Signal processing method and signal processing system
By filtering and processing subsets of signal data, the problem of difficult signal baseline positioning was solved, achieving high-accuracy signal processing in complex signal environments and improving the detection accuracy of in vitro diagnostic equipment.
Patent Information
- Application Number
- CN202511793218.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-01
- Publication Date
- 2026-03-03
AI Technical Summary
Existing technologies suffer from difficulties in signal baseline positioning and inaccurate signal value calculation, especially in complex signals where it is difficult to accurately capture the original baseline, which affects the detection accuracy of in vitro diagnostic equipment.
By acquiring signal curves within a specified time period, signal data within the fluctuation threshold range are filtered out to form a data subset. The two data subsets with the most data are selected as the first and second data subsets, and data processing is performed on them to obtain the first baseline and the second baseline. The effective baseline signal value is calculated using weighting coefficients.
Even when signal baseline changes are caused by environmental variations, the original baseline can be accurately located, maintaining high accuracy in signal processing and reducing the rate of misdiagnosis and missed diagnosis.
Smart Images
Figure CN121598015A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical device technology, and in particular to a signal processing method and signal processing system. Background Technology
[0002] Signal detection scenarios are common in in vitro diagnostic equipment. For example, during sample loading, an analog electrical signal is converted into an optical signal, which is then received by a light receiver and converted back into a digital electrical signal. This signal data is then analyzed to obtain the corresponding detection information. Similarly, in biological sample analysis, diagnostic information related to biomolecules can be extracted from image data signals. Ideally, when the signal is stable, the signal transmitted by the circuit should appear as a regular straight line. However, in real-world environments, various interference signals inevitably superimpose, resulting in a composite signal that is usually the superposition of multiple waveforms with different periods. This composite signal deviates significantly from the target detection signal and exhibits waveform irregularities. If the original baseline of the target waveform cannot be accurately obtained from the composite signal, it will directly lead to deviations in the signal analysis results, affecting the detection accuracy of the in vitro diagnostic equipment and potentially misleading clinical diagnostic results.
[0003] Commonly used baseline extraction methods in the prior art include the moving average method and the polynomial fitting method. The moving average method is easily affected by strong interference waveforms, causing the baseline to shift. The polynomial fitting method has poor adaptability to complex interference waveforms and is difficult to accurately capture the original baseline from composite waveform signals. In particular, when changes in the usage environment cause changes in the signal baseline, it will seriously affect the accuracy of baseline extraction and cause significant interference to the diagnostic results. Summary of the Invention
[0004] In order to overcome the shortcomings of the prior art, the present invention aims to provide a signal processing method and a signal processing system to solve the problems of difficult signal baseline positioning and inaccurate signal value calculation in the prior art.
[0005] The present invention provides a signal processing method in a signal acquisition device, comprising: acquiring a signal curve collected within a specified time period, and forming a total data set from the signal data within the signal curve; sequentially using each signal data as a reference value, summarizing the signal data within the fluctuation threshold range corresponding to the reference value in the total data set and forming a data subset from the signal data; selecting the two data subsets containing the largest number of signal data in all the data subsets as a first data subset and a second data subset; performing data processing on the first data subset and the second data subset respectively to obtain a first baseline and a second baseline; and obtaining an effective baseline signal value based on the first baseline and the second baseline.
[0006] According to the signal processing method provided in the first aspect of the present invention, the largest dataset within the fluctuation threshold range is selected from the signal data collected within a specified time. By utilizing the statistical data of the number of overlaps of waveforms within the amplitude range, the main baseline corresponding to the main waveform and the transition baseline corresponding to the secondary waveform can be accurately selected. Even when the baseline of the signal changes due to changes in the usage environment, the tracking method using dual baselines can still accurately locate the original baseline. Furthermore, it can maintain high accuracy in signal processing when the output signal frequency is switched, providing more reliable data support for the analysis of in vitro diagnostic results and indirectly reducing the rate of misdiagnosis and missed diagnosis.
