A method and system for fast processing of pulse signals

Abstract

The present application provides a method and system for rapid processing of pulse signals. Among them, a pulse signal containing multiple frequency components is received; a spectrum analysis of the pulse signal is performed using an adaptive frequency segmentation algorithm based on spectrum entropy to obtain multiple frequency band signals; the parameter configuration of the filter is dynamically adjusted according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal; multiplexing technology is used to process the optimized frequency band signals in parallel to accelerate the processing flow and ensure signal integrity; wherein the multiplexing technology ensures that the time alignment between the frequency band signals is accurate during the parallel processing process, and takes into account the mutual influence between the signals. The method for rapid processing of pulse signals of the technical solution provided in the present application not only improves the efficiency and quality of signal processing, but also ensures the integrity and robustness of the signal processing process, and is suitable for applications where pulse signals containing multiple frequency components need to be processed quickly and accurately.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of wireless communication technology, and in particular, to a method and system for quickly processing a pulse signal. Background Art

[0002] With the development of communication technology, especially in the fields of wireless communication, radar systems and biomedical engineering, it is necessary to process complex pulse signals containing multiple frequency components. Such signals often carry important information, but due to the diversity and complexity of their spectral components, it brings challenges to the fast and accurate processing of signals. In application scenarios such as radar signal processing, multi-carrier transmission in wireless communication, and biomedical signal processing, signals are often composed of multiple frequency components, which requires the processing system to not only be able to quickly identify and separate these components, but also to be able to effectively remove noise and maintain signal integrity for subsequent data analysis and decision support. Therefore, a technology is needed that can efficiently process multi-frequency component pulse signals without affecting signal quality.

[0003] At present, traditional pulse signal processing methods usually use fixed filter banks to perform spectrum analysis and processing on signals. Although this method can meet basic needs in some cases, it lacks flexibility and adaptability when facing complex and changing signal environments. In addition, existing solutions often do not take into account the mutual influence between signals in different frequency bands, resulting in phase deviation or synchronization problems in the processed signals.

[0004] A major drawback of traditional methods is the inability to dynamically adjust filter parameters to adapt to changes in the signal spectrum. This means that when signal characteristics change, fixed filter settings may cause the useful signal to be weakened or noise to fail to be effectively removed. In addition, existing processing schemes usually adopt a serial processing method, which limits the processing speed and makes it difficult to meet the real-time processing requirements of modern communication systems. Finally, due to the lack of consideration of the interaction between signals, existing processing methods may cause the processed signals to be time-misaligned when they are recombined, affecting the integrity and effectiveness of the signal. Summary of the invention

[0005] The embodiments of the present application provide a method and system for rapid processing of pulse signals, which are used to solve the problem in the prior art that the processed signals are not aligned in time when they are recombined, thus affecting the integrity and effectiveness of the signals.

[0006] In a first aspect, an embodiment of the present application provides a method for quickly processing a pulse signal, comprising:

[0007] receiving a pulse signal including a plurality of frequency components;

[0008] Performing spectrum analysis on the pulse signal using a preset adaptive frequency segmentation algorithm to obtain multiple frequency band signals;

[0009] Dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal;

[0010] Multiplexing technology is used to process the optimized frequency band signals in parallel to speed up the processing flow and ensure the integrity of the frequency band signals.

[0011] Optionally, the using a preset adaptive frequency segmentation algorithm to perform spectrum analysis on the pulse signal to obtain multiple frequency band signals includes:

[0012] Acquire the frequency spectrum of the pulse signal by fast Fourier transform;

[0013] Calculating the total energy entropy of all frequency points in the spectrum;

[0014] According to the distribution of the total energy entropy, the frequency band boundary is automatically determined, and the pulse signal is divided into a plurality of frequency band signals.

[0015] Optionally, dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal includes:

[0016] Monitor the entropy value of each frequency band signal in real time;

[0017] Based on the change of the entropy value, the cutoff frequency and the passband gain of the filter are dynamically adjusted to ensure that the filter can effectively remove noise and retain useful frequency band signals.

