Method and device for eliminating signal noise, equipment and storage medium
By linearly interpolation and adjustment of the uneven periodic signal sequence, converting it into a uniform sequence, and filtering, the problem of insufficient noise cancellation in signals with large periodic differences is solved, and a more efficient noise cancellation effect is achieved.
Patent Information
- Application Number
- CN202311555817.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-21
- Publication Date
- 2025-05-23
Smart Images
Figure CN120030273A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of signal processing technology, and in particular to a method, device, equipment and storage medium for eliminating signal noise. Background Art
[0002] In the process of signal processing, signal filtering is a common operation step, which can eliminate the noise in the signal. There are many technologies to choose from. For example, the sliding window average method is used to filter various signals. The specific process is: take a window of equal length forward and backward at the position point of the original signal, and take the mean value of the original signal in the window as the output value of the position point.
[0003] The above signal noise elimination processing method has a good filtering effect on signals with uniform period, but there are currently some signals with large period differences, which may result in insufficient noise elimination. For example, for the spectral signal of optical critical dimension (OCD) measurement, the period of the low wavelength band is usually small, and the period of the high wavelength band is large. Summary of the invention
[0004] The present disclosure provides a method, device, equipment and storage medium for eliminating signal noise, which can eliminate noise for signals with large period differences. The technical solution is as follows:
[0005] In a first aspect, the present disclosure provides a method for eliminating signal noise, the method comprising: obtaining a first signal sequence, wherein the period of a first feature of the first signal sequence is uneven, performing linear interpolation processing based on the period of the first feature to obtain a first sequence, wherein the number of sequence values in the first sequence is the same as the number of signal values in the first signal sequence, and the first sequence is a periodic sequence or a frequency sequence, adjusting the sequence values in the first sequence to obtain a second sequence, interpolating a second signal sequence corresponding to a sequence with equal sequence value intervals based on the second sequence and the first signal sequence, filtering the second signal sequence to obtain a third signal sequence, and interpolating a signal value corresponding to each sequence value in the second sequence based on the third signal sequence, wherein the signal value group corresponding to each sequence value constitutes a signal sequence corresponding to the first signal sequence after noise elimination.
[0006] In the scheme shown in the present disclosure, the first signal sequence is converted into a second signal sequence corresponding to a sequence with equal intervals, so that when the signal sequence is filtered, the restrictions on the selection of the filter window can be reduced, thereby making it easier to select a suitable filter window, and through filtering processing, the noise can be eliminated as much as possible.
[0007] In an optional manner, the first signal sequence is a spectral sequence, in which the period of the first feature in the first wavelength range is smaller than the period of the first feature in the second wavelength range, the wavelength in the first wavelength range is smaller than the wavelength in the second wavelength range, the i-th sequence value in the first sequence is smaller than the i+1-th sequence value, i ranges from 1 to n-1, n is the number of sequence values in the first sequence, the first sequence is a periodic sequence, and the sequence values in the first sequence are adjusted to obtain the second sequence, including: reversing the sequence values in the first sequence, determining the first sequence value after the reversal processing as the first sequence value in the second sequence, and determining the sum of the first to j-th sequence values after the reversal processing as the j-th sequence value in the second sequence, and j ranges from 2 to n.
[0008] In the scheme shown in the present disclosure, for a spectrum sequence with uneven period, a second sequence is obtained by reversing and accumulating the first sequence, so that the difference between adjacent sequence values from the front to the back of the second sequence becomes smaller and smaller, thereby shortening the period distance of signal values with large period during interpolation.
[0009] In an optional manner, the first signal sequence is a spectral sequence, in which the period of the first feature in the first wavelength range is smaller than the period of the first feature in the second wavelength range, the wavelength in the first wavelength range is smaller than the wavelength in the second wavelength range, the i-th sequence value in the first sequence is greater than the i+1-th sequence value, i is between 1 and n-1, n is the number of sequence values in the first sequence, the first sequence is a frequency sequence, and the sequence values in the first sequence are adjusted to obtain the second sequence, including: determining the first sequence value in the first sequence as the first sequence value in the second sequence, determining the sum of the first to j-th sequence values in the first sequence as the j-th sequence value in the second sequence, and j is between 2 and n.
[0010] In the scheme shown in the present disclosure, for a spectrum sequence with uneven period, a second sequence is obtained by accumulating the first sequence, so that the difference between adjacent sequence values from the front to the back of the second sequence becomes smaller and smaller, thereby shortening the period distance of signal values with large period during interpolation.
[0011] In an optional manner, the method of interpolating a second signal sequence corresponding to a sequence with equal intervals of sequence values based on the second sequence and the first signal sequence includes: obtaining a third sequence, wherein the number of sequence values in the third sequence is greater than the number of signal values in the first signal sequence, the sequence values in the third sequence are arranged from small to large, and the intervals between every two adjacent sequence values are the same, establishing a first mapping relationship between sequence values in the second sequence and signal values in the first signal sequence, and interpolating a second signal sequence corresponding to the third sequence based on the first mapping relationship; the method of interpolating a signal value corresponding to each sequence value in the second sequence based on the third signal sequence includes: establishing a second mapping relationship between sequence values in the third sequence and signal values in the third signal sequence, and interpolating a signal value corresponding to each sequence value in the second sequence based on the second mapping relationship.
[0012] In an optional manner, the minimum sequence value in the third sequence is equal to the minimum sequence value in the first sequence, and the maximum sequence value is equal to the maximum sequence value in the second sequence, and the number of sequence values in the third sequence is equal to the ratio of the maximum sequence value in the second sequence to the minimum sequence value in the first sequence. In this way, the third sequence value is constructed based on the first sequence and the second sequence, so that the boundary of the interpolation range remains unchanged.
[0013] In an optional manner, the first feature is a peak, a trough, or an average value of a signal value of the first signal sequence.
[0014] In an optional manner, based on the period of the first feature, a linear interpolation process is performed to obtain a first sequence, including: performing a linear interpolation process on the period of the first feature to obtain a first periodic sequence, taking the inverse of the period in the first periodic sequence to obtain the first sequence, or taking the inverse of the period of the first feature to obtain the frequency of the first feature, and performing a linear interpolation process on the frequency of the first feature to obtain the first sequence. In this way, there are two ways to obtain the first sequence, which is easier to implement.