[0007] In a preferred embodiment of the present invention, the step of summarizing the signal data within the fluctuation threshold range corresponding to the benchmark value in the total data set and forming the signal data into a data subset includes: determining whether the fluctuation amplitude of each signal data in the total data set relative to the benchmark value does not exceed a preset threshold; if so, the signal data that meets the condition is incorporated into the data subset of the benchmark value.
[0008] In a preferred embodiment of the present invention, the step of forming a total data set from the signal data within the signal curve includes: sorting all the signal data from smallest to largest or from largest to smallest, and summarizing them into the total data set.
[0009] In a preferred embodiment of the present invention, the step of sequentially using each of the signal data as a reference value includes: changing the reference value according to the sorting order of each of the signal data; determining whether the fluctuation amplitude of each of the signal data in the total data set relative to the reference value does not exceed a preset threshold includes: comparing each of the signal data with the reference value according to the sorting order of each of the signal data in the total data set and obtaining the relevant fluctuation amplitude; summarizing the signal data within the fluctuation threshold range corresponding to the reference value in the total data set and forming a data subset of the signal data further includes: if the fluctuation amplitude is greater than the preset threshold, then changing the reference value.
[0010] In a preferred embodiment of the present invention, the signal data within the data subset are all within the positive fluctuation threshold range of the reference value corresponding to the data subset or are all within the negative fluctuation threshold range of the reference value corresponding to the data subset.
[0011] In a preferred embodiment of the present invention, the step of summarizing the signal data within the fluctuation threshold range corresponding to the benchmark value in the total data set and forming a data subset from the signal data further includes: if the fluctuation amplitude is greater than the preset threshold, using the signal data corresponding to the fluctuation amplitude as the starting signal data of the next modified benchmark value; the step of determining whether the fluctuation amplitude of each signal data in the total data set relative to the benchmark value does not exceed the preset threshold includes: after modifying the benchmark value, sequentially determining whether the fluctuation amplitude of subsequent signal data relative to the modified benchmark value does not exceed the preset threshold, starting from the starting signal data; the step of incorporating the signal data that meets the conditions into the data subset of the benchmark value includes: when it is determined that the current signal data does not meet the incorporation conditions of the data subset corresponding to the current benchmark value, incorporating all the original signal data corresponding to the current benchmark value up to the previous signal data into the data subset.
[0012] In a preferred embodiment of the present invention, the step of obtaining a first baseline and a second baseline by performing data processing on the first data subset and the second data subset respectively includes: calculating the average of all the signal data in the first data subset to obtain the first baseline; and calculating the average of all the signal data in the second data subset to obtain the second baseline.
[0013] In a preferred embodiment of the present invention, the number of signal data contained in the first data subset is greater than or equal to that in the second data subset, and the effective baseline signal value L is obtained based on the following formula: L=T1*K+T2*(1-K); where T1 is the first baseline, T2 is the second baseline, and K is the weighting coefficient.
[0014] In a second aspect, the present invention provides a signal processing system, comprising: an acquisition unit for acquiring signals; a storage unit for storing signal data acquired by the acquisition unit to form a data set; a filtering unit for, based on a total data set composed of all signal data acquired by the acquisition unit within a specified time, sequentially identifying signal data within the total data set that falls within a fluctuation threshold range of the reference value, using each signal data as a reference value, and merging the signal data into the storage unit to form a data subset corresponding to the reference value; a processing unit for, taking the two data subsets containing the largest number of signal data in all the data subsets as a first data subset and a second data subset, respectively processing the first data subset and the second data subset to obtain a first baseline and a second baseline; and a calculation unit for, obtaining a valid baseline signal value based on the first baseline and the second baseline.
[0015] In a third aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the signal processing method as described in the first aspect embodiment.
