Signal analysis method and device of particle detector and storage medium

Abstract

The invention discloses a signal analysis method and device of a particle detector and a storage medium, and belongs to the technical field of detectors. The method is applied to a particle detector, and comprises the following steps: acquiring an initial measurement signal, and determining a first response function based on the initial measurement signal and a pulse voltage; performing full-peak fitting on the initial measurement waveform based on a first response function to obtain a first measurement waveform, and determining an amplitude and a width corresponding to the initial measurement signal; performing time-frequency transformation on the first measurement signal and the first response function, and determining an input signal; determining an initial parameter of a first filtering function based on the input signal, and optimizing the initial parameter; filtering the input signal to obtain an output signal; and performing time-frequency inverse transformation on the output signal, and determining the starting time corresponding to the initial measurement signal. According to the method and the device, the accuracy of the obtained waveform information can be improved by performing signal reconstruction on the initial measurement signal, so that the signal is accurately analyzed.

Description

Technical Field

[0001] The present invention relates to the field of detector technology, and in particular to a signal analysis method, device and storage medium for a particle detector. Background Art

[0002] Traditional signal processing methods for TPC (Time Projection Chamber) particle detectors often use waveform front fitting technology, which obtains the width, amplitude and start time of the waveform by fitting the front linear region of the waveform, thereby analyzing the waveform. However, since the waveform front fitting technology only obtains the front linear region of the waveform, in the case of more complex waveforms, due to the inability to obtain more complete waveform information, large errors may occur, thereby reducing the accuracy of signal analysis. In addition, for waveforms with a long start time, there may be a timing offset problem when using waveform front fitting technology to process the signal, resulting in a decrease in time accuracy. Summary of the invention

[0003] The present application provides a signal analysis method, device and storage medium for a particle detector, which is not only applicable to situations where the waveform is relatively complex, but also can improve the accuracy of the acquired waveform information, thereby accurately analyzing the signal. The technical solution is as follows:

[0004] In one aspect, a signal analysis method for a particle detector is provided, the method comprising:

[0005] Acquire an initial measurement signal, and determine a first response function based on the initial measurement signal and a pulse voltage, wherein the initial measurement signal is a signal to be analyzed, the initial measurement signal corresponds to an initial measurement waveform, and the pulse voltage is a voltage input from a test end of the particle detector;

[0006] Performing full-peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determining the amplitude and width corresponding to the initial measurement signal based on the first measurement waveform;

[0007] Performing time-frequency transformation on the first measurement signal and the first response function to obtain a second measurement signal and a second response function, and determining an input signal based on the second measurement signal and the second response function, wherein the input signal is a signal that needs to be filtered;

[0008] Determine initial parameters of a first filter function based on the input signal, and map the initial parameters to a parameter space through an activation function to optimize the initial parameters to obtain optimized parameters and a second filter function, wherein the parameter space is a parameter space optimized by an optimization algorithm, and the parameters of the second filter function are the optimized parameters;

[0009] Filtering the input signal according to the second filter function to obtain an output signal;

[0010] The output signal is subjected to an inverse time-frequency transform to obtain a target signal, and a start time corresponding to the initial measurement signal is determined based on a target waveform, wherein the target waveform is a waveform corresponding to the target signal.

[0011] Optionally, before performing time-frequency transformation on the first measurement signal and the first response function, the method further includes:

[0012] Performing a signal improvement operation on the first measurement signal, wherein the signal improvement operation includes time truncation, smoothing and zero padding;

[0013] A function improvement operation is performed on the first response function, wherein the function improvement operation includes time truncation and zero padding.

[0014] Optionally, the activation function is a Sigmoid function, and / or the optimization algorithm is an Adam (Adaptive Moment Estimation) method.

[0015] Optionally, determining the input signal based on the second measurement signal and the second response function comprises:

[0016] A ratio of the second measurement signal to the second response function is used as an input signal.

[0017] Optionally, determining initial parameters of a filter function based on the input signal comprises:

[0018] The maximum value of the local signal-to-noise ratio of the input signal is used as the initial parameter of the filter function.