[0015] In a second aspect, the present disclosure provides a device for eliminating signal noise, which has the function of implementing the above-mentioned first aspect. The device includes at least one module, and the at least one module is used to implement the method for eliminating signal noise provided in the above-mentioned first aspect or any optional manner of the first aspect.
[0016] In some embodiments, the module in the device for eliminating signal noise is implemented by software, and the module in the device for eliminating signal noise is a program module. In other embodiments, the module in the device for eliminating signal noise is implemented by hardware or firmware.
[0017] In a third aspect, the present disclosure provides a computing device, comprising a processor and a memory, wherein the memory stores computer instructions, and the processor executes the computer instructions so that the computing device performs the method for eliminating signal noise provided in the above-mentioned first aspect or any optional method of the first aspect.
[0018] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing at least one computer instruction, which is read by a processor to enable a computing device to execute the method for eliminating signal noise provided in the first aspect or any optional method of the first aspect.
[0019] In a fifth aspect, the present disclosure provides a computer program product, the computer program product comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a security protection device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the security protection device performs the method for eliminating signal noise provided in the first aspect or any optional manner of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 is a schematic diagram of a system architecture provided by an exemplary embodiment of the present disclosure;
[0021] Figure 2 is a schematic diagram of a system architecture provided by another exemplary embodiment of the present disclosure;
[0022] Figure 3 is a schematic diagram of a system architecture provided by yet another exemplary embodiment of the present disclosure;
[0023] Figure 4 is a schematic diagram of the structure of a device provided by an exemplary embodiment of the present disclosure;
[0024] Figure 5 is a flow chart of a method for eliminating signal noise provided by an exemplary embodiment of the present disclosure;
[0025] Figure 6 is a schematic diagram of a process for eliminating spectral sequence noise provided by an exemplary embodiment of the present disclosure;
[0026] Figure 7 is a schematic diagram of a process for eliminating spectral sequence noise provided by another exemplary embodiment of the present disclosure;
[0027] Figure 8 is a schematic diagram of a spectrum sequence after noise elimination provided by an exemplary embodiment of the present disclosure;
[0028] Fig. 9It is a schematic diagram of the structure of a device for eliminating signal noise provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION
[0029] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.
[0030] Some terminology concepts involved in the embodiments of the present disclosure are explained below.
[0031] 1. Metrology: refers to the process of measuring certain key dimensions of manufactured devices after many process steps during the semiconductor device manufacturing process. Metrology is to locate device problems so as to make improvements to improve yield.
[0032] 2. Optical feature size is one of the key dimensions to be measured and is widely used in the measurement of features such as groove depth, line width and film thickness.
[0033] 3. Spectrum signal refers to the data obtained through hardware measurement and processing, which can reflect the intensity of the signal collected at different wavelengths. The characteristics of the spectrum can reflect the size of the optical feature size.
[0034] 4. Smoothing and denoising refers to the steps in the preprocessing of the spectrum, which can filter out the noise in the spectral signal to a certain extent, making the spectrum smoother.
[0035] In the process of signal processing, signal filtering is a common operation step, which can eliminate the noise in the signal, and there are many technologies to choose from. However, there are some signals with large period differences, which may lead to insufficient noise elimination. For example, in the spectrum measured by OCD, the period difference between the front and back signals is large. Usually, in the low wavelength band, the signal period is small, and in the high wavelength band, the signal period is large. If the window is too large, it will cause a peak clipping effect in the low wavelength band, and if the window is too small, the filtering effect in the high wavelength band will be unsatisfactory. Therefore, it is necessary to provide a method for eliminating signal noise for signals with large period differences.
[0036] In the disclosed embodiment, a signal sequence with equal period or frequency intervals is obtained by interpolation and uniform period or frequency processing, and then the signal sequence is filtered to achieve noise elimination. Since filtering a signal sequence with equal period or frequency intervals can reduce the restrictions on the selection of the filter window, it is easier to select a suitable filter window, so that the noise can be eliminated as much as possible through filtering.
[0037] The embodiments of the present disclosure are described below in the order of execution subject, system architecture, equipment, method flow and apparatus.
[0038] 1. Execution entity.
[0039] The execution subject of the method for eliminating signal noise is a device for eliminating signal noise. The device is a hardware device, such as a computing device such as a server or a terminal. Alternatively, the device is a software device, such as a software program deployed on a hardware device.
[0040] 2. System architecture.
[0041] Figure 1 Provides a system architecture. Figure 1 The system architecture includes an object to be measured 101, a measuring device 102 and a computing device 103. The object to be measured 101 is a device that needs to measure the key dimensions, and the measuring device 102 is a device that measures the object to be measured 101 to obtain a measurement signal. The measuring device 102 and the computing device 103 are connected by wire or wirelessly, and the measuring device 102 sends the measured measurement signal to the computing device 103. The computing device 103 processes the measurement signal to obtain a signal sequence, and processes the signal sequence to eliminate noise and calculate information such as the key dimensions of the object to be measured 101.
[0042] Figure 2 Provides an alternative system architecture. Figure 2 The system architecture includes an object to be measured 101 and a measuring device 102. The object to be measured 101 is a device that needs to measure key dimensions. The measuring device 102 measures the object to be measured 101 to obtain a measurement signal, processes the measurement signal to obtain a signal sequence, and processes the signal sequence to eliminate noise and calculate information such as the key dimensions of the object to be measured 101.
[0043] Figure 3 Provides another system architecture. Figure 3, the system architecture includes the object to be measured 101, the measuring device 102, and the public cloud 104. The measuring device 102 is connected to the public cloud 104 through a wired or wireless network. The object to be measured 101 is a device for which critical dimension measurement is required, and the measuring device 102 is a device that measures the object to be measured 101 to obtain signals. The public cloud 104 is an entity that provides cloud services to users using basic resources in the cloud computing mode. The public cloud 104 can also be considered as a cloud environment. The public cloud 104 includes a cloud data center, and the cloud data center includes a large number of basic resources owned by the cloud service provider. The large number of basic resources includes computing resources, storage resources, and network resources. The computing resources included in the cloud data center can be a cluster of computing devices, and the cluster of computing devices includes at least one computing device 103. The computing device 103 can be a server, etc. When a user uses cloud services, the control measuring device 102 uploads the measurement signals obtained by measurement to the public cloud 104, etc. The cluster of computing devices in the public cloud 104 receives the measurement signals, processes the measurement signals to obtain a signal sequence, and performs noise cancellation on the signal sequence.
