Signal analysis method, device and storage medium of particle detector
By reconstructing the TPC particle detector signal using full-peak fitting and filtering techniques, the problems of analysis accuracy and time precision in complex waveform conditions in traditional methods are solved, achieving higher-precision signal analysis.
Patent Information
- Application Number
- CN202411772625.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-12-09
- Estimated Expiration
- 2044-12-04
AI Technical Summary
Traditional TPC particle detector signal processing methods cannot obtain complete waveform information when the waveform is complex, resulting in reduced analysis accuracy and timing offset issues that affect time precision.
The initial measurement signal is reconstructed using full-peak fitting technology, which includes determining the first response function, time-frequency transformation, filtering, and inverse time-frequency transformation. Signal analysis is then performed by optimizing the parameter space and filtering function.
It improves the accuracy and time resolution of signal analysis, enhances the ability to process complex waveforms, and improves the accuracy of amplitude, width, and start time acquisition.
Smart Images
Figure CN119807723B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of detectors, in particular to a signal analysis method and device of a particle detector and a storage medium. BACKGROUND
[0002] The signal processing method of a conventional TPC (Time Projection Chamber) particle detector often adopts a waveform front fitting technology, which obtains the width, amplitude and starting time of a waveform by fitting the linear region of the waveform front, so as to analyze the waveform. However, since the waveform front fitting technology only obtains the linear region of the waveform front, in the case of a relatively complex waveform, a larger error may occur due to the inability to obtain more complete waveform information, thereby reducing the accuracy of signal analysis. In addition, for a waveform with a long starting time, there may be a timing offset problem when using the waveform front fitting technology to process the signal, thereby causing a decrease in time accuracy. SUMMARY
[0003] The present application provides a signal analysis method and device of a particle detector and a storage medium, which can not only be suitable for a relatively complex waveform, but also can improve the accuracy of the obtained waveform information, thereby accurately analyzing the signal. The technical solution is as follows:
[0004] In one aspect, a signal analysis method of a particle detector is provided, the method comprising:
[0005] obtaining an initial measurement signal and determining a first response function based on the initial measurement signal and a pulse voltage, the initial measurement signal being a signal to be analyzed, the initial measurement signal corresponding to an initial measurement waveform, and the pulse voltage being 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 an amplitude and a 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, the input signal being a signal that needs to be filtered;
[0008] determining an initial parameter of a first filter function based on the input signal, and mapping the initial parameter to a parameter space through an activation function to optimize the initial parameter to obtain an optimized parameter and a second filter function, the parameter space being a parameter space after optimization by an optimization algorithm, and the parameter of the second filter function being the optimized parameter;
[0009] filtering the input signal according to the second filter function to obtain an output signal;
[0010] performing time-frequency inverse transformation on the output signal to obtain a target signal, and determining a starting time corresponding to the initial measurement signal based on a target waveform, the target waveform being a waveform corresponding to the target signal.
[0011] Optionally, before the time-frequency transformation on the first measurement signal and the first response function, the method further comprises:
[0012] performing a signal improvement operation on the first measurement signal, the signal improvement operation including time truncation, smoothing processing and zero padding;
[0013] performing a function improvement operation on the first response function, the function improvement operation including 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, the determination of the input signal based on the second measurement signal and the second response function comprises:
[0016] taking a ratio of the second measurement signal and the second response function as the input signal.
[0017] Optionally, the determination of the initial parameter of the filter function based on the input signal comprises:
[0018] taking a maximum value of a local signal-to-noise ratio of the input signal as the initial parameter of the filter function.
[0019] Optionally, the filtering of the input signal according to the second filter function to obtain an output signal comprises:
[0020] multiplying the input signal by the second filter function to obtain the output signal.
[0021] Optionally, the determination of the first response function based on the initial measurement signal and the pulse voltage comprises:
[0022] Subtracting a base noise from the initial measurement signal to obtain a noise-removed initial measurement signal;
[0023] Dividing the noise-removed initial measurement signal by a current intensity corresponding to the pulse voltage to obtain a first response function.
