Processing method and processing device for seed spectrum, storage medium and processor
By performing segmented processing and fitting of seed spectra and combining digital low-pass filtering technology, the problem of insufficient filtering capabilities of seed spectral in the existing technology is solved, and a higher discrimination accuracy is achieved.
Patent Information
- Application Number
- CN202510327583.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-06-24
AI Technical Summary
When filtering the seed spectrum, it is difficult to effectively remove noise data, resulting in the impact of the accuracy of seed vitality identification.
By performing segmentation processing of the seed spectral waveform, the target segmentation function of each spectral data segment is determined, and the fitted data is obtained through fitting, and the deviation value is calculated. When the deviation value is within the preset range, the original data is digitally low-pass filtered to obtain the filtered data and spliced into complete filtered data.
This method can more effectively remove noise data in the seed spectrum, retain characteristic information, and improve the accuracy of seed vigor identification.
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Figure CN120196876A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of seed spectrum processing, and specifically relates to a method for processing seed spectrum, a processing device, a storage medium, and a processor. Background Art
[0002] Seed spectrum is an important element for analyzing and identifying seed vigor. Therefore, the measurement of seed spectrum is particularly important. The noise components in the spectrum are difficult to eliminate due to their randomness and are important components affecting the accuracy of spectrum data. Therefore, it is necessary to filter and eliminate the noise data in the spectrum. In the prior art, the spectrum waveform is regarded as a continuous signal waveform, and filtering technology is selected to filter this signal waveform. However, the frequency domain characteristics of the spectrum waveform itself are difficult to describe by a single frequency range, and the existing filtering methods will result in insufficient filtering ability for noise, resulting in the suppression of useful feature information and affecting the accuracy of seed vigor identification. Summary of the Invention
[0003] The purpose of the embodiments of this application is to provide a method for processing seed spectrum, a processing device, a storage medium, and a processor.
[0004] To achieve the above purpose, the first aspect of this application provides a method for processing seed spectrum, including: Obtain the original spectrum waveform of the seed, where the original spectrum waveform includes a plurality of original data; Perform segmentation processing on the original spectrum waveform to obtain a plurality of spectrum data segments; Determine the target segmentation function corresponding to each spectrum data segment; For each spectrum data segment, fit the target segmentation function of the spectrum data segment and the original data included in the spectrum data segment to obtain the fitting data corresponding to the spectrum data segment; For each spectrum data segment, determine the deviation value of the spectrum data segment according to the fitting data and the original data; When the deviation values of all spectrum data segments are within a preset range, perform filtering processing on each original data to obtain the filtering data corresponding to each spectrum data segment; Stitch all the filtering data to obtain the complete filtering data corresponding to the original spectrum waveform.
[0005] In an embodiment of the present application, the original spectral waveform is segmented to obtain a plurality of spectral data segments, including: determining a target waveform corresponding to the original spectral waveform, where each waveform data of the target waveform corresponds to an original data; dividing the target waveform according to a preset step length to obtain a plurality of sub-target waveforms; determining the peaks and valleys of each sub-target waveform, and determining the waveform data between any one peak and the two adjacent valleys of the peak as a target data segment; and determining the original data corresponding to the target data segment as a spectral data segment.
[0006] In an embodiment of the present application, determining a target piecewise function corresponding to each spectral data segment includes: for any one spectral data segment, using a plurality of piecewise functions to process the spectral data segment to obtain the fitting deviation corresponding to each piecewise function; and determining the piecewise function corresponding to the smallest fitting deviation as the target piecewise function.
[0007] In an embodiment of the present application, for each spectral data segment, determining the deviation value of the spectral data segment according to the fitting data and the original data includes calculating the deviation value according to formula (1): (1) where represents the deviation value, n represents the number of original data of the spectral data segment, represents the i-th original data of the spectral data segment, represents the i-th fitting data of the spectral data segment.