[0044] In this system architecture, the method for eliminating signal noise can be abstracted by the cloud service provider in the public cloud 104 into a cloud service and provided to users. After the user obtains the permission to use this cloud service, the user can use this cloud service to perform noise cancellation on the signals. This cloud service is a noise cancellation service.
[0045] Among them, in the above system architecture, the object to be measured 101 can be an OCD machine tool, and the measuring device 102 can be a device for testing the OCD machine tool. When the measuring device 102 measures the OCD machine tool, light is emitted and incident on the object to be measured, and the signal is obtained after being reflected by the OCD machine tool. The signal values of the same wavelength in the signal are added to obtain a signal sequence. The signal sequence includes the signal values of each wavelength, and the signal value is the amplitude, etc. Optionally, in Figure 1 the shown system architecture, the computing device 103 can be considered as an OCD algorithm platform, which is not bound to the object to be measured 101 and the measuring device 102. The computing device 103 processes the measurement signals and establishes a model of the corresponding relationship from the measurement signals to the optical feature dimensions through physical modeling and regression problems, etc. In Figure 2 the shown system-level architecture, the measuring device 102 can be considered as an OCD software and hardware platform, including a measurement part, and an online or offline software system adapted to the OCD machine tool. The software system is used to process the measurement signals and establish a model of the corresponding relationship from the measurement signals to the optical feature dimensions through physical modeling and regression problems, etc.
[0046] In addition, the object to be measured 101 can also be an object in the fields of radar, wireless, power system, or precision electronics, etc., and the measured signals are also periodically non-uniform signals.
[0047] 3. Equipment.
[0048] The device 400 may be optionally implemented by a general bus architecture. Figure 4 , the device 400 includes at least one processor 401 , a communication bus 402 , a memory 403 , and at least one network interface 404 . Figure 4 The device 400 of the structure shown is attached Figure 1 computing device 103 in, or Figure 2 In the measuring device 102. In addition, when the device 400 is the measuring device 102, the device 400 also has an optical signal transceiver for measuring the key dimensions of the object to be measured.
[0049] The processor 401 is, for example, a general-purpose central processing unit (CPU), a network processor (NP), a graphics processing unit (GPU), a neural-network processing unit (NPU), a data processing unit (DPU), a microprocessor, or one or more integrated circuits for implementing the disclosed solution. For example, the processor 401 includes an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD is, for example, a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.
[0050] The communication bus 402 is used to transmit information between the above components. The communication bus 402 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0051] The memory 403 is, for example, a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 403 is, for example, independent and connected to the processor 401 via the communication bus 402. The memory 403 can also be integrated with the processor 401.
[0052] Optionally, the memory 403 is used to store signal sequences and the like described below.
[0053] The network interface 404 uses any transceiver-like device for communicating with other devices or communication networks. The network interface 404 includes a wired network interface and may also include a wireless network interface. The wired network interface may be, for example, an Ethernet interface. The Ethernet interface may be an optical interface, an electrical interface, or a combination thereof. The wireless network interface may be a wireless local area network (WLAN) interface, a cellular network network interface, or a combination thereof, etc.
[0054] In a specific implementation, as an example, the processor 401 may include one or more CPUs.
[0055] In a specific implementation, as an example, the security protection device 400 may include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).
[0056] In a specific implementation, as an example, the security protection device 400 may also include an output device and an input device. The output device communicates with the processor 401 and can display information in a variety of ways. For example, the output device may be a liquid crystal display (LCD), a light emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector. The input device communicates with the processor 401 and receives user input in a variety of ways. For example, the input device may be a mouse, a keyboard, a touch screen device, or a sensor device.
[0057] In some embodiments, the memory 403 is used to store the program code 4031 for executing the signal noise elimination in the present disclosure, and the processor 401 executes the program code 4031 stored in the memory 403. That is, the safety protection device 400 can implement the method for eliminating signal noise provided by the method embodiment through the processor 401 and the program code 4031 in the memory 403.
[0058] 4. Method flow for eliminating signal noise.
[0059] Figure 5 Provides a method flow for eliminating signal noise, see Figure 5 Steps 501 to 506. Figure 5 In Figure 1 The system architecture shown is used as an example for explanation, and the computing device is the computing device 103 mentioned above.
[0060] Step 501: Acquire a first signal sequence, wherein a period of a first feature of the first signal sequence is non-uniform.
[0061] In this embodiment, the computing device receives a first signal sequence sent by the measuring device, and the period of the first feature of the first signal sequence is uneven. The uneven period of the first feature means that the periods of the first feature are not completely the same. For example, the first feature is a peak, and the wavelength difference between the first peak and the second peak is 10nm, and the wavelength difference between the second peak and the third peak is 15nm.
[0062] In an optional manner, the first feature is a peak, a trough, or an average of a peak value and a trough value, then the period is equal to the distance between adjacent peaks, or equal to the distance between adjacent troughs, or equal to the distance between adjacent averages.
[0063] Step 502: Perform linear interpolation processing based on the period of the first feature to obtain a first sequence, wherein the number of sequence values in the first sequence is the same as the number of signal values in the first signal sequence, and the first sequence is a periodic sequence or a frequency sequence.
[0064] In this embodiment, the computing device finds the first feature in the first signal sequence, and determines the distance between the first features as the period of the first feature. For example, the first feature is a peak, and there are 5 peaks in the first signal sequence. The distance between the 1st peak and the 2nd peak is determined as the 1st period of the peak, the distance between the 2nd peak and the 3rd peak is determined as the 2nd period of the peak, the distance between the 3rd peak and the 4th peak is determined as the 3rd period of the peak, and the distance between the 4th peak and the 5th peak is determined as the 4th period of the peak. And the computing device determines the position point of the period of the first feature in the first signal sequence. For example, the 1st period of the first feature is the distance between the 1st peak and the 2nd peak, and the position point of the 1st period in the first signal sequence is the position point of the 1st peak, or the position point of the 2nd peak, or the center position point of the position point of the 1st peak and the position point of the 2nd peak. The position point where the wave crest is located is understood as follows: assuming that the first signal sequence includes n signal values, the peak value of the wave crest is the mth wavelength sorted from front to back, then the position point where the wave crest is located is the mth position point.
[0065] The computing device then uses the period of the first feature and the position point at which it is located to perform linear interpolation on the period of the first feature to obtain the period of each position point in the first signal sequence. The periods of each position point constitute a first sequence, and the number of periods in the first sequence is the same as the number of signal values in the first signal sequence.
