CN116842301A - Curve fitting method, system, computer device and computer readable storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a curve fitting method, a system, computer equipment and a computer readable storage medium, relating to the technical field of data processing, comprising the following steps: obtaining scattered light intensity of a sample to be detected to obtain a first data sequence, calculating a target difference value of the maximum scattered light intensity and the minimum scattered light intensity, determining a first differential sequence and a filtering value according to the first data sequence, obtaining a second data sequence according to the filtering value and a first preset value range, obtaining a second time sequence according to the filtering value, obtaining a second differential sequence from the second data sequence, determining a third differential sequence from the second time sequence, calculating a fluctuation value according to the target difference value, the second differential sequence and the third differential sequence, determining a target fitting function with a preset threshold value, and finally fitting the second data sequence to obtain a target fitting curve. The method can obtain the fitting reaction curve of the sample to be tested accurately, further calculate the accurate reaction amplitude and improve the accuracy and reliability of the experimental result.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a curve fitting method, a system, a computer device, and a computer readable storage medium.
Background
With the development of science and technology, the amount of data generated and accumulated by people in daily life is increased, and whether scientific research or social management is performed, the data needs to be analyzed and applied, and curve fitting is used as a common data analysis method, so that the data can be accurately described and predicted. The precondition that the data can be accurately described and predicted according to the fitted curve is that an accurate fitting reaction curve can be obtained based on the data, especially in scientific researches such as the research in the field of biochemistry, the reaction amplitude of the unknown concentration sample can be calculated according to the fitting reaction curve, but the fitting reaction curve of the unknown concentration sample cannot be obtained accurately at present, and the accuracy and the reliability of the experimental result are affected.
Disclosure of Invention
Accordingly, it is an objective of the present application to provide a curve fitting method, system, computer device and computer readable storage medium, which can solve at least some of the above problems.
In a first aspect, an embodiment of the present application provides a curve fitting method, where the method includes:
Acquiring scattered light intensity of a sample to be detected to obtain a first data sequence, wherein all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time;
calculating the difference value of the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value;
calculating first order differences of all scattered light intensities in the first data sequence to obtain a first differential sequence, and determining a filtering value according to the first differential sequence;
filtering the first data sequence according to the filtering value and a first preset value range to obtain a second data sequence, and filtering a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence, wherein the first time sequence comprises the acquisition time of all scattered light intensities in the first data sequence;
calculating first order differences of all scattered light intensities in the second data sequence to obtain a second differential sequence, and obtaining first order differences of all the acquisition times in the second time sequence to obtain a third differential sequence, and calculating fluctuation values according to the target difference values, the second differential sequence and the third differential sequence, wherein the fluctuation values are used for representing fluctuation degrees of the second data sequence;
And determining a target fitting function according to a comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the target fitting function to obtain a target fitting curve.
In a possible implementation manner, the determining a filtering value according to the first differential sequence includes:
obtaining a first target value and a second target value in the first differential sequence, wherein the first target value is the ratio of the maximum difference in the first differential sequence divided by the target difference value, and the second target value is the ratio of the minimum difference in the first differential sequence divided by the target difference value;
determining a first target scattered light intensity according to the first target value and a first preset threshold value;
determining a second target scattered light intensity according to the second target value and a second preset threshold value;
and taking the sum of the number of the scattered light intensities of the first target and the number of the scattered light intensities of the second target as the filtering value.
In a possible implementation manner, the determining the first target scattered light intensity according to the first target value and a first preset threshold value includes:
storing the first target scattered light intensity into a first preset array under the condition that the first target value is smaller than the first preset threshold value, wherein the first target scattered light intensity is used for calculating the maximum differential scattered light intensity in the first data sequence;
And marking the maximum difference as zero, and repeating the steps of acquiring a first target value and a second target value in the first difference sequence until the first target value is greater than or equal to the first preset threshold value, so as to obtain a first target array, wherein the first target array comprises at least two first target scattered light intensities.
In a possible implementation manner, the determining the second target scattered light intensity according to the second target value and a second preset threshold value includes:
storing the second target scattered light intensity into a second preset array under the condition that the second target value is smaller than a second preset threshold value, wherein the second target scattered light intensity is used for calculating the scattered light intensity of the minimum difference in the first difference sequence;
marking the minimum difference as zero, and repeating the steps of obtaining a first target value and a second target value in the first difference sequence until the second target value is greater than or equal to the second preset threshold value, so as to obtain a second target array, wherein the second target array comprises at least two second target scattered light intensities;
Said taking as said filtered value the sum of said first target scattered light intensity and said second target scattered light intensity, comprising:
and taking the sum of the number of the first target scattered light intensities in the first target array and the number of the second target scattered light intensities in the second target array as the filtering value.
