Analysis method and system of all-in-one fluorescent quantitative test strip with self-adaptive threshold adjustment function

Abstract

The invention relates to the technical field of pesticide detection, and discloses an analysis method and system of an all-in-one fluorescent quantitative test strip with a self-adaptive threshold adjustment function, the method comprises the following steps: firstly obtaining historical detection data of N fluorescent signal channels of a historical detection sample, and determining a first threshold parameter of each fluorescent signal channel; then acquiring real-time detection data of a to-be-detected sample, calculating first dynamic parameters of the N fluorescence signal channels, and dynamically adjusting the first threshold parameter to obtain a second threshold parameter; a fluorescence characteristic parameter, a sample matrix characteristic parameter and an environment interference parameter are obtained to correct the second threshold parameter, and self-adaptive thresholds of N fluorescence signal channels are obtained; and finally performing quantitative analysis and result evaluation on the to-be-detected sample according to the self-adaptive threshold value. According to the invention, the threshold value can be dynamically adjusted according to the change of the detection environment, and the accuracy and reliability of the detection result of the all-in-one fluorescent quantitative test strip are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pesticide detection, and more specifically, to an analysis method and system for an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function. Background Art

[0002] With the rapid development of modern agriculture and pharmaceutical industry, the problem of pesticide and drug residues has become increasingly serious and has become one of the important factors affecting human health. In order to effectively monitor and control pesticide and drug residues, it is urgent to develop rapid, sensitive and accurate detection methods and devices. Fluorescence quantitative detection technology has been widely used in the field of pesticide and drug residue detection due to its advantages of high sensitivity and strong specificity.

[0003] The patent application with publication number CN113624973A discloses a fluorescent immunochromatographic rapid detection method for Alternaria toxin, which utilizes immunochromatographic test paper to react with the sample to be tested, captures and analyzes the fluorescent signal through the fluorescent immunochromatographic test strip detection module, and obtains the test result. However, this method is only for detection of a single pesticide, and it is impossible to achieve simultaneous detection of multiple pesticides. The patent application with publication number CN118421763A discloses a fluorescence crosstalk correction method, device and medium based on S-type curve fitting, which obtains the fluorescence value of the standard test plate, performs S-type curve fitting, removes the background value and calculates the crosstalk coefficient, thereby eliminating non-specific signals and other interfering factors. However, this method is mainly for the problem of fluorescence signal crosstalk in the PCR system, and cannot be directly applied to pesticide residue detection. The patent application with publication number CN114527283A discloses an all-in-one pesticide residue quantitative detection test strip and detection method, in which at least two pesticide gold nanoparticle-labeled antibodies are attached to the colloidal gold binding pad of the test strip, and detection lines corresponding to pesticide antigens are arranged on the chromatographic membrane, thereby achieving simultaneous detection of multiple pesticides. However, the application does not involve how to dynamically adjust the detection threshold for different detection environments to ensure the accuracy and reliability of the detection results.

[0004] In summary, most of the existing pesticide residue detection methods are targeted at single pesticides and cannot achieve simultaneous detection of multiple pesticides. The detection process is easily affected by environmental factors, resulting in false positive or false negative results. There is a lack of the ability to dynamically adjust the detection threshold according to changes in the detection environment, which affects the detection accuracy. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an analysis method and system of an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function, which can simultaneously detect multiple pesticides and has an adaptive threshold adjustment function, and has high sensitivity and high specificity.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The analytical method of the all-in-one fluorescence quantitative test strip with adaptive threshold adjustment function comprises:

[0008] Obtain historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of the historical detection sample, determine a reference threshold for each fluorescence signal channel according to the historical detection data of the N fluorescence signal channels, and obtain the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer;

[0009] Acquire real-time detection data of N fluorescence signal channels of the sample to be tested; calculate first dynamic parameters of the N fluorescence signal channels based on the real-time detection data of the N fluorescence signal channels and first threshold parameters of the N fluorescence signal channels; dynamically adjust the first threshold parameters of the N fluorescence signal channels according to the first dynamic parameters of the N fluorescence signal channels to obtain second threshold parameters of the N fluorescence signal channels; acquire fluorescence characteristic parameters of target substances in the N fluorescence signal channels, sample matrix characteristic parameters and environmental interference parameters, calibrate the second threshold parameters of the N fluorescence signal channels, and obtain adaptive thresholds of the N fluorescence signal channels;

[0010] According to the adaptive thresholds of N fluorescence signal channels, quantitative analysis and result evaluation are performed on the samples to be tested.

[0011] Furthermore, the historical detection data includes historical fluorescence intensity data, historical target substance concentration data and historical reaction time data; each fluorescence signal channel corresponds to a target substance;

[0012] The first threshold parameter includes a first intensity threshold [I1, I2, ..., I N ], the first concentration threshold [C1, C2, ..., C N ] and the first time threshold [T1,T2,…,T N ]; among them, I N is the first intensity threshold of the Nth fluorescence signal channel; C N is the first concentration threshold of the Nth fluorescence signal channel; T N is the first time threshold of the Nth fluorescence signal channel; TA N =[I N ,C N ,T N ].

[0013] Furthermore, the first threshold parameters of the N fluorescence signal channels are obtained including:

[0014] Classifying the historical detection data according to N fluorescence signal channels to obtain historical detection data subsets of the N fluorescence signal channels;

[0015] According to the historical fluorescence intensity data DI in the historical detection data subset of the i-th fluorescence signal channel i , get the first intensity threshold I of the i-th fluorescence signal channel i ; 1≤i≤N;

[0016] According to the historical concentration data DC of the target substance in the historical detection data subset of the i-th fluorescence signal channel i , get the first concentration threshold C of the i-th fluorescence signal channel i ;

[0017] According to the historical reaction time data DT in the historical detection data subset of the i-th fluorescence signal channel i , get the first time threshold T of the i-th fluorescence signal channel i .

[0018] Furthermore, the first concentration threshold C of the i-th fluorescence signal channel is obtained. i include:

[0019] Historical concentration data DC of target substances i Perform logarithmic transformation to obtain the historical concentration data LDC of the target substance after logarithmic transformation i ;

[0020] The logarithmically transformed historical concentration data of the target substance LDC i Least squares regression analysis was performed to obtain the concentration regression model;

[0021] The upper limit of the prediction interval of the concentration regression model is used as the upper limit of the first concentration threshold in the logarithmic scale, and then the upper limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale is obtained by exponential transformation. ; The lower limit of the prediction interval of the concentration regression model is used as the lower limit of the first concentration threshold in the logarithmic scale, and then the lower limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale is obtained by exponential transformation ; Set the first concentration threshold upper limit and the first concentration threshold lower limit Composition of the first concentration threshold .

[0022] Furthermore, the first time threshold T of the i-th fluorescence signal channel is obtained. i include:

[0023] Historical reaction time data DT iPerform survival analysis and draw Kaplan-Meier survival curves;

[0024] Extract all time points t from the Kaplan-Meier survival curve i' , as a potential cutoff point;

[0025] Establish the optimal cutoff point objective function J;

[0026] Traverse all potential truncation points and calculate the optimal truncation point objective function J; select the potential truncation point with the largest optimal truncation point objective function J as the optimal truncation point;

[0027] The reaction time corresponding to the optimal cutoff point is taken as the first time threshold T of the i-th fluorescence signal channel. i .

[0028] Furthermore, the real-time detection data includes real-time fluorescence intensity data, real-time concentration data of the target substance and real-time reaction time data; the first dynamic parameter includes real-time signal intensity change rate, real-time concentration change rate and real-time reaction rate;

[0029] The calculating of the first dynamic parameters of the N fluorescence signal channels comprises:

[0030] According to the real-time fluorescence intensity data of the i-th fluorescence signal channel and the first intensity threshold, the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel is calculated i ;

[0031] According to the real-time concentration data of the target substance in the i-th fluorescence signal channel and the first concentration threshold, the real-time concentration change rate ΔRC of the i-th fluorescence signal channel is calculated. i ;

[0032] According to the real-time reaction time data of the i-th fluorescent signal channel and the first time threshold, the real-time reaction rate ΔRT of the i-th fluorescent signal channel is calculated. i .

[0033] Furthermore, the second threshold parameter [TB1, TB2, ..., TB N ] includes a second intensity threshold [BI1, BI2, ..., BI N ], the second concentration threshold [BC1, BC2,…, BC N ] and the second time threshold [BT1, BT2,…, BT N ]; among them, BI N is the second intensity threshold of the Nth fluorescence signal channel; BC N is the second concentration threshold of the Nth fluorescence signal channel; BT N is the second time threshold of the Nth fluorescence signal channel; TB Nis the second threshold parameter of the Nth fluorescence signal channel, TB N =[BI N ,B.C. N ,BT N ];

[0034] The dynamically adjusting the first threshold parameters of the N fluorescence signal channels includes:

[0035] According to the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel i , for the first intensity threshold I i Dynamically adjust to obtain the second intensity threshold BI of the i-th fluorescence signal channel i ;

[0036] According to the real-time concentration change rate ΔRC of the i-th fluorescence signal channel i , for the first concentration threshold C i Dynamically adjust to obtain the second concentration threshold BC of the i-th fluorescence signal channel i ;

[0037] According to the real-time reaction rate ΔRT of the i-th fluorescence signal channel i , for the first time threshold T i Dynamically adjust to obtain the second time threshold BT of the i-th fluorescence signal channel i .

[0038] Furthermore, the second concentration threshold BC of the i-th fluorescence signal channel is obtained. i include:

[0039] If |ΔRC i |≤Δ (C) , then BC i =C i ; Among them, Δ (C) is the preset concentration adjustment threshold, Δ (C) >0;

[0040] If ΔRC i >Δ (C) ,but , , ;in, is the threshold adjustment coefficient of the concentration change rate, is the lower limit of the second concentration threshold, is the upper limit of the second concentration threshold, >0;

[0041] If ΔRC i <-Δ (C) ,but .

[0042] Furthermore, the step of obtaining the adaptive thresholds of the N fluorescence signal channels includes:

[0043] Get the Fluorescence characteristic parameters of the target substance in each fluorescence signal channel ,according to The second intensity threshold Correction is performed to obtain Adaptive intensity thresholding for each fluorescence signal channel ; The fluorescence characteristic parameters Including The maximum excitation wavelength of the target substance in each fluorescence signal channel , Maximum emission wavelength and quantum yield ;

[0044] Get the Sample matrix characteristic parameters for each fluorescence signal channel ,according to The second concentration threshold Correction is performed to obtain Adaptive concentration threshold for each fluorescence signal channel ; The sample matrix characteristic parameters Including Fluorescence signal channel Viscosity of sample matrix value and ionic strength ;

[0045] Get the Environmental interference parameters for each fluorescence signal channel ,according to For the second time threshold Correction is performed to obtain Adaptive time threshold for each fluorescence signal channel ; The environmental interference parameters Including Ambient temperature for each fluorescence signal channel and ambient humidity .

[0046] Further, the obtained Adaptive intensity thresholding for each fluorescence signal channel include:

[0047] Calculate the relative fluorescence intensity correction factor of the target substance in the i-th fluorescence signal channel ;

[0048] Correction factor based on relative fluorescence intensity Calculate the Adaptive intensity threshold for target substances in each fluorescence signal channel ;

[0049] The obtained Adaptive concentration threshold for each fluorescence signal channel include:

[0050] according to Calculate the Relative viscosity of the sample matrix for each fluorescence signal channel ;

[0051] according to Calculate the pH deviation of sample matrix for each fluorescence signal channel ;

[0052] according to Calculate the Relative ionic intensity of the sample matrix for each fluorescence signal channel ;

[0053] According to relative viscosity , pH deviation , relative ionic strength and the second concentration threshold , calculate the Adaptive concentration threshold for each fluorescence signal channel .

[0054] An analysis system for an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function, which is used to implement the above-mentioned analysis method for an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function, the system comprising:

[0055] Reference threshold determination module: obtains historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of historical detection samples, determines the reference threshold for each fluorescence signal channel according to the historical detection data of the N fluorescence signal channels, and obtains the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer;

[0056] Adaptive threshold determination module: obtain real-time detection data of N fluorescence signal channels of the sample to be tested; calculate the first dynamic parameters of the N fluorescence signal channels based on the real-time detection data of the N fluorescence signal channels and the first threshold parameters of the N fluorescence signal channels; dynamically adjust the first threshold parameters of the N fluorescence signal channels according to the first dynamic parameters of the N fluorescence signal channels to obtain the second threshold parameters of the N fluorescence signal channels; obtain the fluorescence characteristic parameters of the target substances of the N fluorescence signal channels, the sample matrix characteristic parameters and the environmental interference parameters, calibrate the second threshold parameters of the N fluorescence signal channels, and obtain the adaptive thresholds of the N fluorescence signal channels;

[0057] Analysis module: Based on the adaptive thresholds of N fluorescence signal channels, quantitative analysis and result evaluation are performed on the samples to be tested.