[0066] Alternatively, the computing device uses the period of the first feature and the position point at which it is located to perform linear interpolation on the period of the first feature to obtain the period of each position point in the first signal sequence. The periods of each position point constitute a first periodic sequence. The number of periods in the first periodic sequence is the same as the number of signal values in the first signal sequence. For example, the number of signal values in the first signal sequence is 1000, and the number of periods in the first periodic sequence is also 1000, which is the period from the 1st position point to the nth position point. Take the reciprocal of each period in the first periodic sequence to obtain a first sequence, which is a frequency sequence. The number of frequencies in the first sequence is the same as the number of signal values in the first signal sequence.
[0067] Alternatively, the computing device takes the inverse of the period of the first feature to obtain the frequency of the first feature, and then uses the frequency of the first feature and the position point at which it is located to perform linear interpolation processing on the frequency of the first feature to obtain the frequencies of each position point in the first signal sequence, and the frequencies of each position point constitute the first sequence. The number of frequencies in the first sequence is the same as the number of signal values in the first signal sequence.
[0068] Step 503: adjust the sequence values in the first sequence to obtain a second sequence.
[0069] In this embodiment, the computing device adjusts the sequence value in the first sequence to obtain the second sequence, and the frequency adjustment does not change the number of frequencies, but only changes the magnitude of the frequencies. That is, the number of frequencies included in the second sequence is the same as the number of frequencies included in the first sequence.
[0070] Step 504: Based on the second sequence and the first signal sequence, interpolate to obtain a second signal sequence corresponding to a sequence with equal sequence value intervals.
[0071] The number of signal values in the second signal sequence is greater than the number of signal values in the first signal sequence.
[0072] In step 504, the interpolation input is the second sequence, the first signal sequence and a sequence with equal intervals of sequence values (the third sequence mentioned later), and the output is the second signal sequence, and the number of sequence values in the third sequence is equal to the number of signal values in the second signal sequence.
[0073] Step 505: filter the second signal sequence to obtain a third signal sequence, wherein the number of signal values in the third signal sequence is the same as the number of signal values in the second signal sequence.
[0074] In this embodiment, the computing device uses Savitzky-Golay (SG) filtering to filter the second signal sequence to obtain a third signal sequence. The SG filtering method is a filtering method based on local polynomial least squares fitting in the time domain, which can ensure that the shape and width of the signal remain unchanged while filtering out noise.
[0075] Alternatively, the second signal sequence is filtered by using a sliding window average method to obtain a third signal sequence. For example, a value window is obtained, and in the second signal sequence, a signal value is taken within the value window corresponding to the kth signal value, and the average value of the taken signal value is calculated, and the average value is determined as the value of the kth signal value in the third signal sequence, and the value window corresponding to the kth signal value is a value window obtained by taking half of the value window before and after the kth signal value, and for edge signal values, points are supplemented by supplementing fixed values or mirror supplementing points, so that the number of signal values in the third signal sequence is equal to the number of signal values in the second signal sequence, and k is 1 to m, and m is the number of signal values in the second signal sequence.
[0076] Step 506: Based on the third signal sequence, interpolate to obtain signal values corresponding to each sequence value in the second sequence, wherein the signal values corresponding to each sequence value form a signal sequence corresponding to the first signal sequence after noise elimination.
[0077] In this embodiment, the number of signal values in the signal sequence after noise elimination is equal to the number of signal values in the first signal sequence. In step 506, the input of the interpolation is the second sequence, the third signal sequence and the third sequence mentioned later, and the output is the signal sequence after noise elimination corresponding to the first signal sequence.
[0078] exist Figure 5 In the process shown, the signal value is first interpolated to obtain a signal sequence with similar sequence values before and after, and then filtered, and finally interpolated back to the original state to eliminate noise. In this way, the sequence value intervals of the signal sequence are close when filtering, and the selection space of the value window (also called the filter window) size is large when filtering, reducing the possibility of being restricted by the filter window selection, so it is easier to select a suitable window, so that through filtering, the noise can be eliminated as much as possible.
[0079] The following takes the first signal sequence as a spectrum sequence as an example Figure 5 The process shown in the figure, in which the first sequence is a periodic sequence, called the first periodic sequence, the second sequence is also a periodic sequence, called the second periodic sequence, and the sequence with equal intervals of sequence values is called the third periodic sequence, see Figure 6 Steps 601 to 607.
[0080] Step 601, obtaining a spectrum sequence.
[0081] In this embodiment, the computing device obtains the measurement signal from the measuring device, preprocesses the measurement signal, and obtains a spectrum sequence, which is represented by S: S(1), S(2)…S(n), where S(n) is the signal value, and the number in the bracket is the position point where the signal value is located. The period of the first feature in the first wavelength range is smaller than the period of the first feature in the second wavelength range, and the wavelength in the first wavelength range is smaller than the wavelength in the second wavelength range, that is, the smaller the wavelength, the shorter the period, and the larger the wavelength, the longer the period. The preprocessing is to add the signal values of the same wavelength to obtain the signal values of each wavelength, thereby obtaining the spectrum sequence.
[0082] Step 602, find the first feature in the spectrum sequence and determine the period of the first feature.
[0083] In this embodiment, the computing device finds the first feature in the first signal sequence, represented as p1, p2..., and determines the period of the first feature, represented as Tp1, Tp2..., where Tp1 is equal to the distance between p2 and p1.
[0084] Step 603: Perform linear interpolation processing based on the period of the first feature to obtain a first periodic sequence.
[0085] In this embodiment, the computing device performs linear interpolation on Tp1, Tp2, ... to obtain a first periodic sequence, which is represented by T(1), T(2), ..., T(n). The i-th period is less than the i+1-th period, and i ranges from 1 to n-1.
[0086] Step 604, reverse the sequence values in the first periodic sequence, determine the first sequence value after the reverse processing as the first sequence value in the second periodic sequence, and determine the sum of the first to jth sequence values after the reverse processing as the jth sequence value in the second periodic sequence, where j ranges from 2 to n.
[0087] In this embodiment, the cycles in the first cycle sequence are processed in reverse order to obtain a sequence after the processing in reverse order, and the first cycle in the sequence after the processing in reverse order is the nth cycle in the first cycle sequence, the second cycle is the n-1th cycle in the first cycle sequence, and so on. Then the value of the first cycle in the sequence after the processing in reverse order is determined as the value of the first cycle in the second cycle sequence, and the sum of the first cycle to the jth cycle in the sequence after the processing in reverse order is calculated to determine the value of the jth cycle in the second cycle sequence, and j is 2 to n. It is equivalent to calculating each cycle in the sequence after the processing in reverse order and adding it to the previous cycle to obtain the second cycle sequence. The second cycle sequence is represented by T1(1), T1(2), ..., T1(n).