[0018] Optionally, the adopting multiplexing technology to perform parallel processing on the optimized frequency band signals to accelerate the processing flow and ensure the integrity of the frequency band signals includes:

[0019] Allocate a dedicated processing channel for each frequency band signal;

[0020] The output signals of each channel are synchronized through time interleaving technology to ensure that all processed frequency band signals are correctly aligned on the time axis;

[0021] The phase difference generated when the frequency band signal is transmitted between different channels is determined, and the phase difference is compensated to speed up the processing flow and ensure the integrity of the frequency band signal.

[0022] Optionally, the total energy entropy calculation of the total energy entropy of all frequency points in the spectrum includes:

[0023] The total energy entropy at each frequency point is calculated by the following formula :

[0024] ;

[0025] in, It is the frequency The energy probability density is It is the frequency The energy probability density is It is the frequency The energy probability density is is the Lagrange multiplier, which is used to ensure that the sum of the probability energy density is , is the weight factor to adjust the cross-correlation, Indicates frequency and The phase difference between them, N is the number of frequency points.

[0026] Optionally, dynamically adjusting the cutoff frequency and passband gain of the filter based on the change of the entropy value includes:

[0027] Dynamically adjust the filter cutoff frequency and passband gain The formulas include:

[0028]

[0029]

[0030] in, represents the cutoff frequency of the filter, and Respectively represent the lowest and highest frequencies allowed to pass, which are used for the working range of the filter. and is the temperature parameter used to simulate the entropy distribution, and is the regulating factor, and is the correction factor, and is the exponential factor, Indicates The entropy value of the frequency band signal, Indicates the total number of signals in the frequency band, Represents the average entropy value of all frequency band signals. Represents the passband gain of the filter, which is used to determine the signal strength passing through the filter. and Represent the minimum and maximum allowable passband gains, respectively, defining the range of the filter gain.

[0031] Optionally, synchronizing the output signals of each channel by using a time interleaving technique to ensure that all processed frequency band signals are correctly aligned on the time axis includes:

[0032] For each of the frequency band signals, adjusting the output delay of each of the frequency band signals;

[0033] Synchronizing the output signals of each channel based on the output delay to ensure that all processed frequency band signals are correctly aligned on the time axis;

[0034] Among them, the following formula is used for the frequency band signals, and determining the output delay of each of the frequency band signals :

[0035] ;

[0036] in, is a fixed time interval, is an exponential factor used to adjust the delay, It is the highest cutoff frequency among all frequency band signals. is the amplitude of the sinusoidal oscillation, which is used to introduce additional time shift to ensure good synchronization even at high frequencies. is the weight factor used to compensate for the phase difference, Indicates The output delay of each frequency band signal is adjusted to ensure that the signals of each frequency band are aligned on the time axis when they are finally merged. is the exponential factor used to adjust the delay; Indicates The cutoff frequency of the frequency band signal, Indicates frequency and The phase difference between Indicates the total number of frequency band signals, Indicates frequency The energy probability density of .

[0037] In a second aspect, an embodiment of the present application provides a pulse signal fast processing system, comprising:

[0038] A receiving module, used for receiving a pulse signal containing multiple frequency components;

[0039] An analysis module, used to perform spectrum analysis on the pulse signal using a preset adaptive frequency segmentation algorithm to obtain multiple frequency band signals;

[0040] An optimization module, used for dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal;

[0041] The acceleration module is used to process the optimized frequency band signals in parallel by using multiplexing technology to accelerate the processing flow and ensure the integrity of the frequency band signals.

[0042] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for quickly processing a pulse signal as described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for quickly processing a pulse signal as described in the first aspect.

[0044] In an embodiment of the present application, a pulse signal containing multiple frequency components is received; a spectrum analysis is performed on the pulse signal using an adaptive frequency segmentation algorithm based on spectrum entropy to obtain multiple frequency band signals; the parameter configuration of the filter is dynamically adjusted according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal; multiplexing technology is used to process the optimized frequency band signals in parallel to accelerate the processing flow and ensure signal integrity; wherein the multiplexing technology ensures that the time alignment between the frequency band signals is accurate during the parallel processing process, and takes into account the mutual influence between the signals. The technical solution provided by the present application, the method for rapid processing of pulse signals, not only improves the efficiency and quality of signal processing, but also ensures the integrity and robustness of the signal processing process, and is suitable for applications where pulse signals containing multiple frequency components need to be processed quickly and accurately.