[0058] An electronic device comprises a memory, a central processing unit and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function is implemented.

[0059] A computer-readable storage medium stores a computer program, which, when executed, implements the above-mentioned analysis method of the all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention determines the first threshold parameter by acquiring historical detection data, calculates the dynamic parameter using real-time detection data, and dynamically adjusts the threshold, which can adapt to the threshold changes under different detection environments and ensure the rationality of the detection threshold.

[0062] On the basis of dynamically adjusting the threshold, the present invention further introduces fluorescence characteristic parameters, sample matrix characteristic parameters and environmental interference parameters to calibrate the threshold to obtain an adaptive threshold. The influence of multiple factors such as target substance, sample matrix and environment is comprehensively considered, making the adaptive threshold more accurate and reliable.

[0063] The present invention combines dynamic threshold adjustment with multi-factor correction, overcomes the limitations of the traditional fixed threshold method, and has stronger adaptability and robustness in complex and changeable actual detection environments.

[0064] The present invention realizes adaptive adjustment of thresholds in multi-index parallel detection, can be used for multi-channel, multi-substance complex sample detection, and improves the practicality and versatility of the all-in-one fluorescent quantitative test strip.

[0065] The present invention provides a set of systematic threshold adaptive adjustment schemes, including key steps such as reference threshold determination, dynamic threshold adjustment and adaptive threshold correction, laying a methodological foundation for the intelligent analysis of all-in-one fluorescent quantitative test strips.

[0066] The present invention improves the accuracy, sensitivity and reliability of the detection results of the all-in-one fluorescent quantitative test strip, reduces the ratio of false positives to false negatives, is of great significance for accurate quantitative analysis and result evaluation, and can better guide decision-making and response in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 It is a principle flow chart of the analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function in the present invention;

[0069] Figure 2 A flow chart of a method for obtaining first threshold parameters of N fluorescence signal channels in an analysis method of an all-in-one fluorescence quantitative test strip with an adaptive threshold adjustment function according to the present invention;

[0070] Figure 3 A flow chart of a method for obtaining a first intensity threshold of an i-th fluorescent signal channel in an analysis method of an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function of the present invention;

[0071] Figure 4 A flow chart of a method for obtaining a first time threshold of an i-th fluorescent signal channel in an analysis method of an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function of the present invention;

[0072] Figure 5 A flow chart of a method for calculating the first dynamic parameters of N fluorescence signal channels in the analysis method of the all-in-one fluorescence quantitative test strip with an adaptive threshold adjustment function of the present invention;

[0073] Figure 6 A flow chart of a method for obtaining second threshold parameters of N fluorescence signal channels in the analysis method of the all-in-one fluorescence quantitative test strip with adaptive threshold adjustment function of the present invention;

[0074] Figure 7 It is a functional module diagram of the analysis system of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function in the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Example 1

[0077] See also Figure 1 As shown, this embodiment provides an analysis method of an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function, comprising:

[0078] Step S1000, obtaining historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of the historical detection sample, determining a reference threshold for each fluorescence signal channel according to the historical detection data of the N fluorescence signal channels, and obtaining the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer;

[0079] Furthermore, step S1000 includes:

[0080] Step S1100, obtaining historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of the historical detection sample, wherein the historical detection data includes historical fluorescence intensity data, historical concentration data of the target substance, and historical reaction time data; each fluorescence signal channel corresponds to a target substance; N is a positive integer;

[0081] Specifically, an all-in-one fluorescent quantitative test strip is a test strip that detects multiple substances at the same time. There are multiple fluorescent signal channels on the test strip, and each channel detects a specific target substance through fluorescent response. For example: a test strip may contain two fluorescent signal channels for detecting aldicarb and carbofuran at the same time.

[0082] The historical fluorescence intensity data is a record of the change in the fluorescence signal intensity monitored by each fluorescence signal channel over time. The fluorescence signal intensity refers to the strength of the fluorescence signal emitted by the test strip during the detection process. The intensity of the fluorescence signal is related to the concentration of the target substance. Generally, the higher the concentration of the target substance, the stronger the fluorescence signal. The historical concentration data of the target substance is the known concentration of the target substance in different samples. The target substance concentration refers to the actual concentration of the target substance in the sample, which is usually determined by a standard detection method (such as liquid chromatography). The historical reaction time data is the time required from the start of the reaction to the completion of the detection. The reaction time refers to the time required from the sample contacting the test strip to the stabilization of the fluorescence signal. For example, a test strip may take 3 minutes to reach the peak of the fluorescence signal, and the reaction time at this time is 3 minutes.

[0083] Each fluorescent signal channel is dedicated to detecting a target substance, which can be pesticide residues (such as aldicarb, carbofuran, hexachlorobenzene, DDT, etc.). The historical detection data of these channels reflects the historical status of the target substance (such as concentration distribution, fluorescent signal response law, etc.).

[0084] The data sources include test results of test strips from different batches and different periods. The data needs to be preprocessed, including the elimination of abnormal data: abnormal data refers to test results that are beyond the normal range. For example, the fluorescence intensity of a channel is suddenly more than 10 times higher than that of other channels, which may be caused by ambient light interference or equipment failure. Eliminating abnormal data can improve the reliability of the analysis. Interpolation of missing values: Reasonably fill in the missing data caused by experimental errors or equipment failure. Interpolation refers to a method of filling in missing data. Common methods include mean interpolation (filling missing points with the average value of the data) and linear interpolation (filling missing points with the trend of adjacent data points). Data normalization or standardization: Eliminate systematic deviations caused by batch or equipment differences.

[0085] For example, a laboratory is using an all-in-one fluorescent quantitative test strip to detect four pesticide residues: aldicarb, carbofuran, hexachloride and DDT. There are four fluorescent signal channels on the test strip, each corresponding to one substance.

[0086] Data Collection:

[0087] 100 samples were tested, and the test data of each sample included:

[0088] The fluorescence intensity of each channel changes with time.

[0089] Known concentrations of 4 substances in each sample (determined by liquid chromatography).

[0090] Total reaction time for each assay (time from sample contact to signal stabilization).

[0091] Data collation:

[0092] Classify the data of these 100 samples by channel:

[0093] Channel 1 (Aldicarb): Data on the fluorescence intensity, concentration and reaction time of Aldicarb are sorted out.

[0094] Channel 2 (Carbofuran): Organizes the data of fluorescence intensity, concentration and reaction time of Carbofuran.

[0095] The same goes for other channels.

[0096] After classifying and arranging the data of each channel, the detection parameters of each substance can be optimized separately to ensure the detection sensitivity and accuracy of each substance. For example, when the fluorescence signal of aldicarb is strong, a higher intensity threshold can be set; while when the fluorescence signal of DDT is weak, a lower intensity threshold needs to be set. The core of step S1100 is to obtain and organize the historical detection data of multi-channel fluorescence signals, laying the foundation for subsequent threshold parameter calculation and dynamic adjustment. By systematically collecting, preprocessing and classifying historical data, it can be ensured that the calculation of threshold parameters is based on a reliable data foundation.

[0097] Step S1200: According to the historical detection data of the N fluorescence signal channels, a reference threshold is determined for each fluorescence signal channel to obtain the first threshold parameters [TA1, TA2, ..., TA N ]; the first threshold parameter includes a first intensity threshold [I1, I2, ..., I N ], the first concentration threshold [C1, C2, ..., C N ] and the first time threshold [T1,T2,…,T N ]; among them, I N is the first intensity threshold of the Nth fluorescence signal channel; C N is the first concentration threshold of the Nth fluorescence signal channel; T N is the first time threshold of the Nth fluorescence signal channel; TA N is the first threshold parameter of the Nth fluorescence signal channel, TA N =[I N ,C N ,T N ];

[0098] The intensity threshold refers to a critical value of the fluorescence signal intensity. In real-time detection, its role is to help determine whether the signal has reached sufficient intensity for effective detection. When the fluorescence signal intensity (i.e., the light intensity detected by each fluorescence signal channel) exceeds the threshold, it means that the sample being tested has reached a certain degree of fluorescence response, and the response value is high enough, which means that the concentration of the substance is already within the detectable range. If the real-time fluorescence intensity is lower than the intensity threshold, it may be considered that the concentration of the target substance in the sample is too low to be reliably analyzed. Therefore, the intensity threshold helps ensure that the data is analyzed and processed only when the detection signal is strong enough.

[0099] The concentration threshold is a critical value based on the concentration of the target substance in the sample. This threshold is used to determine whether the concentration of the target substance in the sample exceeds the specified range, affecting subsequent data analysis and result determination. The concentration threshold determines the upper and lower limits of the concentration of the target substance. Test data outside this range may be considered invalid or abnormal. For example, when the concentration data exceeds the threshold, it may mean that an error has occurred in the test process, or that the concentration of the substance in the sample is too high or too low, and is not suitable for detection with the test strip. During real-time testing, the concentration threshold helps determine whether the current sample concentration is suitable for testing. For example, if the concentration is too high, it may cause the instrument to be saturated and unable to continue accurate measurement; if the concentration is too low, it may result in an inability to detect sufficient signals.

[0100] The time threshold is usually related to the reaction time, which is used to control the duration of the sample reaction on the test strip. The time threshold defines a maximum reaction time. In the test strip test, the reaction needs to be completed within a certain time range. If the reaction time is too long or too short, the reaction result may be inaccurate, so setting a time threshold can ensure that the test is completed within the appropriate time window. The time threshold helps determine whether the sample is measured within the optimal reaction time. When the reaction time is too long, the signal change may become stable and fail to reflect the concentration change of the target substance; conversely, if the reaction time is too short, the signal has not stabilized yet, which may lead to incorrect measurements.

[0101] In actual use, these three thresholds work together to ensure that each fluorescent signal channel can be effectively detected under optimal conditions. Specifically: Intensity threshold: helps ensure that the signal intensity is sufficient for effective analysis. Concentration threshold: ensures that the concentration of the target substance is within the appropriate range to avoid values ​​that are too high or too low affecting the results. Time threshold: ensures that the reaction is carried out within the specified time to avoid errors caused by too long or too short reaction time.

[0102] In actual detection, real-time detection data will be compared and evaluated based on the baseline threshold (the threshold set initially). If the detection signal changes and exceeds the preset adjustment conditions (such as the signal change rate exceeds a specific range), these thresholds will be dynamically adjusted to ensure the accuracy and sensitivity of the detection. This dynamic adjustment can optimize the threshold according to the real-time detection situation and further improve the reliability of the detection results. For example, if the intensity threshold of a channel is triggered too frequently, the threshold of the channel can be dynamically lowered to be more sensitive to small changes; if the concentration exceeds a certain range, the upper and lower limits of the concentration threshold can be adjusted to avoid the generation of erroneous data.

[0103] Furthermore, if Figure 2 As shown, step S1200 includes:

[0104] Step S1210, classifying the historical detection data according to N fluorescence signal channels to obtain a subset of historical detection data of the N fluorescence signal channels;

[0105] Specifically, the purpose of classifying historical test data by channel is to analyze and optimize the characteristics of each target substance separately. For example, the historical test data subset of channel 1 contains all the historical fluorescence intensity data of aldicarb, historical concentration data of target substances, and historical reaction time data. The historical test data subset of channel 2 contains all the historical fluorescence intensity data of carbofuran, historical concentration data of target substances, and historical reaction time data. The same applies to other channels.

[0106] By classifying and organizing the data, the detection parameters can be optimized for each target substance to ensure the detection sensitivity and accuracy of different target substances. For example, the fluorescence response characteristics of aldicarb and carbofuran are different. By independently analyzing the historical data of the two, appropriate detection thresholds can be set to avoid misjudgment due to differences in fluorescence intensity.