[0088] In this way, in the second periodic sequence, the periods from the front to the back become larger and larger, and the distance between adjacent periods becomes smaller and smaller.
[0089] Step 605: Acquire a third periodic sequence, establish a first mapping relationship between periods in the second periodic sequence and signal values in the first signal sequence, and interpolate to obtain a second signal sequence corresponding to the third periodic sequence based on the first mapping relationship.
[0090] In this embodiment, the computing device obtains a third periodic sequence, and the third periodic sequence is represented as T2(1), T2(2), ..., T2(m), where m is the number of periods in the third periodic sequence, and m is greater than n. The number of periods in the third periodic sequence is greater than the number of signal values in the first signal sequence, and the periods in the third periodic sequence are arranged from small to large, and the interval between every two adjacent periods is the same. For example, in the period of the first feature, the minimum period is determined, the minimum period is determined as the minimum period in the third periodic sequence, the maximum period in the second periodic sequence is determined as the maximum period in the third periodic sequence, the period of the kth position point in the third periodic sequence is equal to k multiplied by the minimum period, and the number of periods in the third periodic sequence is equal to the ratio of the maximum period in the second periodic sequence to the minimum period. Alternatively, in the period of the first feature, half of the minimum period is determined, the half of the minimum period is determined as the minimum period in the third periodic sequence, the period of the kth position point in the third periodic sequence is equal to k multiplied by the half of the minimum period, and the number of periods in the third periodic sequence is equal to the ratio of the maximum period in the second periodic sequence to the half of the minimum period. These are just two examples. The minimum period in the third period sequence may be a value smaller than the minimum period in the second period sequence.
[0091] Then the computing device corresponds the first cycle in the second periodic sequence to the first signal value in the first signal sequence, and the second cycle to the second signal value. In this way, n cycles are sequentially corresponded to n signal values to obtain a first mapping relationship. The computing device uses the first mapping relationship to interpolate the signal values corresponding to the 1st to nth cycles in the third periodic sequence, and the signal values corresponding to the 1st to nth cycles constitute the second signal sequence. The principle of interpolation here is: use the first mapping relationship to construct a function with the independent variable as the cycle and the dependent variable as the signal value, and then substitute each cycle in the second periodic sequence into the function to obtain the signal value corresponding to each cycle. The second signal sequence is represented by S1(1), S1(2), ..., S1(m), where the value of m is equal to the number of cycles in the third periodic sequence.
[0092] Step 606: filter the second signal sequence to obtain a third signal sequence, wherein the number of signal values in the third signal sequence is the same as the number of signal values in the second signal sequence.
[0093] The third signal sequence is represented by S2(1), S2(2), ..., S2(m). The processing process of step 606 is shown in Figure 4 The description in will not be repeated here.
[0094] Step 607: establish a second mapping relationship between the periods in the third periodic sequence and the signal values in the third signal sequence, and interpolate to obtain a noise-eliminated spectrum sequence based on the second mapping relationship.
[0095] In this embodiment, the computing device corresponds the first period in the third period sequence to the first signal value in the third signal sequence, and corresponds the second period to the second signal value. In this way, m periods are sequentially corresponded to m signal values to obtain a second mapping relationship.
[0096] Then the computing device uses the second mapping relationship to interpolate the signal values corresponding to the 1st to nth periods in the second periodic sequence. The signal values corresponding to the 1st to nth periods form a spectrum sequence that eliminates noise, expressed as S3(1), S3(2), ..., S3(n). The principle of interpolation here is: use the second mapping relationship to construct a function with the independent variable being the period and the dependent variable being the signal value, and then substitute each period in the second periodic sequence into the function to obtain the signal value corresponding to each period.
[0097] The following takes the first signal sequence as a spectrum sequence as an example Figure 5 In the process shown in FIG. 1 , the first sequence is a frequency sequence, which is called the first frequency sequence. The second sequence is also a frequency sequence, which is called the second frequency sequence. The sequence with equal intervals of sequence values is called the third frequency sequence. Figure 7 Steps 701 to 707.
[0098] Step 701, obtaining a spectrum sequence.
[0099] Step 702, find the first feature in the spectrum sequence and determine the period of the first feature.
[0100] For the processing in step 701 and step 702, refer to the processing in step 601 and step 602 in the previous text, which will not be repeated here.
[0101] Step 703: Perform linear interpolation processing based on the period of the first feature to obtain a first frequency sequence.
[0102] In this embodiment, the computing device performs linear interpolation on Tp1, Tp2, ... to obtain a first periodic sequence, which is represented by T1, T2, ..., Tn. The i-th period is less than the i+1-th period, and i is 1 to n-1. The period in the first periodic sequence is reciprocated to obtain a first frequency sequence, which is represented by f(1), f(2), ..., f(n).
[0103] Alternatively, the computing device takes the reciprocal of Tp1, Tp2… to obtain 1 / Tp1, 1 / Tp2…, and then performs linear interpolation on 1 / Tp1, 1 / Tp2… to obtain a first frequency sequence, represented as f(1), f(2),…, f(n).
[0104] Step 704: determine the first frequency in the first frequency sequence as the first frequency in the second frequency sequence, and determine the sum of the first to j-th frequencies in the first frequency sequence as the j-th frequency in the second frequency sequence, where j ranges from 2 to n.
[0105] In this embodiment, the value of the first frequency in the first frequency sequence is determined as the value of the first frequency in the second frequency sequence, and the sum of the first frequency to the jth frequency in the first frequency sequence is calculated to determine the value of the jth frequency in the second frequency sequence, where j is 2 to n. This is equivalent to calculating the sum of each frequency in the first frequency sequence and the previous frequency to obtain the second frequency sequence. The second frequency sequence is represented by f1(1), f1(2), ..., f1(n).
[0106] In this way, in the second frequency sequence, the frequencies increase from the front to the back, and the distances between adjacent frequencies decrease.
[0107] Step 705: Acquire a third frequency sequence, establish a first mapping relationship between frequencies in the second frequency sequence and signal values in the first signal sequence, and interpolate to obtain a second signal sequence corresponding to the third frequency sequence based on the first mapping relationship.