[0016] Other features and advantages of the invention will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures and / or processes particularly pointed out in the description, claims, and drawings. Attached Figure Description
[0017] Figure 1 A flowchart of a signal processing method provided in an embodiment of the present invention;
[0018] Figure 2 A flowchart illustrating the signal processing method provided in an embodiment of the present invention;
[0019] Figure 3 This is a schematic diagram of the structure of a signal processing system provided in an embodiment of the present invention. Detailed Implementation
[0020] The following detailed description of the embodiments of the present invention, in conjunction with the accompanying drawings, will provide a thorough understanding of how the present invention uses technical means to solve technical problems and achieve technical effects, enabling its implementation. It should be noted that these specific descriptions are merely intended to facilitate a clearer understanding of the present invention by those skilled in the art, and are not intended to limit the scope of the invention. For example, the terms "first" and "second" mentioned in the embodiments of the present invention are not intended to limit the invention, but are merely used to indicate the sequence numbers of multiple identical or similar devices or mechanisms. Those skilled in the art can readjust these sequence numbers for ease of description or during the organization of technical solutions. Furthermore, alternative solutions are described for some mechanisms in different embodiments, and these alternatives can be applied to other identical or similar devices or mechanisms. As long as there is no conflict, the various embodiments and features in each embodiment of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.
[0021] Signals can be categorized into electrical signals, optical signals, and wireless signals based on their form. Signal acquisition devices typically include a signal acquisition module and a signal conversion module. The signal acquisition module can acquire corresponding signals from the environment, such as optical signals, wireless signals, or electrical signals. The acquired signals are then further converted into electrical signals by the signal conversion module for data processing. The signal acquisition device includes a signal processing unit that can identify valid information in the electrical signals so that the control system can perform corresponding processing actions based on this valid information. Of course, a signal transmission module can be added to the signal acquisition device if design requirements dictate. The output electrical signals are transmitted through the signal transmission module and acquired by the signal acquisition module. This implementation method is also found in the signal acquisition device to which this invention is applicable.
[0022] Electrical signals in existing technologies typically include analog and digital signals. During signal processing, it may be necessary to remove redundant signals, filter out unwanted noise, or transform the signal into a form that is easier to process, transmit, analyze, and recognize—all common signal processing techniques. However, in in vitro diagnostic equipment, due to the actual operating environment, signals with various periodic waveforms or other irregular noises may inevitably be superimposed on the original baseline, becoming a composite signal acquired by the device.
[0023] When a signal curve contains multiple signals with different periods and waveforms, it may deviate significantly from the signal being detected. The waveforms become irregular and difficult to process, especially since the baseline of the target signal is hard to determine, which can directly lead to deviations in signal analysis results. Common methods for handling composite signals include the moving average method and polynomial fitting. The moving average method filters out interference signals with large amplitude deviations by taking values at reasonable intervals, but it is easily affected by strong interference waveforms, leading to baseline shifts and affecting subsequent signal analysis. The polynomial fitting method, when used with complex interference waveforms containing multiple signals of different periods, is not only computationally intensive, but also struggles to accurately capture the original baseline corresponding to the target waveform, especially as the environment changes and interference signals vary, resulting in inaccurate signal value calculations.
[0024] Accordingly, see Figure 1 In a first aspect embodiment, the present invention proposes a signal processing method applied to the aforementioned signal acquisition device, the method specifically comprising: S100: Acquire the signal curve collected within a specified time period and make the signal data within the signal curve form a total data set; Taking a specified time T as an example, the signal acquisition device can be set to acquire one data signal per unit time, where the unit time can be set to 1 second, 0.1 seconds, etc., and designed reasonably according to actual use. Each signal data is usually set as an electrical signal and represented as a corresponding signal value in the signal coordinate system. After the specified time T, a signal curve S with the specified time T as the width and the signal value A as the height can be acquired. However, because the signal acquisition device cannot distinguish between the target signal and the interference signal during the signal acquisition process, the signal acquired at a certain moment is the result of the superposition of signals with different periods and waveforms at the same time. For example, at that moment, the signal value of the original baseline is B1, and the signal values of the interference waveforms are C1, D1, etc. Then, the signal value acquired by the signal acquisition device at this time is A1 = B1 + C1 + D1... Therefore, it is necessary to identify the original baseline from the acquired signal curve to obtain accurate analysis results.
[0025] For the acquired signal curve S, all signal data A1,...,A within the signal curve S can be made... i (i∈1,2,3...,n) all form a set, namely the total data set E={A1,...,Ai} (i∈1,2,3...,n), so as to extract, analyze and process all signal data within the obtained signal curve S.