[0107] Step S1220: based on the historical fluorescence intensity data DI in the historical detection data subset of the i-th fluorescence signal channel i , get the first intensity threshold I of the i-th fluorescence signal channel i ; 1≤i≤N;

[0108] Furthermore, if Figure 3 As shown, step S1220 includes:

[0109] Step S1221, historical fluorescence intensity data DI i Conduct normality test;

[0110] Step S1222: If the historical fluorescence intensity data DI i If normality is satisfied, the historical fluorescence intensity data DI is calculated iThe mean μ I and standard deviation σ I , take μ I +2σ I As the first intensity threshold I of the i-th fluorescence signal channel i , that is I i =μ I +2σ I ;

[0111] Step S1223, if the historical fluorescence intensity data DI i If normality is not satisfied, the quantile method is used and the 95% quantile is taken as the first intensity threshold I of the i-th fluorescence signal channel. i .

[0112] Specifically, historical fluorescence intensity data refers to the intensity curve of the fluorescence signal recorded in the i-th fluorescence signal channel over time. The magnitude of the fluorescence intensity reflects the concentration of the target substance. Generally, the higher the concentration of the target substance, the stronger the fluorescence signal. The normality test is a statistical method used to determine whether the data distribution conforms to the normal distribution. Commonly used normality test methods include the Shapiro-Wilk test and KS (Kolmogorov-Smirnov test). The Shapiro-Wilk test is suitable for small sample data, and the KS test is suitable for large sample data. If the data conforms to the normal distribution, the mean and standard deviation can be used to calculate the threshold; otherwise, the quantile method is used. The normality test can ensure the rationality of the threshold calculation method.

[0113] According to the characteristics of normal distribution, the mean μ of the data is I represents the center position, standard deviation σ I Measures the degree of discreteness of the data. I +2σ I The threshold is set to cover approximately 95% of the data points within the normal distribution range (theoretically 95.45%). This method can effectively eliminate outliers that deviate significantly from the data center and ensure that the threshold is applicable to most normal detection situations. For different fluorescence signal channels, the mean and standard deviation of historical fluorescence intensity may vary depending on the fluorescence characteristics of the target substance. For example: for target substances with higher signal intensity (such as aldicarb), μ I and σ I If the value is larger, the threshold value will automatically adapt to the historical data to improve the tolerance to high-intensity signals. For target substances with weaker signals (such as DDT), the threshold value will be automatically adjusted down to improve the detection sensitivity of weak signals. Under normal distribution conditions, if the value exceeds μ I +2σ IThe signal is considered a significant signal, which can effectively reduce the impact of background noise or random interference. This ensures that only signals that significantly deviate from the background noise are included in the analysis, reducing misjudgment. The normality assumption is usually applicable to the case where there is no significant skewed distribution in large-scale sample data. Under this condition, μ I +2σ I It can more accurately reflect the overall law of data distribution and is suitable for intensity threshold calculation of most detection channels.

[0114] In actual detection, the fluorescence intensity data may deviate from the normal distribution due to factors such as the characteristics of the target substance, environmental interference, and differences in reaction efficiency. For example:

[0115] Highly skewed: The fluorescence intensity of some target substances may be concentrated in the low value area, showing a right-skewed distribution.

[0116] Multi-peak distribution: Different batches of test strips may result in multiple peaks in the data distribution.

[0117] In these cases, the normal distribution assumption does not hold and μ is used directly I +2σ I This may cause the threshold to deviate from the true signal distribution and reduce the detection reliability. The quantile method directly takes the 95% quantile point, avoiding excessive assumptions about the data distribution form and is more flexible.

[0118] The quantile method determines the threshold based on the ranking position of the data rather than relying on the mean and standard deviation, so it is less sensitive to outliers. For example:

[0119] If there are extremely high values ​​in the historical fluorescence intensity data (such as abnormal intensity caused by ambient light interference), the quantile method will not be significantly affected by the extreme values, while the threshold calculation based on the mean and standard deviation may be pulled up by the outliers.

[0120] Taking the 95% quantile as the threshold means that only the strongest 5% of signals will be identified as significant signals. This method can eliminate most of the background noise and reduce the damage of outliers to the threshold.

[0121] The quantile method does not depend on the specific form of data distribution, so it can adapt to various complex distributions. For example:

[0122] For right-skewed distribution or long-tailed distribution, the 95% quantile can effectively reflect the significance of signal strength.

[0123] For multi-peak distribution, the 95% quantile can avoid the interference of local peaks and find a more robust threshold.

[0124] As an alternative to normality test failure, the quantile method can ensure that a reasonable intensity threshold can still be calculated when the normal distribution assumption fails. Through this mechanism, even if the distribution characteristics of historical fluorescence intensity data change, the threshold setting method can be dynamically adjusted to enhance the stability and adaptability of the detection process.

[0125] Step S1230: based on the historical concentration data DC of the target substance in the historical detection data subset of the i-th fluorescence signal channel i , get the first concentration threshold C of the i-th fluorescence signal channel i ;

[0126] Further, step S1230 includes:

[0127] Step S1231: Calculate the target substance historical concentration data DC i Perform logarithmic transformation to obtain the historical concentration data LDC of the target substance after logarithmic transformation i ;

[0128] Step S1232: Logarithmically transform the historical concentration data of the target substance LDC i Least squares regression analysis was performed to obtain the concentration regression model;

[0129] Step S1233: Use the upper limit of the prediction interval of the concentration regression model as the upper limit of the first concentration threshold in the logarithmic scale, and then obtain the upper limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale through exponential transformation. ; The lower limit of the prediction interval of the concentration regression model is used as the lower limit of the first concentration threshold in the logarithmic scale, and then the lower limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale is obtained by exponential transformation ; Set the first concentration threshold upper limit and the first concentration threshold lower limit Composition of the first concentration threshold .

[0130] Specifically, DC i It is a set of historical concentration data of the target substance corresponding to the i-th fluorescence signal channel. These data may be unevenly distributed, with long-tail distribution or skewed distribution, for example, some concentration values ​​are significantly higher or lower than most data points. Logarithmic transformation is a data processing technique used to convert skewed or long-tailed distributed data into a form closer to normal distribution. Normal distribution is the basis of many statistical analysis methods, such as least squares regression analysis. If the data deviates from the normal distribution (such as right-skewed distribution, most of the data is concentrated in low values, but a small number of high values ​​are far from the mean), direct use may lead to modeling deviation. Through logarithmic transformation, the dynamic range of the data can be reduced and the impact of extreme values ​​on the model can be reduced.

[0131] By fitting historical concentration data, the changing pattern of the concentration of the target substance can be captured. The concentration regression model can predict the range of concentration data, especially the upper limit, which is used to set the concentration threshold. Data quality is crucial to the fitting effect of the concentration regression model. If the data is noisy or deviates from the normal distribution, the model may fail. Therefore, the logarithmic transformation of step S1231 is the key to ensuring the reliability of the concentration regression model.

[0132] For example, assuming that the historical concentration data of the target substance after logarithmic transformation is , regression analysis found that the concentration and time have a linear relationship:

[0133]

[0134] Where t is the reaction time. The concentration range at different time points can be predicted through the concentration regression model.

[0135] The prediction interval is the prediction range of the concentration regression model for new data points, usually including a confidence interval (such as 95%). The upper limit of the prediction interval represents the possible maximum concentration, which is used to set the concentration threshold to ensure that abnormally high concentrations of target substances can be identified; the lower limit of the prediction interval represents the possible minimum concentration, which is used to set the concentration threshold to ensure that abnormally low concentrations of target substances can be identified; the exponential transformation is used to convert the concentration threshold in the logarithmic scale back to the original scale. By setting the thresholds at the upper and lower limits of the prediction interval, the concentration range of the target substance corresponding to the signal can be identified, which can avoid missing low-concentration target substances and prevent misjudgment of high-concentration abnormal values.

[0136] Step S1240: based on the historical reaction time data DT in the historical detection data subset of the i-th fluorescence signal channel i , get the first time threshold T of the i-th fluorescence signal channel i .

[0137] Furthermore, if Figure 4 As shown, step S1240 includes:

[0138] Step S1241, historical reaction time data DT i Perform survival analysis and draw Kaplan-Meier survival curves;

[0139] Step S1242, selecting the optimal cutoff point on the Kaplan-Meier survival curve, and using the reaction time corresponding to the optimal cutoff point as the first time threshold T of the i-th fluorescence signal channel i .

[0140] Further, step S1242 includes:

[0141] Step S12421, extract all time points t from the Kaplan-Meier survival curve i' , as a potential cutoff point;

[0142] Step S12422, establishing the optimal truncation point objective function J;

[0143]

[0144] in:

[0145] For the The time points corresponding to the potential cutoff points; The smaller it is, the shorter the reaction time is and the higher the detection efficiency is;

[0146] : Reaction time index (the shorter the cutoff point, the better); the time corresponding to the potential cutoff point is directly taken ;

[0147] : Detection accuracy indicator (the higher the survival probability, the better); specifically, Calculate the survival probability ; or calculate the event rate after a potential cutoff point , reflecting the proportion of completed reactions;

[0148] : The weight coefficient of the reaction time indicator, indicating the preference for efficiency; for fast detection scenarios, increase α;

[0149] : The weight coefficient of the detection accuracy index, indicating the preference for accuracy; for high accuracy scenarios, increase β;

[0150] Step S12423, traverse all potential truncation points, calculate the optimal truncation point objective function J; select the potential truncation point with the largest optimal truncation point objective function J as the optimal truncation point.

[0151] Specifically, step S1240 is to determine the first time threshold T of the i-th fluorescence signal channel using the survival analysis method. i Survival analysis is a statistical method used to analyze the time data of event occurrence. In this embodiment, the event refers to the fluorescent signal reaching a stable state, that is, the reaction is completed.

[0152] Historical reaction time data DT i The time required for the i-th fluorescence signal channel from the start of the reaction to the signal stabilization in the historical detection samples is recorded. i Survival analysis can be performed to obtain the time distribution of reaction completion, and the first time threshold T can be determined accordingly.i .

[0153] The Kaplan-Meier survival curve is a commonly used tool for survival analysis. The curve can intuitively reflect the change in the probability of an event occurring over time. The ordinate of the Kaplan-Meier survival curve represents the probability that the event has not occurred (i.e., the survival probability), and the abscissa represents time. Each point on the curve represents an event occurrence, and the overall trend of the curve reflects the occurrence pattern of events over time. In the test strip test, the non-occurrence of the event means that the reaction has not been completed and the signal has not stabilized. Therefore, the higher the survival probability, the higher the proportion of tests that have not been completed at that time point.

[0154] The optimal cutoff point is a time point selected on the survival curve to balance detection efficiency and accuracy. Data before the optimal cutoff point is considered reliable and used for subsequent analysis; data after the optimal cutoff point is eliminated and regarded as unstable signals in the reaction process. The process of selecting the optimal cutoff point is actually looking for a balance point: the earlier the optimal cutoff point, the shorter the detection time and the higher the efficiency; but premature cutoff may lose effective signals and reduce accuracy. Conversely, the later the optimal cutoff point, the greater the probability of obtaining a stable signal and the higher the accuracy; but the detection time is also correspondingly extended and the efficiency is reduced.

[0155] In step S12421, all time points are extracted as potential truncation points in order to comprehensively evaluate the pros and cons of different time points. Each time point represents a possible truncation strategy, and the optimal solution can be found by comparing their objective function values.

[0156] Step S12422 constructs the optimal cutoff point objective function J, which is used to quantify the comprehensive effect of different cutoff points. Objective function J consists of two parts: reaction time index and detection accuracy index. Among them, the reaction time index directly takes the time value corresponding to the potential cutoff point. The shorter the time, the higher the efficiency. The detection accuracy index is measured by survival probability or event incidence rate. The higher the survival probability (or the lower the event incidence rate), the lower the proportion of completed reactions before this time point, and there are still more unstable signals after the cutoff point, and the greater the accuracy risk.

[0157] The weight coefficients α and β are used to balance the relative importance of efficiency and accuracy. In the scenario of rapid detection, the time cost is high, and the weight of α can be appropriately increased; in the scenario with extremely high accuracy requirements, the weight of β can be appropriately increased. By adjusting the weights, the objective function can be adapted to different usage requirements.

[0158] For example: Suppose the historical test data of a batch of test strips shows that the survival probability is 0.9 at 2 minutes, 0.5 at 3 minutes, and 0.1 at 4 minutes.

[0159] If 2 minutes is selected as the cutoff point, 90% of the tests will not be completed at this time, and the accuracy risk is relatively high; however, the detection time is the shortest and the efficiency is the highest.

[0160] If 4 minutes is selected as the cutoff point, 90% of the detection has been completed, the probability of obtaining a stable signal is very high, and the accuracy is guaranteed; but the detection time is longer and the efficiency is reduced.