[0108] In this embodiment, the computing device obtains a third frequency sequence, and the third frequency sequence is represented as f2(1), f2(2), ..., f2(m), where m is the number of frequencies in the third frequency sequence, and m is greater than n. The number of frequencies in the third frequency sequence is greater than the number of signal values in the first signal sequence, and the frequencies in the third frequency sequence are arranged from small to large, and the interval between every two adjacent frequencies is the same. For example, in the frequency of the first feature, the minimum frequency is determined, and the minimum frequency is determined as the minimum frequency in the third frequency sequence, and the maximum frequency in the second frequency sequence is determined as the maximum frequency in the third frequency sequence, and the frequency of the kth position point in the third frequency sequence is equal to k multiplied by the minimum frequency, and the number of frequencies in the third frequency sequence is equal to the ratio of the maximum frequency in the second frequency sequence to the minimum frequency. Alternatively, in the frequency of the first feature, half of the minimum frequency is determined, and half of the minimum frequency is determined as the minimum frequency in the third frequency sequence, and the frequency of the kth position point in the third frequency sequence is equal to k multiplied by the half of the minimum frequency, and the number of frequencies in the third frequency sequence is equal to the ratio of the maximum frequency in the second frequency sequence to the half of the minimum frequency. These are just two examples. The minimum frequency in the third frequency sequence may be a value smaller than the minimum frequency in the second frequency sequence.
[0109] Then the computing device corresponds the first frequency in the second frequency sequence to the first signal value in the first signal sequence, and the second frequency to the second signal value. In this way, n frequencies are sequentially matched to n signal values to obtain a first mapping relationship. The computing device uses the first mapping relationship to interpolate the signal values corresponding to the 1st frequency to the nth frequency in the third frequency sequence, and the signal values corresponding to the 1st frequency to the nth frequency constitute the second signal sequence. The principle of interpolation here is: use the first mapping relationship to construct a function with the independent variable being the frequency and the dependent variable being the signal value, and then substitute each frequency in the second frequency sequence into the function to obtain the signal value corresponding to each frequency. The second signal sequence is represented as S11(1), S11(2), ..., S11(m), where the value of m is equal to the number of frequencies in the third frequency sequence.
[0110] Step 706: filter the second signal sequence to obtain a third signal sequence, wherein the number of signal values in the third signal sequence is the same as the number of signal values in the second signal sequence.
[0111] The third signal sequence is represented by S12(1), S12(2), ..., S12(m). The processing process of step 706 is shown in Figure 4 The description in will not be repeated here.
[0112] Step 707: establish a second mapping relationship between frequencies in the third frequency sequence and signal values in the third signal sequence, and interpolate to obtain a noise-eliminated spectrum sequence based on the second mapping relationship.
[0113] In this embodiment, the computing device corresponds the first frequency in the third frequency sequence to the first signal value in the third signal sequence, and corresponds the second frequency to the second signal value. In this way, m frequencies are sequentially corresponded to m signal values to obtain a second mapping relationship.
[0114] Then the computing device uses the second mapping relationship to interpolate the signal values corresponding to the 1st frequency to the nth frequency in the second frequency sequence. The signal values corresponding to the 1st frequency to the nth frequency form a noise-eliminated spectrum sequence, which is represented as S13(1), S13(2), ..., S13(n). The principle of interpolation here is: use the second mapping relationship to construct a function with the independent variable being the frequency and the dependent variable being the signal value, and then substitute each frequency in the second frequency sequence into the function to obtain the signal value corresponding to each frequency.
[0115] In the disclosed embodiment, the signal sequence is transformed into a signal sequence with uniform front and rear sequence values through sequence value adjustment and signal value interpolation, so that during filtering processing, the selection space of the filter window is relatively large and is no longer restricted by the original signal being a periodically varying signal. Figure 8 FIG. 1 is a schematic diagram showing a method of performing noise elimination using an embodiment of the present disclosure. Figure 8 The middle left picture is the signal sequence before filtering. Figure 8 The filtered part of the middle right image corresponds to Figure 8 Draw the part marked by the arrow. Figure 8 It can be seen that after adopting the solution shown in the embodiment of the present disclosure, there will be no peak clipping phenomenon (peak clipping refers to reducing the amplitude of the peak), and the smoothing effect is relatively good.
[0116] In addition, when the filtering method is selected as SG filtering or sliding window average filtering, the phase of the spectrum will not shift.
[0117] exist Figure 6 and Figure 7 In the process shown, when eliminating noise, it is aimed at the situation where the period is small in the low wavelength band and the period is large in the high wavelength band. The same is also applicable to the situation where the period is large in the low wavelength band and the period is small in the high wavelength band.
[0118] 5. Device for eliminating signal noise.
[0119] Fig. 9 : is a structural diagram of a device for eliminating signal noise provided by an embodiment of the present disclosure. The device can be implemented as part or all of the device through software, hardware, or a combination of both. The device provided by an embodiment of the present disclosure can implement the embodiment of the present disclosure Figure 5 , Figure 6 or Figure 7The process described above, the device includes: an acquisition module 910, an interpolation module 920, an adjustment module 930 and a filtering module 940, wherein:
[0120] An acquisition module 910 is used to acquire a first signal sequence, wherein the period of the first feature of the first signal sequence is uneven, and can be specifically used to implement the acquisition function of step 501 and execute the implicit steps included in step 501;
[0121] An interpolation module 920 is used to perform linear interpolation processing based on the period of the first feature to obtain a first sequence, wherein the number of sequence values in the first sequence is the same as the number of signal values in the first signal sequence, and the first sequence is a periodic sequence or a frequency sequence, and can be specifically used to implement the interpolation function of step 502 and execute the implicit steps included in step 502;
[0122] An adjustment module 930, used to adjust the sequence values in the first sequence to obtain a second sequence, and specifically can be used to implement the adjustment function of step 503 and execute the implicit steps included in step 503;
[0123] The interpolation module 920 is further used to interpolate the second signal sequence corresponding to the sequence with equal sequence value intervals based on the second sequence and the first signal sequence, and can be specifically used to implement the interpolation function of step 504 and execute the implicit steps included in step 504;
[0124] A filtering module 940 is used to filter the second signal sequence to obtain a third signal sequence, which can be used to implement the filtering function of step 505 and execute the implicit steps included in step 505;
[0125] The interpolation module 920 is further used to interpolate the signal values corresponding to each sequence value in the second sequence based on the third signal sequence, wherein the signal value composition corresponding to each sequence value constitutes the signal sequence after noise elimination corresponding to the first signal sequence, which can be specifically used to implement the interpolation function of step 506 and execute the implicit steps included in step 506.