[0019] Optionally, filtering the input signal according to the second filter function to obtain an output signal includes:

[0020] The input signal is multiplied by the second filter function to obtain the output signal.

[0021] Optionally, determining a first response function based on the initial measurement signal and the pulse voltage comprises:

[0022] Subtracting basic noise from the initial measurement signal to obtain the initial measurement signal after noise removal;

[0023] The first response function is obtained by dividing the initial measurement signal after noise removal by the current intensity, where the current intensity is the current intensity corresponding to the pulse voltage.

[0024] On the other hand, a signal analysis device of a particle detector is provided, the device comprising:

[0025] An acquisition module, the acquisition module is used to acquire an initial measurement signal, and determine a first response function based on the initial measurement signal and a pulse voltage, the initial measurement signal is a signal to be analyzed, the initial measurement signal corresponds to an initial measurement waveform, and the pulse voltage is a voltage input from a test end of the particle detector;

[0026] A fitting module, the fitting module is used to perform full-peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determine the amplitude and width corresponding to the initial measurement signal based on the first measurement waveform;

[0027] A first transformation module, the first transformation module is used to perform time-frequency transformation on the first measurement signal and the first response function to obtain a second measurement signal and a second response function, and determine an input signal based on the second measurement signal and the second response function, wherein the input signal is a signal that needs to be filtered;

[0028] A determination module, the determination module is used to determine initial parameters of a first filter function based on the input signal, and map the initial parameters to a parameter space through an activation function to optimize the initial parameters to obtain optimized parameters and a second filter function, the parameter space is a parameter space optimized by an optimization algorithm, and the parameters of the second filter function are the optimized parameters;

[0029] A filtering module, the filtering module is used to filter the input signal according to the second filtering function to obtain an output signal;

[0030] The second transformation module is used to perform an inverse time-frequency transformation on the output signal to obtain a target signal, and determine a start time corresponding to the initial measurement signal based on a target waveform, wherein the target waveform is a waveform corresponding to the target signal.

[0031] On the other hand, a computer-readable storage medium is provided, in which a computer program is stored. The computer program can be executed by a processor to implement the steps of the signal analysis method of the particle detector described above.

[0032] On the other hand, a computer program product comprising instructions is provided. When the instructions are executed on a computer, the computer executes the steps of the signal analysis method of the particle detector described above.

[0033] The technical solution provided by this application can at least bring the following beneficial effects:

[0034] By performing full-peak fitting on the initial measurement waveform, it is not only applicable to situations where the waveform is more complex, but also can obtain more complete information about the waveform. Therefore, according to the initial measurement waveform after full-peak fitting, that is, the first measurement waveform, the accuracy of the acquired amplitude and width information can be improved; in addition, after determining the initial parameters of the first filter function based on the input signal, the initial parameters will be mapped to the parameter space through the activation function, so as to optimize the initial parameters, and the input signal is filtered using the second filter function whose parameters are the optimized parameters, which can improve the signal-to-noise ratio, thereby improving the accuracy of the analysis of the initial measurement signal; and, after filtering the input signal through the second filter function to obtain the output signal, the output signal can also be inversely transformed in time and frequency to obtain the target signal, so that the start time of the initial measurement signal can be obtained according to the target waveform corresponding to the target signal, which can improve the accuracy of the acquired start time. In summary, the present application can improve the accuracy of the acquired signal's width information, amplitude information, and start time, thereby improving the accuracy of the signal analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of a signal analysis method for a particle detector provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the structure of a signal analysis device for a particle detector provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0038] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0039] The serial numbers of the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0040] Traditional signal processing methods for TPC particle detectors often use waveform front fitting technology, which obtains the width, amplitude, and start time of the waveform by fitting the linear region of the waveform front, thereby analyzing the waveform. However, since the waveform front fitting technology only obtains the linear region of the waveform front, in the case of complex waveforms, due to the inability to obtain more complete waveform information, large errors may occur, thereby reducing the accuracy of signal analysis. In addition, for waveforms with a long start time, there may be a timing offset problem when using waveform front fitting technology to process the signal, resulting in a decrease in time accuracy.

