Signal value processing method and system

Abstract

The invention provides a signal value processing method and system, and the method comprises the following steps: obtaining a group of continuous signal value increase data in the total time according to a preset collection method, and processing the signal value increase data; acquiring a signal value difference according to the processed signal value increase data so as to determine an abnormal point; performing signal correction on the residual signal points according to the abnormal points; according to the method, aiming at a unique mechanism of homogeneous-phase light-excited chemiluminescence, the exciting light related noise can be accurately inhibited, especially, the noise is effectively removed under the condition of low concentration, the signal-to-noise ratio is remarkably improved, and the equipment cost is saved; the system has the same beneficial effects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of homogeneous photo-induced chemiluminescence, and in particular to a signal value processing method and system suitable for homogeneous photo-induced chemiluminescence. BACKGROUND

[0002] In the process of homogeneous photo-induced chemiluminescence testing, the source of noise contains both the common characteristics of the homogeneous reaction system and the unique interference of the photoexcitation process: firstly, the random fluctuations of chemiluminescence reactions still produce intrinsic noise, such as the probabilistic difference in energy transfer between donors and acceptors leading to fluctuations in luminescence intensity; secondly, the inherent noise (dark current, thermal noise, etc.) of the photoelectric detection component still exists, and due to the additional light signal introduced by photoexcitation, the influence of shot noise is more prominent; thirdly, among external environmental disturbances, the scattering and leakage of excitation light become unique sources of noise, and the excitation light that is not absorbed by the donor is scattered by the reaction system and enters the detector, or the excitation light directly leaks into the detection light path, which will form strong background noise; in addition, the stability fluctuations of the excitation light source (such as light intensity drift, wavelength shift) will also introduce additional noise, further interfering with the extraction of effective signals. As a core indicator for evaluating system performance, the signal-to-noise ratio (SNR) plays a decisive role in the sensitivity and accuracy of homogeneous photo-induced chemiluminescence testing. High signal-to-noise ratio can ensure that the system accurately identifies the specific luminescence generated by the target binding event, thereby realizing reliable detection of low-abundance analytes (such as trace amounts of antibodies, small molecule antigens); on the contrary, low signal-to-noise ratio will cause non-specific signals (such as weak luminescence caused by non-specific binding) to be confused with noise, significantly reducing the specificity of detection, and even causing misjudgment of results, which may cause serious consequences in clinical diagnosis.

[0003] Currently, the homogeneous photo-induced chemiluminescence testing system faces special challenges in noise suppression and signal-to-noise ratio improvement: on the one hand, the coupling of excitation light-related noise (scattering, leakage, light source fluctuation) and traditional noise makes it difficult to suppress noise; on the other hand, traditional signal processing methods cannot effectively distinguish between specific luminescence signals and background noise caused by excitation light interference, resulting in limited signal-to-noise ratio improvement of the system in low concentration detection scenarios.

[0004] Therefore, how to accurately suppress excitation light-related noise based on the unique mechanism of homogeneous photo-induced chemiluminescence, especially to effectively remove noise at low concentrations and significantly improve the signal-to-noise ratio is a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0005] To solve the above technical problems, the present application aims to provide a signal value processing method and system, based on the unique mechanism of homogeneous photochemical chemiluminescence, which can accurately suppress the excitation light related noise, especially effectively remove noise in low concentration, significantly improve the signal-to-noise ratio, and save equipment cost.

[0006] The first object of the present application is to provide a signal value processing method; The technical solutions provided by the present application are as follows: A signal value processing method, comprising the following steps: According to a preset acquisition method, a set of continuous signal value growth data within a total time is obtained, and the signal value growth data is processed; According to the processed signal value growth data, signal value difference is obtained to determine abnormal points; According to the abnormal points, signal correction is performed on the remaining signal points.

[0007] Preferably, the signal value growth data within the total time is obtained according to the preset acquisition method, and the signal value growth data is processed, specifically including: According to a single counter counting method, a set of continuous signal value growth data within a total time is obtained; The signal value growth data is subjected to exponential fitting to obtain a corresponding exponential fitting function, and the exponential fitting value corresponding to the signal value growth data is obtained through the exponential fitting function; The signal value growth data and the exponential fitting value corresponding to the signal value growth data are subjected to residual analysis to obtain the deviation of each signal value growth data; According to the deviation, initial abnormal points are obtained.

