Morse code message content.

[0042] Specifically, the method involves dividing the stored Morse code audio message into multiple Morse code audio message segments based on the signal-to-noise ratio (SNR) of the stored Morse code audio message at different stages. This includes: sampling the stored Morse code audio message frame by frame and calculating the SNR of the audio signal in each frame; setting an SNR change threshold, and determining a segmentation node when the SNR change value of adjacent frames exceeds the SNR change threshold; and dividing the Morse code audio message into multiple consecutive Morse code audio message segments using the segmentation nodes as boundaries, with the SNR fluctuation of the audio signal within each message segment remaining within a preset range.

[0043] Specifically, based on the average signal-to-noise ratio (SNR) of each Morse code audio segment, a noise reduction threshold is determined for each Morse code audio segment. This involves: calculating the average SNR of all sampled frames within each Morse code audio segment as the average SNR of that segment; establishing a mapping relationship between the average SNR and the noise reduction threshold, where the noise reduction threshold T is positively correlated with the average SNR, satisfying the formula T = k × SNR + b, where k is a proportionality coefficient and b is a base threshold coefficient, both k and b being calibrated according to the actual wireless channel environment; and matching a corresponding noise reduction threshold to each Morse code audio segment based on this mapping relationship.

[0044] Specifically, the noise in each Morse code audio message segment is eliminated by using a noise reduction threshold for each segment. This includes: employing an adaptive filtering algorithm, using the noise reduction threshold of each Morse code audio message segment as a filtering threshold, identifying signals below the filtering threshold as noise signals and filtering them out; and performing signal enhancement processing on the filtered effective signal to amplify the amplitude of the effective signal, thereby obtaining a clean Morse code audio signal.

[0045] The process involves statistical analysis of the data in the high and low level duration data table to dynamically generate a reference time interval for Morse code recognition. Specifically, this includes: extracting a preset number of high-level duration samples from the high and low level duration data table, sorting the sample data and removing outliers, calculating the minimum value of the remaining samples as the initial dot code reference duration, and calculating the maximum value of the remaining samples as the initial dash code reference duration, with the dash code reference duration being 3 times ± 10% of the dot code reference duration; extracting a preset number of low-level duration samples from the high and low level duration data table, sorting the sample data and removing outliers, and dividing the remaining samples into three intervals corresponding to the code interval, character interval, and word interval, respectively. The character interval reference duration is 3 times ± 10% of the dot code reference duration, the word interval reference duration is 7 times ± 10% of the dot code reference duration, and the code interval reference duration is 1 times ± 10% of the dot code reference duration; in subsequent Morse code recognition processes, after recognizing a preset number of dot-dash coding units, high and low level duration samples are re-extracted and a reference time interval is generated, achieving dynamic adaptive adjustment of the reference time interval.

[0046] The pre-set Morse code lookup table contains a one-to-one correspondence between all English letters, numbers, punctuation marks, and dot-stroke code sequences in the internationally used Morse code, and also supports the addition of custom codes. The technical solution of this invention will be described in detail below with reference to specific embodiments. These embodiments are only used to explain this invention and are not intended to limit the scope of protection of this invention.

[0047] The Morse code recognition method provided in this embodiment is applied to the Morse code reception and recognition scenario of civilian shortwave radio communication. In this scenario, there is channel transmission noise and environmental electromagnetic noise, and the received Morse code is manually transmitted, with non-standard fluctuations in code rate and dot duration.

[0048] Step 1: Receive and save the complete Morse code audio message:

[0049] The Morse code audio messages in the shortwave channel are collected by a radio receiving device and saved to the local storage unit in WAV format. The sampling rate is set to 44.1KHz and the sampling precision is 16bit.

[0050] Step 2: Segment and buffer Morse code audio messages:

[0051] The saved WAV format audio messages are sampled frame by frame, with a frame length of 10ms and a frame shift of 5ms. The signal-to-noise ratio (SNR) of each frame is calculated. The SNR change threshold is set to 15dB. When the SNR change value of adjacent frames exceeds 15dB, it is identified as a segmentation node. Using the segmentation node as the boundary, the audio message is divided into multiple consecutive audio message segments (e.g., 3 consecutive audio message segments), which are buffered into buffer 1, buffer 2, and buffer 3 respectively. The SNR fluctuation within each message segment is controlled within 5dB.

[0052] Step 3: Determine the noise reduction threshold for each message segment:

[0053] The average signal-to-noise ratio (SNR) of the three segments was calculated separately. The average SNR of the segments in buffer 1 was SNR1 = 28 dB, the average SNR of the segments in buffer 2 was SNR2 = 19 dB, and the average SNR of the segments in buffer 3 was SNR3 = 25 dB. Based on the actual shortwave channel environment calibration ratio k = 0.02 and the basic threshold coefficient b = 0.1, according to the formula T = k × SNR + b, the noise reduction thresholds were calculated as follows: Buffer 1 noise reduction threshold T1 = 0.02 × 28 + 0.1 = 0.66 V; Buffer 2 noise reduction threshold T2 = 0.02 × 19 + 0.1 = 0.48 V; Buffer 3 noise reduction threshold T3 = 0.02 × 25 + 0.1 = 0.60 V.