[0024] In another aspect, a signal analysis device of a particle detector is provided, the device comprising:
[0025] An acquisition module configured 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 being a signal to be analyzed, the initial measurement signal corresponding to an initial measurement waveform, and the pulse voltage being a voltage input from a test terminal of the particle detector;
[0026] A fitting module configured to perform a full peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determine an amplitude and a width corresponding to the initial measurement signal based on the first measurement waveform;
[0027] A first transformation module configured to perform a 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, the input signal being a signal that needs to be filtered;
[0028] A determination module configured to determine an initial parameter of a first filter function based on the input signal, and map the initial parameter to a parameter space through an activation function to optimize the initial parameter to obtain an optimized parameter and a second filter function, the parameter space being a parameter space after optimization by an optimization algorithm, and a parameter of the second filter function being the optimized parameter;
[0029] A filtering module configured to filter the input signal according to the second filter function to obtain an output signal;
[0030] A second transformation module configured to perform a time-frequency inverse 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, the target waveform being a waveform corresponding to the target signal.
[0031] In another aspect, a computer-readable storage medium is provided, the storage medium storing a computer program, the computer program being executable by a processor to implement the steps of the signal analysis method of the particle detector described above.
[0032] In another aspect, a computer program product comprising instructions which, when executed on a computer, cause the computer to carry out the steps of the signal analysis method of the particle detector described above is provided.
[0033] The technical solutions provided in the present application can bring at least the following beneficial effects:
[0034] By performing full-peak fitting on the initial measurement waveform, not only can it be applied to a more complex waveform, but also more complete information of the waveform can be obtained, and thus, according to the initial measurement waveform, i.e., the first measurement waveform, after full-peak fitting, the accuracy of the obtained 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 are mapped to the parameter space through the activation function, so as to optimize the initial parameters, and the second filter function with the optimized parameters is used to filter the input signal, which can improve the signal-to-noise ratio, and further improve the accuracy of analyzing the initial measurement signal; and after filtering the input signal through the second filter function to obtain the output signal, the time-frequency inverse transform can be performed on the output signal to obtain the target signal, so that the starting time of the initial measurement signal can be obtained according to the target waveform corresponding to the target signal, and the accuracy of the obtained starting time can be improved. In summary, the present application can improve the accuracy of the width information, amplitude information and starting time of the obtained signal, thereby improving the accuracy of analyzing the signal. BRIEF DESCRIPTION OF DRAWINGS
[0035] Figure 1 A flowchart of a signal analysis method of a particle detector provided by an embodiment of the present application;
[0036] Figure 2 A structural schematic diagram of a signal analysis device of a particle detector provided by an embodiment of the present application. DETAILED DESCRIPTION
[0037] The present application will be further described in details by specific embodiments in combination with the drawings. In different embodiments, similar elements are associated with similar element reference numbers. In the following embodiments, many details are described in order to make the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different cases, or can be replaced by other elements, materials or methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid the core part of the present application being overwhelmed by too much description, and it is not necessary for those skilled in the art to describe these related operations in detail according to the description in the specification and the general technical knowledge in the art.
[0038] In addition, features described in the specification, operations or characteristics can be combined in any appropriate manner to form various embodiments. Meanwhile, the steps or actions in the method description can also be sequentially changed or adjusted in a manner that can be apparent 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 do not mean that the sequence is necessary, unless otherwise stated that a certain sequence must be followed.
[0039] The serial numbers of components in this paper, such as "first", "second", etc., are only used to distinguish the described objects, and do not have any order or technical meaning. The "connection" and "coupling" in this application include direct and indirect connection (coupling) unless otherwise specified.
[0040] The signal processing method of the conventional TPC particle detector often uses waveform front fitting technology. The waveform front fitting technology obtains the width, amplitude and starting time of the waveform by fitting the linear region of the waveform front, so as to analyze the waveform. However, since the waveform front fitting technology only obtains the linear region of the waveform front, in the case of a complex waveform, a larger error may occur due to the inability to obtain more complete waveform information, thereby reducing the accuracy of signal analysis. In addition, for a waveform with a long starting time, there may be a timing offset problem when using the waveform front fitting technology to process the signal, thereby causing a decrease in time accuracy.
[0041] Based on this, the embodiment of the present application provides a signal analysis method of a particle detector. The signal analysis method of the particle detector can reconstruct the initial measurement signal, improve the accuracy of signal analysis of the TPC particle detector, for example, improve the signal analysis capability and time resolution accuracy of the MTPC (Multi-purpose Time Projection Chamber) particle detector in a complex situation, thereby enhancing the overall performance of the MTPC particle detector.