In one possible implementation manner, the objective fitting function includes at least one of a preset linear fitting function and a preset polynomial fitting function, the determining an objective fitting function according to a comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the objective fitting function to obtain an objective fitting curve, including:
and under the condition that the fluctuation value is detected to be larger than the preset threshold value, fitting the second data sequence according to the preset linear fitting function to obtain a first target fitting curve, and under the condition that the fluctuation value is detected to be smaller than or equal to the preset threshold value, fitting the second data sequence based on the preset polynomial fitting function to obtain a second target fitting curve.
In a possible implementation manner, the calculating a fluctuation value according to the target difference value, the second differential sequence and the third differential sequence includes:
Calculating the ratio of corresponding elements in the second differential sequence and the third differential sequence one by one to obtain a ratio data set;
calculating the sum of all ratio data in the ratio data set to obtain a target ratio;
dividing the target ratio by the target difference to obtain the fluctuation value.
In one possible embodiment, after the obtaining the scattered light intensity of the sample to be measured obtains the first data sequence, the method further includes:
filtering the first data sequence according to a preset threshold range to obtain an intermediate data sequence;
the calculating a difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value includes:
and calculating the difference value of the maximum scattered light intensity and the minimum scattered light intensity in the intermediate data sequence to obtain the target difference value.
In a second aspect, embodiments of the present application provide a curve fitting system, the system comprising:
the acquisition module is used for acquiring scattered light intensity of a sample to be detected to obtain a first data sequence, and all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time;
the first calculation module is used for calculating the difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value;
The second calculation module is used for calculating the first order difference of all scattered light intensities in the first data sequence to obtain a first difference sequence, and determining a filtering value according to the first difference sequence;
the filtering module is used for filtering the first data sequence according to the filtering value and a first preset value range to obtain a second data sequence, and filtering a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence, wherein the first time sequence comprises acquisition time of all scattered light intensities in the first data sequence;
a third calculation module, configured to calculate first order differences of all scattered light intensities in the second data sequence to obtain a second differential sequence, and calculate a fluctuation value according to the target difference value, the second differential sequence, and the third differential sequence, where the fluctuation value is used to characterize a fluctuation degree of the second data sequence;
and the determining module is used for determining a target fitting function according to the comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the target fitting function to obtain a target fitting curve.
In a third aspect, an embodiment of the present application provides a computer device, where the computer device includes a memory and a processor, where the memory stores a computer program, and where the computer program, when executed by the processor, implements the curve fitting method provided in the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by one or more processors, implements the curve fitting method provided in the first aspect.
According to the curve fitting method provided by the embodiment of the application, the first data sequence can be obtained by acquiring the scattered light intensity of the sample to be tested, all the scattered light intensities in the first data sequence are arranged according to the sequence of the acquisition time, then the difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence is calculated to obtain the target difference value, the first difference of all the scattered light intensities in the first data sequence is calculated to obtain the first difference sequence, and the filter value is determined according to the first difference sequence, so that the interference of abnormal data is avoided, and the accuracy of an experimental result is influenced. And then filtering the first data sequence according to the filtering value and the first preset value range to obtain a second data sequence, and filtering the first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence. And then calculating the first order difference of all scattered light intensities in the second data sequence to obtain a second difference sequence, and obtaining the first order difference of all acquisition time in the second time sequence to obtain a third difference sequence, calculating a fluctuation value according to the target difference value, the second difference sequence and the third difference sequence, determining a target fitting function according to a comparison result of the fluctuation value and a preset threshold value, fitting the second data sequence according to the target fitting function to obtain a target fitting curve, and obtaining a fitting response curve of a precise sample to be tested, thereby improving the accuracy and reliability of an experimental result.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being understood that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a curve fitting method according to an embodiment of the present application;
FIG. 2 is a graph showing the response of a high concentration sample to be tested according to the curve fitting method according to the embodiment of the present application;
FIG. 3 is a graph showing a reaction curve of a low-concentration sample to be measured according to the curve fitting method according to the embodiment of the present application;
fig. 4 is a fitting graph of a high-concentration sample to be measured according to a curve fitting method according to an embodiment of the present application;
FIG. 5 is a graph showing a curve fitting method of the present application for a low concentration sample to be tested;
FIG. 6 is a graph showing a reaction curve of a low concentration sample to be measured according to the curve fitting method according to the embodiment of the present application;
FIG. 7 is a graph showing a curve fitting method of the present application for a low concentration sample to be tested;
FIG. 8 is a schematic diagram of functional blocks of a curve fitting system according to an embodiment of the present application;
fig. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment of the present application.
Reference numerals illustrate:
curve fitting system 800, acquisition module 810, first calculation module 820, second calculation module 830, filter module 840, third calculation module 850, determination module 860.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In various embodiments of the application, the expression "or" at least one of a or/and B "includes any or all combinations of the words listed simultaneously. For example, the expression "a or B" or "at least one of a or/and B" may include a, may include B or may include both a and B.