[0008] Preferably, the initial abnormal points are obtained according to the deviation, specifically including: The deviation is judged: If the deviation is more than 3 or equal to 3 points above the mean ± standard deviation of residual deviation and none of them is continuous, the remaining points are directly removed and refitted; If the deviation is more than 3 or equal to 3 points above the mean ± standard deviation of residual deviation and any two of them are adjacent and continuous, sliding window analysis is performed from the first data to obtain initial abnormal points.

[0009] Preferably, the initial abnormal points are obtained by sliding window analysis from the first data, specifically including: Sliding window fitting is performed within a preset window to obtain the fitting value corresponding to the preset window; Residual deviation analysis is performed in each preset window and judged respectively: If one of the points exceeds the | mean ± standard deviation | in ≥ 2 consecutive windows, the point is determined as an initial abnormal point.

[0010] Preferably, after the determination of the initial abnormal point, the method further comprises: performing exponential fitting on the remaining points after the initial abnormal point is removed to obtain corresponding fitting values; re-performing residual analysis on the remaining points according to the corresponding fitting values to obtain current deviations; judging according to the current deviations.

[0011] Preferably, the judging according to the current deviations comprises: if the number of points with current deviations above the mean ± standard deviation of residual deviations is less than 3, or equal to 3 and all are not continuous, then directly removing and re-fitting using the remaining points; if the number of points with current deviations above the mean ± standard deviation of residual deviations is greater than 3, or equal to 3 and any two consecutive deviations are above the mean ± standard deviation of residual deviations, then starting from the first data, performing sliding window analysis.

[0012] Preferably, the method of obtaining signal value differences from the processed signal value growth data to determine abnormal points comprises: comparing the data of the same position fitted by each window to obtain signal value differences; determining abnormal points according to the signal value differences.

[0013] Preferably, the signal correction of the remaining signal points according to the abnormal points comprises: obtaining the mutation signal difference of each abnormal point according to the position of the abnormal point; subtracting the mutation signal difference from the normal signal points before each abnormal point to perform signal correction.

[0014] Preferably, after the signal correction of the remaining signal points according to the abnormal points, the method further comprises: calculating unit integral data from the obtained corrected signal value growth data, and correcting the unit integral data.

[0015] The second object of the present application is to provide a signal value processing system. The technical scheme provided by the present application is as follows: A signal value processing system comprises a processing module, a determination module and a correction module. The processing module is configured to obtain a set of continuous signal value growth data within a total time according to a preset acquisition method, and process the signal value growth data. The determination module is used for obtaining signal value difference according to the processed signal value growth data to determine abnormal points. The correction module is used for performing signal correction on the remaining signal points according to the abnormal points.

[0016] Compared with the prior art, the signal value processing method provided by the application comprises the following steps: obtaining a group of continuous signal value growth data in total time according to a preset acquisition method, and processing the signal value growth data; obtaining signal value difference according to the processed signal value growth data to determine abnormal points; and performing signal correction on the remaining signal points according to the abnormal points. Through the method, the excitation light related noise can be precisely suppressed according to the unique mechanism of homogeneous photochemical chemiluminescence, especially in the case of low concentration, the noise can be effectively removed, the signal-to-noise ratio is significantly improved, and the equipment cost is saved.

[0017] The application further provides a signal value processing system. Since the system and the signal value processing method solve the same technical problem and belong to the same technical concept, the system should have the same beneficial effects as the signal value processing method, and thus will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 A flowchart of a signal value processing method provided by an embodiment of the application is shown in the figure. Figure 2 A flowchart of step S1 provided by an embodiment of the application is shown in the figure. Figure 3 An index fitting diagram provided by an embodiment of the application is shown in the figure. Figure 4 A window fitting diagram provided by an embodiment of the application is shown in the figure. Figure 5 An index fitting diagram after abnormal point rejection provided by an embodiment of the application is shown in the figure. Figure 6 A structural diagram of a signal value processing system provided by an embodiment of the application is shown in the figure. Figure 7 A structural diagram of an electronic device provided by an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0020] In order for those skilled in the art to better understand the technical solutions in the present application, the technical solutions provided by the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] As shown in Figure 1 The present application provides a signal value processing method, comprising the following steps: S1. A set of continuous signal value growth data in total time is obtained according to a preset acquisition method, and the signal value growth data is processed; S2. The signal value difference is obtained according to the processed signal value growth data to determine an abnormal point; S3. The remaining signal points are corrected according to the abnormal point.