[0042] Next, the signal analysis method of the particle detector provided by the embodiment of the present application will be explained in detail.
[0043] Figure 1 is a flowchart of a signal analysis method of a particle detector provided by the embodiment of the present application. The method is applied to a particle detector. Please refer to Figure 1 , the method includes the following steps.
[0044] Step 101: obtaining an initial measurement signal, and determining a first response function based on the initial measurement signal and a pulse voltage, the initial measurement signal being a signal to be analyzed, the initial measurement signal corresponding to an initial measurement waveform, and the pulse voltage being a voltage input from a test end of the particle detector.
[0045] In some embodiments, the initial measurement signal is a signal to be analyzed, and the initial measurement signal corresponds to an initial measurement waveform, so that the initial measurement signal can be accurately analyzed by reconstructing the initial measurement signal in a subsequent process.
[0046] It should be noted that the initial measurement waveform can be pre-set in the particle detector by the technician, so that 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 by the particle detector after statistical analysis of 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 as shown in the following formula (1);
[0048]
[0049] Wherein, 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, which 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 by the following process: subtracting the basic noise from the initial measurement signal to obtain the initial measurement signal after removing the noise; dividing the initial measurement signal after removing the noise by the current intensity to obtain the first response function, and the current intensity is the current intensity corresponding to the pulse voltage.
[0051] Since the initial measurement signal can be disturbed by noise, if the noise is not eliminated, it can greatly reduce the accuracy and reliability of the analysis result of the initial measurement signal, so it is necessary to subtract the basic noise from the initial measurement signal to obtain the initial measurement signal after removing the noise. The basic noise can be pre-set by the technician, so that the basic noise can be directly obtained to continue the subsequent process.
[0052] Moreover, the technician can also input the pulse voltage at the test end of the particle detector, so that the particle detector can directly obtain the current intensity corresponding to the pulse voltage after obtaining the initial measurement signal after removing the noise, and the current intensity of the initial measurement signal after removing the noise, thereby obtaining the first response function.
[0053] Step 102: 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 of the initial measurement signal based on the first measurement waveform.
[0054] In the related art, only the front linear region of the waveform is fitted, which is not suitable for complex waveforms and can result in low accuracy of the obtained signal information. Therefore, the particle accelerator can perform full peak fitting on the initial measurement waveform based on the first response function after obtaining the first response function. In this way, the amplitude and width of the initial measurement signal can be obtained more accurately based on the first measurement waveform after full peak fitting.
[0055] As an example, the positions of the peaks in the initial measurement waveform can be identified first, and the initial parameters of each peak, such as position, width, and height, can be estimated based on the identified peaks. Then, the first response function is used as a fitting model, and a loss function is defined, which is usually the sum of the squares of the errors between the actual data and the fitting model. A function optimization algorithm such as the nonlinear least squares method is used to adjust the first response function, thereby performing iterative fitting, i.e., repeatedly adjusting the parameters to minimize the loss function. Result verification can also be performed to determine whether there is systematic bias. Post-processing can also be performed, and the amplitude and width of the initial measurement signal can be determined based on the fitted first measurement waveform.
[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, the input signal being 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 can be subjected to time-frequency transformation to obtain the second measurement signal and the second response function. That is, because the second measurement signal and the second response function are in the frequency domain, the initial measurement signal can be more accurately analyzed based on the second measurement signal and the second response function in subsequent processes. For example, the time-frequency transformation can be Fourier transformation, that is, the first measurement signal can be subjected to Fourier transformation to obtain the second measurement signal, and similarly, the first response function can also be subjected to Fourier transformation to obtain the second response function.
[0058] After obtaining the second measurement signal and the second response function, the input signal can also be determined based on the second measurement signal and the second response function, the input signal being a signal to be input to the filter, i.e., the input signal being a signal that needs to be filtered.
[0059] In some embodiments, a ratio of the second measurement signal and the second response function can be taken as the input signal.
[0060] For example, the input signal can be determined according to the following formula (2);
[0061]
[0062] where 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 the time-frequency transformation of the first measurement signal and the first response function, a signal improvement operation can also be performed on the first measurement signal, which includes time truncation, smoothing processing, and zero padding; and a function improvement operation can also be performed on the first response function, which includes time truncation and zero padding.