[0026] S200, taking each signal data as a reference value, summarizes the signal data within the fluctuation threshold range corresponding to the reference value in the total data set and makes the signal data into a data subset; Continuing with the aforementioned embodiment as an example, using each signal data as a reference value, such as A1 as the reference value, the signal data A1,...,A are continuously judged. i Whether it is within the fluctuation threshold range of A1, where the fluctuation threshold range is determined based on the fluctuation range of the target waveform, and the signal data within the fluctuation threshold range is the signal data that matches the reference value. This step uses a one-by-one screening method. Under the premise that there is sufficient signal data in the signal curve S, the reference value is set as the signal value near the baseline or directly assumed to be the baseline value. Assuming each signal data as the reference value, the signal data that conforms to the fluctuation threshold range of the target waveform curve is screened and formed into a data subset. Under normal circumstances, the main baseline corresponding to the target waveform runs through the entire signal curve. Even under the interference of other different signals, the signal data that falls into the main baseline due to interference from the target waveform should be the most. Therefore, the data subset with the most data signals in the data subset obtained from each signal data has a higher correlation with the baseline of the target waveform, thereby achieving accurate positioning of the target baseline.
[0027] In this embodiment, the threshold value within the fluctuation threshold range is a set value, so step S200 can be implemented as follows: S210, determine whether the fluctuation amplitude of each signal data in the total data set relative to the reference value does not exceed the preset threshold; S220, if so, then all signal data that meet the conditions will be merged into the data subset of the reference value.
[0028] Taking the total dataset E={A1,...,A7} as an example, when A1 is used as the baseline value, assuming the preset threshold value is K, the fluctuation amplitude △A1 of A1 relative to A1 is 0, the fluctuation amplitude △A2 of A2 relative to A1 is △A2=|A2-A1|, and the fluctuation amplitudes of the remaining data △A3,...,△A7 are compared with the preset threshold value K, and those that meet the △A1 value are selected. i Given signal data A1, A3, and A5 that meet the condition ≤ K, the subset of data with A1 as the baseline is E1 = {A1, A3, A5}. Similarly, when A2 is the baseline, the fluctuation amplitude ΔA1 of A1 relative to A2 is ΔA1 = |A1 - A2|, the fluctuation amplitude ΔA2 of A2 relative to A2 is 0, and the fluctuation amplitudes of the remaining data ΔA3, ..., ΔA7 are compared with the preset threshold K. Data that meets the condition ΔA1, A3, and A5 is then selected. i Given signal data A2, A4, A6, and A7 with a value ≤ K, the subset of data with A2 as the reference value is E2 = {A2, A4, A6, A7}. The same logic applies to other reference values. Thus, the subset of data E1 to E7 for each signal data in the total data set can be obtained.
[0029] To reduce the computational load of data processing in S200, the signal data within the total data set can be pre-processed in step S100 when assembling the total data set. For example, in this embodiment, the data signals in the total data set are sorted, specifically as follows: S110 sorts all signal data from smallest to largest or largest to smallest and summarizes them into a total data set.
[0030] After sorting all data signals by size, when obtaining the corresponding data subset based on each signal data as a baseline value, it is no longer necessary to check each signal data in the total data set one by one, significantly reducing the amount of computation and lowering the risk of calculation errors. For example, when the signal data obtained in signal curve S corresponds to signal values of 10, 6, 2, 9, 8, 5, the original total data set is E0={10,6,2,9,8,5}. Regardless of the preset threshold value, the total amount of computation in S210 is 6*6=36 times. However, after sorting all signal data in the original total data set E0, taking the order from smallest to largest as an example, the new total data set E0 becomes... p={3,5,6,7,9,10}, and set a preset threshold K=2. When A1=3 is used as the reference value, make A i After comparing each value up to A2, the remaining signal data with fluctuation amplitudes greater than 2, namely A3, A4, A5, and A6, can be excluded from the calculation and the result is a subset of data {3, 5} of 2. For example, if A3 = 6 is used as the baseline value, then A... i After comparing each item up to A5 (i.e., 9), A6 no longer needs to be calculated, resulting in a significant reduction in the computational workload compared to the total data set before sorting.