[0041] Based on this, an embodiment of the present application provides a signal analysis method for a particle detector, which performs signal reconstruction on the initial measurement signal, and can improve the accuracy of signal analysis of the TPC particle detector. For example, it can improve the signal analysis capability and time resolution accuracy of the MTPC (Multi-purpose time projection chamber) particle detector in complex situations, thereby enhancing the overall performance of the MTPC particle detector.

[0042] Next, the signal analysis method of the particle detector provided in the embodiment of the present application is explained in detail.

[0043] Figure 1 This is a flow chart of a signal analysis method for a particle detector provided in an embodiment of the present application, which is applied to a particle detector. Figure 1 , the method comprises the following steps.

[0044] Step 101: Acquire an initial measurement signal, and determine a first response function based on the initial measurement signal and a pulse voltage, wherein the initial measurement signal is a signal to be analyzed, corresponds to an initial measurement waveform, and the pulse voltage is a voltage input from a test end of a particle detector.

[0045] In some embodiments, the initial measurement signal is the signal to be analyzed, and the initial measurement signal corresponds to an initial measurement waveform. Thus, in subsequent processes, the initial measurement signal can be reconstructed to accurately analyze the initial measurement signal.

[0046] It should be noted that the initial measurement waveform can be preset into the particle detector by a technician. Thus, the particle detector can directly obtain the initial measurement signal and perform subsequent steps. Alternatively, the initial measurement signal can also be a signal obtained after the particle detector statistically analyzes any particles. The embodiments of the present application do not limit this.

[0047] In some embodiments, the theoretical expression of the response function of the particle detector is shown in the following formula (1);

[0048]

[0049] Where f(t) represents the response function, A represents the amplitude of the signal, t0 represents the starting time of the signal, τ represents the width of the signal, and θ(t - t0) represents the step function. The step function is used to ensure that the signal is 0 when t < t0. However, since the response function represented by formula (1) cannot well explain the undershoot of the signal, it is necessary to determine the first response function based on the initial measurement signal and the pulse voltage to improve the accuracy of analyzing the initial measurement signal.

[0050] In some embodiments, the first response function can be determined through the following process: subtract the base noise from the initial measurement signal to obtain the initial measurement signal after removing the noise; divide the initial measurement signal after removing the noise by the current intensity, where the current intensity is the current intensity corresponding to the pulse voltage, to obtain the first response function.

[0051] Since the initial measurement signal may be interfered by noise, if the noise is not eliminated, it may cause a significant reduction in the accuracy and reliability of the analysis result of the initial measurement signal. Therefore, it is necessary to subtract the base noise from the initial measurement signal to obtain the initial measurement signal after removing the noise. The base noise can be preset by a technician. In this way, the base noise can be directly obtained to continue the subsequent process.

[0052] Moreover, a technician can also input a pulse voltage at the test end of the particle detector. Thus, after the particle detector obtains the initial measurement signal after removing the noise, the particle detector can directly obtain the current intensity corresponding to the pulse voltage and use the current intensity of the initial measurement signal after removing the noise to obtain the first response function.

[0053] Step 102: Perform full peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determine the amplitude and width corresponding to the initial measurement signal based on the first measurement waveform.

[0054] In the related art, only fitting the front linear region of the waveform is not only not applicable to the case of a more complex waveform, but also results in low accuracy of the relevant information of the acquired signal. Therefore, after acquiring the first response function, the particle accelerator can perform full-peak fitting on the initial measurement waveform according to the first response function, so that the more accurate amplitude and width of the initial measurement signal can be obtained according to the first measurement waveform after full-peak fitting.