[0064] In order to obtain a more accurate result of reconstructing the initial measurement signal in the subsequent process, a signal improvement operation can also be performed on the first measurement signal before the time-frequency transformation of the first measurement signal, which can include time truncation, smoothing processing, and zero padding. Since the first measurement signal can be relatively long, it can be difficult to perform time-frequency transformation on the entire first measurement signal, and therefore, time truncation can be performed on the first measurement signal, that is, a certain time period in the first measurement signal is selected for subsequent processing, which not only helps to remove irrelevant parts in the first measurement signal, but also removes part of the noise; the first measurement signal after time truncation can also be subjected to smoothing processing, so as to avoid the oscillation effect caused by time truncation, thereby obtaining a more accurate signal reconstruction result of the initial measurement signal in the subsequent process; zero padding can improve the sampling time and avoid noise caused by discrete operation.
[0065] Similarly, before the time-frequency transformation of the first response function, a function improvement operation can also be performed on the first response function, which can include time truncation and zero padding. In this way, not only can irrelevant parts in the first response function be removed, but also noise caused by discrete operation can be avoided.
[0066] It should be noted that the above is described in the case where the signal improvement operation includes time truncation, smoothing processing, and zero padding, and the function improvement operation includes time truncation and zero padding, or in applications, the signal improvement operation and / or the function improvement operation can also include more, fewer, or other steps. That is, the signal improvement operation and the function improvement operation are not limited in the embodiments of the present application.
[0067] Step 104: determining an initial parameter of the first filter function based on the input signal, and mapping the initial parameter to a parameter space through an activation function to optimize the initial parameter to obtain an optimized parameter and a second filter function, the parameter space being a parameter space after optimization by an optimization algorithm, and the parameter of the second filter function being the optimized parameter.
[0068] Based on the above description, the input signal is a signal that needs to be filtered. Filtering the input signal is to reduce signal distortion caused by noise as much as possible, and therefore, the initial parameter 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 taken 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, and the part with the maximum local signal-to-noise ratio indicates the minimum received noise strength. Therefore, taking the maximum value of the local signal-to-noise ratio as the initial parameter of the first filter function can effectively reduce signal distortion caused by noise.
[0070] In addition, in some embodiments, to further improve the signal-to-noise ratio of the signal, the initial parameter can also be optimized. The initial parameter can be mapped to a parameter space through an activation function to optimize the initial parameter. Mapping the initial parameter to the parameter space through the activation function can ensure that the parameter obtained after optimizing the initial parameter is within the expected range. Moreover, the parameter space is a parameter space after optimization by an optimization algorithm, and therefore, the values included in the parameter space are more accurate, thereby enabling more accurate optimization of the initial parameter.
[0071] In some embodiments, the activation function is a Sigmoid function, and / or the optimization algorithm is an Adam method.
[0072] 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, thereby improving the training efficiency and stability.
[0075] It should be noted that the above is described with the activation function being a Sigmoid function and the optimization algorithm being an Adam method, or in applications, the activation function can also be other functions, and the optimization algorithm can also be other algorithms, which are not limited by the embodiments of the present application.
[0076] Continuing the foregoing description, after the initial parameters are optimized, the optimized parameters can be obtained, and the optimized parameters are applied to the first filter function, and the second filter function can be obtained, so that in the subsequent process, the input signal can be filtered according to the second filter function.
[0077] Step 105: filtering the input signal according to the second filter function to obtain an output signal.
[0078] In some embodiments, the input signal can be multiplied by the second filter function to obtain the output signal. For example, the input signal can be filtered according to the following formula (4);
[0079]
[0080] wherein, 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 the optimized parameters, the signal distortion caused by the noise can be further reduced, thereby reducing and the error of S(ω).
[0081] Step 106: performing time-frequency inverse transformation on the output signal to obtain a target signal, and determining the starting time corresponding to the initial measurement signal based on a target waveform corresponding to the target signal.
[0082] In some embodiments, since the output signal is in the frequency domain, in order to obtain the starting time of the initial measurement signal, the output signal needs to be converted to the time domain, that is, the time-frequency inverse transformation is performed on the output signal, thereby obtaining the target signal to complete the reconstruction of the initial measurement signal. For example, the time-frequency inverse transformation can be inverse Fourier transformation, that is, the output signal can be converted from the frequency domain to the time domain by inverse Fourier transformation to obtain the target signal.