[0126] In an optional manner, the first signal sequence is a spectral sequence, in which a period of the first feature in a first wavelength range is smaller than a period of the first feature in a second wavelength range, a wavelength in the first wavelength range is smaller than a wavelength in the second wavelength range, an i-th sequence value in the first sequence is smaller than an i+1-th sequence value, i ranges from 1 to n-1, n is the number of sequence values in the first sequence, and the first sequence is a periodic sequence;
[0127] The adjustment module 930 is used to:
[0128] Reverse the sequence values in the first sequence;
[0129] Determine the first sequence value after the reverse process as the first sequence value in the second sequence;
[0130] Determine the sum of the first to the j-th sequence values after the reverse process as the j-th sequence value in the second sequence, where j ranges from 2 to n.
[0131] In an alternative manner, the first signal sequence is a spectral sequence. In the spectral sequence, the period of the first feature within the first wavelength range is less than the period of the first feature within the second wavelength range, the wavelength within the first wavelength range is less than the wavelength within the second wavelength range, the i-th sequence value in the first sequence is greater than the (i + 1)-th sequence value, where i ranges from 1 to n - 1, n is the number of sequence values in the first sequence, and the first sequence is a frequency sequence;
[0132] The adjustment module 930 is configured to:
[0133] Determine the first sequence value in the first sequence as the first sequence value in the second sequence;
[0134] Determine the sum of the first to the j-th sequence values in the first sequence as the j-th sequence value in the second sequence, where j ranges from 2 to n.
[0135] In an alternative manner, the interpolation module 920 is further configured to:
[0136] Obtain a third sequence, where the number of sequence values in the third sequence is greater than the number of signal values in the first signal sequence, the sequence values in the third sequence are arranged in ascending order, and the interval between every two adjacent sequence values is the same;
[0137] Establish a first mapping relationship between the sequence values in the second sequence and the signal values in the first signal sequence;
[0138] Interpolate to obtain a second signal sequence corresponding to the third sequence based on the first mapping relationship;
[0139] Establish a second mapping relationship between the sequence values in the third sequence and the signal values in the third signal sequence;
[0140] Interpolate to obtain the signal values corresponding to each sequence value in the second sequence based on the second mapping relationship.
[0141] In an alternative manner, the minimum sequence value in the third sequence is equal to the minimum sequence value in the first sequence, and the maximum sequence value is equal to the maximum sequence value in the second sequence;
[0142] The number of sequence values in the third sequence is equal to the ratio of the maximum sequence value in the second sequence to the minimum sequence value in the first sequence.
[0143] In an optional manner, the interpolation module 920 is used to:
[0144] Performing linear interpolation processing on the period of the first feature to obtain a first periodic sequence, and taking the inverse of the period in the first periodic sequence to obtain a first sequence; or,
[0145] The reciprocal of the period of the first feature is taken to obtain the frequency of the first feature, and linear interpolation processing is performed on the frequency of the first feature to obtain a first sequence.
[0146] Fig. 9 For the detailed process of eliminating signal noise by the device for eliminating signal noise shown, please refer to the description in the previous embodiments, which will not be repeated here. Fig. 9 The device shown to eliminate signal noise is attached Figure 1 The computing device 103 or the attached Figure 2 The measuring device 102 in FIG.
[0147] In some embodiments, a computer program product is provided, the computer program product comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computing device performs Figure 5 , Figure 6 or Figure 7 The process shown.
[0148] Those of ordinary skill in the art will appreciate that the various method steps and units described in the embodiments disclosed in this disclosure can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the steps and components of each embodiment have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.
[0149] In the several embodiments provided in the present disclosure, it should be understood that the disclosed system architecture, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, or it can be an electrical, mechanical or other form of connection.
[0150] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.
[0151] In addition, each module in each embodiment of the present disclosure may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules may be implemented in the form of hardware or software modules.
[0152] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks or optical disks.
[0153] In the present disclosure, the words such as the term "first" and "second" are used to distinguish the same or similar items with substantially the same effect and function. It should be understood that there is no logical or temporal dependency between the "first" and "second", and the quantity and execution order are not limited. It should also be understood that although the following description uses the terms "first" and "second" etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of various examples, the first signal sequence can be referred to as the second signal sequence, and similarly, the second signal sequence can be referred to as the first signal sequence. Both the first signal sequence and the second signal sequence can be signal sequences, and in some cases, can be separate and different signal sequences.
[0154] The above description is an exemplary embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A method for eliminating signal noise, It is characterized in that The method comprises: Acquire a first signal sequence, wherein a period of a first feature of the first signal sequence is uneven; Performing linear interpolation processing based on the period of the first feature to obtain a first sequence, wherein the number of sequence values in the first sequence is the same as the number of signal values in the first signal sequence, and the first sequence is a periodic sequence or a frequency sequence; Adjusting the sequence values in the first sequence to obtain a second sequence; Based on the second sequence and the first signal sequence, interpolate to obtain a second signal sequence corresponding to a sequence with equal sequence value intervals; performing filtering processing on the second signal sequence to obtain a third signal sequence; Based on the third signal sequence, signal values corresponding to each sequence value in the second sequence are interpolated, wherein the signal values corresponding to each sequence value form a signal sequence corresponding to the first signal sequence after noise elimination.
2. The method according to claim 1, It is characterized in that The first signal sequence is a spectral sequence, in which a period of the first feature in a first wavelength range is smaller than a period of the first feature in a second wavelength range, a wavelength in the first wavelength range is smaller than a wavelength in the second wavelength range, an i-th sequence value in the first sequence is smaller than an i+1-th sequence value, i ranges from 1 to n-1, n is the number of sequence values in the first sequence, and the first sequence is a periodic sequence; The step of adjusting the sequence values in the first sequence to obtain a second sequence includes: Reversing the sequence values in the first sequence; The first sequence value after the reverse processing is determined as the first sequence value in the second sequence; The sum of the 1st to jth sequence values after the reverse processing is determined as the jth sequence value in the second sequence, where j ranges from 2 to n.