[0055] As an example, the position of the peak in the initial measurement waveform can be identified first, and based on the identified peaks, the initial parameters of each peak, such as position, width and height, etc., can be estimated, and then the first response function can be used as a fitting model, and then a loss function can be defined. The loss function is usually the sum of squares of the error between the actual data and the fitting model, and a function optimization algorithm (such as nonlinear least squares method) is used to adjust the first response function, so as to perform iterative fitting, that is, repeatedly adjust the parameters to minimize the loss function. The result verification can also be performed to determine that there is no systematic deviation. Post-processing can also be performed afterwards, and the amplitude and width of the initial measurement signal can be determined based on the first measurement waveform obtained by fitting.

[0056] Step 103: performing time-frequency transformation on the first measurement signal and the first response function to obtain a second measurement signal and a second response function, and determining an input signal based on the second measurement signal and the second response function, where the input signal is a signal that needs to be filtered.

[0057] In some embodiments, the first measurement signal and the first response function are in the time domain, but it is difficult to see the characteristics of the first measurement signal and the first response function in the time domain, therefore, the first measurement signal and the first response function may be subjected to a time-frequency transform to obtain a second measurement signal and a second response function. That is, because the second measurement signal and the second response function are in the frequency domain, the initial measurement signal may be analyzed more accurately according to the second measurement signal and the second response function in the subsequent process. For example, the time-frequency transform may be a Fourier transform, that is, the first measurement signal may be subjected to a Fourier transform to obtain the second measurement signal, and similarly, the first response function may be subjected to a Fourier transform to obtain the second response function.

[0058] After obtaining the second measurement signal and the second response function, an input signal may be determined based on the second measurement signal and the second response function. The input signal is a signal to be input into the filter, that is, the input signal is a signal that needs to be filtered.

[0059] In some embodiments, a ratio of the second measurement signal and the second response function may be used as the input signal.

[0060] For example, the input signal may be determined according to the following formula (2):

[0061]

[0062] Wherein, S(ω) represents the input signal, M(ω) represents the second measurement signal, and R(ω) represents the second response function.

[0063] In some embodiments, before performing time-frequency transformation on the first measurement signal and the first response function, a signal improvement operation may be performed on the first measurement signal, and the signal improvement operation includes time truncation, smoothing and zero padding; and a function improvement operation may be performed on the first response function, and the function improvement operation includes time truncation and zero padding.

[0064] In order to obtain a more accurate reconstruction result of the initial measurement signal in the subsequent process, before the first measurement signal is subjected to time-frequency transformation, a signal improvement operation may be performed on the first measurement signal, and the signal improvement operation may include time truncation, smoothing and zero padding. Since the first measurement signal may be relatively long, it may be difficult to perform time-frequency transformation on the entire first measurement signal. Therefore, the first measurement signal may be subjected to time truncation, that is, a certain time period in the first measurement signal may be selected for subsequent processing, which can not only help remove irrelevant parts in the first measurement signal, but also remove some noise; the first measurement signal after time truncation may also be subjected to smoothing processing, thereby avoiding the oscillation effect caused by time truncation, so as to obtain a more accurate signal reconstruction result for the initial measurement signal in the subsequent process; zero padding may increase the sampling time and avoid noise caused by discrete operations.

[0065] Similarly, before performing time-frequency transformation on the first response function, a function improvement operation may be performed on the first response function, and the function improvement operation may include time truncation and zero padding. In this way, not only irrelevant parts in the first response function can be eliminated, but also noise caused by discrete operation can be avoided.

[0066] It should be noted that the above description is based on the signal improvement operation including time truncation, smoothing and zero padding, and the function improvement operation including time truncation and zero padding. Alternatively, in an application, the signal improvement operation and / or the function improvement operation may include more, fewer or other steps. In other words, the embodiments of the present application do not limit the signal improvement operation and the function improvement operation.

[0067] Step 104: Determine the initial parameters of the first filter function based on the input signal, and map the initial parameters to the parameter space through an activation function to optimize the initial parameters to obtain optimized parameters and a second filter function. The parameter space is the parameter space after optimization by the optimization algorithm, and the parameters of the second filter function are the optimized parameters.

[0068] Based on the above description, it can be known that the input signal is a signal that needs to be filtered. The purpose of filtering the input signal is to reduce the signal distortion caused by noise as much as possible. Therefore, the initial parameters of the first filter function can be determined based on the input signal.