[0008] In an embodiment of the present application, the processing method further includes: in the case where the deviation value of any one spectral data segment is outside the preset range, returning to the step of segmenting the original spectral waveform to obtain a plurality of spectral data segments again until the deviation values of all spectral data segments are within the preset range.
[0009] In an embodiment of the present application, filtering each original data to obtain filtered data corresponding to each spectral data segment includes: using digital low-pass filtering to process each original data to obtain the filtered data.
[0010] In an embodiment of the present application, the processing method further includes: after splicing all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform, performing visualization processing on the complete filtered data to obtain the spectral waveform corresponding to the complete filtered data.
[0011] The second aspect of the present application provides a processor configured to execute the above-mentioned processing method for the seed spectrum.
[0012] The third aspect of the present application provides a processing device for the seed spectrum, and the processing device includes the above-mentioned processor.
[0013] The fourth aspect of this application provides a machine-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above-mentioned processing method for seed spectra.
[0014] In the above technical solution, by obtaining the original spectral waveform of the seed, where the original spectral waveform includes a plurality of original data; performing segmentation processing on the original spectral waveform to obtain a plurality of spectral data segments; determining the target segmentation function corresponding to each spectral data segment; for each spectral data segment, fitting the target segmentation function of the spectral data segment and the original data included in the spectral data segment to obtain the fitting data corresponding to the spectral data segment; for each spectral data segment, determining the deviation value of the spectral data segment according to the fitting data and the original data; in the case where the deviation values of all spectral data segments are within a preset range, performing filtering processing on each original data to obtain the filtered data corresponding to each spectral data segment; splicing all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform. By adopting this technical solution, it is possible to better filter the seed spectrum, better remove the noise data in the spectrum, effectively retain the characteristic information in the seed spectrum, and improve the accuracy of seed identification.
[0015] Other features and advantages of the embodiments of this application will be described in detail in the subsequent specific implementation part. Description of the Drawings
[0016] The drawings are used to provide a further understanding of the embodiments of this application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of this application, but do not constitute a limitation to the embodiments of this application. In the drawings: Figure 1 Schematically shows a first flowchart of the processing method for seed spectra according to an embodiment of this application; Figure 2 Schematically shows a second flowchart of the processing method for seed spectra according to an embodiment of this application; Figure 3 Schematically shows the internal structure diagram of a computer device according to an embodiment of this application. Detailed Description of the Embodiments
[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. It should be understood that the specific embodiments described herein are only for explaining and illustrating the embodiments of this application, and are not used to limit the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope protected by this application.
[0018] Figure 1 Schematically shows a first flowchart of a method for processing a seed spectrum according to an embodiment of this application. As Figure 1 shown, in an embodiment of this application, a method for processing a seed spectrum is provided, including the following steps: Step 101, obtain the original spectral waveform of the seed, where the original spectral waveform includes a plurality of original data.
[0019] Step 102, perform segmentation processing on the original spectral waveform to obtain a plurality of spectral data segments.
[0020] Step 103, determine the target piecewise function corresponding to each spectral data segment.
[0021] Step 104, for each spectral data segment, fit the target piecewise function of the spectral data segment and the original data included in the spectral data segment to obtain the fitting data corresponding to the spectral data segment.
[0022] Step 105, for each spectral data segment, determine the deviation value of the spectral data segment according to the fitting data and the original data.
[0023] Step 106, when the deviation values of all spectral data segments are within a preset range, perform filtering processing on each original data to obtain the filtered data corresponding to each spectral data segment.
[0024] Step 107, splice all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform.