[0054] Step 4: Noise reduction processing to obtain a clean audio signal:

[0055] The audio message segments in the three buffers are denoised using an adaptive filtering algorithm. The denoising threshold of each message segment is used as the filtering threshold to filter out noise signals below the threshold. Then, the effective signal amplitude is amplified by 2 times by a signal enhancement unit to obtain a pure Morse code audio signal without noise interference.

[0056] Step 5: Bitstream conversion and debouncing:

[0057] A clean Morse code audio signal is input into a bitstream converter chip and converted into a 48kHz bitstream. At the same time, an RC dejitting circuit is used to dejitter the bitstream, filtering out glitches with a width of less than 0.5ms in the signal to ensure signal stability.

[0058] Step 6: Search the message database for a segment bitstream that matches each segment bitstream. If a segment bitstream that matches each segment bitstream is found, process each segment bitstream according to the various reference time intervals of the found segment bitstream, i.e., execute steps 10-11. If no segment bitstream that matches the dotted code and dashed code in each segment bitstream is found, generate various reference time intervals, and then process each segment bitstream using the generated reference time intervals, i.e., execute steps 7-11.

[0059] Step 7: Edge capture and time recording when generating various reference time intervals:

[0060] The edge detection circuit captures the edges of the dejittered bitstream. When a rising or falling edge is detected, a high-precision timer (1μs precision) is triggered to record the current time. The time information of all rising and falling edges is stored in the temporary data area.

[0061] Step 8: Create a high and low level duration data table

[0062] Based on the time records in the temporary data area, calculate the duration of each high level (falling edge - the next adjacent rising edge) and the duration of each low level (rising edge - the next adjacent falling edge); organize all the calculated high and low level duration data in chronological order to create a high and low level duration data table.

[0063] Step 9: Dynamically generate reference time intervals

[0064] The first 50 high-level duration samples are extracted from the high and low level duration data table. After sorting, the top 5% and bottom 5% outliers are removed. The minimum value of the remaining 40 samples is calculated to be 0.2ms, and the initial dot code reference duration is determined to be 0.2ms, and the initial dash code reference duration is 0.6ms (3 times the dot code reference duration). The first 50 low-level duration samples are extracted, sorted, and outliers are removed. The intervals are divided as follows: code interval reference duration 0.2ms ± 10%, character interval reference duration 0.6ms ± 10%, and word interval reference duration 1.4ms ± 7 times the dot code reference duration. In the subsequent recognition process, every 20 dot-dash coding units are recognized, 50 high and low level samples are extracted again to generate various reference time intervals.

[0065] Step 10: Form a dot-dash code sequence

[0066] Compare the high-level data in the high / low level duration data table with the reference durations of dot and dash codes. If the high-level duration is between 0.18ms and 0.22ms, it is determined to be a dot code "·"; if it is between 0.54ms and 0.66ms, it is determined to be a dash code "—". Compare the low-level data with the reference durations of various intervals. If it is between 0.18ms and 0.22ms, it is determined to be a code interval; if it is between 0.54ms and 0.66ms, it is determined to be a character interval; if it is between 1.26ms and 1.54ms, it is determined to be a word interval. Ignore the code intervals, use the character interval as the boundary of the encoding unit, and the word interval as the boundary of the message word group to form a continuous Morse code dot-dash encoding sequence.

[0067] Step 11: Decode and output the message

[0068] The generated dot-and-dash encoding sequence is matched and queried against a pre-set internationally recognized Morse code encoding lookup table, and each encoding unit is converted into the corresponding English letters and numbers. The decoding results are then concatenated according to the character intervals and word intervals, and finally the complete Morse code message content is output.

[0069] Correspondingly, the Morse code recognition device in this embodiment consists of a receiving and storage module, a segmented buffer module, a threshold determination module, a noise reduction processing module, a bitstream conversion module, an edge capture module, a timing and statistics module, a dynamic adjustment module, an encoding and recognition module, a decoding and output module, and a parameter calibration module. These modules are electrically connected via a circuit board. The core control chip is an STM32F407, with a high-precision timer integrated into the core chip. The multi-buffer unit uses three independent SRAM buffer chips, each corresponding to a different audio message segment. The parameter calibration module completes coefficient calibration via host computer software. The calibrated parameters are stored in the chip's Flash memory and can be recalibrated and modified according to the actual usage environment.

[0070] The present invention has the following technical effects:

[0071] Adaptive segmented noise reduction to accommodate random noise fluctuations: This invention segments the Morse code audio message according to the signal-to-noise ratio at different stages, independently determines the noise reduction threshold for each message segment and performs noise reduction, solving the problems of existing noise reduction methods that do not consider random fluctuations in noise levels and have poor noise reduction effects. It effectively filters out various noises introduced by channel transmission, equipment and environment, and improves the foundation for signal recognition in noisy environments.