[0069] In some embodiments, the maximum value of the local signal-to-noise ratio of the input signal can be used as the initial parameter of the filter function. The signal-to-noise ratio is the ratio of the input signal strength to the noise strength. The part with the largest local signal-to-noise ratio indicates that the received noise strength is the smallest. Therefore, using the maximum value of the local signal-to-noise ratio as the initial parameter of the first filter function can effectively reduce the signal distortion caused by noise.

[0070] In addition, in some embodiments, in order to further improve the signal-to-noise ratio of the signal, the initial parameters can also be optimized. The initial parameters can be mapped to the parameter space through an activation function, so as to optimize the initial parameters. By mapping the initial parameters to the parameter space through the activation function, it can be ensured that the parameters obtained after the optimization of the initial parameters are within the expected range. Moreover, the parameter space is the parameter space after being optimized by the optimization algorithm, so the values ​​included in the parameter space are more accurate, thereby enabling the initial parameters to be optimized more accurately.

[0071] In some embodiments, the activation function is a Sigmoid function, and / or the optimization algorithm is an Adam method.

[0072] Among them, the Sigmoid function is a commonly used activation function, and its shape is similar to the letter "S", hence the name. The mathematical expression of the Sigmoid function is shown in the following formula (3);

[0073]

[0074] The Adam method is a commonly used optimization algorithm, which is widely used in training deep learning models. It combines the advantages of gradient descent and can adaptively adjust the learning rate to improve training efficiency and stability.

[0075] It should be noted that the above description is based on the activation function being the Sigmoid function and the optimization algorithm being the Adam method. Alternatively, in an application, the activation function may be other functions and the optimization algorithm may be other algorithms, which are not limited in the embodiments of the present application.

[0076] Continuing with the above description, after optimizing the initial parameters, optimized parameters can be obtained, and the optimized parameters are applied to the first filter function to obtain the second filter function. In this way, in the subsequent process, the input signal can be filtered according to the second filter function.

[0077] Step 105: Filter the input signal according to the second filter function to obtain an output signal.

[0078] In some embodiments, the input signal may be multiplied by the second filter function to obtain an output signal. For example, the input signal may be filtered according to the following formula (4):

[0079]

[0080] in, represents the output signal, S(ω) represents the input signal, F(ω) represents the second filter function, M(ω) represents the second measurement signal, and R(ω) represents the second response function. Since the parameters of the second filter function are optimized parameters, the signal distortion caused by noise can be further reduced, thereby reducing and the error of S(ω).

[0081] Step 106: Perform an inverse time-frequency transform on the output signal to obtain a target signal, and determine the start time corresponding to the initial measurement signal based on a target waveform, where the target waveform is a waveform corresponding to the target signal.

[0082] In some embodiments, since the output signal is in the frequency domain, in order to obtain the start time of the initial measurement signal, it is necessary to transfer the output signal to the time domain, that is, to perform an inverse time-frequency transform on the output signal, thereby obtaining a target signal, so as to complete the reconstruction of the initial measurement signal. For example, the inverse time-frequency transform may be an inverse Fourier transform, that is, the output signal may be transferred from the frequency domain to the time domain by an inverse Fourier transform to obtain the target signal.

[0083] Continuing with the above description, the target signal corresponds to a target waveform, and thus, the start time corresponding to the initial measurement signal can be determined from the target waveform.

[0084] It should be noted that the above description is based on the inverse time-frequency transform being the inverse Fourier transform, or, in application, the inverse time-frequency transform may also be other transformation methods, which is not limited in the embodiments of the present application.