[0025] A seed refers to a unique reproductive body of gymnosperms and angiosperms, which is formed by the ovule after pollination and fertilization. The spectral waveform of the seed includes spectral information in forms such as the projection, reflection, and Raman spectra of the seed. The piecewise function can include linear functions, quadratic functions, cubic functions, etc. Filtering is an operation that filters out specific band frequencies in the spectral data segment and is an important measure to suppress and prevent interference. The processor can obtain the original spectral waveform of the seed, where the original spectral waveform includes multiple original data. After obtaining the original spectral waveform of the seed, the processor can perform piecewise processing on the original spectral waveform to obtain multiple spectral data segments. After obtaining multiple spectral data segments, the processor can determine the target piecewise function corresponding to each spectral data segment. For each spectral data segment, the processor can fit the target piecewise function of the spectral data segment and the original data included in the spectral data segment to obtain the fitting data corresponding to the spectral data segment. Among them, fitting means using the target piecewise function to describe all the data in the spectral data segment. For each spectral data segment, after obtaining the fitting data corresponding to the spectral data segment, the processor can determine the deviation value of the spectral data segment based on the fitting data and the original data. After obtaining the deviation values of all spectral data segments, the processor can determine whether the deviation values of all spectral data segments are within a preset range. When it is determined that the deviation values of all spectral data segments are within the preset range, the processor can perform filtering processing on each original data to obtain the filtered data corresponding to each spectral data segment. After obtaining the filtered data of each spectral data segment, the processor can splice all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform.
[0026] In one embodiment, the processing method further includes: when the deviation value of any one spectral data segment is outside the preset range, returning to the step of performing piecewise processing on the original spectral waveform to obtain multiple spectral data segments until the deviation values of all spectral data segments are within the preset range.
[0027] The processor can obtain the original spectral waveform of the seed, where the original spectral waveform includes multiple original data. After obtaining the original spectral waveform of the seed, the processor can perform piecewise processing on the original spectral waveform to obtain multiple spectral data segments. After obtaining multiple spectral data segments, the processor can determine the target piecewise function corresponding to each spectral data segment. For each spectral data segment, the processor can fit the target piecewise function of the spectral data segment and the original data included in the spectral data segment to obtain the fitting data corresponding to the spectral data segment. For each spectral data segment, after obtaining the fitting data corresponding to the spectral data segment, the processor can determine the deviation value of the spectral data segment based on the fitting data and the original data. In one embodiment, the processor can calculate the deviation value according to formula (1): (1) Among them, represents the deviation value, n represents the number of original data in the spectral data segment, represents the i-th original data in the spectral data segment, represents the i-th fitted data in the spectral data segment.
[0028] After obtaining the deviation values of all spectral data segments, the processor can determine whether the deviation values of all spectral data segments are within a preset range. In the case where the deviation value of any one spectral data segment is outside the preset range, the processor can return to the step of segmenting the original spectral waveform again to obtain multiple spectral data segments. Until the deviation values of all spectral data segments are within the preset range. In the case where it is determined that the deviation values of all spectral data segments are within the preset range, the processor can perform filtering processing on each original data to obtain filtered data corresponding to each spectral data segment. In one embodiment, the processor can use digital low-pass filtering to process each original data to obtain filtered data. Among them, digital low-pass filtering is a filtering method, and the rule is that low-frequency signals can pass through normally, while high-frequency signals exceeding the set critical value are blocked and attenuated.
[0029] After obtaining the filtered data of each spectral data segment, the processor can splice all the filtered data to obtain complete filtered data corresponding to the original spectral waveform.
[0030] For example, the processor can obtain the original spectral waveform X of the seed. After obtaining the original spectral waveform X, the processor can perform segmentation processing on the original spectral waveform X to obtain spectral data segments , spectral data segments and spectral data segments . After obtaining multiple spectral data segments, the processor can determine the target piecewise function corresponding to the spectral data segment , the target piecewise function corresponding to the spectral data segment , and the target piecewise function corresponding to the spectral data segment .