3. The method according to claim 1, It is characterized in that The first signal sequence is a spectrum sequence, in which a period of the first feature in a first wavelength range is smaller than a period of the first feature in a second wavelength range, a wavelength in the first wavelength range is smaller than a wavelength in the second wavelength range, an i-th sequence value in the first sequence is greater than an i+1-th sequence value, i ranges from 1 to n-1, n is the number of sequence values in the first sequence, and the first sequence is a frequency sequence; The step of adjusting the sequence values in the first sequence to obtain a second sequence includes: Determine the first sequence value in the first sequence as the first sequence value in the second sequence; The sum of the 1st to jth sequence values in the first sequence is determined as the jth sequence value in the second sequence, where j ranges from 2 to n.
4. The method according to any one of claims 1 to 3, It is characterized in that The interpolating, based on the second sequence and the first signal sequence, to obtain a second signal sequence corresponding to a sequence with equal sequence value intervals comprises: Acquire a third sequence, wherein the number of sequence values in the third sequence is greater than the number of signal values in the first signal sequence, the sequence values in the third sequence are arranged from small to large, and the interval between every two adjacent sequence values is the same; Establishing a first mapping relationship between sequence values in the second sequence and signal values in the first signal sequence; Based on the first mapping relationship, interpolate to obtain a second signal sequence corresponding to the third sequence; The interpolating, based on the third signal sequence, to obtain signal values corresponding to each sequence value in the second sequence includes: Establishing a second mapping relationship between sequence values in the third sequence and signal values in the third signal sequence; Based on the second mapping relationship, signal values corresponding to each sequence value in the second sequence are obtained by interpolation.
5. The method according to claim 4, It is characterized in that The minimum sequence value in the third sequence is equal to the minimum sequence value in the first sequence, and the maximum sequence value is equal to the maximum sequence value in the second sequence; The number of sequence values in the third sequence is equal to the ratio of the maximum sequence value in the second sequence to the minimum sequence value in the first sequence.
6. The method according to any one of claims 1 to 5, It is characterized in that The first feature is a peak, a trough or an average value of a signal value of the first signal sequence.
7. The method according to claim 3, It is characterized in that The step of performing linear interpolation processing based on the period of the first feature to obtain a first sequence includes: Performing linear interpolation processing on the period of the first feature to obtain a first periodic sequence, and taking the inverse of the period in the first periodic sequence to obtain a first sequence; or, The reciprocal of the period of the first feature is taken to obtain the frequency of the first feature, and linear interpolation processing is performed on the frequency of the first feature to obtain a first sequence.
8. A device for eliminating signal noise, It is characterized in that The device comprises: An acquisition module, configured to acquire a first signal sequence, wherein a period of a first feature of the first signal sequence is uneven; an interpolation module, configured to perform linear interpolation processing based on the period of the first feature to obtain a first sequence, wherein the number of sequence values in the first sequence is the same as the number of signal values in the first signal sequence, and the first sequence is a periodic sequence or a frequency sequence; An adjustment module, used for adjusting the sequence values in the first sequence to obtain a second sequence; The interpolation module is further used to interpolate, based on the second sequence and the first signal sequence, a second signal sequence corresponding to a sequence with equal sequence value intervals; A filtering module, configured to filter the second signal sequence to obtain a third signal sequence; The interpolation module is further used to interpolate signal values corresponding to each sequence value in the second sequence based on the third signal sequence, wherein the signal values corresponding to each sequence value form a signal sequence corresponding to the first signal sequence after noise elimination.
9. The device according to claim 8, It is characterized in that The first signal sequence is a spectral sequence, in which a period of the first feature in a first wavelength range is smaller than a period of the first feature in a second wavelength range, a wavelength in the first wavelength range is smaller than a wavelength in the second wavelength range, an i-th sequence value in the first sequence is smaller than an i+1-th sequence value, i ranges from 1 to n-1, n is the number of sequence values in the first sequence, and the first sequence is a periodic sequence; The adjustment module is used for: Reversing the sequence values in the first sequence; The first sequence value after the reverse processing is determined as the first sequence value in the second sequence; The sum of the 1st to jth sequence values after the reverse processing is determined as the jth sequence value in the second sequence, where j ranges from 2 to n.
10. The device according to claim 8, It is characterized in that The first signal sequence is a spectrum sequence, in which a period of the first feature in a first wavelength range is smaller than a period of the first feature in a second wavelength range, a wavelength in the first wavelength range is smaller than a wavelength in the second wavelength range, an i-th sequence value in the first sequence is greater than an i+1-th sequence value, i ranges from 1 to n-1, n is the number of sequence values in the first sequence, and the first sequence is a frequency sequence; The adjustment module is used for: Determine the first sequence value in the first sequence as the first sequence value in the second sequence; The sum of the 1st to jth sequence values in the first sequence is determined as the jth sequence value in the second sequence, where j ranges from 2 to n.
11. The device according to any one of claims 8 to 10, It is characterized in that The interpolation module is further used for: Acquire a third sequence, wherein the number of sequence values in the third sequence is greater than the number of signal values in the first signal sequence, the sequence values in the third sequence are arranged from small to large, and the interval between every two adjacent sequence values is the same; Establishing a first mapping relationship between sequence values in the second sequence and signal values in the first signal sequence; Based on the first mapping relationship, interpolate to obtain a second signal sequence corresponding to the third sequence; Establishing a second mapping relationship between sequence values in the third sequence and signal values in the third signal sequence; Based on the second mapping relationship, signal values corresponding to each sequence value in the second sequence are obtained by interpolation.
12. The device according to claim 11, It is characterized in that The minimum sequence value in the third sequence is equal to the minimum sequence value in the first sequence, and the maximum sequence value is equal to the maximum sequence value in the second sequence; The number of sequence values in the third sequence is equal to the ratio of the maximum sequence value in the second sequence to the minimum sequence value in the first sequence.
13. The device according to claim 10, It is characterized in that The interpolation module is used to: Performing linear interpolation processing on the period of the first feature to obtain a first periodic sequence, and taking the inverse of the period in the first periodic sequence to obtain a first sequence; or, The reciprocal of the period of the first feature is taken to obtain the frequency of the first feature, and linear interpolation processing is performed on the frequency of the first feature to obtain a first sequence.
14. A computing device, It is characterized in that The computing device comprises a processor and a memory, wherein: The memory stores computer instructions; The processor executes the computer instructions to cause the computing device to perform the method of any one of claims 1 to 7.
15. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions. When the computer instructions in the computer-readable storage medium are executed by a computing device, the computing device executes the method according to any one of claims 1 to 7.