In the description of the present application, it should be noted that, if the terms "upper", "lower", "inner", "outer", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present application and simplifying the description, and it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus it should not be construed as limiting the present application.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
Before describing the curve fitting method in the following embodiments of the present application in detail, an immune scattering nephelometry method is described, wherein antigens in serum are utilized to form insoluble immune complexes with corresponding antibodies in a liquid phase environment, light with a certain wavelength irradiates along a horizontal axis, when passing through a solution, the light encounters antigen-antibody complex particles, the light is refracted by the particles and deflected, and the angle of light deflection is closely related to the wavelength of emitted light and the size of the antigen-antibody complex particles. The intensity of scattered light is proportional to the content of the complex, namely, the more antigens to be detected are, the stronger the scattered light is, so that the quantitative determination of the antigens or the antibodies can be realized by an immune scattering turbidimetry method.
In clinical immunology detection, a dose response curve is usually measured by using a series of concentration calibrators, which is also called a standard curve, and the concentration of a sample to be detected is calculated according to the standard curve, so that the concentration of the sample to be detected is mainly detected by an instrument to obtain a response amplitude according to the principle of an immune scattering turbidimetry, and the response amplitude is calculated according to the standard curve.
Referring to fig. 1, fig. 1 is a flowchart of a curve fitting method according to an embodiment of the present application, and each step of the method will be described in detail below.
S110, acquiring scattered light intensity of a sample to be detected to obtain a first data sequence, wherein all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time.
In this embodiment, the sample to be measured is a sample with unknown concentration, and the scattered light intensity of the sample to be measured may be obtained by at least one of a laser particle analyzer, a scatterometer, and a multi-angle laser light scattering detector, which may be selected according to actual situations. The first data sequence is a sequence of scattered light intensities acquired sequentially in time, such as T over time during the process of acquiring a specific binding substance between a liquid phase environment and a corresponding antibody of a sample to be tested according to the above-mentioned instrument i (i.epsilon.1, 2,3, …, N) varying scattered light intensity R i (i.epsilon.1, 2,3, …, N), i.e., the first data sequence may be represented by L 1 (R 1 ,R 2 ,R 3 ,…,R i ) And (3) representing i epsilon 1,2,3, … and N.
S120, calculating the difference value of the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value.
In particular, the target difference may be used to characterize the magnitude of the variation interval of the response amplitude of the first data sequence, for example, if R range Representing the target difference, using R max Representing the maximum scattered light intensity, using R min Representing the minimum scattered light intensity, R range ＝R max -R min 。
S130, calculating first order differences of all scattered light intensities in the first data sequence to obtain a first differential sequence, and determining a filtering value according to the first differential sequence.
Specifically, the first-order difference is the difference between two adjacent data points on the time sequence, and since all scattered light intensities in the first data sequence are arranged according to the sequence of the acquisition time, the first-order difference of all scattered light intensities in the first data sequence is calculated, that is, the first-order difference of all adjacent scattered light intensities in the first data sequence is calculated.
In the present embodiment, the first differential sequence may be represented by S (S 1 ,S 2 ,S 3 ,…,S i ) Represented by, wherein i.epsilon.1, 2,3, …, N-1, S i ＝R i +1-R i 。
In addition, the first differential sequence can be further analyzed to determine the filtering value, the abnormal scattered light intensity in the first data sequence can be determined according to the filtering value, abnormal data in the first data sequence can be filtered conveniently, accurate fitting response curves are ensured to be obtained, and accuracy and reliability of experimental results are improved.
And S140, filtering the first data sequence according to the filtering value and the first preset value range to obtain a second data sequence, and filtering a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence, wherein the first time sequence comprises the acquisition time of all scattered light intensities in the first data sequence.
Specifically, the first preset numerical range may be all the scattered light intensity ranges included in the first data sequence, or may be part of the scattered light intensity ranges in the first data sequence, which may be specifically selected according to the actual situation. If the number of scattered light intensity data in the first data sequence exceeds 50, the current scattered light intensity data can be considered to be more, and all scattered light intensities in the first data sequence can be filtered, otherwise, the current scattered light intensity data can be considered to be less, if all scattered light intensity data are filtered, an accurate fitting response curve cannot be obtained, accuracy and reliability of an experimental result can be affected, in this case, the first data sequence can be divided at intervals to obtain a plurality of subareas, and then partial subareas are selected to complete the filtering of abnormal data in a mode such as interval selection, so that accuracy and reliability of the experimental result can be guaranteed conveniently.
Wherein the second data sequence may be L 2 (R 1 ,R 2 ,R 3 ,…,R i ) The first time sequence may be represented by T 1 (t 1 ,t 2 ,t 3 ,…,t i ) The second time series may be represented by T 2 (t 1 ,t 2 ,t 3 ,…,t i ) The expression, i.epsilon.1, 2,3, …, N-C, represents the filter values in the above examples.