[0161] With 3 minutes as a compromise, 50% of the tests are completed, and accuracy and efficiency are balanced.

[0162] The final optimal cutoff point needs to be determined by the objective function. For example, if the time cost is very high, the objective function may be more inclined to choose 2 minutes; if the accuracy requirement is very high, the objective function may be more inclined to choose 4 minutes.

[0163] Step S12423 traverses all potential truncation points to find the time point with the maximum objective function J as the optimal truncation point. Since the efficiency and accuracy of different time points are different, their objective function values ​​are also different. By comparing their objective function values, the time point with the best comprehensive performance under the current weight strategy can be selected. This optimization method can adaptively balance detection speed and accuracy, and make preference adjustments according to actual needs.

[0164] In general, step S1240 uses survival analysis and optimization methods to automatically select the optimal time threshold based on massive historical data, while meeting the detection accuracy requirements and minimizing the detection time, thereby significantly improving the detection efficiency. This data-driven threshold optimization method can fully tap the value of historical data, reduce the blindness of manually setting thresholds, and make the detection scheme more intelligent and efficient. At the same time, through the design of the objective function, the threshold selection can quantitatively balance speed and accuracy, and can be flexibly adjusted according to the actual application scenario, thereby improving the applicability and operability of the detection scheme.

[0165] Step S2000, obtaining real-time detection data of N fluorescence signal channels of the sample to be tested; calculating first dynamic parameters of the N fluorescence signal channels based on the real-time detection data of the N fluorescence signal channels and first threshold parameters of the N fluorescence signal channels; dynamically adjusting the first threshold parameters of the N fluorescence signal channels according to the first dynamic parameters of the N fluorescence signal channels to obtain second threshold parameters of the N fluorescence signal channels; obtaining fluorescence characteristic parameters of target substances in the N fluorescence signal channels, sample matrix characteristic parameters and environmental interference parameters, and correcting the second threshold parameters of the N fluorescence signal channels to obtain adaptive thresholds of the N fluorescence signal channels;

[0166] Furthermore, step S2000 includes:

[0167] Step S2100, obtaining real-time detection data of N fluorescence signal channels of the sample to be tested; the real-time detection data includes real-time fluorescence intensity data, real-time concentration data of the target substance and real-time reaction time data;

[0168] Specifically, step S2100 is the process of obtaining real-time detection data when dynamically adjusting the threshold. The sample to be tested refers to an unknown sample that needs to detect the concentration of the target substance. It can be a water sample, a soil sample, a food sample, etc., to detect pesticide residues; real-time detection refers to the real-time collection and analysis of data during the detection process, corresponding to the historical detection data. The N fluorescence signal channels are consistent with the channels in step S1100, and each channel corresponds to a target substance. Real-time fluorescence intensity data refers to the instant record of the fluorescence signal of each channel changing over time during the detection process. It reflects the instantaneous reaction state of the target substance and the test strip. The real-time concentration data of the target substance refers to the concentration of the target substance calculated based on the real-time fluorescence intensity. The real-time fluorescence intensity data can be converted into real-time concentration data through the pre-established concentration-fluorescence intensity curve. The real-time reaction time data refers to the time from the contact between the sample and the test strip to the collection of the current data. It can reveal the progress and rate of the reaction.

[0169] For example: a water sample may contain carbofuran residues and needs to be tested using the carbofuran detection channel. After the water sample is dripped onto the test strip, the fluorescence intensity of the carbofuran channel begins to change continuously. At the 30th second, the fluorescence intensity reaches 100 intensity units. According to the pre-set standard curve, it can be inferred that the carbofuran concentration in the water sample at this time is about 0.1 mg / L. As time goes by, at the 60th second, the fluorescence intensity rises to 150 intensity units, corresponding to a carbofuran concentration of 0.15 mg / L. By continuously recording the fluorescence intensity at different times, the inferred concentration and the reaction time, a set of real-time detection data for the carbofuran channel is obtained.

[0170] By acquiring real-time detection data from N channels, the reaction dynamics of various target substances can be fully monitored. This process helps to evaluate the concentration level of complex pollutants in samples in real time and provide data support for subsequent dynamic adjustments. Compared with single endpoint detection, real-time detection can provide richer dynamic information and is conducive to capturing abnormal changes in the reaction process. At the same time, multi-channel real-time detection can reveal the mutual influence between different target substances and provide a more comprehensive perspective for the assessment of complex pollution.

[0171] Step S2200, calculating first dynamic parameters of the N fluorescence signal channels based on real-time detection data of the N fluorescence signal channels and first threshold parameters of the N fluorescence signal channels; the first dynamic parameters include real-time signal intensity change rate, real-time concentration change rate and real-time reaction rate;

[0172] Furthermore, if Figure 5 As shown, step S2200 includes:

[0173] Step S2210: Calculate the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel according to the real-time fluorescence intensity data of the i-th fluorescence signal channel and the first intensity threshold. i ;

[0174] Step S2220, calculating the real-time concentration change rate ΔRC of the i-th fluorescent signal channel according to the real-time concentration data of the target substance in the i-th fluorescent signal channel and the first concentration threshold value i ;

[0175] Step S2230, calculating the real-time reaction rate ΔRT of the i-th fluorescent signal channel according to the real-time reaction time data of the i-th fluorescent signal channel and the first time threshold i .

[0176] Specifically, step S2200 is the core link in the dynamic analysis detection process. By calculating and comparing the real-time detection data and the set first threshold parameters (including the first intensity threshold, the first concentration threshold and the first time threshold), the dynamic change characteristics of each fluorescence signal channel, namely the first dynamic parameters, are extracted. These dynamic parameters can be used to evaluate the reaction behavior of the target substance during the detection process, including the change trend of the signal intensity, the change rate of the target substance concentration and the dynamic characteristics of the reaction time. This process provides basic data for subsequent dynamic threshold adjustment and detection optimization.

[0177] The three core indicators of the first dynamic parameter are:

[0178] Real-time signal strength change rate ΔRI i :Describes the rate of change of the fluorescence signal intensity over time, reflecting the dynamic trend of the fluorescence response of the test strip. The intensity change rate is measured by comparing the fluorescence intensity value at the current moment with the value at the previous moment. Combined with the first intensity threshold, it can filter out invalid weak signals and significantly improve the sensitivity and accuracy of detection. It provides the dynamic characteristics of real-time signal intensity for the subsequent dynamic adjustment of the threshold.

[0179] Real-time concentration change rate ΔRC i : Describes the rate of change of the target substance concentration over time, reflecting the dynamic concentration level of the target substance. It is calculated by combining the real-time fluorescence intensity data with the concentration-fluorescence intensity calibration curve. By real-time calculation of ΔRC i , can monitor the dynamic process of target substance concentration changes. Combined with the upper and lower limits of the concentration threshold, it can identify and filter abnormal concentration data and improve the stability of the test results. It provides a basis for the subsequent dynamic adjustment of the concentration threshold and optimizes the detection sensitivity.

[0180] Real-time reaction rate ΔRT i : Describes the rate dynamics of the detection reaction and reflects the real-time progress of the test strip reaction. It measures the speed at which the fluorescence signal approaches a stable state. By calculating ΔRT in real time i , the completion progress of the reaction can be dynamically evaluated. Combined with the first time threshold, it ensures that the detection is completed within a reasonable time window to improve efficiency. It provides a reference for subsequent optimization of the detection time to avoid too long or too short detection time.

[0181] Step S2300: dynamically adjust the first threshold parameters of the N fluorescent signal channels according to the first dynamic parameters of the N fluorescent signal channels to obtain the second threshold parameters [TB1, TB2, ..., TB N ], the second threshold parameter includes a second intensity threshold [BI1, BI2, ..., BI N ], the second concentration threshold [BC1, BC2,…, BC N ] and the second time threshold [BT1, BT2,…, BT N ]; among them, BI N is the second intensity threshold of the Nth fluorescence signal channel; BC N is the second concentration threshold of the Nth fluorescence signal channel; BT N is the second time threshold of the Nth fluorescence signal channel; TB N is the second threshold parameter of the Nth fluorescence signal channel, TB N =[BI N ,B.C. N ,BT N ];

[0182] Furthermore, if Figure 6 As shown, step S2300 includes:

[0183] Step S2310: based on the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel i , for the first intensity threshold I i Dynamically adjust to obtain the second intensity threshold BI of the i-th fluorescence signal channel i ;

[0184] Further, step S2310 includes:

[0185] Step S2311, if |ΔRI i |≤Δ (I) , then BI i =I i ; Among them, Δ (I) is the preset intensity adjustment threshold, Δ (I) >0;

[0186] Step S2312, if ΔRI i >Δ (I) ,but ;in, is the threshold adjustment coefficient of the signal strength change rate, >0;

[0187] Step S2313, if ΔRI i <-Δ (I) ,but .

[0188] Specifically, by comparing ΔRI i and the preset intensity adjustment threshold Δ (I) , it can be determined whether the change in fluorescence intensity exceeds the normal range, thereby determining whether the first intensity threshold I i Make adjustments. (I) is a positive number, indicating the acceptable rate of change of fluorescence intensity. i Absolute value of |ΔRI i |Less than or equal to Δ (I) , indicating that the fluorescence intensity does not change significantly, maintaining the original first intensity threshold I i Then, the second intensity threshold BI i Equal to I i .

[0189] If ΔRI i Greater than Δ (I) , indicating that the fluorescence intensity rises too fast, which may mean that environmental interference or other abnormal factors have caused the abnormal enhancement of the fluorescence signal. At this time, the intensity threshold needs to be increased accordingly to reduce the impact of abnormal signals. The new threshold BI i The formula is used to calculate the result of proportional amplification. This dynamic adjustment can adaptively reduce the impact of environmental interference.

[0190] If ΔRI i Less than -Δ (I) , indicating that the fluorescence intensity is decreasing too fast, which may mean that the concentration of the target substance in the sample has dropped sharply, and the intensity threshold needs to be lowered to improve the detection rate. i The result obtained by the formula is a scaled-down result. < is a positive number less than 1, so that the adjusted threshold is smaller than the original threshold.

[0191] In summary, step S2310 dynamically tracks the rate of change of the fluorescence intensity and adaptively adjusts the intensity threshold. When the fluorescence intensity increases abnormally, the threshold is increased to suppress interference, and when the fluorescence intensity decreases abnormally, the threshold is lowered to increase sensitivity, thereby improving the robustness and reliability of the detection. This dynamic threshold adjustment method can effectively cope with complex and changeable detection environments and has strong practical value.

[0192] Step S2320: based on the real-time concentration change rate ΔRC of the i-th fluorescence signal channel i , for the first concentration threshold C i Dynamically adjust to obtain the second concentration threshold BC of the i-th fluorescence signal channel i ;

[0193] Further, step S2320 includes:

[0194] Step S2321, if |ΔRC i |≤Δ (C) , then BC i =C i ; Among them, Δ (C) is the preset concentration adjustment threshold, Δ (C) >0;

[0195] Step S2322, if ΔRC i >Δ (C) ,but , , ;in, is the threshold adjustment coefficient of the concentration change rate, is the lower limit of the second concentration threshold, is the upper limit of the second concentration threshold, >0;

[0196] Step S2323, if ΔRC i <-Δ (C) ,but .

[0197] Specifically, step S2321 determines the real-time concentration change rate ΔRC i The absolute value of |ΔRC i |Is it less than or equal to the preset concentration adjustment threshold Δ (C) . Δ (C) Is a positive number, indicating the tolerable change rate of the target substance concentration, which is usually pre-set according to the properties and detection requirements of the target substance. i |≤Δ (C) , which means that the current concentration change rate is within the normal range, and there is no obvious abnormal fluctuation, so there is no need to adjust the first concentration threshold C iAdjust and maintain the original threshold value, and make the second concentration threshold value BC i Equal to C i The advantage of keeping the concentration threshold unchanged is that it avoids unnecessary threshold fluctuations and improves the reproducibility of quantitative results. When the concentration change is not obvious, frequent adjustment of the threshold may introduce additional variation and reduce the confidence of the test results. Therefore, step S2321 sets a tolerance range for the rate of change to maintain the threshold stable when the rate of change is small. This is a strategy that takes into account both sensitivity and reproducibility. It can ensure the comparability of a series of tests on the basis of dynamic optimization and improve the credibility of quantitative analysis.