[0031] Based on the sorted total data set, when the preset threshold only takes positive values, i.e., △A i =|A i -A k In this embodiment, step S200 can optimize the calculation method, wherein... Step S210 includes: S211, based on the sorting order of each signal data in the total data set, compare each signal data with the reference value and obtain the fluctuation amplitude.
[0032] Step S200 also includes: S230: If the fluctuation amplitude is greater than the preset amplitude, the benchmark value will be changed.
[0033] That is, to obtain the sorted total data set E p In the case of {A1, A2, ..., A6}, with A1 as the baseline value, A1 to A6 are compared sequentially against A1. Following the previous example, when A3 is compared, its fluctuation amplitude ΔA3 is greater than 2, so the baseline value A1 is changed to the next baseline value A2. Similarly, with A2 as the baseline value, A3 to A6 are compared sequentially against A2. When A5 is compared, its fluctuation amplitude ΔA5 is greater than 2, so the baseline value A2 is changed to the next baseline value A3. The comparison method for the remaining baseline values is the same. This reduces the number of comparisons and thus the number of calculations.
[0034] To further optimize the calculation method of the algorithm in step S200, this embodiment can be further optimized as follows.
[0035] Step S200 further includes: S240, if the fluctuation amplitude is greater than the preset threshold, the signal data corresponding to the fluctuation amplitude is used as the starting signal data of the next changed reference value.
[0036] In step S240, if the current reference value is A1 and the next reference value is A2, combined with the previous step S230 where the current reference value A1 changes to the next reference value A2 at signal data A3, then signal data A3 can be directly used as the starting signal data for the next reference value A2. Since the signal data A1~A2 before A2 have already been compared with the current reference value A1 and are within the data subset that matches the current reference value A1, the comparison result of the current reference value A1 can be directly used. The signal data A1~A2 before A3 do not need to continue being compared with the reference value A2. The ending signal data for the current reference value A2 is A5. Similarly, if the current reference value is A3 and the next reference value is A4, the ending signal data for the current reference value is A5. Since A4 is the last signal data and still satisfies the data subset of A3, A4 does not need to be used as the starting signal data for the next reference value A4 for further comparison.
[0037] According to the judgment and comparison method based on the preset threshold, step S210 also includes: S212, after changing the reference value, judge in order from the starting signal data whether the change amplitude of all subsequent signal data relative to all changed reference values does not exceed the preset threshold. And in step S220, the following is included: S221, when it is determined that the current signal data does not meet the inclusion condition of the data subset corresponding to the current reference value, all the original signal data corresponding to the current reference value are included into the data subset according to the arrangement order up to the previous signal data.
[0038] In step S212, from A1 to A i The comparison is performed sequentially. For example, as described in the previous embodiment, when A3 is the starting signal data with A2 as the reference value, the next signal data to be compared after A3 is A4, and so on. This step significantly reduces the number of comparisons of each signal data in the total data set. Continuing with the complete total data signal E0 as an example, when the reference value is A1, the compared signal data are A1 to A3. A3 is a signal data that does not meet the preset threshold, so it is used as the starting data signal for the reference value A2. Thus, the compared signal data for A2 are A3 to A5. A5 is a signal data that does not meet the preset threshold, so when the next reference value is A3, the compared signal data is A5. Since A4 has already fallen into the data subset corresponding to the reference value A2, no further comparison is needed when the reference values are A3 to A6.
[0039] In step S221, after combining the aforementioned judgment and comparison method, a data subset corresponding to the benchmark value can be obtained. When the preset threshold is set to a positive value, the data subset should consist of the signal data corresponding to the original benchmark value up to the signal data falling within the fluctuation threshold range. Continuing with the aforementioned embodiment, if the signal data that does not meet the judgment and comparison conditions when A1 is the benchmark value is A3, then the previous signal data that meets the comparison conditions is A2. Therefore, the signal data contained in this data subset are A1~A2, and the data subset corresponding to A1 is E1. Given a base value of A1, A2, and a data set of A5 that does not meet the comparison criteria when A2 is the baseline, the data set containing A2 to A4 is E2={A2, A3, A4}. Since A5 does not meet the comparison criteria when A3 is the baseline, the data set for A3 is E3={A3, A4}. Similarly, the data sets for A4, A5, and A6 are E4={A4, A5}, E5={A5, A6}, and E6={A6}, respectively. In this embodiment, the data set corresponding to each signal data can be obtained with minimal computation, significantly improving computational efficiency and quickly locating the baseline position.