In this embodiment, for convenience of unified explanation, the first preset numerical range may be understood as the entire data range of the first data sequence, and the second preset numerical range may be understood as the entire data range of the first time sequence, where the time in the first time sequence is the acquisition time of each scattered light intensity in the first data sequence, that is, the time in the first time sequence and the scattered light intensity in the first data sequence are in one-to-one correspondence in order. According to the embodiment, the first data sequence and the first time sequence are filtered through the filtered value, the first preset value range and the second preset value range, namely, abnormal scattered light intensity in the first data sequence and acquisition time corresponding to the abnormal scattered light intensity in the first time sequence are filtered, so that accuracy of obtaining the target fitting curve can be further improved.
S150, calculating first order differences of all scattered light intensities in the second data sequence to obtain a second differential sequence, and obtaining first order differences of all acquisition time in the second time sequence to obtain a third differential sequence, and calculating fluctuation values according to the target difference value, the second differential sequence and the third differential sequence, wherein the fluctuation values are used for representing the fluctuation degree of the second data sequence.
After filtering the first data sequence and the abnormal data in the first time sequence in the above embodiment, a second data sequence and a second time sequence are obtained, and in this embodiment, the real reaction process of the sample to be tested can be further determined through the second data sequence and the second time sequence. The first-order difference calculation is performed again mainly for the second data sequence and the second time sequence, specifically, the first-order difference calculation is performed on the second time sequence, so that noise on the time sequence can be eliminated, the time sequence is smoother, and accuracy of data of the sample to be detected is improved.
And S160, determining a target fitting function according to a comparison result of the fluctuation value and a preset threshold value, and fitting a second data sequence according to the target fitting function to obtain a target fitting curve.
In this embodiment, the comparison may be performed according to the actually calculated fluctuation value and the preset threshold, so as to determine a target fitting function suitable for performing data fitting on the second data sequence, so as to obtain a real and accurate target fitting curve.
As can be seen from the above analysis, according to the curve fitting method provided by the embodiment of the present application, a first data sequence may be obtained by acquiring the scattered light intensities of a sample to be tested, all the scattered light intensities in the first data sequence are arranged according to the sequence of the acquired time, then the difference between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence is calculated to obtain a target difference, the first differential sequence is obtained by calculating the first differential of all the scattered light intensities in the first data sequence, and the filter value is determined according to the first differential sequence, so that the interference of abnormal data is avoided, and the accuracy of the experimental result is affected. And then filtering the first data sequence according to the filtering value and the first preset value range to obtain a second data sequence, and filtering the first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence. And then calculating the first order difference of all scattered light intensities in the second data sequence to obtain a second difference sequence, and obtaining the first order difference of all acquisition time in the second time sequence to obtain a third difference sequence, calculating a fluctuation value according to the target difference value, the second difference sequence and the third difference sequence, determining a target fitting function according to a comparison result of the fluctuation value and a preset threshold value, fitting the second data sequence according to the target fitting function to obtain a target fitting curve, and obtaining a fitting response curve of a precise sample to be tested, thereby improving the accuracy and reliability of an experimental result.
In one possible embodiment, determining the filter value from the first differential sequence includes:
obtaining a first target value and a second target value in a first differential sequence, wherein the first target value is the ratio of the maximum differential value in the first differential sequence divided by a target differential value, and the second target value is the ratio of the minimum differential value in the first differential sequence divided by the target differential value;
determining a first target scattered light intensity according to the first target value and a first preset threshold value;
determining a second target scattered light intensity according to the second target value and a second preset threshold value;
and taking the sum of the number of the scattered light intensities of the first target and the number of the scattered light intensities of the second target as a filtering value.
In the present embodiment, the first differential sequence S (S 1 ,S 2 ,S 3 ,…,S i ) Example, first target value P 1 ＝S max /R range Second target value P 2 ＝S min /R range After determining the first target scattered light intensity, i e 1,2,3, …, N-1, the total number of the first target scattered light intensity and the second target scattered light intensity may be determined according to the first preset threshold and the second preset threshold, and the filtering value in the above embodiment may be determined.
Optionally, determining the first target scattered light intensity according to the first target value and the first preset threshold value includes:
Storing the first target scattered light intensity into a first preset array under the condition that the first target value is smaller than a first preset threshold value, wherein the first target scattered light intensity is used for calculating the maximum differential scattered light intensity in a first data sequence;
and marking the maximum difference as zero, and repeating the steps of obtaining a first target value and a second target value in the first difference sequence until the first target value is greater than or equal to a first preset threshold value, so as to obtain a first target array, wherein the first target array comprises at least two first target scattered light intensities.
Specifically, the first preset threshold may be modified according to the actual calculation requirement, if the first differential sequence S (S 1 ,S 2 ,S 3 ,…,S i ) In S max ＝R max+1 -R max The scattered light intensity of the first target is R max+1 R can be max+1 Store in a first preset array and store S max Recorded as zero, and the next R is retrieved max+1 Up to a first target value P 1 Stopping when the preset threshold value is greater than or equal to the first preset threshold value. Wherein S is max Is the largest value in the positive differential.