[0072] Dynamically generated reference intervals to adapt to manual variable-speed transmission: Through real-time statistical analysis of high and low level duration data, reference time intervals for dot codes, dash codes, and various intervals are dynamically generated. This can adaptively match the problems of code speed changes and non-standard dot-dash duration ratios in manually transmitted Morse code, solving the problem of low recognition accuracy caused by differences in code speed and duration in existing technologies.

[0073] Automated recognition and decoding improve efficiency and accuracy: It realizes a fully automated process of Morse code from audio message reception, noise reduction, signal processing to decoding output, eliminating the need for manual transcription and calculation. It completely solves the problems of high error rate and low efficiency caused by the skill level and operating habits of personnel in the manual recognition process, and greatly improves the efficiency and accuracy of Morse code recognition.

[0074] Modular design, strong scalability and practicality: The identification device adopts a modular hardware structure, with each module functioning independently yet working closely together. The parameter calibration module supports parameter adjustment according to different wireless channel environments and noise types. At the same time, the Morse code encoding lookup table supports custom encoding extensions, making it applicable to various Morse code identification scenarios such as civilian and amateur radio, and possessing strong practicality and scalability.

[0075] The signal processing steps have been improved to enhance signal quality: a de-jitter processing step has been added after bitstream conversion to filter out glitches and interference in the signal. At the same time, signal enhancement is performed after noise reduction to improve the identification of the effective signal. This provides a high-quality digital signal for subsequent edge capture and time statistics, further ensuring the accuracy of recognition and decoding.

[0076] Although the present invention has been described in detail above, it is not limited thereto, and those skilled in the art can make various modifications based on the principles of the present invention. Therefore, all modifications made in accordance with the principles of the present invention should be understood to fall within the protection scope of the present invention.

Claims

1. A method for fast recognition of Morse code, characterized in that, include: For receiving and saving complete Morse code audio messages, the signal-to-noise ratio of the Morse code audio messages is calculated in real time; The received and completely saved Morse code audio message is divided into multiple Morse code audio message segments according to the real-time calculated signal-to-noise ratio of the Morse code audio message. Calculate the average signal-to-noise ratio (SNR) of each Morse code audio segment, and determine the noise reduction threshold T for each Morse code audio segment based on the average SNR. The noise reduction threshold T is positively correlated with the average SNR. The noise of each Morse code audio message segment is eliminated by using the noise reduction threshold T of each Morse code audio message segment, and then jitter is removed to obtain a clean bitstream of each target message segment. Search the message database for the segment bitstream that matches each target segment bitstream; If a segment bitstream that matches each target segment bitstream is found, then each target segment bitstream is processed according to various reference time intervals of the segment bitstream that matches each target segment bitstream in order to identify Morse code audio messages. If no matching segment bitstream is found for each target segment bitstream, various reference time intervals are generated, and each target segment bitstream is processed using the generated reference time intervals to identify Morse code audio messages. Specifically, when dividing the complete Morse code audio message into multiple Morse code audio message segments, the signal-to-noise ratio (SNR) is calculated by sampling the Morse code audio message frame by frame, and the segmentation point is the position where the SNR change exceeds a preset threshold.

2. The Morse code fast identification method according to claim 1, processing the target segment bitstream according to various reference time intervals of the found segment bitstream includes: The dot code, dash code, and various time intervals of each target segment bit stream are determined according to the various reference time intervals of the found segment bit stream to form a dot and dash coding sequence; The generated dot and dash coding sequence is matched and decoded with a preset lookup table, and then the complete message is output.

3. In the Morse code fast identification method according to claim 2, the various reference time intervals of the message segment bit stream stored in the message database are based on the dot code, cross code, and various time intervals of the target message segment bit stream that has been received and processed previously.

4. The Morse code fast recognition method according to claim 1, generating various reference time intervals includes: Capture the bit stream edge of the target segment and record the time, calculate the duration of high and low levels and establish a time data table; By performing statistical analysis on the time data table, a reference time interval for identifying Morse code is generated.

5. The Morse code fast identification method according to claim 4, processing each target segment bitstream using the generated various reference time intervals includes: The generated reference time intervals are used to determine the dot code, dash code, and various time intervals of each target segment bit stream, so as to form a dot and dash coding sequence; The dot and dash encoding sequences are matched and decoded with a preset lookup table, and the complete message is output by splicing them together.

6. In the Morse code fast identification method according to claim 5, the message database saves the processed target message segment bit stream and the corresponding generated reference time intervals in real time, and uses them as the message segment bit stream and its various time intervals saved in the message database.

7. The Morse code fast recognition method according to claim 1, characterized in that, The noise reduction threshold T = k × SNR + b, where k is the scaling factor, SNR is the average signal-to-noise ratio, and b is the base threshold factor.

8. The Morse code fast recognition method according to claim 2, characterized in that, When generating various reference time intervals, samples of high and low level durations are extracted and outliers are removed. The reference durations for dotted codes, scratch codes, and various intervals are all set based on the dotted code reference duration.

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