[0040] Of course, in addition to being set to a fixed value, the threshold set within the fluctuation threshold range can also be set to a variable threshold based on the target waveform or taking into account other factors, such as the variable threshold changing with the reference value. The setting is based on the actual signal processing needs and is within the scope of this invention.
[0041] Existing data processing methods for baseline positioning typically combine horizontal and vertical information with time factors in the acquired signal curve to locate the baseline. Therefore, when the interference signal changes, it will disrupt the key information of the composite signal in the signal curve, thus significantly increasing the difficulty of baseline positioning. However, this method can ignore the serious impact of changes in the interference signal on the signal processing process in time, and accurately locate the target baseline.
[0042] S300: Select the two data subsets with the largest number of signal data in all data subsets as the first data subset and the second data subset; Having acquired the data subset corresponding to each signal, the baseline of the target waveform can be located by determining which data subset within the fluctuation threshold range contains the most signal data. For example, in the aforementioned embodiment, among the acquired E1~E6, the data subset containing the most signal data is E2. Therefore, the first data subset is located as E2, and the baseline of the main waveform is obtained accordingly. Normally, the data subset corresponding to the main waveform baseline contains the most signal data. However, under certain specific circumstances, due to the influence of data acquisition volume and fluctuation interference, the number of signal data contained in the data subset corresponding to the main waveform baseline may be the same as the number of signal data contained in the data subset corresponding to the other secondary waveform baselines, or there may be multiple data subsets with the second largest number of signal data. Under these conditions, since the main waveform baseline or secondary waveform baseline is generally closer to the zero line than the other interfering waveform baselines, the data subset with the smaller corresponding reference value is taken as the data subset of the main waveform baseline or secondary waveform baseline. For example, in the two data subsets E1, E3, E4 and E5 acquired above, the number of data is 2, so E1 is taken as the second data subset.
[0043] S400, after performing data processing on the first data subset and the second data subset respectively, the first baseline and the second baseline are obtained; In the data processing, the averaging method can be used. For example, in the total data set E, the baseline value of the first baseline is T1 = (A2 + A3 + A4 + A5) / 4, and the baseline value of the second baseline is T2 = (A3 + A4 + A5 + A6) / 4. Of course, the moving average method or the arithmetic average method can also be used, all of which are within the scope of this invention.
[0044] S500 obtains the effective baseline signal value based on the first baseline and the second baseline.
[0045] After acquiring the first and second baselines, the effective baseline signal value L can be obtained based on the following formula: L = T1*K + T2*(1-K) Where T1 is the first baseline, T2 is the second baseline, and K is the weighting coefficient.
[0046] The effective baseline signal is composed of a first baseline and a second baseline. From a signal composition perspective, the composite signal includes a dominant main waveform and an interfering waveform. The interfering waveform periodically fluctuates up and down on the main waveform baseline. The original signal, which remains stable per unit time, can be considered a straight line and defined as the original baseline. The signal on this original baseline is the effective baseline signal. In practical applications, the acquired signal curve is a composite curve resulting from the superposition of multiple different waveforms on the original baseline. Since the main waveform curve intersects with the original baseline the most frequently, and the overlap between the main waveform and the original baseline is highest within the amplitude range above and below the baseline, the region with the most concentrated composite signal waveform intersections corresponds precisely to the baseline of the main waveform. The subset of data containing the most signal data is used as the intersection data between the corresponding main waveform curve and the original baseline. Simultaneously, within this amplitude range, the baseline corresponding to the second-highest overlap with the composite waveform, the secondary waveform curve, is the transition baseline during dynamic signal changes. The acquisition of the original baseline also involves a significant number of intersections, and the subset of data containing the second-highest signal data is used as the intersection data between the corresponding secondary waveform curve and the original baseline.