[0022] In steps S1 to S3, homogeneous photochemical chemiluminescence is a detection technology for biological molecules based on the short-distance diffusion of singlet oxygen energy of two nanometer microspheres to excite the chemiluminescence reaction of the adjacent sites formed. In signal detection, there are three core steps: Light excitation: using a specific wavelength to excite oxygen supply microspheres to generate singlet oxygen; Chemiluminescence reaction: the oxygen receiving microspheres combined with the oxygen supply microspheres receive the singlet oxygen and emit light of another wavelength; at this time, the excitation light should be cut off and the optical detection should be started immediately; Signal decay: as the reaction proceeds, the singlet oxygen is consumed, and the light intensity continues to weaken.

[0023] Here, the signal decay is derived from the first-order reaction characteristics. According to the kinetic equation of the first-order reaction, the change of reactant concentration with time is exponential, and since the luminescent signal is directly related to the concentration of reactants, the decay law of signal intensity with time is exponential decay. Since the light intensity generated by the chemiluminescence reaction is weak, photomultiplier tube is needed for photon counting integral detection, which cannot identify different wavelengths, and it is difficult to distinguish and eliminate noise during detection. In the case that the instrument does not have a shutter component to save cost, the present method uses the decay law of the reaction to obtain a set of continuous signal value growth data in total time according to a preset acquisition method, and processes the signal value growth data; the signal value difference is obtained according to the processed signal value growth data to determine an abnormal point; the remaining signal points are corrected according to the abnormal point, so that the unique mechanism of homogeneous photochemical chemiluminescence can be used to accurately suppress the excitation light related noise, especially to effectively remove the noise in the case of low concentration, significantly improve the signal-to-noise ratio, and save the cost of equipment.

[0024] Preferably, as Figure 2 shown, the signal value growth data in the total time is obtained according to a preset acquisition method, and the signal value growth data is processed, specifically including: A1. obtaining a set of continuous signal value growth data in the total time according to a single counter counting method; A2. performing exponential fitting on the signal value growth data to obtain a corresponding exponential fitting function, and obtaining an exponential fitting value corresponding to the signal value growth data through the exponential fitting function; A3. performing residual analysis on the signal value growth data and the exponential fitting value corresponding to the signal value growth data to obtain a deviation of each signal value growth data; A4. obtaining an initial abnormal point according to the deviation.

[0025] In step A1, homogenous photochemical chemiluminescence requires optical detection in the order of milliseconds (ms), and there are two different optical acquisition methods for test systems with different cost requirements and control methods: (1) double counter alternating counting (ping-pong counting) method: for test systems with low cost requirements, two counters can be used to count alternately. When counter 1 is counting, counter 2 is outputting and clearing data; similarly, when counter 2 is counting, counter 1 is outputting and clearing data. In this way, signal value loss that may exist when the software clears data can be effectively avoided, especially in the case of high signal value frequency. In the design scheme using double counter alternating counting, the required detection time can be divided into N segments, and the signal value growth in each time segment can be outputted.

[0026] (2) single counter counting method: using a single counter to count can save hardware cost and board space. In the hardware design using a single counter to count, if the counter needs to be cleared each time, signal loss will occur, and only continuous integration is possible. Therefore, after dividing the required detection time into N segments, the cumulative integration value of each time node needs to be recorded, and then the signal value growth in each time segment is obtained by calculating the difference.

[0027] In this method, the single counter counting method is used, the excitation light is 680 nm, the detection end receives 610 nm, the total integration time is 300 ms, and the data is processed in 10 interval segments, so as to obtain a set of continuous signal value growth data [Z1,.., Z N ] in the total time T (unit: s), as shown in Table 1: Table 1: A set of continuous signal value growth data table of 10 interval segments

[0028] In step A2, asFigure 3 As shown in the figure, the signal value [Z1,..,ZN] is fitted with [1,2,..,N] as the horizontal coordinate to obtain the corresponding exponential fitting function, and the exponential fitting value corresponding to the signal value growth data is obtained through the exponential fitting function, as shown in Table 2: N Table 2 Exponential fitting value corresponding to signal value growth data

[0029] In step A3, the [1,2,..,N] is brought into the fitted exponential formula, and the residual deviation of each signal value fitting value and the actual value of the signal value growth data is calculated by performing residual analysis on the signal value growth data and the exponential fitting value corresponding to the signal value growth data, wherein the calculation formula of the residual deviation is: Residual deviation=(actual value-fitting value) / fitting value; In step A4, the residual deviation calculated in A3 is judged: Case 1, if the points with deviation above the mean±standard deviation of residual deviation are less than 3 or equal to 3 and are not continuous, then directly eliminate, and re-fit using the remaining points; this step will be iterated several times until R 2 ≥0.99, then enter the step of calculating unit integral data from the corrected signal value growth data obtained and correcting the unit integral data.