[0045] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flowchart of a method for quickly processing a pulse signal provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the structure of a pulse signal fast processing device provided in an embodiment of the present application;

[0049] Figure 3A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0051] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0053] Figure 1 A flowchart of a method for quickly processing a pulse signal is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0054] 101. Receive a pulse signal including multiple frequency components;

[0055] In this step, receiving a pulse signal containing multiple frequency components refers to capturing electrical signals from an external environment or system through a sensor or other receiving device. These signals are usually composed of a series of pulses, each of which may contain different frequency components. For example, in a radar system, the emitted radar wave is reflected back after encountering a target, forming an echo signal containing target information. Such an echo signal is usually a complex signal composed of multiple frequency components.

[0056] In the embodiment of the present application, it is assumed that in a radar system, the radar transmitter transmits a continuous wave signal with a frequency range of 2 GHz to 4 GHz. When this signal encounters a target, it is reflected back to the radar receiving antenna. At this time, the signal received by the receiving antenna is a pulse signal containing multiple frequency components. Specifically, the signal received by the radar receiver may include multiple frequency components, which carry information about the target distance, speed, etc. For ease of understanding, this process can be illustrated by a simplified example:

[0057] Assume that the signal emitted by the radar transmitter is ,in and When this signal hits the target and reflects back, the time it takes for the signal to reach the receiver will vary depending on the target's distance. Assuming the target is at a certain distance from the radar transmitter, the received signal can be expressed as ,in is the time delay required for the signal to travel back and forth.

[0058] In this embodiment, the signal received by the radar receiver is It is a pulse signal containing multiple frequency components (2GHz and 3GHz). The next steps will be based on further processing of this signal.

[0059] 102. Perform spectrum analysis on the pulse signal using a preset adaptive frequency division algorithm to obtain multiple frequency band signals;

[0060] Optionally, the step 102 of "performing spectrum analysis on the pulse signal using a preset adaptive frequency segmentation algorithm to obtain multiple frequency band signals" includes: obtaining the spectrum of the pulse signal through fast Fourier transform; calculating the total energy entropy of all frequency points in the spectrum; and automatically determining the frequency band boundaries based on the distribution of the total energy entropy to divide the pulse signal into multiple frequency band signals.

[0061] In this step, the spectrum of the pulse signal is obtained by Fast Fourier Transform (FFT): this is a technique for converting a time domain signal into a frequency domain signal, which can reveal the intensity distribution of different frequency components in the signal.

[0062] Calculate the total energy entropy of all frequency points in the spectrum: The total energy entropy is an indicator to measure the uniformity of energy distribution in the signal spectrum, which can help identify important frequency components in the signal. The higher the total energy entropy, the more uniform the energy distribution of the signal.

[0063] According to the distribution of the total energy entropy, the frequency band boundary is automatically determined, and the pulse signal is divided into multiple frequency band signals: Based on the distribution of the total energy entropy, the appropriate frequency band boundary can be automatically found, thereby dividing the original signal into multiple frequency band signals, each frequency band signal contains frequency components within a certain range.

[0064] In the embodiment of the present application, it is assumed that in step 101, a pulse signal containing multiple frequency components has been received. Now, a preset adaptive frequency segmentation algorithm is used to perform spectrum analysis on the pulse signal to obtain multiple frequency band signals. The specific steps are as follows: Obtain the spectrum of the pulse signal by fast Fourier transform: First, use fast Fourier transform (FFT) to convert the pulse signal in the time domain into a frequency domain signal. Assume that the received pulse signal is , the frequency domain signal is obtained after FFT transformation . Assume that the received time domain signal is ,in and Through FFT transformation, we get the frequency domain signal .

[0065] Furthermore, the total energy entropy of each frequency point in the spectrum is calculated The total energy entropy can be calculated by the following formula:

[0066] ;

[0067] in, It is the frequency The energy probability density is It is the frequency The energy probability density is It is the frequency The energy probability density is is the Lagrange multiplier, which is used to ensure that the sum of the probability energy density is , is the weight factor to adjust the cross-correlation, Indicates frequency and The phase difference between them, N is the number of frequency points.