[0031] For the spectral data segment , the processor can fit the target piecewise function and the original data included in the spectral data segment to obtain the fitted data corresponding to the spectral data segment . And determine the deviation value of the spectral data segment based on the fitted data . For the spectral data segment The processor can fit the piecewise function and the spectral data segment including the original data to obtain the fitting data corresponding to the spectral data segment . And determine the deviation value of the spectral data segment based on the fitting data and the original data . For the spectral data segment , the processor can fit the piecewise function and the spectral data segment including the original data to obtain the fitting data corresponding to the spectral data segment . And determine the deviation value of the spectral data segment based on the fitting data and the original data . .
[0032] When the deviation value , the deviation value and the deviation value are all within the preset range, the processor can perform filtering processing on each original data to obtain the filtering data corresponding to the spectral data segment , the filtering data corresponding to the spectral data segment , the filtering data corresponding to the spectral data segment , the filtering data corresponding to the spectral data segment and the filtering data corresponding to the spectral data segment . The processor can splice the filtering data , the filtering data and the filtering data to obtain the complete filtering data corresponding to the original spectral waveform X.
[0033] When any one of the deviation value , the deviation value and the deviation value is not within the preset range, the processor can segment the original spectral waveform X again to obtain new spectral data segments. And determine the target piecewise function of the new spectral data segments until the deviation values of all the new spectral data segments are within the preset range.
[0034] In one embodiment, the original spectral waveform is segmented to obtain multiple spectral data segments, including: determining a target waveform corresponding to the original spectral waveform, where each waveform data of the target waveform corresponds to an original data; dividing the target waveform according to a preset step size to obtain multiple sub-target waveforms; determining the peaks and valleys of each sub-target waveform, and determining the waveform data between any one peak and the two valleys adjacent to the peak as a target data segment; determining the original data corresponding to the target data segment as a spectral data segment. A peak refers to the maximum value of the wave amplitude within a wavelength range of the waveform. A valley refers to the minimum value of the wave amplitude within a wavelength range of the waveform.
[0035] The processor can segment the original spectral waveform to obtain multiple spectral data segments. Specifically, the processor can determine a target waveform corresponding to the original spectral waveform, where each waveform data of the target waveform corresponds to an original data. After determining the target waveform, the processor can divide the target waveform according to a preset step size to obtain multiple sub-target waveforms. After obtaining the sub-target waveforms, the processor can determine the peaks and valleys of each sub-target waveform. And determine the waveform data between any one peak and the two valleys adjacent to the peak as a target data segment. After determining the target data segment, the processor can determine the original data corresponding to the target data segment as a spectral data segment.
[0036] For example, the processor can perform a derivative operation on the original spectral waveform and perform a modulo operation on the basis of the derivative to obtain a target waveform corresponding to the original spectral waveform. After obtaining the target waveform, the processor can divide the target waveform into multiple segments according to a preset step size, and each segment is a sub-target waveform. Suppose the preset step size can be selected as 0.5. After dividing the target waveform according to the preset step size, the sub-target waveforms can include waveform data such as the 0-0.5 segment, 0.5-1.0 segment, 1.0-1.5 segment, etc. After obtaining the sub-target waveforms, the processor can determine the peaks and valleys of each sub-target waveform, and determine the waveform data between any one peak and the two valleys adjacent to the peak as a target data segment. For example, for the sub-target waveform of the 0-0.5 segment, the peak is at the 0.3 position, and the 0.2 and 0.5 positions are the two valleys adjacent to the peak respectively. Then the processor can determine the waveform data between 0.2 and 0.5 as a target data segment. After determining the target data segment, the processor can determine the original data corresponding to the target data segment as a spectral data segment.
[0037] In one embodiment, determining the target piecewise function corresponding to each spectral data segment includes: for any one spectral data segment, using multiple piecewise functions to process the spectral data segment to obtain the fitting deviation corresponding to each piecewise function; determining the piecewise function corresponding to the fitting deviation with the smallest value as the target piecewise function.