[0030] Case 2, if the points with deviation above the mean±standard deviation of residual deviation are more than 3 or equal to 3 and any two of them are adjacent and continuous, then sliding window analysis is performed from the first data to obtain initial abnormal points, as shown in Table 3, the residual deviations of the 4th, 5th, 6th and 10th points corresponding to the underlined points are all greater than the mean±standard deviation: Table 3 Residual deviation analysis table of case 2

[0031] Preferably, the sliding window analysis from the first data to obtain initial abnormal points specifically includes: Sliding window fitting is performed within a preset window to obtain the fitting value corresponding to the preset window; Residual deviation analysis is performed in each preset window and judged: If the deviation of one point exceeds |mean±standard deviation| in ≥2 consecutive windows, then this point is determined as an initial abnormal point.

[0032] In actual application process, the sliding window method is used for case 2 in step A4, the window size is set to 5≤window size≤N / 2, and sliding window fitting is performed, which specifically includes the following steps​ The first step is to perform window fitting, each window fitting a corresponding exponential function to obtain a corresponding fitting value, as shown in Figure 4 , wherein, Figure 4 (a) is a window fitting graph of data 1 to data 5; Figure 4 (b) is a window fitting graph of data 2 to data 6; Figure 4 (c) is a window fitting graph of data 3 to data 7; Figure 4 (d) is a window fitting graph of data 4 to data 8; Figure 4 (e) is a window fitting graph of data 5 to data 9; Figure 4 (f) is a window fitting graph of data 6 to data 10; The second step is to perform residual deviation analysis in each window, as shown in Table 4; if a point has a deviation exceeding the mean ± standard deviation (MAD ± SD) in two or more consecutive windows, the point is determined to be an initial abnormal point, and the initial abnormal point is removed, as shown in Table 5, the initial abnormal point determination criterion is that a point has a deviation exceeding the absolute value mean ± standard deviation in two or more consecutive windows; the data with underlined in the table is a point whose residual deviation is not within the range, and the initial abnormal point determination criterion is used to determine that the 3rd, 4th, and 6th points are initial abnormal points. Table 4 Residual deviation table in each window

[0033] Table 5 Initial abnormal point determination table

[0034] Preferably, after determining the initial abnormal point, the method further comprises: performing exponential fitting on the remaining points after removing the initial abnormal point to obtain a corresponding fitting value; re-performing residual analysis on the remaining points according to the corresponding fitting value to obtain a current deviation; determining according to the current deviation.

[0035] In actual application, as shown in Figure 5 , the remaining points after removing the abnormal point are fitted by an exponential function to obtain a corresponding fitting value, and the remaining points are re-analyzed for residual deviation and judged for the situation: Case 1, if the points with residual deviation exceeding the mean ± standard deviation (AV ± SD) of the residual deviation are less than 3, or equal to 3 and not continuous, then they are directly removed, and the remaining points are re-fitted. This step may be iterated several times until R 2 ≥0.99, and then entering the step of calculating unit integral data from the corrected signal value growth data and correcting the unit integral data. Case 2, if the points with residual deviation above the mean ± standard deviation of residual deviation are greater than 3, or equal to 3 and there are any two consecutive (in this step, if the values of the interval between the two points have been removed, it is also considered as consecutive), the deviation is above the AV ± SD of the residual, it is necessary to start from the first data, sliding window analysis; as shown in Table 6, the data with underlined is the point with residual deviation above the mean ± standard deviation of residual deviation, this case in points 2, 5, 10 these three abnormal points, and points 2 and 5 are consecutive, belong to the processing mode of case 2. Among them, for case 2, the remaining points after removing the abnormal points (the removed signal is left blank) are re-analyzed by sliding window, and the exponential function corresponding to each window is obtained, and then the corresponding fitting value is obtained.