[0068] Furthermore, according to the distribution of total energy entropy, the frequency band boundary is automatically determined, and the pulse signal is divided into multiple frequency band signals. Specifically, by analyzing the distribution of energy entropy in the spectrum, the local maximum value or mutation point of energy entropy is found as the potential frequency band boundary.

[0069] For example, first calculate the energy probability density of each frequency point in the spectrum, and use the definition of information entropy to calculate the energy entropy of each frequency point; secondly, set a threshold, which is used to determine which frequency points have higher energy entropy and can be used as potential frequency band boundaries, and use the find_peaks function to find local maxima in the energy entropy distribution, and ensure that the height of these maxima is greater than the threshold; finally, use the local maxima found as frequency band boundaries, and design bandpass filters based on the determined frequency band boundaries to extract the signals in each frequency band.

[0070] Through the above steps, the pulse signal was successfully spectrally analyzed using the preset adaptive frequency segmentation algorithm, and the frequency band boundaries were automatically determined based on the distribution of total energy entropy, dividing the pulse signal into multiple frequency band signals, providing preparation for the next step of signal processing.

[0071] 103. Dynamically adjust the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal;

[0072] Optionally, the step 103 of "dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal" includes: real-time monitoring the entropy value of each of the frequency band signals; based on the change of the entropy value, dynamically adjusting the cutoff frequency and passband gain of the filter to ensure that the filter can effectively remove noise and retain useful frequency band signals.

[0073] In this step, the described technique is a method for signal processing that dynamically adjusts the parameters of the filter by analyzing the entropy value of the signal. Entropy can be used in information theory to measure the uncertainty or randomness of a signal. Higher entropy values ​​usually indicate more noise or a more complex information structure in the signal.

[0074] Real-time monitoring of the entropy value of each frequency band signal: This means that the system continuously calculates the entropy value of the signal in each frequency band to evaluate the purity or complexity of the signal. It should be noted that the entropy value of the frequency band signal can be the sum of the energy entropy of all frequency points in the frequency band, and the total energy entropy refers to the sum of the energy entropy of all frequency points in the entire spectrum.

[0075] Dynamically adjust the filter's cutoff frequency and passband gain based on changes in entropy values: If a high signal entropy value is detected in a certain frequency band (which may mean there is more noise), the system may adjust the parameters of the filter in that frequency band (such as lowering the cutoff frequency, reducing the high-frequency components allowed to pass, or adjusting the passband gain to change the signal strength) to improve signal quality.

[0076] Ensure that the filter can effectively remove noise and retain useful frequency band signals: The ultimate goal is to improve the clarity of the signal through the above adjustments, remove unnecessary interference, and retain those signal features that are important for subsequent processing.

[0077] The advantage of this method is that it can adapt to the changing signal environment and automatically adjust to obtain the best signal processing effect. This is very useful for communication systems, audio processing, and other applications that need to process signals in uncertain environments.

[0078] In the embodiment of the present application, it is assumed that multiple frequency band signals have been acquired by the adaptive frequency segmentation algorithm in step 102. Now, it is necessary to dynamically adjust the parameter configuration of the filter according to the entropy values ​​of these frequency band signals to optimize the signal quality.

[0079] The specific steps are as follows:

[0080] First, for each frequency band signal, calculate its entropy value to evaluate the quality of the signal. Specifically, the entropy value of the frequency band signal can be determined by calculating the sum of the energy entropies of all frequency points in the frequency band. The energy entropy of a single frequency point can be calculated by the following formula: ,in, Indicates that at a specific frequency The energy entropy at It is the frequency The energy probability density of .

[0081] Secondly, based on the change of the entropy value, the cutoff frequency and passband gain of the filter are dynamically adjusted: Dynamically adjust the cutoff frequency of the filter and passband gain , the specific formula is as follows:

[0082]

[0083] ;

[0084] in, represents the cutoff frequency of the filter, and Respectively represent the lowest and highest frequencies allowed to pass, which are used for the working range of the filter. and is the temperature parameter used to simulate the entropy distribution, and is the regulating factor, and is the correction factor, and is the exponential factor, Indicates The entropy value of the frequency band signal, Indicates the total number of signals in the frequency band, Represents the average entropy value of all frequency band signals. Represents the passband gain of the filter, which is used to determine the signal strength passing through the filter. and Represent the minimum and maximum allowable passband gains, respectively, defining the range of the filter gain.