[0038] The processor can determine the target piecewise function corresponding to each spectral data segment. Specifically, for any spectral data segment, the processor can process the spectral data segment with multiple piecewise functions to obtain the fitting deviation corresponding to each piecewise function. After obtaining the fitting deviation, the processor can determine the piecewise function corresponding to the smallest fitting deviation as the target piecewise function.
[0039] For example, after the processor obtains the original spectral waveform of the seed and performs piecewise processing on the original spectral waveform, 3 spectral data segments are obtained. These 3 spectral data segments are respectively , and . The piecewise functions include a linear function , a quadratic function and a cubic function . For the spectral data segment , the processor can sequentially use the linear function , the quadratic function and the cubic function to process the spectral data segment , and obtain the fitting deviation corresponding to the linear function , the fitting deviation corresponding to the quadratic function , and the fitting deviation corresponding to the cubic function . Among them, . The processor can determine the cubic function corresponding to the fitting deviation as the target piecewise function. For the spectral data segment , the processor can sequentially use the linear function , the quadratic function and the cubic function to process the spectral data segment , and obtain the fitting deviation corresponding to the linear function , the fitting deviation corresponding to the quadratic function , and the fitting deviation corresponding to the cubic function . Among them, . The processor can determine the quadratic function corresponding to the fitting deviation as the target piecewise function. For the spectral data segment , the processor can sequentially use the linear function , the quadratic function and the cubic function Process spectral data segments to obtain a linear function and the corresponding fitting deviation a quadratic function and the corresponding fitting deviation and a cubic function and the corresponding fitting deviation . Among them, . The processor can use the quadratic function corresponding to the fitting deviation as the target piecewise function.
[0040] In one embodiment, the processing method further includes: after splicing all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform, performing visualization processing on the complete filtered data to obtain the spectral waveform corresponding to the complete filtered data.
[0041] The processor can obtain the original spectral waveform of the seed, where the original spectral waveform includes a plurality of original data. After obtaining the original spectral waveform of the seed, the processor can perform segmentation processing on the original spectral waveform to obtain a plurality of spectral data segments. After obtaining a plurality of spectral data segments, the processor can determine the target piecewise function corresponding to each spectral data segment. For each spectral data segment, the processor can fit the target piecewise function of the spectral data segment and the original data included in the spectral data segment to obtain the fitting data corresponding to the spectral data segment. For each spectral data segment, after obtaining the fitting data corresponding to the spectral data segment, the processor can determine the deviation value of the spectral data segment according to the fitting data and the original data. After obtaining the deviation values of all spectral data segments, the processor can determine whether the deviation values of all spectral data segments are within a preset range. When it is determined that the deviation values of all spectral data segments are within the preset range, the processor can perform filtering processing on each original data to obtain the filtered data corresponding to each spectral data segment. After obtaining the filtered data of each spectral data segment, the processor can splice all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform. After obtaining the complete filtered data, the processor can perform visualization processing on the complete filtered data to obtain the spectral waveform corresponding to the complete filtered data.
[0042] In one embodiment, as Figure 2 shown, the processor can obtain the original spectral waveform of the seed, where the original spectral waveform includes a plurality of original waveform data. The processor can perform a derivative-solving process on the original waveform data to obtain the target waveform corresponding to the original spectral waveform. After obtaining the target waveform, the processor can divide the target waveform into multiple segments according to a preset step length and perform segmented statistics to construct a segmented template. The processor can perform segmentation processing on the original spectral waveform according to the segmented template to obtain a plurality of spectral data segments.
[0043] After obtaining the spectral data segments, the processor can use piecewise functions to fit each spectral data segment to obtain the fitting data corresponding to each spectral data segment. After obtaining the fitting data, the processor can evaluate the deviation between the fitting data and the original waveform data. When the deviation evaluations of all spectral data segments are within a preset range, the processor can segment the original spectral waveform according to a segmentation template and perform frequency domain filtering on each spectral data segment of the segmented data to obtain the filtered data corresponding to each spectral data segment. The processor can synthesize the waveforms of each segment of filtered data to obtain the complete filtered waveform data.