Optionally, determining the second target scattered light intensity according to the second target value and a second preset threshold value includes:
storing the second target astigmatism intensity into a second preset array under the condition that the second target numerical value is smaller than a second preset threshold, wherein the second target astigmatism intensity is the scattered light intensity used for calculating the minimum difference in the first difference sequence;
Marking the minimum difference as zero, and repeating the steps of obtaining a first target value and a second target value in the first difference sequence until the second target value is greater than or equal to a second preset threshold value, so as to obtain a second target array, wherein the second target array comprises at least two second target scattered light intensities;
taking the sum of the number of the scattered light intensities of the first target and the number of the scattered light intensities of the second target as a filter value, wherein the filter value comprises:
and taking the sum of the number of the first target scattered light intensities in the first target array and the number of the second target scattered light intensities in the second target array as a filtering value.
In this embodiment, the second preset threshold value can be modified according to the actual calculation requirement, if the first differential sequence S (S 1 ,S 2 ,S 3 ,…,S i ) In S min ＝R min+1 -R min The scattered light intensity of the second target is R min R can be min Store in the second preset array and store S min Recorded as zero, and the next R is retrieved min Stopping until the second target value is greater than or equal to a second preset threshold value. Wherein S is min The first target array and the second target array can be the same array, namely, all obtained first target scattered light intensities and all obtained second target scattered light intensities can be stored as one array, and all data total numbers in the same array can be counted as filtering values.
It should be noted that, whether the first target scattered light intensity is stored in the first preset array or the second target scattered light intensity is stored in the second preset array, the acquisition time corresponding to the first target scattered light intensity and/or the second target scattered light intensity is recorded and stored at the same time, the acquisition time is not counted in the statistics of the filtering value, and the recording of the acquisition time is convenient for finding the corresponding filtering time point in the first data sequence according to the filtering value and filtering the abnormal scattered light intensity at the time point.
In one possible implementation manner, the objective fitting function includes at least one of a preset linear fitting function and a preset polynomial fitting function, the objective fitting function is determined according to a comparison result of the fluctuation value and a preset threshold value, and the objective fitting curve is obtained by fitting the second data sequence according to the objective fitting function, including:
and under the condition that the fluctuation value is detected to be larger than a preset threshold value, fitting a second data sequence according to a preset linear fitting function to obtain a first target fitting curve, and under the condition that the fluctuation value is detected to be smaller than or equal to the preset threshold value, fitting the second data sequence based on a preset polynomial fitting function to obtain a second target fitting curve.
In this embodiment, the preset linear fitting function includes at least one of a least square linear fitting function, a least absolute error linear fitting function, a global regression linear fitting function, and a local regression linear fitting function, and the preset polynomial fitting korean includes any one of a first order polynomial fitting function, a second order polynomial fitting function, and an n-degree polynomial fitting function. According to the calculated fluctuation value in the embodiment, the fluctuation degree of the second data sequence can be accurately reflected, and a proper fitting function can be conveniently selected to obtain an accurate and reliable target fitting curve for all data in the second data sequence. The first target fitting curve and the second target fitting curve in this embodiment are obtained by different curve fitting modes, and the two target fitting curves are different.
In one possible embodiment, calculating the ripple value from the target difference value, the second differential sequence, and the third differential sequence includes:
calculating the ratio of corresponding elements in the second differential sequence and the third differential sequence one by one to obtain a ratio data set;
calculating the sum of all ratio data in the ratio data set to obtain a target ratio;
Dividing the target ratio by the target difference to obtain a fluctuation value.
In this embodiment, if the second differential sequence is denoted by S ' (S '1, S '2, S '3, …, S ' i), and the third differential sequence is denoted by H ' (H '1, H '2, H '3, …, H ' i), where i e 1,2,3, …, N-C-1, H ' is calculated by first order difference over all the acquisition times in the second time sequence in the above embodiment.
The ratio dataset in this embodiment includes zi=s' i /H’ i Wherein i.epsilon.1, 2,3, …, N-C-1, if the differential sequence of the first time sequence exists in the above embodiment, H (H) 1 ,H 2 ,H 3 ,…,H i ) The representation, i e 1,2,3, …, N-1, is shown in consideration of the differential sequence of the first time sequence that is temporarily unused in the above embodiment.
In this embodiment, a threshold may be set to perform screening and filtering on all ratio data, and then the Sum of all the filtered ratio data is calculated to obtain a target ratio, where a mode of screening and filtering on all the ratio data may be implemented by setting a threshold, and setting of the threshold may be performed according to actual situations, and screening and filtering on all the ratio data may fully take into consideration rationality of the ratio data, so as to obtain an accurate and reliable target ratio Sum.
Optionally, an optionalThe target ratio obtained by calculating the sum of all ratio data in the ratio data set can also be obtained by judging Z i In the case of being smaller than zero, the threshold range is set and the ratio data satisfying the threshold range is summed to obtain the target ratio Sum, sum/R range I.e. characterize the fluctuation values in this example, where i.epsilon.1, 2,3, …, N-C-1.