[0036] Table 6 residual analysis table

[0037] Preferably, the signal value difference is obtained according to the processed signal value growth data to determine the abnormal point, specifically comprising: The data of the same position fitted by each window is compared to obtain the signal value difference; The abnormal point is determined according to the signal value difference.

[0038] In actual application process, the data of the same position fitted by each window is compared, and the signal value difference is calculated: (Wa,b-Wa+1,b-1) / Wa+1,b-1, wherein Wa,b is the fitting value of the bth value in the ath window, if there is a signal value difference greater than the signal value difference threshold, it is judged that the point is an abnormal point, as shown in Table 7; wherein the signal value difference threshold can be determined according to the actual situation, in this embodiment, the signal value difference threshold is 10%, through Table 7, it can be judged that the underlined points 3, 4, 6 are abnormal points; Table 7 signal value difference representation table

[0039] Preferably, the signal correction is performed on the remaining signal points according to the abnormal point, specifically comprising: The mutation signal difference of each abnormal point is obtained according to the position of the abnormal point; The mutation signal difference is deducted from the normal signal point in front of each abnormal point to perform signal correction.

[0040] In actual application process, the data correction of the remaining points: based on the position of the abnormal points obtained in Table 7, the corresponding index function is obtained by re-sliding window analysis of the points remaining after the abnormal points are removed (where the removed signal is left blank and removed), and then the corresponding fitting value is obtained to calculate the mutation signal difference, wherein the mutation signal difference = Wa,b-Wa+1,b-1, all abnormal points in Table 7 are calculated in turn to obtain the corresponding mutation signal difference of each abnormal point, as shown in Table 8; further, the normal points in front of each abnormal point need to be deducted by the corresponding mutation signal difference for data correction, as shown in Table 9.

[0041] Table 8 Calculation table of mutation signal difference corresponding to each abnormal point

[0042] Table 9 Data correction table

[0043] Preferably, after the signal correction of the remaining signal points according to the abnormal points, it further comprises: According to the obtained corrected signal value growth data, the unit integral data is calculated, and the unit integral data is corrected.

[0044] In actual application process, the corrected signal value growth data [C1,..,C N ] is obtained by inputting [1,2,..,N] into the fitted exponential formula, as shown in Table 10: Table 10 Corrected signal value growth data table

[0045] Then the corrected signal value growth data [C1,..,C N ] is summed to obtain the integral signal value CT; then the unit integral data under 1s is obtained according to the integral signal value CT, and the specific calculation formula of the unit integral data is: ; According to the pulse resolution time λ of the photomultiplier module, the integral data is corrected to obtain the final correction output result M, wherein the specific correction output result M calculation formula is: ; In this embodiment, T=0.3s, λ=43ns, the final correction output result M=38997 is obtained by calculation, compared with the result 43561 before correction, the noise that makes the unit integral data deviate from the exponential fitting curve due to the external interference such as residual excitation light source can be removed.

[0046] Two different biological samples (PCT (procalcitonin), CKMB (creatine kinase isoenzyme MB)) are listed to realize the signal-to-noise ratio improvement effect comparison by the method, as shown in Tables 11 and 12, including 0 concentration calibrators, 5 concentrations of working calibrators are selected at the low value end, each concentration is tested 3 times, and the average value is taken as the test result of the concentration; then the ratio between the test results of Cn (n = 1~4) and the test results of C0 is calculated, and the signal-to-noise ratio before and after optimization is compared; As can be seen from Tables 11 and 12, after optimization algorithm, the signal-to-noise ratios of PCT and CKMB at different low concentrations are improved.

[0047] Table 11 Signal-to-noise ratio of PCT (procalcitonin)

[0048] Table 12 Signal-to-noise ratio of CKMB (creatine kinase isoenzyme MB)

[0049] The following verifies the improvement effect on sensitivity after the signal-to-noise ratio is improved by blank limit and detection limit: (1) Blank limit The zero concentration calibrator C0 is repeatedly measured for 20 times, and the average value (x) and standard deviation (SD) of the relative luminescence value (RLU) are calculated, and (x+2SD) is substituted into the dose-response curve to calculate the corresponding concentration value, which is the blank limit, as shown in Tables 13 and 14: Table 13 Comparison table of PCT blank limit

[0050] Table 14 Comparison table of CKMB blank limit

[0051] (2) Detection limit 5 detection limit reference samples are tested, each sample is tested 5 times, and the test results are sorted according to size, and the number of test results lower than the blank limit is less than or equal to 3, then the detection limit is considered to be basically reasonable, as shown in Tables 15 and 16, wherein the detection limit sample concentration of PCT detection limit is 0.04 ng / mL; the detection limit sample concentration of CKMB detection limit is 0.4 ng / mL.