[0198] Step S2322 handles the situation where the concentration of the target substance increases too fast. i Greater than the preset concentration adjustment threshold Δ (C) When the concentration is above 0.05, it means that the rate of increase of concentration exceeds the normal range, and there may be interference factors such as matrix effect and cross contamination. The concentration threshold needs to be increased accordingly to reduce the risk of false positives. Used to control the amplification degree of the threshold; The larger the value of , the larger the adjustment range of the concentration threshold, and vice versa. It can be obtained by fitting experimental data, for example, by analyzing the relationship between the rate of change and the false alarm rate in historical data. . The formula in step S2322 dynamically adapts to drastic changes in concentration to avoid misjudgment due to a too low threshold. The proportional amplification mechanism makes the adjustment process smoother and does not introduce new errors due to excessive adjustments. By dynamically increasing the concentration threshold, the impact of interference factors can be adaptively reduced. When the concentration increases sharply, raising the threshold can avoid misjudging the contamination signal as the target substance, thereby reducing the false positive rate. At the same time, the magnitude of the threshold increase is related to the degree of the rate of change exceeding the standard, which can take into account both the specificity and sensitivity of the detection.

[0199] When ΔRC i <-Δ (C) , it means that the real-time concentration change rate has dropped beyond the preset concentration adjustment threshold, which may be caused by a sudden drop in the concentration of the target substance or a change in environmental conditions. way to improve the detection sensitivity of low-concentration target substances. When the real-time concentration change rate is too low, it may mean that the concentration of the target substance is approaching the lower limit of detection. At this time, reducing the concentration adjustment threshold can be more sensitive to weak signals, thereby avoiding missed detection. The formula in step S2323 improves the detection sensitivity of low-concentration target substances and reduces the risk of missed detection. In the case of a sudden drop in concentration, the responsiveness to weak signals is ensured by reducing the threshold. Automatically adapting to the dynamic changes of concentration drop makes the detection process more intelligent.

[0200] Step S2330, based on the real-time reaction rate ΔRT of the i-th fluorescent signal channel i , for the first time threshold T i Dynamically adjust to obtain the second time threshold BT of the i-th fluorescence signal channel i .

[0201] Further, step S2330 includes:

[0202] Step S2331, if |ΔRT i |≤Δ (T) , then BT i =T i ; Among them, Δ (T) is the preset time adjustment threshold, Δ (T) >0;

[0203] Step S2332, if ΔRT i >Δ (T) ,but ;in, is the threshold adjustment coefficient of the reaction rate, >0;

[0204] Step S2333, if ΔRT i <-Δ (T) ,but .

[0205] Specifically, when |ΔRT i |≤Δ (T) , it means that the current reaction time change is within the normal range and there is no need to adjust the time threshold. Keeping the time threshold unchanged can avoid errors caused by slight fluctuations in reaction time and reduce unnecessary calculations.

[0206] Steps S2332 and S2333 are similar in logic to steps S2322 and S2323, except that the time threshold is adjusted instead of the concentration threshold. i >Δ (T) When ΔRT is too high, the time threshold needs to be enlarged; when the reaction time becomes longer, enlarging the time threshold can avoid premature termination of the test due to incomplete reaction. i <-Δ (T)) When the reaction time becomes shorter, reducing the time threshold can improve the detection efficiency and avoid wasting time.

[0207] Step S2300 can perceive the fluctuation of the signal and the deviation of the reaction rate in the detection process in real time by dynamically analyzing the real-time data change characteristics (such as the change rate of the fluorescence signal intensity, the change rate of the concentration, etc.) during the detection process. Based on these dynamic parameters, the first threshold parameter is adjusted to generate a second threshold parameter that is more in line with the current detection environment and sample characteristics, so that it can adapt to complex and changeable detection conditions. For example, when the concentration of the target substance is low or the reaction rate is slow, the intensity threshold and time threshold can be dynamically reduced to avoid missed detection; and when the signal change rate is fast, the concentration threshold and time threshold can be appropriately increased to prevent misjudgment. This dynamic adjustment capability significantly enhances the ability to respond to real-time data changes.

[0208] The setting of the second threshold parameter comprehensively considers the changing trend of real-time data (dynamic parameters) and the requirements of the detection target (such as detection speed and accuracy). The dynamic adjustment of the intensity threshold, concentration threshold and time threshold can find a balance between detection efficiency and detection accuracy. For example, by dynamically optimizing the time threshold, the reaction time can be shortened and the detection speed can be increased while ensuring that the signal is fully stable; and by dynamically optimizing the intensity threshold and concentration threshold, background noise or abnormal signals can be effectively filtered to improve the accuracy and reliability of detection. This multi-objective optimization mechanism not only improves the efficiency and accuracy of the detection process, but also provides greater flexibility and adaptability for different detection scenarios (such as rapid screening or high-precision detection).

[0209] Multi-channel fluorescence quantitative test strips usually involve the simultaneous detection of multiple target substances (such as aldicarb, carbofuran, etc.), and the fluorescence characteristics, concentration ranges and reaction rates of different target substances may differ significantly. Step S2300 adjusts the intensity threshold, concentration threshold and time threshold of each channel respectively for the first dynamic parameter of each fluorescence signal channel to ensure that the detection sensitivity and accuracy of each channel can be optimized according to its characteristics. For example, for channels with weaker fluorescence intensity (such as DDT detection channel), the intensity threshold can be dynamically lowered to improve the detection sensitivity; and for channels with stronger fluorescence intensity (such as aldicarb detection channel), the intensity threshold can be dynamically increased to avoid false detection. This adaptive optimization mechanism significantly improves the overall performance of multi-channel collaborative detection and ensures that the detection results of different target substances are highly reliable and consistent.

[0210] Step S2400, obtaining fluorescence characteristic parameters of target substances, sample matrix characteristic parameters and environmental interference parameters of N fluorescence signal channels, and correcting the second threshold parameters of the N fluorescence signal channels to obtain adaptive thresholds of the N fluorescence signal channels; the adaptive thresholds include adaptive intensity thresholds, adaptive concentration thresholds and adaptive time thresholds;

[0211] Further, step S2400 includes:

[0212] Step S2410, obtain the Fluorescence characteristic parameters of the target substance in each fluorescence signal channel ,according to The second intensity threshold Correction is performed to obtain Adaptive intensity thresholding for each fluorescence signal channel ; The fluorescence characteristic parameters Including The maximum excitation wavelength of the target substance in each fluorescence signal channel , Maximum emission wavelength and quantum yield ;

[0213] Further, step S2410 includes:

[0214] Step S2411, calculate the relative fluorescence intensity correction factor of the target substance in the i-th fluorescence signal channel ;

[0215]

[0216] in:

[0217] is the maximum value of the maximum emission wavelength of each target substance; is the maximum value of the maximum excitation wavelength of each target substance; is the maximum value of the quantum yield of each target substance.

[0218] Step S2412, based on the relative fluorescence intensity correction factor Calculate the Adaptive intensity threshold for target substances in each fluorescence signal channel .

[0219]

[0220] Specifically, the fluorescence characteristic parameters reflect the fluorescence properties of the target substance itself. Different substances have different fluorescence properties, for example:

[0221] Maximum excitation wavelength Refers to the wavelength that can most effectively excite the target substance to produce fluorescence. The maximum excitation wavelength is different for different substances, depending on the molecular structure and electronic transition characteristics of the substance. For example, for polycyclic aromatic hydrocarbons, the maximum excitation wavelength is usually in the ultraviolet region (200-400nm); while for some proteins, the maximum excitation wavelength may be in the visible light region (400-700nm). Maximum emission wavelength It refers to the wavelength at which the target substance emits the strongest fluorescence after being excited. Similarly, different substances have different maximum emission wavelengths. The emission wavelength is always greater than the excitation wavelength because some energy is lost during the relaxation process of the excited state. The difference between the excitation wavelength and the emission wavelength is called the Stokes shift, which reflects the energy loss of the fluorescence process. Quantum yield It refers to the ratio of the number of absorbed photons to the number of emitted photons, reflecting the efficiency of the fluorescence process. The quantum yield ranges from 0 to 1, and the closer it is to 1, the higher the fluorescence efficiency. The quantum yield of most organic fluorescent substances is between 0.1 and 1.

[0222] Fluorescence characteristic parameters of different target substances For example, the most common target substances in pesticide residue detection, chlorpyrifos and aldicarb, have maximum excitation wavelengths of 290nm and 265nm, maximum emission wavelengths of 330nm and 290nm, and quantum yields of different wavelengths. For the second intensity threshold BI i After correction, we can get the adaptive intensity threshold AI i "Adaptive" means that the threshold can be automatically adjusted according to the fluorescence characteristics of the target substance to meet the needs of different detection objects. The correction process takes into account the influence of the maximum excitation wavelength, maximum emission wavelength and quantum yield on the fluorescence intensity, making AI i It can more accurately reflect the actual fluorescence level of the target substance.

[0223] Relative fluorescence intensity correction factor The fluorescence intensity difference of the i-th target substance relative to the substance with the best fluorescence characteristics (i.e., the hypothetical substance whose maximum emission wavelength, maximum excitation wavelength and quantum yield all reach the maximum value) is measured. The molecular part It represents the characteristic fluorescence intensity of the i-th target substance, which is proportional to the Stokes shift (the difference between the maximum emission wavelength and the maximum excitation wavelength) and the quantum yield. The denominator is normalized to the characteristic fluorescence intensity of all target substances with the optimal fluorescent substance as reference.

[0224] Step S2412, based on the relative fluorescence intensity correction factor Calculate the Adaptive intensity threshold for target substances in each fluorescence signal channel ; If the fluorescence characteristics of the target substance are significantly different from those of the optimal substance (i.e. smaller), then The value of will be adjusted accordingly to adapt to the weaker fluorescence signal intensity. This adaptive adjustment can effectively improve the detection sensitivity and avoid missed detection due to differences in fluorescence intensity. Only when the substance is detected is it considered to be effectively detected.

[0225] Through adaptive intensity threshold correction, the influence of differences in the fluorescence characteristics of the target substance can be eliminated to a certain extent, making the detection results more reliable and accurate. This method fully considers the fluorescence characteristic parameters such as the maximum excitation wavelength, the maximum emission wavelength and the quantum yield. By comparing with the optimal fluorescent substance, the threshold is reasonably adjusted to ensure that it can effectively adapt to different substances to be tested. This is of great significance for the detection of multi-component residues in complex matrices, which can effectively improve the detection rate and accuracy, and provide a strong guarantee for the quality and safety control of agricultural products.

[0226] Step S2420, obtain the Sample matrix characteristic parameters for each fluorescence signal channel ,according to The second concentration threshold Correction is performed to obtain Adaptive concentration threshold for each fluorescence signal channel ; The sample matrix characteristic parameters Including Fluorescence signal channel Viscosity of sample matrix value and ionic strength ;

[0227] Further, step S2420 includes:

[0228] Step S2421, according to Calculate the Relative viscosity of the sample matrix for each fluorescence signal channel ;

[0229]

[0230] in, is the viscosity of pure water, through the relative viscosity , which can be measured The difference in viscosity of the sample matrix relative to pure water.

[0231] Step S2422, according to Calculate the pH deviation of sample matrix for each fluorescence signal channel ;

[0232]

[0233] By pH deviation , which can be measured The pH value of the sample matrix is ​​relative to the neutral condition ( 7.0).

[0234] Step S2423, according to Calculate the Relative ionic intensity of the sample matrix for each fluorescence signal channel ;

[0235]

[0236] in, is the ionic strength of the reference solution (such as the ionic strength of saline). , which can be measured The difference in ionic strength of a sample matrix relative to a reference solution.

[0237] Step S2424, according to the relative viscosity , pH deviation , relative ionic strength and the second concentration threshold , calculate the Adaptive concentration threshold for each fluorescence signal channel .

[0238]

[0239]

[0240]

[0241] in, is the lower limit of the adaptive concentration threshold, is the upper limit of the adaptive concentration threshold, is the correction index of relative viscosity, is the correction index for pH deviation, is the correction index of relative ionic intensity, , , Obtained through experimental optimization.

[0242] Specifically, step S2420 aims to adaptively correct the second concentration threshold according to the characteristics of the sample matrix to improve the accuracy and reliability of the detection. The sample matrix refers to the sum of all other components in the sample except the target substance, such as solvents, impurities, interferents, etc. The matrix differences of different samples will affect the detection results, so threshold correction needs to be performed for different matrices.

[0243] Sample matrix characteristic parameters It includes three aspects: viscosity , pH value and ionic strength These three parameters describe the characteristics of the sample matrix in terms of rheological properties, pH and electrolyte concentration.