[0085] In the embodiment of the present application, full-peak fitting is performed on the initial measurement waveform, which is not only applicable to the case where the waveform is more complex, but also can obtain more complete information of the waveform. Therefore, according to the initial measurement waveform after full-peak fitting, that is, the first measurement waveform, the accuracy of the acquired amplitude and width information can be improved; in addition, after determining the initial parameters of the first filter function based on the input signal, the initial parameters will be mapped to the parameter space through the activation function, so as to optimize the initial parameters, and the input signal is filtered using the second filter function whose parameters are the optimized parameters, which can improve the signal-to-noise ratio, thereby improving the accuracy of the analysis of the initial measurement signal; and after filtering the input signal through the second filter function to obtain the output signal, the output signal can also be subjected to a time-frequency inverse transform to obtain the target signal, so that the start time of the initial measurement signal can be obtained according to the target waveform corresponding to the target signal, which can improve the accuracy of the acquired start time. In summary, the embodiment of the present application can improve the accuracy of the acquired signal width information, amplitude information and start time, thereby improving the accuracy of the signal analysis.

[0086] Figure 2 is a schematic diagram of a signal analysis device for a particle detector provided in an embodiment of the present application, see Figure 2 The signal analysis device includes: an acquisition module 201, a fitting module 202, a first transformation module 203, a determination module 204, a filtering module 205 and a second transformation module 206.

[0087] An acquisition module 201 is used to acquire an initial measurement signal and determine a first response function based on the initial measurement signal and a pulse voltage, wherein the initial measurement signal is a signal to be analyzed and corresponds to an initial measurement waveform, and the pulse voltage is a voltage input from a test end of a particle detector;

[0088] A fitting module 202 is used to perform full peak fitting on the initial measurement waveform based on a first response function to obtain a first measurement waveform, and determine the amplitude and width corresponding to the initial measurement signal based on the first measurement waveform;

[0089] A first transformation module 203 is used to perform time-frequency transformation on the first measurement signal and the first response function to obtain a second measurement signal and a second response function, and determine an input signal based on the second measurement signal and the second response function, where the input signal is a signal that needs to be filtered;

[0090] A determination module 204 is used to determine initial parameters of the first filter function based on the input signal, and map the initial parameters to a parameter space through an activation function to optimize the initial parameters to obtain optimized parameters and a second filter function, wherein the parameter space is a parameter space optimized by an optimization algorithm, and the parameters of the second filter function are optimized parameters;

[0091] A filtering module 205, configured to filter the input signal according to a second filtering function to obtain an output signal;

[0092] The second transformation module 206 is used to perform time-frequency inverse transformation on the output signal to obtain a target signal, and determine the start time corresponding to the initial measurement signal based on a target waveform, where the target waveform is a waveform corresponding to the target signal.

[0093] In the embodiment of the present application, full-peak fitting is performed on the initial measurement waveform, which is not only applicable to the case where the waveform is more complex, but also can obtain more complete information of the waveform. Therefore, according to the initial measurement waveform after full-peak fitting, that is, the first measurement waveform, the accuracy of the acquired amplitude and width information can be improved; in addition, after determining the initial parameters of the first filter function based on the input signal, the initial parameters will be mapped to the parameter space through the activation function, so as to optimize the initial parameters, and the input signal is filtered using the second filter function whose parameters are the optimized parameters, which can improve the signal-to-noise ratio, thereby improving the accuracy of the analysis of the initial measurement signal; and after filtering the input signal through the second filter function to obtain the output signal, the output signal can also be subjected to a time-frequency inverse transform to obtain the target signal, so that the start time of the initial measurement signal can be obtained according to the target waveform corresponding to the target signal, which can improve the accuracy of the acquired start time. In summary, the embodiment of the present application can improve the accuracy of the acquired signal width information, amplitude information and start time, thereby improving the accuracy of the signal analysis.