[0047] In this embodiment, by calculating the weighting coefficients of the dual baselines, changes in the signal baseline can be tracked in real time, and high accuracy can still be maintained even when the signal switching frequency is used.
[0048] According to the foregoing embodiments, see Figure 2 The specific implementation process of this signal processing method may include the following steps: S1, set i=1, k=1; S2, within a specified time T, collect m signal data points of the signal curve, and combine multiple signal data points a i The total dataset is composed of (i∈1,...,m). After merging the signal data in the total dataset, E is obtained by sorting the signal data in the total dataset from smallest to largest. p ={A1,A2,...,A m}, enter S3; S3, determine if i is less than or equal to m. If yes, proceed to S4; otherwise, proceed to S5. S4, with A i Using the baseline value, determine |A k -A i |≤K, if yes, proceed to S41; otherwise, proceed to S42; S41, determine if k does not exceed m. If yes, proceed to S411; otherwise, proceed to S413. S411, k=k+1, proceed to S412; S412, determine if k is greater than m. If yes, proceed to S42; otherwise, return to S4. S413, records the subset of data that meets the conditions, Ei ={A i A k}, the cumulative count num=k-i+1, proceed to S43; S42, records the subset of data that meets the conditions, E i ={A i A k-1}, cumulative count num=ki, proceed to S43; S43, i=i+1, return to S3; S5, from all data subsets E i The first data subset P1 and the second data subset P2 that obtain the most signal data in (i∈1,...,m); S6, after averaging the signal data in P1 and P2, we obtain the first baseline T1 and the second baseline T2 respectively; S7, effective baseline signal value L=T1*K+T2*(1-K).
[0049] To implement the signal processing method of the first aspect embodiment described above, the present invention provides a signal processing system 10 in a second aspect, see below. Figure 3 The signal processing system 10 includes: Acquisition unit 11 is used to acquire signals; Storage unit 12 is used to store signal data acquired by acquisition unit 11 to form a data set; The filtering unit 13 is used to identify signal data within the fluctuation threshold range of the reference value in the total data set composed of all signal data collected by the acquisition unit 11 within a specified time, and to import the signal data into the storage unit 12 to form a data subset corresponding to the reference value. Processing unit 14 is used to take the two data subsets with the largest number of signal data in all data subsets as the first data subset and the second data subset, and to process the first data subset and the second data subset respectively to obtain the first baseline and the second baseline. The calculation unit 15 is used to obtain the effective baseline signal value based on the first baseline and the second baseline.
[0050] In a third aspect, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored program, wherein, when the program is running, the computer-readable storage medium controls the execution of the signal processing method of the first aspect embodiment of the present invention within a device.
[0051] This invention also provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the signal processing methods described in the embodiments; to avoid repetition, these methods are not described in detail here. Alternatively, when executed by the processor, the computer program implements the functions of each model / unit of the control device described in the embodiments; to avoid repetition, these functions are not described in detail here.
[0052] Computer devices include, but are not limited to, processors and memory. Those skilled in the art will understand that the above are merely examples of computer devices and do not constitute a limitation on computer devices. A computer device may include more or fewer components than illustrated, or a combination of certain components, or different components. For example, a computer device may also include input / output devices, network access devices, buses, etc.
[0053] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.
[0054] Memory can be an internal storage unit of a computer device, such as a hard drive or RAM. Memory can also be an external storage device of a computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the computer device. Memory can also be used to temporarily store data that has been output or will be output.
[0055] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).
[0056] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
[0057] Finally, it should be noted that the above description is merely the preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any person skilled in the art can make many possible variations and simple substitutions to the technical solutions of the present invention using the disclosed methods and techniques without departing from the scope of the present invention; all of these variations fall within the protection scope of the present invention.