[0244] Viscosity Indicates the flow resistance of the sample matrix, that is, the resistance of the sample matrix to flow. The greater the viscosity, the worse the sample fluidity and the slower the diffusion rate, which may prolong the reaction time and affect the detection sensitivity. It reflects the acidity and alkalinity of the sample matrix. Too high or too low pH value may affect the stability and fluorescence intensity of the fluorescent substance, thus interfering with the test results. The concentration of electrolytes in the sample matrix is ​​measured. Too high ion concentration may cause fluorescence quenching and reduce detection sensitivity; too low ion concentration may affect the stability of the buffer system and lead to unstable test results.

[0245] In steps S2421-S2423, by calculating the relative viscosity , pH deviation and relative ionic strength , the characteristic parameters of the sample matrix are dimensionless, which is convenient for quantitative comparison and correction. It indicates the ratio of the sample matrix viscosity to the pure water viscosity, reflecting the difference in the sample matrix viscosity relative to pure water. Indicates the deviation of the pH value of the sample from the neutral condition (pH=7.0), reflecting the abnormality of the sample's pH. The ratio of the sample ion strength to the reference solution (such as saline) ion strength reflects the difference in sample ion concentration relative to normal physiological conditions. In step S2424, the adaptive concentration threshold is calculated by multiplying the relative viscosity, pH deviation, relative ion strength and the second concentration threshold. .

[0246] In general, step S2420 can effectively improve the applicability and reliability of the detection method by characterizing the sample matrix characteristics from three aspects: viscosity, pH value, and ionic strength, and quantitatively correcting the concentration threshold accordingly. This method fully considers the impact of sample matrix differences on the test results, and optimizes the detection performance through adaptive correction. In particular, for special matrices such as viscous samples, abnormal pH samples, and high-salt samples, this method can automatically adjust the detection threshold to ensure the accuracy of the test results. Compared with the traditional fixed threshold detection method, this method has stronger robustness and practicality, and provides strong technical support for the rapid screening of complex matrix samples.

[0247] Step S2430, obtain the Environmental interference parameters for each fluorescence signal channel ,according to For the second time threshold Correction is performed to obtain Adaptive time threshold for each fluorescence signal channel ; The environmental interference parameters Including Ambient temperature for each fluorescence signal channel and ambient humidity .

[0248] Further, step S2430 includes:

[0249] Step S2431, according to Calculate the Temperature deviation of each fluorescence signal channel ;

[0250]

[0251] in, It is the optimal reaction temperature of the test strip.

[0252] Step S2432, according to Calculate the Relative humidity of each fluorescence signal channel ;

[0253]

[0254] in, It is the optimal response humidity of the test strip.

[0255] Step S2433, according to the temperature deviation and relative humidity , calculate the Adaptive time threshold for each fluorescence signal channel .

[0256]

[0257] in, is the correction factor for temperature deviation, is the correction factor for relative humidity, Represents the correction factor for the relative humidity versus time threshold.

[0258] Specifically, step S2430 obtains the Environmental interference parameters for each fluorescence signal channel ,according to For the second time threshold Correction is performed to obtain Adaptive time threshold for each fluorescence signal channel ; Environmental interference parameters Including Ambient temperature for each fluorescence signal channel and ambient humidity .

[0259] Ambient humidity Refers to the environmental humidity of the i-th fluorescent signal channel during the test strip reaction. The storage and reaction process of the test strip have certain requirements for humidity. Too high humidity may cause the test strip to get damp, affecting the activity and stability of the reagent; while too low humidity may cause the test strip to dry out, affecting the diffusion and reaction efficiency of the sample. Therefore, the test strip usually has an optimal reaction humidity. At this humidity, the reagent activity is optimal and the sample diffusion and reaction efficiency are highest.

[0260] Step S2431 is based on Calculate the temperature deviation of the i-th fluorescence signal channel . Temperature deviation Indicates the actual ambient temperature Optimal reaction temperature with test strips The absolute value of the difference between . The larger the value is, the farther the ambient temperature deviates from the optimal temperature, which may have a greater impact on the reaction rate and the fluorescent signal. Calculate the relative humidity of the i-th fluorescence signal channel . Relative humidity Indicates the actual ambient humidity Best response humidity with test strips The ratio between . When it is greater than 1, it means that the ambient humidity is higher than the optimal humidity; When the relative humidity is less than 1, it means that the ambient humidity is lower than the optimal humidity. The further the relative humidity deviates from 1, the greater the impact on reagent activity, sample diffusion and reaction efficiency may be. Step S2433 and relative humidity , calculate the adaptive time threshold of the i-th fluorescence signal channel . Adaptive time threshold is the second time threshold Based on this, the time threshold is dynamically adjusted by considering the influence of ambient temperature and humidity.

[0261] In the formula of step S2433, Represents the correction factor of temperature deviation to time threshold. is the correction factor for temperature deviation, which is used to quantify the influence of temperature deviation on reaction time. >0), the correction factor is greater than 1, and the time threshold is enlarged; when the ambient temperature is higher than the optimal temperature ( <0), the correction factor is less than 1, and the time threshold is reduced. This adjustment can compensate for the change in reaction rate caused by temperature deviation and make the time threshold adapt to the actual reaction progress. Represents the correction factor for the relative humidity versus time threshold. is the correction factor for relative humidity, which is used to quantify the effect of relative humidity on reaction time. <1), the correction factor is greater than 1, and the time threshold is enlarged; when the ambient humidity is higher than the optimal humidity ( >1), the correction factor is less than 1, and the time threshold is narrowed. This adjustment can compensate for the changes in reagent activity, sample diffusion, and reaction efficiency caused by relative humidity deviation, so that the time threshold is adapted to the actual reaction conditions.

[0262] Step S2430 can significantly improve the adaptability and reliability of the test strip detection; the adaptive time threshold can be adjusted in real time according to the changes in ambient temperature and humidity, ensuring that the time point of reaction completion can be accurately judged under different environmental conditions, avoiding missed detection or misjudgment due to environmental interference. Ambient temperature and humidity correction can compensate for changes in reaction rate and fluorescence signal intensity caused by environmental deviation from optimal conditions, making the test results comparable and consistent in different environments. Consider the effects of temperature and humidity separately, and introduce a temperature deviation correction coefficient and relative humidity correction factor , the environmental interference can be quantified more finely and the calculation accuracy of the adaptive time threshold can be improved.

[0263] Step S3000: performing quantitative analysis and result evaluation on the sample to be tested according to the adaptive thresholds of N fluorescence signal channels.

[0264] Furthermore, step S3000 includes:

[0265] Step S3100, using the adaptive threshold of each fluorescent signal channel to analyze the real-time detection data of the sample to be tested, to obtain the quantitative detection results of each target substance;

[0266] Specifically, during the real-time detection process, each fluorescence signal channel will continuously monitor the fluorescence signal of the corresponding target substance in the sample. By comparing the real-time collected fluorescence intensity and reaction time with the adaptive intensity threshold and adaptive time threshold calculated in step S2000, it can be determined whether the current detection signal meets the effective detection standard. When the real-time fluorescence intensity exceeds the adaptive intensity threshold and the reaction time is within the adaptive time threshold range, it is considered that the channel has detected a valid signal.

[0267] At the same time, the actual concentration of the target substance can be estimated using real-time fluorescence intensity data and adaptive concentration thresholds. The specific estimation method is as follows: a standard curve of fluorescence intensity and target substance concentration is established based on historical data, and the corresponding point of the real-time fluorescence intensity on the curve is the estimated concentration. If the estimated concentration falls within the adaptive concentration threshold range, the concentration value is considered credible; if the estimated concentration exceeds the adaptive concentration threshold range, there may be interference or anomalies, and the data needs to be marked for review.

[0268] By performing threshold comparison and concentration estimation on the real-time data of each channel, the quantitative detection results of each target substance, that is, the effective concentration value of each substance, can be obtained. These effective concentration values ​​are screened and calibrated by adaptive thresholds and can represent the actual content of the target substance in the sample.

[0269] The dynamic adjustment mechanism of the adaptive threshold can adapt to the fluctuations of different batches of test strips and different testing environments, and always maintain the optimal detection sensitivity and reliability. The traditional fixed threshold method is difficult to cope with such fluctuations, which can easily lead to missed detection or misjudgment. The adaptive threshold dynamically optimizes the detection parameters through real-time data feedback and historical data mining, thereby improving sensitivity while reducing the misjudgment rate.

[0270] For example, for pesticide residue detection, samples with different matrices and different degrees of contamination will result in large differences in background signals. Fixed thresholds are difficult to balance the strong signals of highly contaminated samples and the weak signals of low-contaminated samples. The adaptive intensity threshold can specifically adjust the judgment criteria for different concentration levels, being more stringent for high-concentration pollution and more relaxed for low-concentration pollution, to minimize missed detections. The upper and lower limits of the adaptive concentration threshold can be calibrated according to the high and low value intervals commonly seen in actual detection to eliminate interference from outliers. Optimizing the threshold range using a large amount of historical data can make the test results more consistent with the actual pollution conditions.

[0271] In summary, the adaptive threshold gives the test strip flexible and intelligent judgment capabilities, which is the key to improving the performance of multi-channel detection. Through real-time comparison and dynamic adjustment, the adaptive threshold can adapt to complex and changing detection environments, maximize the multi-channel advantages of the test strip, and provide a strong guarantee for accurate quantitative detection.

[0272] Step S3200, respectively compare the quantitative detection results of each substance with the detection results of the reference method, and calculate the relative error; if the relative errors of each fluorescence signal channel are less than the preset error threshold, it is considered that the adaptive thresholds of the current N fluorescence signal channels are reasonable; otherwise, the fluorescence signal channel whose relative error is greater than or equal to the preset error threshold is marked as a poor channel; execute step S3300 to further optimize the adaptive threshold of the poor channel.

[0273] Specifically, in order to evaluate the effectiveness of the multi-channel adaptive threshold scheme, it is necessary to use a reliable reference method to perform parallel detection of the target substance to obtain an authoritative reference value. Commonly used reference methods include chromatography (such as gas chromatography, liquid chromatography), mass spectrometry, etc. These methods can accurately determine the actual content of the target substance in the sample through physical separation and chemical analysis. Their detection results have a high degree of credibility and can be used as a standard for judging the adaptive threshold scheme. The relative error reflects the detection accuracy of the adaptive threshold scheme. The smaller the relative error, the closer the detection value is to the true value, and the higher the effectiveness of the adaptive threshold scheme. Usually, an error threshold (such as 10%) is preset as a standard for judging whether the adaptive threshold scheme is qualified. If the relative error of all channels is less than the preset threshold, the current multi-channel adaptive threshold scheme is considered to be effective. This means that the adaptive threshold can adapt well to the current detection environment and test strip performance, and give detection results consistent with the reference method. Qualified adaptive threshold schemes can be directly used in subsequent batch tests to ensure the stability of detection quality.

[0274] However, if the relative error of some channels is large and exceeds the preset error threshold, these poor channels need to be optimized. The reasons for the unsatisfactory detection results of some channels may be:

[0275] The performance of the test strip itself is unstable, and the correlation between the fluorescence response and the concentration of the target substance is poor;

[0276] The sample matrix has serious interference and the background signal is strong, which interferes with the detection of the target substance;

[0277] Environmental factors fluctuate greatly, such as changes in temperature and humidity that exceed the adjustment range of the adaptive threshold.

[0278] For poor channels, it is necessary to focus on analyzing the causes of detection deviations and perform targeted algorithm optimization and threshold adjustment. The specific optimization ideas are as follows:

[0279] Collect additional detection data from poor channels to enrich training samples and improve the adaptability of the algorithm;

[0280] Introduce more influencing factors (such as environmental parameters, substrate types, etc.) as inputs to the algorithm to improve the threshold adaptive capability;

[0281] In a complex matrix with strong interference, one can try to increase the lower limit of the adaptive concentration threshold and reduce the detection sensitivity, in order to sacrifice some weak signals in exchange for the reliability of the results;

[0282] When environmental factors fluctuate greatly, the range of the adaptive intensity / time threshold can be relaxed to give the test strip a larger tolerance space.

[0283] By further optimizing the adaptive threshold of the poor channel, the overall detection performance of the test strip can be continuously improved, making the multi-channel detection results more accurate and reliable. At the same time, incorporating the optimization process of the poor channel into the iterative optimization of the entire adaptive threshold can help the algorithm summarize experience and learn strategies to deal with various complex detection environments.