Optionally, after obtaining the scattered light intensity of the sample to be measured to obtain the first data sequence, the method further includes:
filtering the first data sequence according to a preset threshold range to obtain an intermediate data sequence;
calculating a difference between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference, including:
and calculating the difference value of the maximum scattered light intensity and the minimum scattered light intensity in the intermediate data sequence to obtain a target difference value.
Specifically, in this embodiment, after the scattered light intensity of the sample to be measured is obtained for the first time, the scattered light intensity with a part that is too large or too small may be initially filtered through the preset threshold range, so that accuracy and reliability of data are further improved, and accuracy and reliability of an experimental result are ensured.
It should be noted that, after obtaining an accurate and reliable target fitting curve, the reaction amplitude of the sample to be measured can be accurately calculated, where, taking measuring the scattered light intensity as an example, the scattered light intensity of each sampling point in a fixed measurement time point is recorded to calculate the variation of the scattered light intensity of two specific time points in the fixed measurement time point, that is, the reaction amplitude.
In order to further explain the curve fitting method provided by the embodiment of the application, the target fitting function which is suitable for the reaction process of the sample to be tested can be determined through the scattering light intensity of the sample to be tested, so that an accurate target fitting curve can be obtained, the accuracy and stability of an experimental result can be ensured, and the following description is made through the reaction process curve of the sample to be tested aiming at low concentration:
the inventor finds that in the process of generating a specific conjugate with a corresponding antibody in a liquid phase environment by an unknown concentration sample through a large number of researches, if the concentration of the concentration sample at a position is low, the content of the specific conjugate is low and is influenced by noise, so that the fluctuation degree of a reaction curve of the sample is large, and if the reaction curve is fitted by a polynomial fitting function, a fitting curve which cannot reflect the reaction trend of the unknown concentration is very easy to obtain, and the reaction amplitude of the unknown concentration sample cannot be accurately calculated.
For example, please refer to fig. 2 and fig. 3, in which fig. 2 is a reaction graph of a high-concentration sample to be measured related to a curve fitting method provided by an embodiment of the present application, fig. 3 is a reaction graph of a low-concentration sample to be measured related to a curve fitting method provided by an embodiment of the present application, in fig. 2, a horizontal axis represents time, and may be seconds, milliseconds or microseconds, and may be specifically selected according to practical situations, a vertical axis represents scattered light intensity, and the units of candela, and the coordinates and units of the horizontal axis and the vertical axis in fig. 3 and fig. 4 to fig. 7 are the same as those of fig. 2, which may be referred to the description about the horizontal axis and the vertical axis in fig. 2, and will not be repeated. By comparison, the high-concentration sample to be detected has a relatively strong reaction, a large scattered light intensity change degree and is not easily influenced by other interference factors such as noise, while the low-concentration sample to be detected has a gentle reaction, a small scattered light intensity change and is easily influenced by other interference factors such as noise.
Referring to fig. 4 and fig. 5, fig. 4 is a fitting graph of a high-concentration sample to be measured related to a curve fitting method according to an embodiment of the present application, and fig. 5 is a fitting graph of a low-concentration sample to be measured related to a curve fitting method according to an embodiment of the present application, if the reaction processes of all the concentrations of the sample to be measured use the same curve fitting function, such as a polynomial fitting function, the calculated reaction amplitude distortion, especially the reaction amplitude distortion of the low-concentration sample to be measured, is caused.
Referring to fig. 6, fig. 6 is a reaction curve diagram of a low-concentration sample to be measured, which is included in the curve fitting method according to the embodiment of the present application, wherein the reaction curve may be a reaction curve of all scattered light intensities in the second data sequence of the low-concentration sample to be measured, which is filtered by the filtering values in the above embodiment, and it is obvious that after the abnormal scattered light intensities in the first data sequence are filtered, abnormal data caused by noise interference is filtered, and the change of the reaction curve tends to be stable.
Referring to fig. 7, fig. 7 is a fitting graph of a low-concentration sample to be measured, which is included in the curve fitting method according to the embodiment of the present application, after the calculation and comparison process in the above embodiment, a linear fitting function is selected to fit all scattered light intensities in the second data sequence of the low-concentration sample to be measured, so that the distortion phenomenon of the response amplitude of the low-concentration sample to be measured can be reduced, and an accurate response amplitude can be obtained based on the obtained accurate fitting curve. It should be noted that, in the case of unknown concentration of the sample to be measured, a suitable fitting function may be determined by the curve fitting method in the above embodiment of the present application, and determining the fitting function according to the sample to be measured with known high and low concentrations in fig. 2 to 7 may be regarded as an experiment, and in particular, fig. 6 and 7 may be regarded as verification of the curve fitting method in the above embodiment.