Claims
1. A signal processing method, applied in a signal acquisition device, characterized in that, include: Acquire signal curves collected within a specified time period, and combine the signal data within the signal curves into a total data set; Using each of the signal data as a reference value, the signal data within the fluctuation threshold range corresponding to the reference value in the total data set are aggregated and the signal data are made into a data subset. Select the two data subsets containing the largest number of signal data from all the data subsets as the first data subset and the second data subset; After processing the first data subset and the second data subset respectively, a first baseline and a second baseline are obtained; The effective baseline signal value is obtained based on the first baseline and the second baseline.
2. The method according to claim 1, characterized in that, The process of summarizing the signal data within the fluctuation threshold range corresponding to the benchmark value in the total data set and forming a data subset from the signal data includes: Determine whether the fluctuation amplitude of each signal data in the total data set relative to the reference value does not exceed a preset threshold; If so, the signal data that meets the conditions will be incorporated into the data subset of the reference value.
3. The method according to claim 2, characterized in that, The step of forming a total data set from the signal data within the signal curve includes: All the signal data are sorted from smallest to largest or from largest to smallest and then summarized into the total data set.
4. The method according to claim 3, characterized in that, The step of using each of the signal data as a reference value sequentially includes: The reference value is changed according to the sorting order of the signal data; The step of determining whether the fluctuation amplitude of each signal data in the total data set relative to the reference value does not exceed a preset threshold includes: Based on the sorting order of the signal data within the total data set, each signal data is compared with the benchmark value to obtain the relevant fluctuation amplitude. The step of summarizing the signal data within the fluctuation threshold range corresponding to the benchmark value in the total data set and forming a data subset from the signal data further includes: If the fluctuation amplitude is greater than the preset threshold, then the benchmark value is changed.
5. The method according to claim 4, characterized in that, The signal data within the data subset are all within the positive fluctuation threshold range of the reference value corresponding to the data subset, or are all within the negative fluctuation threshold range of the reference value corresponding to the data subset.
6. The method according to claim 5, characterized in that, The step of summarizing the signal data within the fluctuation threshold range corresponding to the benchmark value in the total data set and forming a data subset from the signal data further includes: If the fluctuation amplitude is greater than the preset threshold, the signal data corresponding to the fluctuation amplitude is used as the starting signal data of the next modified reference value. The step of determining whether the fluctuation amplitude of each signal data in the total data set relative to the reference value does not exceed a preset threshold includes: After changing the reference value, the fluctuation amplitude of the subsequent signal data relative to the changed reference value is determined sequentially from the initial signal data in the order of arrangement to see if it does not exceed the preset threshold. The step of incorporating the signal data that meets the conditions into the data subset of the reference value includes: When it is determined that the current signal data does not meet the import conditions of the data subset corresponding to the current reference value, all the original signal data corresponding to the current reference value are imported into the data subset according to the order of arrangement up to the previous signal data.
7. The method according to any one of claims 1 to 6, characterized in that, The step of processing the first data subset and the second data subset respectively to obtain the first baseline and the second baseline includes: The first baseline is obtained by averaging all the signal data within the first data subset. The second baseline is obtained by averaging all the signal data within the second data subset.
8. The method according to any one of claims 1 to 6, characterized in that, The number of signal data contained in the first data subset is greater than or equal to that in the second data subset, and the effective baseline signal value L is obtained based on the following formula: L = T1*K + T2*(1-K) Where T1 is the first baseline, T2 is the second baseline, and K is the weighting coefficient.
9. A signal processing system, characterized in that, include: The acquisition unit is used to acquire signals; A storage unit is used to store the signal data collected by the acquisition unit to form a data set; The filtering unit is used to identify, based on the total data set composed of all signal data collected by the acquisition unit within a specified time, the signal data within the total data set that are within the fluctuation threshold range of the reference value, and to incorporate the signal data into the storage unit to form a data subset corresponding to the reference value. The processing unit is configured to take the two data subsets containing the largest number of signal data in all the data subsets as the first data subset and the second data subset, and to process the first data subset and the second data subset respectively to obtain the first baseline and the second baseline. The calculation unit is used to obtain the effective baseline signal value based on the first baseline and the second baseline.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the signal processing method as described in any one of claims 1 to 8.