[0083] Continuing the foregoing description, and the target signal corresponds to a target waveform, so that the starting time corresponding to the initial measurement signal can be determined from the target waveform.
[0084] It should be noted that the above is described by taking the time-frequency inverse transformation as the inverse Fourier transformation, or in application, the time-frequency inverse transformation can also be other transformation methods, and the embodiments of the present application do not limit this.
[0085] In the embodiment of the present application, the initial measurement waveform is subjected to full peak fitting, which can be applicable to a more complex waveform and can obtain more complete information of the waveform. Therefore, the accuracy of the obtained amplitude and width information can be improved according to the initial measurement waveform, i.e., the first measurement waveform, after full peak fitting. In addition, after determining the initial parameter of the first filter function based on the input signal, the initial parameter is mapped to the parameter space through the activation function, so as to optimize the initial parameter and filter the input signal by using the second filter function with the optimized parameter. The signal-to-noise ratio can be improved, and the accuracy of analyzing the initial measurement signal can be improved. Furthermore, after filtering the input signal by the second filter function to obtain the output signal, the time-frequency inverse transformation can be performed on the output signal to obtain the target signal. Therefore, the starting time of the initial measurement signal can be obtained according to the target waveform corresponding to the target signal, and the accuracy of the obtained starting time can be improved. In summary, the accuracy of the width information, amplitude information and starting time of the obtained signal can be improved, so as to improve the accuracy of analyzing the signal.
[0086] Figure 2 FIG. 1 is a structural schematic diagram of a signal analysis device of a particle detector provided by the embodiment of the present application, referring to Figure 2 The signal analysis device comprises 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] The acquisition module 201 is configured 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 being a signal to be analyzed, the initial measurement signal corresponding to an initial measurement waveform, and the pulse voltage being a voltage input from a test terminal of a particle detector.
[0088] The fitting module 202 is configured to perform full peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determine an amplitude and a width corresponding to the initial measurement signal based on the first measurement waveform.
[0089] The first transformation module 203 is configured 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, the input signal being a signal to be subjected to a filtering operation.
[0090] The determination module 204 is configured to determine an initial parameter of a first filter function based on the input signal, and map the initial parameter to a parameter space through an activation function to optimize the initial parameter, so as to obtain an optimized parameter and a second filter function, the parameter space being a parameter space after optimization by an optimization algorithm, and the parameter of the second filter function being the optimized parameter.
[0091] a filtering module 205, configured to filter the input signal according to a second filter function to obtain an output signal;
[0092] a second transforming module 206, configured to perform time-frequency inverse transform on the output signal to obtain a target signal, and determine a starting time corresponding to the initial measurement signal based on a target waveform corresponding to the target signal.
[0093] In the embodiments of the present application, the initial measurement waveform is subjected to full-peak fitting, which can not only be applicable to the case where the waveform is relatively complex, but also can obtain more complete information of the waveform. Therefore, according to the initial measurement waveform, i.e., the first measurement waveform, after full-peak fitting, the accuracy of the obtained 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 are mapped to the parameter space through the activation function, so as to optimize the initial parameters, and the input signal is filtered by using the second filter function with the optimized parameters, which can improve the signal-to-noise ratio, and further improve the accuracy of analyzing the initial measurement signal. Moreover, after filtering the input signal by the second filter function to obtain the output signal, the output signal is subjected to time-frequency inverse transform to obtain the target signal, so that the starting time of the initial measurement signal can be obtained according to the target waveform corresponding to the target signal, and the accuracy of the obtained starting time can be improved. In summary, the embodiments of the present application can improve the accuracy of the width information, amplitude information and starting time of the obtained signal, thereby improving the accuracy of analyzing the signal.
[0094] It should be noted that the signal analysis device provided in the above embodiments is only used as an example for the division of the above functional modules during signal analysis. In actual applications, the above functions can be completed by different functional modules according to needs, i.e., the internal structure of the signal analysis device is divided into different functional modules to complete all or part of the above described functions. In addition, the signal analysis device of the particle detector provided in the above embodiments and the signal analysis method of the signal detector provided in the above embodiments belong to the same concept, and the specific implementation process is described in detail in the method embodiments, which will not be described here.