In summary, according to the curve fitting method provided by the embodiment of the application, a first data sequence can be obtained by acquiring the scattered light intensity of a sample to be tested, all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time, then the difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence is calculated to obtain a target difference value, the first difference of all the scattered light intensities in the first data sequence is calculated to obtain a first difference sequence, and a filtering value is determined according to the first difference sequence, so that the interference of abnormal data is avoided, and the accuracy of an experimental result is affected. And then filtering the first data sequence according to the filtering value and the first preset value range to obtain a second data sequence, and filtering the first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence. And then calculating the first order difference of all scattered light intensities in the second data sequence to obtain a second difference sequence, and calculating the first order difference of all acquisition time in the second time sequence to obtain a third difference sequence, calculating a fluctuation value according to the target difference value, the second difference sequence and the third difference sequence, determining a target fitting function according to a comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the target fitting function to obtain a target fitting curve, wherein a secondary filtering process is led out, so that a fitting response curve of an accurate sample to be tested can be obtained, the response amplitude of the sample to be tested can be conveniently determined, and the accuracy and the reliability of an experimental result are improved.
In correspondence to the above method embodiment, the present application further provides a curve fitting system, please refer to fig. 8, fig. 8 is a schematic diagram of functional modules of a curve fitting system 800 provided in the embodiment of the present application, including:
the acquiring module 810 is configured to acquire scattered light intensities of a sample to be measured to obtain a first data sequence, where all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time;
a first calculation module 820, configured to calculate a difference between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence, to obtain a target difference;
the second calculation module 830 is configured to calculate first order differences of all scattered light intensities in the first data sequence to obtain a first differential sequence, and determine a filtering value according to the first differential sequence;
the filtering module 840 is configured to filter the first data sequence according to the filtering value and the first preset value range to obtain a second data sequence, and filter a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence, where the first time sequence includes an acquisition time of all scattered light intensities in the first data sequence;
a third calculation module 850, configured to calculate first order differences of all scattered light intensities in the second data sequence to obtain a second differential sequence, and first order differences of all acquisition times in the second time sequence to obtain a third differential sequence, and calculate a fluctuation value according to the target difference, the second differential sequence, and the third differential sequence, where the fluctuation value is used to characterize a fluctuation degree of the second data sequence;
The determining module 860 is configured to determine a target fitting function according to a comparison result between the fluctuation value and a preset threshold, and fit the second data sequence according to the target fitting function to obtain a target fitting curve.
According to the curve fitting system provided by the embodiment of the application, the first data sequence can be obtained by acquiring the scattered light intensity of the sample to be tested through the acquisition module, all the scattered light intensities in the first data sequence are arranged according to the sequence of the acquisition time, then the difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence is calculated through the first calculation module to obtain the target difference value, the first differential sequence is obtained by calculating the first differential of all the scattered light intensities in the first data sequence through the second calculation module, the filtering value is determined according to the first differential sequence, the interference of abnormal data is avoided, and the accuracy of an experimental result is influenced. And then filtering the first data sequence through a filtering module according to the filtering value and a first preset value range to obtain a second data sequence, and filtering a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence. And then calculating the first order difference of all scattered light intensities in the second data sequence through a third calculation module to obtain a second difference sequence, and obtaining the third difference sequence through the first order difference of all acquisition time in the second time sequence, calculating a fluctuation value according to the target difference value, the second difference sequence and the third difference sequence, finally determining a target fitting function through a determination module according to a comparison result of the fluctuation value and a preset threshold value, fitting the second data sequence according to the target fitting function to obtain a target fitting curve, and thus obtaining a fitting response curve of a precise sample to be tested, and improving the accuracy and reliability of an experimental result.
The application also provides a computer device, please refer to fig. 9, fig. 9 is an internal structure diagram of the computer device provided by the embodiment of the application. The computer device includes a processor, a memory, and a network interface coupled by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program, where the computer program when executed by the processor may cause the processor to implement the curve fitting method applied to the computer device in the above embodiment. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform a curve fitting method. It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The embodiment of the application also discloses a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program realizes the curve fitting method as in the method embodiment when being executed by a processor.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAMs), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRD RAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
Claims (10)
1. A method of curve fitting, the method comprising:
acquiring scattered light intensity of a sample to be detected to obtain a first data sequence, wherein all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time;
calculating the difference value of the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value;
calculating first order differences of all scattered light intensities in the first data sequence to obtain a first differential sequence, and determining a filtering value according to the first differential sequence;
filtering the first data sequence according to the filtering value and a first preset value range to obtain a second data sequence, and filtering a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence, wherein the first time sequence comprises the acquisition time of all scattered light intensities in the first data sequence;
Calculating first order differences of all scattered light intensities in the second data sequence to obtain a second differential sequence, and obtaining first order differences of all the acquisition times in the second time sequence to obtain a third differential sequence, and calculating fluctuation values according to the target difference values, the second differential sequence and the third differential sequence, wherein the fluctuation values are used for representing fluctuation degrees of the second data sequence;
and determining a target fitting function according to a comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the target fitting function to obtain a target fitting curve.