[0052] Table 15 Comparison table of PCT detection limit

[0053] Table 16 Comparison table of CKMB detection limit

[0054] AsFigure 6 As shown, the embodiment of the present application provides a signal value processing system, comprising a processing module, a determining module and a correction module; The processing module is configured to acquire a set of continuous signal value growth data in total time according to a preset acquisition method, and process the signal value growth data; The determining module is configured to acquire signal value difference according to the processed signal value growth data, so as to determine an abnormal point; The correction module is configured to perform signal correction on the remaining signal points according to the abnormal point.

[0055] In actual application process, the processing module, the determining module and the correction module are arranged in the signal value processing system; the determining module is connected with the processing module and the correction module respectively; the processing module acquires a set of continuous signal value growth data in total time according to a preset acquisition method, then processes the signal value growth data, and transmits the processed signal value growth data to the determining module; the determining module acquires signal value difference according to the processed signal value growth data, so as to determine an abnormal point, and transmits the abnormal point to the correction module; the correction module performs signal correction on the remaining signal points according to the abnormal point; the system can precisely suppress excitation light related noise according to the unique mechanism of homogeneous photochemical chemiluminescence through the cooperative work of the processing module, the determining module and the correction module, especially effectively removes noise in low concentration, significantly improves signal-to-noise ratio, and saves equipment cost.

[0056] Further, the embodiment of the present application further discloses an electronic device, Figure 7 The electronic device structure diagram shown according to an exemplary embodiment, the content in the figure cannot be considered as any limitation on the use range of the present application.

[0057] Figure 7 The electronic device structure diagram provided by the embodiment of the present application. The electronic device 20, specifically can include: at least one processor 21, at least one memory 22, power supply 23, communication interface 24, input output interface 25 and communication bus 26. Wherein, the memory 22 is used for storing computer program, the computer program is loaded and executed by the processor 21, to realize the related steps in the signal value processing method disclosed by any preceding embodiment. In addition, the electronic device 20 in the embodiment of the present application can be an electronic computer.

[0058] In this embodiment, the power supply 23 is configured to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 is configured to create a signal value processing channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which will not be specifically limited herein; the input / output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application requirements, which will not be specifically limited herein.

[0059] In addition, the memory 22 as a carrier for storing resources can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222 and data 223, etc., and the storage mode can be temporary storage or permanent storage.

[0060] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, so as to realize the operation and processing of the processor 21 on the data 223 in the memory 22, and the operating system 221 can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the signal value processing method executed by the electronic device 20 disclosed in any one of the foregoing embodiments, the computer program 222 can further include a computer program capable of completing other specific work. In addition to the data received by the signal value processing device from the external device, the data 223 can also include data collected by the input / output interface 25 itself and the like.

[0061] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be directly implemented by hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0062] Further, the present application also discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the signal value processing method disclosed above. For the specific steps of the method, please refer to the corresponding content disclosed in the foregoing embodiments, which will not be described here again.

[0063] In the embodiments of the present application, it should be understood that the disclosed method and system can be implemented in other manners. The embodiments described above are merely schematic, and the division of the modules is merely logical function division. There can be other division manners in actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical, mechanical or other forms.

[0064] In addition, each functional module in each embodiment of the present application can be integrated in one processor, or each module can be a separate device, or two or more modules can be integrated in one device. Each functional module in each embodiment of the present application can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0065] It can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by program instructions and related hardware, and the above-mentioned program instructions can be stored in a computer readable storage medium. When the program instructions are executed, the steps of the above-mentioned method embodiments are executed. The above-mentioned storage medium includes mobile storage devices, read only memory (ROM), magnetic discs or optical discs and various storage program codes.

[0066] It should be understood that if "system", "device", "unit" and / or "module" are used in the present application, it is only a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0067] As shown in the present application and claims, unless the context clearly indicates otherwise, "one", "a", "an" and / or "the" do not refer to the singular, but also include the plural. Generally, the terms "include" and "contain" only indicate that the steps and elements explicitly identified are included, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements. The element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, product or device including the element.