[0044] In the above technical solution, by obtaining the original spectral waveform of the seed, where the original spectral waveform includes a plurality of original data; segmenting the original spectral waveform to obtain a plurality of spectral data segments; determining the target piecewise function corresponding to each spectral data segment; for each spectral data segment, fitting the target piecewise function of the spectral data segment and the original data included in the spectral data segment to obtain the fitting data corresponding to the spectral data segment; for each spectral data segment, determining the deviation value of the spectral data segment according to the fitting data and the original data; when the deviation values of all spectral data segments are within a preset range, performing filtering processing on each original data to obtain the filtered data corresponding to each spectral data segment; splicing all the filtered data to obtain the complete filtered data corresponding to the original spectral waveform. By adopting this technical solution, it is possible to better filter the seed spectrum, better remove the noise data in the spectrum, effectively retain the characteristic information in the seed spectrum, and improve the accuracy of seed identification.
[0045] Figure 1 and Figure 2 is a schematic flowchart of a processing method for a seed spectrum in an embodiment. It should be understood that although Figure 1 and Figure 2 the steps in the flowchart are shown sequentially in the direction of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 and Figure 2 at least a part of the steps in
[0046] An embodiment of the present application provides a processor for running a program. When the program runs, it executes the above-mentioned processing method for the seed spectrum.
[0047] An embodiment of the present application provides a processing device for the seed spectrum. The processing device includes the above-mentioned processor.
[0048] An embodiment of the present application provides a storage medium with a program stored thereon. When the program is executed by the processor, it implements the above-mentioned processing method for the seed spectrum.
[0049] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 3 shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store raw data, fitting data, and filtering data. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a processing method for the seed spectrum.
[0050] Those skilled in the art can understand that Figure 3 the structure shown in
[0051] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining an original spectral waveform of a seed, where the original spectral waveform includes a plurality of original data; performing segmentation processing on the original spectral waveform to obtain a plurality of spectral data segments; determining a target segmentation function corresponding to each spectral data segment; for each spectral data segment, fitting the target segmentation function of the spectral data segment and the original data included in the spectral data segment to obtain fitting data corresponding to the spectral data segment; for each spectral data segment, determining a deviation value of the spectral data segment according to the fitting data and the original data; in the case where the deviation values of all spectral data segments are within a preset range, performing filtering processing on each original data to obtain filtering data corresponding to each spectral data segment; splicing all the filtering data to obtain complete filtering data corresponding to the original spectral waveform.
[0052] In one embodiment, performing segmentation processing on the original spectral waveform to obtain a plurality of spectral data segments includes: determining a target waveform corresponding to the original spectral waveform, where each waveform data of the target waveform corresponds to an original data; dividing the target waveform according to a preset step length to obtain a plurality of sub-target waveforms; determining the peaks and valleys of each sub-target waveform, and determining the waveform data between any one peak and the two adjacent valleys of the peak as a target data segment; determining the original data corresponding to the target data segment as a spectral data segment.
[0053] In one embodiment, determining a target segmentation function corresponding to each spectral data segment includes: for any one spectral data segment, processing the spectral data segment with a plurality of segmentation functions to obtain a fitting deviation corresponding to each segmentation function; determining the segmentation function corresponding to the smallest fitting deviation as the target segmentation function.
[0054] In one embodiment, for each spectral data segment, determining the deviation value of the spectral data segment according to the fitting data and the original data includes calculating the deviation value according to formula (1): (1) Where represents the deviation value, n represents the number of original data of the spectral data segment, represents the i-th original data of the spectral data segment, represents the i-th fitting data of the spectral data segment.
[0055] In one embodiment, the processing method further includes: in the case where the deviation value of any one spectral data segment is outside the preset range, returning to the step of performing segmentation processing on the original spectral waveform to obtain a plurality of spectral data segments again until the deviation values of all spectral data segments are within the preset range.