[0284] For example, suppose a batch of test strips had a large deviation in the methyl parathion channel when testing for organophosphorus pesticides in agricultural products, with a relative error of up to 15%, exceeding the preset threshold of 10%. Analysis revealed that the batch of agricultural products generally contained a high concentration of organic acids, which interfered with the detection of methyl parathion.

[0285] To address this problem, the following optimization measures can be taken:

[0286] Screen out the test data with similar matrices (such as other samples containing organic acids) from the historical data for retraining the threshold of the methamidophos channel;

[0287] The concentration of organic acid was introduced as a new input parameter in the algorithm, and a mathematical model of methamidophos fluorescence signal and organic acid concentration was established to achieve active compensation for interference factors.

[0288] The lower limit of the adaptive concentration threshold of the methylamidophos channel can be appropriately increased (e.g., from 0.1 ppm to 0.5 ppm) to reduce the interference of organic acids, in exchange for the stability of the detection results at the cost of an increase in the background signal.

[0289] Through the above optimization, the relative error of the methamidophos channel can be reduced to less than 8%, meeting the preset threshold requirements, and the multi-channel detection performance is improved. At the same time, the algorithm has learned from the experience of detecting methamidophos in a high organic acid background, enriched the adaptive threshold response strategy, and enhanced the universality of the solution.

[0290] With the continuous iteration of optimization-evaluation-re-optimization, the adaptive threshold scheme will become more and more perfect, so that the test strips can always maintain the best state in complex and changeable detection tasks, providing more powerful protection for food safety and environmental monitoring.

[0291] Example 2

[0292] This embodiment provides an analysis system of an all-in-one fluorescence quantitative test strip with an adaptive threshold adjustment function on the basis of embodiment 1. Figure 7 As shown, including:

[0293] Reference threshold determination module: obtains historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of historical detection samples, determines the reference threshold for each fluorescence signal channel according to the historical detection data of the N fluorescence signal channels, and obtains the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer;

[0294] Adaptive threshold determination module: obtain real-time detection data of N fluorescence signal channels of the sample to be tested; calculate the first dynamic parameters of the N fluorescence signal channels based on the real-time detection data of the N fluorescence signal channels and the first threshold parameters of the N fluorescence signal channels; dynamically adjust the first threshold parameters of the N fluorescence signal channels according to the first dynamic parameters of the N fluorescence signal channels to obtain the second threshold parameters of the N fluorescence signal channels; obtain the fluorescence characteristic parameters of the target substances of the N fluorescence signal channels, the sample matrix characteristic parameters and the environmental interference parameters, calibrate the second threshold parameters of the N fluorescence signal channels, and obtain the adaptive thresholds of the N fluorescence signal channels;

[0295] Analysis module: Based on the adaptive thresholds of N fluorescence signal channels, quantitative analysis and result evaluation are performed on the samples to be tested.

[0296] In the reference threshold determination module, the historical detection data includes historical fluorescence intensity data, historical target substance concentration data and historical reaction time data; each fluorescence signal channel corresponds to a target substance; the first threshold parameter includes a first intensity threshold [I1, I2, ..., I N ], the first concentration threshold [C1, C2, ..., C N ] and the first time threshold [T1,T2,…,T N ]; among them, I N is the first intensity threshold of the Nth fluorescence signal channel; C N is the first concentration threshold of the Nth fluorescence signal channel; T N is the first time threshold of the Nth fluorescence signal channel; TA N =[I N ,C N ,T N ].

[0297] In the reference threshold determination module, the first threshold parameters of the N fluorescence signal channels are obtained including:

[0298] Step S1210, classifying the historical detection data according to N fluorescence signal channels to obtain a subset of historical detection data of the N fluorescence signal channels;

[0299] Step S1220: based on the historical fluorescence intensity data DI in the historical detection data subset of the i-th fluorescence signal channel i , get the first intensity threshold I of the i-th fluorescence signal channel i ; 1≤i≤N;

[0300] Step S1230: based on the historical concentration data DC of the target substance in the historical detection data subset of the i-th fluorescence signal channel i , get the first concentration threshold C of the i-th fluorescence signal channel i ;

[0301] Step S1240: based on the historical reaction time data DT in the historical detection data subset of the i-th fluorescence signal channel i , get the first time threshold T of the i-th fluorescence signal channel i .

[0302] The step S1220 includes:

[0303] Step S1221, historical fluorescence intensity data DI i Conduct normality test;

[0304] Step S1222: If the historical fluorescence intensity data DI i If normality is satisfied, the historical fluorescence intensity data DI is calculated i The mean μ I and standard deviation σ I , take μ I +2σ I As the first intensity threshold I of the i-th fluorescence signal channel i , that is I i =μ I +2σ I ;

[0305] Step S1223, if the historical fluorescence intensity data DI i If normality is not satisfied, the quantile method is used and the 95% quantile is taken as the first intensity threshold I of the i-th fluorescence signal channel. i .

[0306] The step S1230 includes:

[0307] Step S1231: Calculate the target substance historical concentration data DC i Perform logarithmic transformation to obtain the historical concentration data LDC of the target substance after logarithmic transformation i ;

[0308] Step S1232: Logarithmically transform the historical concentration data of the target substance LDC i Least squares regression analysis was performed to obtain the concentration regression model;

[0309] Step S1233: Use the upper limit of the prediction interval of the concentration regression model as the upper limit of the first concentration threshold in the logarithmic scale, and then obtain the upper limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale through exponential transformation. ; The lower limit of the prediction interval of the concentration regression model is used as the lower limit of the first concentration threshold in the logarithmic scale, and then the lower limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale is obtained by exponential transformation ; Set the first concentration threshold upper limit and the first concentration threshold The lower limit constitutes the first concentration threshold .

[0310] The step S1240 includes:

[0311] Step S1241, historical reaction time data DT i Perform survival analysis and draw Kaplan-Meier survival curves;

[0312] Step S1242, selecting the optimal cutoff point on the Kaplan-Meier survival curve, and using the reaction time corresponding to the optimal cutoff point as the first time threshold T of the i-th fluorescence signal channel i .

[0313] The step S1242 includes:

[0314] Step S12421, extract all time points t from the Kaplan-Meier survival curve i' , as a potential cutoff point;

[0315] Step S12422, establishing the optimal truncation point objective function J;

[0316] Step S12423, traverse all potential truncation points, calculate the optimal truncation point objective function J; select the potential truncation point with the largest optimal truncation point objective function J as the optimal truncation point.

[0317] In the adaptive threshold determination module, the real-time detection data includes real-time fluorescence intensity data, real-time concentration data of the target substance and real-time reaction time data;

[0318] In the adaptive threshold determination module, the calculation of the first dynamic parameters of the N fluorescence signal channels includes:

[0319] Step S2210: Calculate the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel according to the real-time fluorescence intensity data of the i-th fluorescence signal channel and the first intensity threshold. i ;

[0320] Step S2220, calculating the real-time concentration change rate ΔRC of the i-th fluorescent signal channel according to the real-time concentration data of the target substance in the i-th fluorescent signal channel and the first concentration threshold value i ;

[0321] Step S2230, calculating the real-time reaction rate ΔRT of the i-th fluorescent signal channel according to the real-time reaction time data of the i-th fluorescent signal channel and the first time threshold i .

[0322] In the adaptive threshold determination module, the second threshold parameter [TB1, TB2, ..., TB N ] includes a second intensity threshold [BI1, BI2, ..., BI N ], the second concentration threshold [BC1, BC2,…, BC N ] and the second time threshold [BT1, BT2,…, BT N ]; among them, BI N is the second intensity threshold of the Nth fluorescence signal channel; BC N is the second concentration threshold of the Nth fluorescence signal channel; BT N is the second time threshold of the Nth fluorescence signal channel; TB N is the second threshold parameter of the Nth fluorescence signal channel, TB N =[BI N ,B.C. N ,BT N ];

[0323] The second threshold parameters of the N fluorescence signal channels are obtained as follows:

[0324] Step S2310: based on the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel i , for the first intensity threshold I i Dynamically adjust to obtain the second intensity threshold BI of the i-th fluorescence signal channel i ;

[0325] Step S2320: based on the real-time concentration change rate ΔRC of the i-th fluorescence signal channel i , for the first concentration threshold C i Dynamically adjust to obtain the second concentration threshold BC of the i-th fluorescence signal channel i ;

[0326] Step S2330, based on the real-time reaction rate ΔRT of the i-th fluorescent signal channel i , for the first time threshold T i Dynamically adjust to obtain the second time threshold BT of the i-th fluorescence signal channel i .

[0327] The step S2310 includes:

[0328] Step S2311, if |ΔRI i |≤Δ (I) , then BI i =I i ; Among them, Δ (I) is the preset intensity adjustment threshold, Δ (I) >0;

[0329] Step S2312, if ΔRI i >Δ (I) ,but ;in, is the threshold adjustment coefficient of the signal strength change rate, >0;

[0330] Step S2313, if ΔRI i <-Δ (I) ,but .

[0331] The step S2320 includes:

[0332] Step S2321, if |ΔRC i |≤Δ (C) , then BC i =C i ; Among them, Δ (C) is the preset concentration adjustment threshold, Δ (C) >0;

[0333] Step S2322, if ΔRC i >Δ (C) ,but , , ;in, is the threshold adjustment coefficient of the concentration change rate, is the lower limit of the second concentration threshold, is the upper limit of the second concentration threshold, >0;

[0334] Step S2323, if ΔRC i <-Δ (C) ,but .

[0335] The step S2330 includes:

[0336] Step S2331, if |ΔRT i |≤Δ (T) , then BT i =T i; Among them, Δ (T) is the preset time adjustment threshold, Δ (T) >0;

[0337] Step S2332, if ΔRT i >Δ (T) ,but ;in, is the threshold adjustment coefficient of the reaction rate, >0;

[0338] Step S2333, if ΔRT i <-Δ (T) ,but .

[0339] In the adaptive threshold determination module, the adaptive thresholds of the N fluorescence signal channels are obtained including:

[0340] Step S2410, obtain the Fluorescence characteristic parameters of the target substance in each fluorescence signal channel ,according to The second intensity threshold Correction is performed to obtain Adaptive intensity thresholding for each fluorescence signal channel ; The fluorescence characteristic parameters Including The maximum excitation wavelength of the target substance in each fluorescence signal channel , Maximum emission wavelength and quantum yield ;

[0341] Step S2420, obtain the Sample matrix characteristic parameters for each fluorescence signal channel ,according to The second concentration threshold Correction is performed to obtain Adaptive concentration threshold for each fluorescence signal channel ; The sample matrix characteristic parameters Including Fluorescence signal channel Viscosity of sample matrix value and ionic strength ;

[0342] Step S2430, obtain the Environmental interference parameters for each fluorescence signal channel ,according to For the second time threshold Correction is performed to obtain Adaptive time threshold for each fluorescence signal channel ; The environmental interference parameters Including Ambient temperature for each fluorescence signal channel and ambient humidity .

[0343] The step S2410 includes:

[0344] Step S2411, calculate the relative fluorescence intensity correction factor of the target substance in the i-th fluorescence signal channel ;

[0345] Step S2412, based on the relative fluorescence intensity correction factor Calculate the Adaptive intensity threshold for target substances in each fluorescence signal channel .

[0346] The step S2420 includes:

[0347] Step S2421, according to Calculate the Relative viscosity of the sample matrix for each fluorescence signal channel ;

[0348] Step S2422, according to Calculate the pH deviation of sample matrix for each fluorescence signal channel ;

[0349] Step S2423, according to Calculate the Relative ionic intensity of the sample matrix for each fluorescence signal channel ;

[0350] Step S2424, according to the relative viscosity , pH deviation , relative ionic strength and the second concentration threshold , calculate the Adaptive concentration threshold for each fluorescence signal channel .

[0351] The step S2430 includes:

[0352] Step S2431, according to Calculate the Temperature deviation of each fluorescence signal channel ;

[0353] Step S2432, according to Calculate the Relative humidity of each fluorescence signal channel ;

[0354] Step S2433, according to the temperature deviation and relative humidity , calculate the Adaptive time threshold for each fluorescence signal channel .

[0355] Example 3

[0356] This embodiment discloses an electronic device, which may include one or more processors and one or more memories, wherein the memories store computer-readable codes, which, when executed by the one or more processors, may execute the above-mentioned analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function.