[0068] Hereinafter, the terms "first", "second", etc. are used only for the purpose of description, and are not to be construed as indicating or implying relative importance or a specific number of the technical features indicated. Thus, the features defined with "first", "second", etc. can explicitly or implicitly include one or more of the features.

[0069] If flowcharts are used in the present application, the flowcharts are used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. Instead, each step can be processed in reverse order or simultaneously. Meanwhile, other operations can be added to these processes, or one or more steps of operations can be removed from these processes.

[0070] The above description of disclosed embodiments enables a person skilled in the art to implement or use the invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A signal value processing method, characterized in that, Includes the following steps: A set of continuous signal value growth data within a total time period is obtained according to a preset acquisition method, and the signal value growth data is processed. Based on the processed signal value growth data, the signal value difference is obtained to identify anomalies; The remaining signal points are corrected based on the anomaly points.

2. The signal value processing method according to claim 1, characterized in that, The step of acquiring a set of continuous signal value growth data over a total time period according to a preset acquisition method, and processing the signal value growth data, specifically includes: A set of continuous signal value growth data within the total time period is obtained using a single counter counting method; The signal value growth data is subjected to exponential fitting to obtain the corresponding exponential fitting function, and the exponential fitting value corresponding to the signal value growth data is obtained through the exponential fitting function. Residual analysis is performed between the signal value growth data and the corresponding exponential fitting value to obtain the deviation of each signal value growth data. The initial outlier point is obtained based on the deviation.

3. The signal value processing method according to claim 2, characterized in that, The step of obtaining the initial outlier point based on the deviation specifically includes: The deviation is judged as follows: If there are less than 3 or more points with deviations above the mean ± standard deviation of the residual deviation and none of them are continuous, they are directly removed, and the remaining points are used for refitting. If there are more than 3 or more points where the deviation is above the mean ± standard deviation of the residual deviation, and any two of them are adjacent and consecutive, then a sliding window analysis is performed starting from the first data point to obtain the initial outliers.

4. The signal value processing method according to claim 3, characterized in that, The process of performing sliding window analysis starting from the first data point to obtain initial outliers specifically includes: A sliding window fitting is performed within a preset window to obtain the fitting value corresponding to the preset window; Perform residual deviation analysis and make judgments within each preset window: If a point deviates from the mean ± standard deviation for ≥2 consecutive windows, then that point is identified as an initial outlier.

5. The signal value processing method according to claim 4, characterized in that, After determining the initial outlier, the process also includes: The remaining points after removing the initial outliers are subjected to exponential fitting to obtain the corresponding fitted values. Residual analysis is performed again on the remaining points based on the corresponding fitted values ​​to obtain the current deviation; Make a judgment based on the current deviation.

6. The signal value processing method according to claim 5, characterized in that, The judgment based on the current deviation specifically includes: If there are fewer than 3 points in the current deviation that are above the mean ± standard deviation of the residual deviation, or if there are 3 or more points that are not consecutive, then these points are directly removed and the remaining points are used for refitting. If there are more than 3 points where the current deviation is above the mean ± standard deviation of the residual deviation, or if there are 3 or more points where any two consecutive deviations are above the mean ± standard deviation of the residual, then a sliding window analysis needs to be performed starting from the first data point.

7. The signal value processing method according to claim 1, characterized in that, The step of obtaining signal value differences based on the processed signal value growth data to determine outliers specifically includes: Compare the data fitted from the same location in each window to obtain the differences in signal values; Anomalies are determined based on the differences in the signal values.

8. The signal value processing method according to claim 1, characterized in that, The step of correcting the remaining signal points based on the anomalies specifically includes: The mutation signal difference of each anomaly point is obtained based on its location. The abrupt signal difference is subtracted from the normal signal points preceding each anomalous point to perform signal correction.

9. The signal value processing method according to claim 1, characterized in that, After correcting the remaining signal points based on the anomalies, the method further includes: Calculate the unit integral data based on the acquired corrected signal value growth data, and then correct the unit integral data.

10. A signal value processing system, characterized in that, include: Processing module, determination module, and correction module; The processing module is used to acquire a set of continuous signal value growth data within a total time period according to a preset acquisition method, and to process the signal value growth data. The determining module is used to obtain signal value differences based on the processed signal value growth data in order to determine anomalies. The correction module is used to correct the remaining signal points based on the abnormal points.

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