[0094] It should be noted that: when the signal analysis device provided in the above embodiment performs signal analysis, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the signal analysis device is divided into different functional modules to complete all or part of the functions described above. In addition, the signal analysis device of the particle detector provided in the above embodiment and the signal analysis method embodiment of the signal detector described above belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0095] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer program. When all or part of the functions in the above-mentioned embodiments are implemented by computer program, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, CD, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer program, the program can also be stored in a storage medium such as a server, another computer, disk, CD, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0096] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A signal analysis method for a particle detector, characterized in that: The method comprises: Acquire an initial measurement signal, and determine a first response function based on the initial measurement signal and a pulse voltage, wherein the initial measurement signal is a signal to be analyzed, the initial measurement signal corresponds to an initial measurement waveform, and the pulse voltage is a voltage input from a test end of the particle detector; Performing full-peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determining the amplitude and width corresponding to the initial measurement signal based on the first measurement waveform; Performing time-frequency transformation on the first measurement signal and the first response function to obtain a second measurement signal and a second response function, and determining an input signal based on the second measurement signal and the second response function, wherein the input signal is a signal that needs to be filtered; Determine initial parameters of a first filter function based on the input signal, and map the initial parameters to a parameter space through an activation function to optimize the initial parameters to obtain optimized parameters and a second filter function, wherein the parameter space is a parameter space optimized by an optimization algorithm, and the parameters of the second filter function are the optimized parameters; Filtering the input signal according to the second filter function to obtain an output signal; The output signal is subjected to an inverse time-frequency transform to obtain a target signal, and a start time corresponding to the initial measurement signal is determined based on a target waveform, wherein the target waveform is a waveform corresponding to the target signal.

2. The method according to claim 1, characterized in that Before performing time-frequency transformation on the first measurement signal and the first response function, the method further includes: Performing a signal improvement operation on the first measurement signal, wherein the signal improvement operation includes time truncation, smoothing and zero padding; A function improvement operation is performed on the first response function, wherein the function improvement operation includes time truncation and zero padding.

3. The method according to claim 1, characterized in that The activation function is a Sigmoid function, and / or the optimization algorithm is an Adam method.

4. The method according to claim 1, characterized in that Determining the input signal based on the second measurement signal and the second response function comprises: A ratio of the second measurement signal to the second response function is used as an input signal.

5. The method according to claim 4, characterized in that The determining of initial parameters of the filter function based on the input signal comprises: The maximum value of the local signal-to-noise ratio of the input signal is used as the initial parameter of the filter function.

6. The method according to claim 5, characterized in that The filtering the input signal according to the second filter function to obtain an output signal comprises: The input signal is multiplied by the second filter function to obtain the output signal.

7. The method according to claim 1, characterized in that Determining a first response function based on the initial measurement signal and the pulse voltage comprises: Subtracting basic noise from the initial measurement signal to obtain the initial measurement signal after noise removal; The first response function is obtained by dividing the initial measurement signal after noise removal by the current intensity, where the current intensity is the current intensity corresponding to the pulse voltage.

8. A signal analysis device for a particle detector, characterized in that: The device comprises: An acquisition module, the acquisition module is used to acquire an initial measurement signal, and determine a first response function based on the initial measurement signal and a pulse voltage, the initial measurement signal is a signal to be analyzed, the initial measurement signal corresponds to an initial measurement waveform, and the pulse voltage is a voltage input from a test end of the particle detector; A fitting module, the fitting module is used to perform full-peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determine the amplitude and width corresponding to the initial measurement signal based on the first measurement waveform; A first transformation module, the first transformation module is used to perform time-frequency transformation on the first measurement signal and the first response function to obtain a second measurement signal and a second response function, and determine an input signal based on the second measurement signal and the second response function, wherein the input signal is a signal that needs to be filtered; A determination module, the determination module is used to determine initial parameters of a first filter function based on the input signal, and map the initial parameters to a parameter space through an activation function to optimize the initial parameters to obtain optimized parameters and a second filter function, the parameter space is a parameter space optimized by an optimization algorithm, and the parameters of the second filter function are the optimized parameters; A filtering module, wherein the filtering module is used to filter the input signal according to the second filtering function to obtain an output signal; The second transformation module is used to perform time-frequency inverse transformation on the output signal to obtain a target signal, and determine the start time corresponding to the initial measurement signal based on a target waveform, wherein the target waveform is a waveform corresponding to the target signal.

9. A computer-readable storage medium, characterized in that: A computer program is stored on the medium, and the computer program can be executed by a processor to implement the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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