[0357] The method or system according to the embodiment of the present application can also be implemented with the help of the architecture of an electronic device. The electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, etc. The storage device in the electronic device, such as a ROM or a hard disk, can store the analysis method of the all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function provided in the present application. The analysis method of the all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function may, for example, include: obtaining historical detection data of N fluorescent signal channels in the all-in-one fluorescent quantitative test strip of historical detection samples, and determining a reference threshold for each fluorescent signal channel based on the historical detection data of the N fluorescent signal channels, and obtaining the first threshold parameters [TA1, TA2,…, TA] of the N fluorescent signal channels. N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer; real-time detection data of the N fluorescence signal channels of the sample to be tested is obtained; based on the real-time detection data of the N fluorescence signal channels and the first threshold parameters of the N fluorescence signal channels, the first dynamic parameters of the N fluorescence signal channels are calculated; according to the first dynamic parameters of the N fluorescence signal channels, the first threshold parameters of the N fluorescence signal channels are dynamically adjusted to obtain the second threshold parameters of the N fluorescence signal channels; fluorescence characteristic parameters of the target substances of the N fluorescence signal channels, sample matrix characteristic parameters and environmental interference parameters are obtained, the second threshold parameters of the N fluorescence signal channels are corrected to obtain the adaptive thresholds of the N fluorescence signal channels; according to the adaptive thresholds of the N fluorescence signal channels, quantitative analysis and result evaluation are performed on the sample to be tested.

[0358] Furthermore, the electronic device may also include a user interface. Of course, the architecture disclosed in the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0359] Example 4

[0360] This embodiment discloses a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the analysis method of the all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function of the embodiment of the present application can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0361] In addition, according to the implementation of the present application, the process described in the above reference flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, for example: obtaining historical detection data of N fluorescent signal channels in an all-in-one fluorescent quantitative test strip of a historical detection sample, determining a reference threshold for each fluorescent signal channel based on the historical detection data of the N fluorescent signal channels, and obtaining the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer; obtain the real-time detection data of the N fluorescence signal channels of the sample to be tested; based on the real-time detection data of the N fluorescence signal channels and the first threshold parameters of the N fluorescence signal channels, calculate the first dynamic parameters of the N fluorescence signal channels; according to the first dynamic parameters of the N fluorescence signal channels, dynamically adjust the first threshold parameters of the N fluorescence signal channels to obtain the second threshold parameters of the N fluorescence signal channels; obtain the fluorescence characteristic parameters of the target substances of the N fluorescence signal channels, the sample matrix characteristic parameters and the environmental interference parameters, correct the second threshold parameters of the N fluorescence signal channels, and obtain the adaptive threshold values ​​of the N fluorescence signal channels; according to the adaptive threshold values ​​of the N fluorescence signal channels, perform quantitative analysis and result evaluation on the sample to be tested. When the computer program is executed by the central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0362] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0363] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0364] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An analysis method for an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function, characterized in that: The method comprises: Obtain historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of the historical detection sample, determine a reference threshold for each fluorescence signal channel according to the historical detection data of the N fluorescence signal channels, and obtain the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer; Acquire real-time detection data of N fluorescence signal channels of the sample to be tested; calculate first dynamic parameters of the N fluorescence signal channels based on the real-time detection data of the N fluorescence signal channels and first threshold parameters of the N fluorescence signal channels; dynamically adjust the first threshold parameters of the N fluorescence signal channels according to the first dynamic parameters of the N fluorescence signal channels to obtain second threshold parameters of the N fluorescence signal channels; acquire fluorescence characteristic parameters of target substances in the N fluorescence signal channels, sample matrix characteristic parameters and environmental interference parameters, calibrate the second threshold parameters of the N fluorescence signal channels, and obtain adaptive thresholds of the N fluorescence signal channels; According to the adaptive thresholds of N fluorescence signal channels, quantitative analysis and result evaluation are performed on the samples to be tested.

2. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 1, characterized in that: The historical detection data includes historical fluorescence intensity data, historical target substance concentration data and historical reaction time data; each fluorescence signal channel corresponds to a target substance; The first threshold parameter includes a first intensity threshold [I1, I2, ..., I N ], the first concentration threshold [C1, C2, ..., C N ] and the first time threshold [T1,T2,…,T N ]; among them, I N is the first intensity threshold of the Nth fluorescence signal channel; C N is the first concentration threshold of the Nth fluorescence signal channel; T N is the first time threshold of the Nth fluorescence signal channel; TA N =[I N ,C N ,T N ].

3. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 2, characterized in that: The first threshold parameters of the N fluorescence signal channels are obtained as follows: Classifying the historical detection data according to N fluorescence signal channels to obtain historical detection data subsets of the N fluorescence signal channels; According to the historical fluorescence intensity data DI in the historical detection data subset of the i-th fluorescence signal channel i , get the first intensity threshold I of the i-th fluorescence signal channel i ; 1≤i≤N; According to the historical concentration data DC of the target substance in the historical detection data subset of the i-th fluorescence signal channel i , get the first concentration threshold C of the i-th fluorescence signal channel i ; According to the historical reaction time data DT in the historical detection data subset of the i-th fluorescence signal channel i , get the first time threshold T of the i-th fluorescence signal channel i .

4. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 3, characterized in that: The first concentration threshold C of the i-th fluorescence signal channel is obtained i include: Historical concentration data DC of target substances i Perform logarithmic transformation to obtain the historical concentration data LDC of the target substance after logarithmic transformation i ; The logarithmically transformed historical concentration data of the target substance LDC i Least squares regression analysis was performed to obtain the concentration regression model; The upper limit of the prediction interval of the concentration regression model is used as the upper limit of the first concentration threshold in the logarithmic scale, and then the upper limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale is obtained by exponential transformation. ; The lower limit of the prediction interval of the concentration regression model is used as the lower limit of the first concentration threshold in the logarithmic scale, and then the lower limit of the first concentration threshold of the i-th fluorescence signal channel in the original scale is obtained by exponential transformation ; Set the first concentration threshold upper limit and the first concentration threshold lower limit Composition of the first concentration threshold .

5. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 4, characterized in that: The first time threshold T of the i-th fluorescence signal channel is obtained i include: Historical reaction time data DT i Perform survival analysis and draw Kaplan-Meier survival curves; Extract all time points t from the Kaplan-Meier survival curve i' , as a potential cutoff point; Establish the optimal cutoff point objective function J; Traverse all potential truncation points and calculate the optimal truncation point objective function J; select the potential truncation point with the largest optimal truncation point objective function J as the optimal truncation point; The reaction time corresponding to the optimal cutoff point is taken as the first time threshold T of the i-th fluorescence signal channel. i .

6. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 5, characterized in that: The real-time detection data includes real-time fluorescence intensity data, real-time concentration data of the target substance and real-time reaction time data; the first dynamic parameter includes real-time signal intensity change rate, real-time concentration change rate and real-time reaction rate; The calculating of the first dynamic parameters of the N fluorescence signal channels comprises: According to the real-time fluorescence intensity data of the i-th fluorescence signal channel and the first intensity threshold, the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel is calculated i ; According to the real-time concentration data of the target substance in the i-th fluorescence signal channel and the first concentration threshold, the real-time concentration change rate ΔRC of the i-th fluorescence signal channel is calculated. i ; According to the real-time reaction time data of the i-th fluorescent signal channel and the first time threshold, the real-time reaction rate ΔRT of the i-th fluorescent signal channel is calculated. i .

7. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 6, characterized in that: The second threshold parameter [TB1, TB2, ..., TB N ] includes a second intensity threshold [BI1, BI2, ..., BI N ], the second concentration threshold [BC1, BC2,…, BC N ] and the second time threshold [BT1, BT2,…, BT N ]; Among them, BI N is the second intensity threshold of the Nth fluorescence signal channel; BC N is the second concentration threshold of the Nth fluorescence signal channel; BT N is the second time threshold of the Nth fluorescence signal channel; TB N is the second threshold parameter of the Nth fluorescence signal channel, TB N =[BI N ,B.C. N ,BT N ]; The dynamically adjusting the first threshold parameters of the N fluorescence signal channels includes: According to the real-time signal intensity change rate ΔRI of the i-th fluorescence signal channel i , for the first intensity threshold I i Dynamically adjust to obtain the second intensity threshold BI of the i-th fluorescence signal channel i ; According to the real-time concentration change rate ΔRC of the i-th fluorescence signal channel i , for the first concentration threshold C i Dynamically adjust to obtain the second concentration threshold BC of the i-th fluorescence signal channel i ; According to the real-time reaction rate ΔRT of the i-th fluorescence signal channel i , for the first time threshold T i Dynamically adjust to obtain the second time threshold BT of the i-th fluorescence signal channel i .

8. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 7, characterized in that: The second concentration threshold BC of the i-th fluorescence signal channel is obtained i include: If |ΔRC i |≤Δ (C) , then BC i =C i ; Among them, Δ (C) is the preset concentration adjustment threshold, Δ (C) >0; If ΔRC i >Δ (C) ,but , , ;in, is the threshold adjustment coefficient of the concentration change rate, is the lower limit of the second concentration threshold, is the upper limit of the second concentration threshold, >0; If ΔRC i <-Δ (C) ,but .

9. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 7, characterized in that: The method of obtaining the adaptive thresholds of N fluorescence signal channels includes: Get the Fluorescence characteristic parameters of the target substance in each fluorescence signal channel ,according to The second intensity threshold Correction is performed to obtain Adaptive intensity thresholding for each fluorescence signal channel ; The fluorescence characteristic parameters Including The maximum excitation wavelength of the target substance in each fluorescence signal channel , Maximum emission wavelength and quantum yield ; Get the Sample matrix characteristic parameters for each fluorescence signal channel ,according to The second concentration threshold Correction is performed to obtain Adaptive concentration threshold for each fluorescence signal channel ; The sample matrix characteristic parameters Including Fluorescence signal channel Viscosity of sample matrix value and ionic strength ; Get the Environmental interference parameters for each fluorescence signal channel ,according to For the second time threshold Correction is performed to obtain Adaptive time threshold for each fluorescence signal channel ; The environmental interference parameters Including Ambient temperature for each fluorescence signal channel and ambient humidity .

10. The analysis method of the all-in-one fluorescent quantitative test strip with adaptive threshold adjustment function according to claim 9, characterized in that: The obtained Adaptive intensity thresholding for each fluorescence signal channel include: Calculate the relative fluorescence intensity correction factor of the target substance in the i-th fluorescence signal channel ; Correction factor based on relative fluorescence intensity Calculate the Adaptive intensity threshold for target substances in each fluorescence signal channel ; The obtained Adaptive concentration threshold for each fluorescence signal channel include: according to Calculate the Relative viscosity of the sample matrix for each fluorescence signal channel ; according to Calculate the pH deviation of sample matrix for each fluorescence signal channel ; according to Calculate the Relative ionic intensity of the sample matrix for each fluorescence signal channel ; According to relative viscosity , pH deviation , relative ionic strength and the second concentration threshold , calculate the Adaptive concentration threshold for each fluorescence signal channel .

11. An analysis system for an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function, which is used to implement the analysis method for an all-in-one fluorescent quantitative test strip with an adaptive threshold adjustment function as claimed in any one of claims 1 to 10, characterized in that: The system comprises: Reference threshold determination module: obtains historical detection data of N fluorescence signal channels in the all-in-one fluorescence quantitative test strip of historical detection samples, determines the reference threshold for each fluorescence signal channel according to the historical detection data of the N fluorescence signal channels, and obtains the first threshold parameters [TA1, TA2, ..., TA N ]; among them, TA N is the first threshold parameter of the Nth fluorescence signal channel; N is a positive integer; Adaptive threshold determination module: obtain real-time detection data of N fluorescence signal channels of the sample to be tested; calculate the first dynamic parameters of the N fluorescence signal channels based on the real-time detection data of the N fluorescence signal channels and the first threshold parameters of the N fluorescence signal channels; dynamically adjust the first threshold parameters of the N fluorescence signal channels according to the first dynamic parameters of the N fluorescence signal channels to obtain the second threshold parameters of the N fluorescence signal channels; obtain the fluorescence characteristic parameters of the target substances of the N fluorescence signal channels, the sample matrix characteristic parameters and the environmental interference parameters, calibrate the second threshold parameters of the N fluorescence signal channels, and obtain the adaptive thresholds of the N fluorescence signal channels; Analysis module: Based on the adaptive thresholds of N fluorescence signal channels, quantitative analysis and result evaluation are performed on the samples to be tested.

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