2. The curve fitting method of claim 1 wherein said determining a filtered value from said first differential sequence comprises:
obtaining a first target value and a second target value in the first differential sequence, wherein the first target value is the ratio of the maximum difference in the first differential sequence divided by the target difference value, and the second target value is the ratio of the minimum difference in the first differential sequence divided by the target difference value;
determining a first target scattered light intensity according to the first target value and a first preset threshold value;
Determining a second target scattered light intensity according to the second target value and a second preset threshold value;
and taking the sum of the number of the scattered light intensities of the first target and the number of the scattered light intensities of the second target as the filtering value.
3. The curve fitting method of claim 2 wherein said determining a first target scattered light intensity based on said first target value and a first preset threshold comprises:
storing the first target scattered light intensity into a first preset array under the condition that the first target value is smaller than the first preset threshold value, wherein the first target scattered light intensity is used for calculating the maximum differential scattered light intensity in the first data sequence;
and marking the maximum difference as zero, and repeating the steps of acquiring a first target value and a second target value in the first difference sequence until the first target value is greater than or equal to the first preset threshold value, so as to obtain a first target array, wherein the first target array comprises at least two first target scattered light intensities.
4. The curve fitting method of claim 3 wherein said determining a second target scattered light intensity based on said second target value and a second preset threshold comprises:
Storing the second target scattered light intensity into a second preset array under the condition that the second target value is smaller than a second preset threshold value, wherein the second target scattered light intensity is used for calculating the scattered light intensity of the minimum difference in the first difference sequence;
marking the minimum difference as zero, and repeating the steps of obtaining a first target value and a second target value in the first difference sequence until the second target value is greater than or equal to the second preset threshold value, so as to obtain a second target array, wherein the second target array comprises at least two second target scattered light intensities;
said taking as said filtered value the sum of said first target scattered light intensity and said second target scattered light intensity, comprising:
and taking the sum of the number of the first target scattered light intensities in the first target array and the number of the second target scattered light intensities in the second target array as the filtering value.
5. The curve fitting method of claim 1, wherein the objective fitting function comprises at least one of a preset linear fitting function and a preset polynomial fitting function, the determining the objective fitting function according to the comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the objective fitting function to obtain an objective fitting curve comprises:
And under the condition that the fluctuation value is detected to be larger than the preset threshold value, fitting the second data sequence according to the preset linear fitting function to obtain a first target fitting curve, and under the condition that the fluctuation value is detected to be smaller than or equal to the preset threshold value, fitting the second data sequence based on the preset polynomial fitting function to obtain a second target fitting curve.
6. The curve fitting method of claim 1, wherein said calculating a fluctuation value from said target difference value, said second differential sequence, and said third differential sequence comprises:
calculating the ratio of corresponding elements in the second differential sequence and the third differential sequence one by one to obtain a ratio data set;
calculating the sum of all ratio data in the ratio data set to obtain a target ratio;
dividing the target ratio by the target difference to obtain the fluctuation value.
7. The curve fitting method of claim 1, wherein after obtaining the first data sequence from the scattered light intensities of the sample under test, the method further comprises:
filtering the first data sequence according to a preset threshold range to obtain an intermediate data sequence;
The calculating a difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value includes:
and calculating the difference value of the maximum scattered light intensity and the minimum scattered light intensity in the intermediate data sequence to obtain the target difference value.
8. A curve fitting system, the system comprising:
the acquisition module is used for acquiring scattered light intensity of a sample to be detected to obtain a first data sequence, and all the scattered light intensities in the first data sequence are arranged according to the sequence of acquisition time;
the first calculation module is used for calculating the difference value between the maximum scattered light intensity and the minimum scattered light intensity in the first data sequence to obtain a target difference value;
the second calculation module is used for calculating the first order difference of all scattered light intensities in the first data sequence to obtain a first difference sequence, and determining a filtering value according to the first difference sequence;
the filtering module is used for filtering the first data sequence according to the filtering value and a first preset value range to obtain a second data sequence, and filtering a first time sequence corresponding to the first data sequence according to the filtering value and the second preset value range to obtain a second time sequence, wherein the first time sequence comprises acquisition time of all scattered light intensities in the first data sequence;
A third calculation module, configured to calculate first order differences of all scattered light intensities in the second data sequence to obtain a second differential sequence, and calculate a fluctuation value according to the target difference value, the second differential sequence, and the third differential sequence, where the fluctuation value is used to characterize a fluctuation degree of the second data sequence;
and the determining module is used for determining a target fitting function according to the comparison result of the fluctuation value and a preset threshold value, and fitting the second data sequence according to the target fitting function to obtain a target fitting curve.
9. A computer device, characterized in that it comprises a memory and a processor, said memory having stored thereon a computer program which, when executed by said processor, implements the curve fitting method according to any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by one or more processors, implements the curve fitting method of any of claims 1-7.
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