[0095] Those skilled in the art can understand that all or part of the functions of various methods in the above embodiments can be realized by hardware or by a computer program. When all or part of the functions in the above embodiments are realized by a computer program, the program can be stored in a computer readable storage medium, which can include a read-only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, and the like. The above functions are realized by executing the program by a computer. For example, the program is stored in a memory of a device, and the above functions are realized by executing the program in the memory by a processor. In addition, when all or part of the functions in the above embodiments are realized by a computer program, the program can also be stored in a storage medium such as a server, another computer, a disk, an optical disk, a flash disk, or a mobile hard disk, and is saved in a memory of a local device by downloading or copying, or the system of the local device is updated, and the above functions are realized by executing the program in the memory by a processor.
[0096] The above application of specific examples to the present application is described, which is only used to help understand the present application and does not limit the present application. For those skilled in the art, according to the idea of the present application, a number of simple deductions, deformations or substitutions can be made.
Claims
1. A method of signal analysis of a particle detector, characterized by, The method comprises: acquiring an initial measurement signal, and determining a first response function based on the initial measurement signal and a pulse voltage, the initial measurement signal being a signal to be analyzed, the initial measurement signal corresponding to an initial measurement waveform, and the pulse voltage being 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 an amplitude and a 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, the input signal being a signal that needs to be filtered; determining initial parameters of a first filter function based on the input signal, and mapping 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 being a parameter space optimized through an optimization algorithm, and parameters of the second filter function being the optimized parameters; filtering the input signal according to the second filter function to obtain an output signal; performing time-frequency inverse transformation on the output signal to obtain a target signal, and determining a starting time corresponding to the initial measurement signal based on a target waveform, the target waveform being a waveform corresponding to the target signal.
2. The method of claim 1, wherein, Before the time-frequency transformation on the first measurement signal and the first response function, the method further comprises: performing a signal improvement operation on the first measurement signal, the signal improvement operation including time truncation, smoothing processing, and zero padding; performing a function improvement operation on the first response function, the function improvement operation including time truncation and zero padding.
3. The method of claim 1, wherein, The activation function is a Sigmoid function, and / or the optimization algorithm is an Adam method.
4. The method of claim 1, wherein, The determination of the input signal based on the second measurement signal and the second response function comprises: taking a ratio of the second measurement signal and the second response function as the input signal.
5. The method of claim 4, wherein, The determination of the initial parameters of the filter function based on the input signal comprises: taking a maximum value of a local signal-to-noise ratio of the input signal as the initial parameters of the filter function.
6. The method of claim 5, wherein, The filtering of the input signal according to the second filter function to obtain the output signal comprises: multiplying the input signal by the second filter function to obtain the output signal.
7. The method of claim 1, wherein, The determination of the first response function based on the initial measurement signal and the pulse voltage comprises: subtracting a basic noise from the initial measurement signal to obtain a noise-removed initial measurement signal; dividing the noise-removed initial measurement signal by a current intensity to obtain the first response function, the current intensity being a current intensity corresponding to the pulse voltage.
8. A signal analysis device of a particle detector, characterized by The device comprises: An acquisition module is configured 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 being a signal to be analyzed, the initial measurement signal corresponding to an initial measurement waveform, and the pulse voltage being a voltage input from a test terminal of the particle detector; A fitting module is configured to perform full peak fitting on the initial measurement waveform based on the first response function to obtain a first measurement waveform, and determine an amplitude and a width corresponding to the initial measurement signal based on the first measurement waveform; A first transformation module is configured 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, the input signal being a signal to be filtered; A determination module is configured to determine an initial parameter of a first filter function based on the input signal, and map the initial parameter to a parameter space through an activation function to optimize the initial parameter to obtain an optimized parameter and a second filter function, the parameter space being a parameter space after optimization by an optimization algorithm, and a parameter of the second filter function being the optimized parameter; A filtering module is configured to filter the input signal according to the second filter function to obtain an output signal; A second transformation module is configured to perform time-frequency inverse 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, the target waveform being a waveform corresponding to the target signal.
9. A computer-readable storage medium, characterized in that, The medium has stored thereon a computer program, which can be executed by a processor to implement the method of any one of claims 1-7.
10. A computer program product comprising computer programs and / or instructions, characterized in that, The computer program and / or instructions are executed by the processor to implement the method of any one of claims 1-7.
Citation Information
Patent Citations
Power spectrum analysis method, power spectrum analysis system and Gamma ray detection system
CN105301627A
Dynamic response analysis prober
CN108351377A