[0085] Here is a specific example:

[0086] Suppose we have two frequency band signals and The corresponding extraction values ​​are and .

[0087] Assumptions:

[0088]

[0089] For frequency band signals , adjust its cutoff frequency and passband gain:

[0090]

[0091] For frequency band signals , adjust its cutoff frequency and passband gain:

[0092]

[0093] Through the above steps, the cutoff frequency and passband gain of the filter are dynamically adjusted according to the entropy value of the frequency band signal, the quality of the frequency band signal is optimized, and a good foundation is laid for subsequent processing.

[0094] 104. Use multiplexing technology to process the optimized frequency band signals in parallel to speed up the processing flow and ensure the integrity of the frequency band signals.

[0095] Optionally, the step 104 of "using multiplexing technology to perform parallel processing on the optimized frequency band signals to accelerate the processing flow and ensure the integrity of the frequency band signals" includes: allocating a dedicated processing channel to each of the frequency band signals; synchronizing the output signals of each channel through time interleaving technology to ensure that all processed frequency band signals are correctly aligned on the time axis; determining the phase difference generated when the frequency band signals are transmitted between different channels, and compensating for the phase difference to accelerate the processing flow and ensure the integrity of the frequency band signals.

[0096] In this step, multiplexing technology is used to process the optimized frequency band signals in parallel: multiplexing technology allows multiple signals to be processed simultaneously through multiple independent channels in the same time period. In this way, the speed of signal processing can be significantly accelerated. Common multiplexing technologies include frequency division multiplexing (FDM), time division multiplexing (TDM), etc.

[0097] Allocate a dedicated processing channel for each frequency band signal: Allocate each optimized frequency band signal to an independent processing channel, so as to avoid interference between signals of different frequency bands and improve processing efficiency.

[0098] Synchronize the output signals of each channel through time interleaving technology: Time interleaving technology can ensure that the output signals of each processing channel are aligned in time. Even if the processing time of the signals in different channels is slightly different, the synchronization of the signals can be guaranteed by adjusting the delay.

[0099] Determine the phase difference generated when the frequency band signal is transmitted between different channels, and compensate for the phase difference: Since the signal processing time of different channels may be different, resulting in phase inconsistency, the consistency and integrity of the signal when it is finally merged can be ensured by detecting and compensating the phase difference.

[0100] In the embodiment of the present application, it is assumed that the quality of the frequency band signal has been optimized by dynamically adjusting the filter parameters in step 103. Now, multiplexing technology will be used to process the optimized frequency band signals in parallel to speed up the processing flow and ensure the integrity of the frequency band signals. The specific steps are as follows:

[0101] Assign a dedicated processing channel to each frequency band signal: Assume there are two optimized frequency band signals and , respectively and frequency band signal. Assigned to channel 1, Assigned to channel 2. Synchronize the output signals of each channel through time interleaving technology: In order to ensure that the output signals of each processing channel are aligned in time, it is necessary to adjust the output delay of each frequency band signal .

[0102] The specific adjustment formula is as follows:

[0103] ;

[0104] in, is a fixed time interval, is an exponential factor used to adjust the delay, It is the highest cutoff frequency among all frequency band signals. is the amplitude of the sinusoidal oscillation, which is used to introduce additional time shift to ensure good synchronization even at high frequencies. is the weight factor used to compensate for the phase difference, Indicates The output delay of each frequency band signal is adjusted to ensure that the signals of each frequency band are aligned on the time axis when they are finally merged. is an exponential factor used to adjust the delay, Indicates The cutoff frequency of the frequency band signal, Indicates frequency and The phase difference between Indicates the total number of frequency band signals, Indicates frequency The energy probability density of .

[0105] Determine the phase difference generated when the frequency band signal is transmitted between different channels, and compensate for the phase difference:

[0106] Assume that it is found through measurement that the frequency band signal and Phase difference occurs when transmitting in different channels and , these phase differences need to be compensated. Compensating for phase differences can be achieved by adjusting the phase of the signals so that the signals are correctly aligned when combined.