[0056] In one embodiment, filtering each piece of original data to obtain filtered data corresponding to each spectral data segment includes: performing digital low-pass filtering on each piece of original data to obtain filtered data.
[0057] In one embodiment, the processing method further includes: after splicing all the filtered data to obtain complete filtered data corresponding to the original spectral waveform, performing visualization processing on the complete filtered data to obtain a spectral waveform corresponding to the complete filtered data.
[0058] The present application also provides a computer program product which, when executed on a data processing device, is adapted to execute a program for initializing steps of a processing method such as that for a seed spectrum.
[0059] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0060] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.
[0061] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.
[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.
[0063] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.
[0064] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.
[0065] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.
[0066] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity or device including the element.
[0067] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.
Claims
1. A method for processing a seed spectrum, characterized in that: The processing method comprises: Acquire an original spectrum waveform of the seed, wherein the original spectrum waveform includes a plurality of original data; Segmenting the original spectrum waveform to obtain a plurality of spectrum data segments; determining a target piecewise function corresponding to each spectral data segment; For each spectral data segment, fitting the target piecewise function of the spectral data segment and the original data included in the spectral data segment to obtain fitting data corresponding to the spectral data segment; For each spectral data segment, determining a deviation value of the spectral data segment according to the fitting data and the original data; When the deviation values of all spectral data segments are within a preset range, each raw data is filtered to obtain filtered data corresponding to each spectral data segment; All filtered data are concatenated to obtain complete filtered data corresponding to the original spectral waveform.
2. The method for processing a seed spectrum according to claim 1, characterized in that: The segmenting of the original spectrum waveform to obtain a plurality of spectrum data segments comprises: Determine a target waveform corresponding to the original spectrum waveform, wherein each waveform data of the target waveform corresponds to one original data; Dividing the target waveform according to a preset step size to obtain a plurality of sub-target waveforms; Determine the peaks and troughs of each sub-target waveform, and determine the waveform data between any peak and two troughs adjacent to the peak as a target data segment; The original data corresponding to the target data segment is determined as a spectral data segment.
3. The method for processing seed spectrum according to claim 1, characterized in that: Determining the target piecewise function corresponding to each spectral data segment comprises: For any spectral data segment, a plurality of piecewise functions are used to process the spectral data segment to obtain a fitting deviation corresponding to each piecewise function; The piecewise function corresponding to the fitting deviation with the smallest value is determined as the target piecewise function.
4. The method for processing a seed spectrum according to claim 1, characterized in that: For each spectral data segment, determining the deviation value of the spectral data segment according to the fitting data and the original data includes calculating the deviation value according to formula (1): （1） in, represents the deviation value, n represents the number of original data of the spectral data segment, represents the i-th original data of the spectral data segment, represents the i-th fitting data of the spectral data segment.
5. The method for processing seed spectrum according to claim 1, characterized in that: The processing method also includes: When the deviation value of any spectral data segment is outside the preset range, the process returns to the step of segmenting the original spectral waveform to obtain multiple spectral data segments until the deviation values of all spectral data segments are within the preset range.
6. The method for processing seed spectrum according to claim 1, characterized in that: The filtering process for each raw data to obtain filtered data corresponding to each spectral data segment includes: Each raw data is processed by digital low-pass filtering to obtain the filtered data.
7. The method for processing seed spectrum according to claim 1, characterized in that: The processing method also includes: After all the filter data are spliced to obtain complete filter data corresponding to the original spectrum waveform, the complete filter data are visualized to obtain the spectrum waveform corresponding to the complete filter data.
8. A processor, characterized in that: The method is configured to execute the seed spectrum processing method according to any one of claims 1 to 7.
9. A processing device for seed spectrum, characterized in that: The processing means comprises a processor according to claim 8.
10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the processor is configured to perform the processing method for seed spectra according to any one of claims 1 to 7.