[0107] Specific embodiment: Allocate a dedicated processing channel for each frequency band signal: Assume there are two frequency band signals and The corresponding cut-off frequencies are and ,in .Will Assigned to channel 1, Assigned to channel 2. The output signals of each channel are synchronized by time interleaving technology: Assume that , and assuming and The energy probability density Same, and phase difference is zero. For the frequency band signal , adjust its output delay:

[0108] ;

[0109] For frequency band signals , adjust its output delay:

[0110] ;

[0111] Through the above calculations, we get and The output delays are and .

[0112] Determine the phase difference generated when the frequency band signal is transmitted between different channels, and compensate for the phase difference:

[0113] Assuming that the measurement and The phase differences generated when transmitting in different channels are and To compensate for these phase differences, the phase of the signal can be adjusted. For example, an opposite phase offset can be added to the phase of the signal to cancel the phase difference.

[0114] for , compensate the phase difference: ;

[0115] for , compensate the phase difference: ;

[0116] Through the above steps, the optimized frequency band signals are successfully processed in parallel, and the time alignment and integrity of the signals are ensured through time interleaving technology and phase difference compensation. This not only speeds up the processing flow, but also ensures the high quality of the signal processing results.

[0117] Figure 2 A schematic diagram of a structure of a pulse signal fast processing device is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0118] A receiving module 21, used for receiving a pulse signal containing multiple frequency components;

[0119] An analysis module 22, configured to perform spectrum analysis on the pulse signal using a preset adaptive frequency segmentation algorithm to obtain signals of multiple frequency bands;

[0120] An optimization module 23, used for dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal;

[0121] The acceleration module 24 is used to process the optimized frequency band signals in parallel using a multiplexing technology to accelerate the processing flow and ensure the integrity of the frequency band signals.

[0122] Figure 2 The pulse signal fast processing device can execute Figure 1 The implementation principle and technical effect of the pulse signal fast processing method described in the embodiment are not repeated here. The specific way in which each module and unit performs operations in the pulse signal fast processing device in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0123] In one possible design, Figure 2 A pulse signal fast processing device of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0124] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0125] The processing component 32 is used to: receive a pulse signal containing multiple frequency components; use an adaptive frequency segmentation algorithm based on spectral entropy to perform spectral analysis on the pulse signal to obtain multiple frequency band signals; dynamically adjust the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal; use multiplexing technology to parallel process the optimized frequency band signals to accelerate the processing flow and ensure signal integrity; wherein the multiplexing technology ensures that during the parallel processing process, the time alignment between the frequency band signals is accurate and the mutual influence between the signals is taken into account.

[0126] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0127] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0128] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0129] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0130] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0131] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0132] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A method for quickly processing a pulse signal in the illustrated embodiment.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0135] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for rapid processing of pulse signals, characterized in that: include: receiving a pulse signal including a plurality of frequency components; Performing spectrum analysis on the pulse signal using a preset adaptive frequency segmentation algorithm to obtain multiple frequency band signals; Dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal; Multiplexing technology is used to process the optimized frequency band signals in parallel to speed up the processing flow and ensure the integrity of the frequency band signals; The using a preset adaptive frequency division algorithm to perform spectrum analysis on the pulse signal to obtain multiple frequency band signals comprises: Acquire the frequency spectrum of the pulse signal by fast Fourier transform; Calculating the total energy entropy of all frequency points in the spectrum; Automatically determining frequency band boundaries according to the distribution of the total energy entropy, and dividing the pulse signal into multiple frequency band signals; The dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal includes: Monitor the entropy value of each frequency band signal in real time; Based on the change of the entropy value, dynamically adjust the cutoff frequency and passband gain of the filter to ensure that the filter can effectively remove noise and retain useful frequency band signals; The dynamically adjusting the cutoff frequency and passband gain of the filter based on the change of the entropy value includes: Dynamically adjust the filter cutoff frequency and passband gain The formulas include: ； ； in, represents the cutoff frequency of the filter, and Respectively represent the lowest and highest frequencies allowed to pass, which are used for the working range of the filter. and is the temperature parameter used to simulate the entropy distribution, and is the regulating factor, and is the correction factor, and is the exponential factor, Indicates The entropy value of the frequency band signal, Indicates the total number of signals in the frequency band, Represents the average entropy value of all frequency band signals. Represents the passband gain of the filter, which is used to determine the signal strength passing through the filter. and Represent the minimum and maximum allowable passband gains, respectively, defining the range of the filter gain.

2. The method according to claim 1, characterized in that The method of using multiplexing technology to process the optimized frequency band signals in parallel to speed up the processing flow and ensure the integrity of the frequency band signals includes: Allocate a dedicated processing channel for each frequency band signal; The output signals of each channel are synchronized through time interleaving technology to ensure that all processed frequency band signals are correctly aligned on the time axis; The phase difference generated when the frequency band signal is transmitted between different channels is determined, and the phase difference is compensated to speed up the processing flow and ensure the integrity of the frequency band signal.

3. The method according to claim 1, characterized in that Calculating the total energy entropy of all frequency points in the spectrum includes: The total energy entropy of all frequency points is calculated by the following formula : ； in, It is the frequency The energy probability density is It is the frequency The energy probability density is It is the frequency The energy probability density is is the Lagrange multiplier, which is used to ensure that the sum of the probability energy density is , is the weight factor to adjust the cross-correlation, Indicates frequency and The phase difference between them, N is the number of frequency points.

4. The method according to claim 2, characterized in that: The method of synchronizing the output signals of each channel by time interleaving technology to ensure that all processed frequency band signals are correctly aligned on the time axis includes: For each of the frequency band signals, adjusting the output delay of each of the frequency band signals; Synchronizing the output signals of each channel based on the output delay to ensure that all processed frequency band signals are correctly aligned on the time axis; Among them, the following formula is used for the frequency band signals, and determining the output delay of each of the frequency band signals : ； in, is a fixed time interval, is an exponential factor used to adjust the delay, It is the highest cutoff frequency among all frequency band signals. is the amplitude of the sinusoidal oscillation, which is used to introduce additional time shift to ensure good synchronization even at high frequencies. is the weight factor used to compensate for the phase difference, Indicates The output delay of each frequency band signal is adjusted to ensure that the signals of each frequency band are aligned on the time axis when they are finally merged. is an exponential factor used to adjust the delay, Indicates The cutoff frequency of the frequency band signal, Indicates frequency and The phase difference between Indicates the total number of frequency band signals, Indicates frequency The energy probability density of .

5. A pulse signal fast processing system, characterized in that: include: A receiving module, used for receiving a pulse signal containing multiple frequency components; An analysis module, used to perform spectrum analysis on the pulse signal using a preset adaptive frequency segmentation algorithm to obtain multiple frequency band signals; An optimization module, used for dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal; An acceleration module, used for processing the optimized frequency band signals in parallel by using a multiplexing technology, so as to accelerate the processing flow and ensure the integrity of the frequency band signals; The using a preset adaptive frequency division algorithm to perform spectrum analysis on the pulse signal to obtain multiple frequency band signals comprises: Acquire the frequency spectrum of the pulse signal by fast Fourier transform; Calculating the total energy entropy of all frequency points in the spectrum; According to the distribution of the total energy entropy, the frequency band boundary is automatically determined, and the pulse signal is divided into a plurality of frequency band signals; The dynamically adjusting the parameter configuration of the filter according to the entropy value of the frequency band signal to optimize the quality of the frequency band signal includes: Monitor the entropy value of each frequency band signal in real time; Based on the change of the entropy value, dynamically adjust the cutoff frequency and passband gain of the filter to ensure that the filter can effectively remove noise and retain useful frequency band signals; The dynamically adjusting the cutoff frequency and passband gain of the filter based on the change of the entropy value includes: Dynamically adjust the filter cutoff frequency and passband gain The formulas include: ； ； in, represents the cutoff frequency of the filter, and Respectively represent the lowest and highest frequencies allowed to pass, which are used for the working range of the filter. and is the temperature parameter used to simulate the entropy distribution, and is the regulating factor, and is the correction factor, and is the exponential factor, Indicates The entropy value of the frequency band signal, Indicates the total number of signals in the frequency band, Represents the average entropy value of all frequency band signals. Represents the passband gain of the filter, which is used to determine the signal strength passing through the filter. and Represent the minimum and maximum allowable passband gains, respectively, defining the range of the filter gain.

6. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a pulse signal rapid processing method as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a pulse signal fast processing method as described in any one of claims 1 to 4 is implemented.

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