A rapid detection device and method for pesticide residues

Abstract

The application relates to the technical field of pesticide residue detection, in particular to a rapid pesticide residue detection device and method, which solves the technical problem that it is difficult to accurately compensate for the asymmetric attenuation of enzyme activity caused by temperature influence and to affect the accuracy of pesticide residue detection. The method comprises the following steps: obtaining detection data of a detection session; the detection session comprises a complete pesticide residue detection process; determining an interval cumulative thermal damage potential vector according to an environmental temperature sequence; performing competitive diagnosis according to the interval cumulative thermal damage potential vector and initial blank enzyme activity and terminal blank enzyme activity, determining an optimal loss distribution order of nonlinearly distributing the activity loss between the initial blank enzyme activity and the terminal blank enzyme activity to each time interval; determining the dynamic reference enzyme activity corresponding to each sample detection time according to the optimal loss distribution order; and calculating the compensated pesticide residue detection result according to the dynamic reference enzyme activity and the corresponding sample enzyme activity.

Description

Technical Field

[0001] This application relates to the field of pesticide residue detection technology, specifically to a rapid detection device and method for pesticide residues. Background Technology

[0002] Current pesticide residue detection widely employs enzyme inhibition methods. These methods are based on the principle that acetylcholinesterase (AChE) activity can be inhibited by organophosphates and carbamate pesticides. The inhibition rate is calculated by comparing the enzyme activity of a sample with a blank baseline, thus determining the residue level. The standard testing procedure performs a blank calibration at the start of the session to obtain the initial blank enzyme activity as a fixed baseline for calculating the inhibition rate of all subsequent samples. However, in actual field testing, fluctuations in ambient temperature and the heat generated by the testing equipment itself can cause continuous thermal inactivation of the enzyme reagent during the testing process. Because the thermal inactivation process of enzymes has non-linear characteristics, its decay may exhibit temporal asymmetry: for example, early thermal shock due to equipment preheating or conformational instability at the beginning of the test, or later fatigue due to heat accumulation in the later stages of the test. This asymmetric decay means that the blank enzyme activity measured at the beginning cannot accurately represent the true enzyme activity state at subsequent testing times.

[0003] Existing technologies typically employ time-based linear interpolation to compensate for enzyme activity decay, assuming a uniform temporal distribution of the decay process. However, when faced with asymmetric decay patterns such as early concentrated damage or later cumulative fatigue, it is difficult to accurately compensate for the asymmetric enzyme activity decay caused by temperature effects, thus affecting the accuracy of pesticide residue detection. Summary of the Invention

[0004] To address the current technical problem of accurately compensating for the asymmetric decline in enzyme activity caused by temperature, which affects the accuracy of pesticide residue detection, this application aims to provide a rapid detection method for pesticide residues. The specific technical solution adopted is as follows: Acquire detection data from the detection session; the detection session includes a complete pesticide residue detection process; the detection data includes the initial blank enzyme activity measured at the start of the detection session, the endpoint blank enzyme activity measured at the end of the detection session, the sample enzyme activity and corresponding sample detection time at the completion of each sample detection, and the ambient temperature sequence during the detection session. Based on the ambient temperature sequence, the interval cumulative thermal damage potential vector is determined; the interval cumulative thermal damage potential vector is used to characterize the effective heat load borne by the enzyme reagent in each time interval during the detection session. Based on the cumulative thermal damage potential vector of the interval and the initial blank enzyme activity and the final blank enzyme activity, competitive diagnosis is performed to determine the optimal loss allocation order for nonlinearly distributing the activity loss between the initial blank enzyme activity and the final blank enzyme activity to each time interval. Based on the optimal loss allocation order, determine the dynamic baseline enzyme activity corresponding to each sample detection time. Based on the dynamic baseline enzyme activity and its corresponding sample enzyme activity, the compensated pesticide residue detection results are calculated.

[0005] In one possible implementation, acquiring detection data for a detection session includes: recording the initial blank enzyme activity at the start of the detection session; the start time being the time when the first blank calibration occurs in the detection session; recording the sample enzyme activity and its corresponding sample detection time each time a sample detection is completed between the start and end times; recording the endpoint blank enzyme activity at the end time; the end time being the time when the second blank calibration occurs in the detection session; continuously acquiring the raw temperature sequence during the detection session using a temperature sensor, and smoothing the raw temperature sequence to obtain the ambient temperature sequence; the smoothing process is used to eliminate high-frequency electrical noise in the raw temperature sequence.

[0006] In one possible implementation, determining the interval cumulative thermal damage potential vector based on the ambient temperature sequence includes: dividing the duration of the detection session into multiple consecutive time intervals; determining the difference between the temperature at each sampling moment and the enzyme thermal inactivation reference temperature within each time interval, based on the ambient temperature sequence within each time interval; performing a nonlinear transformation on the difference within each time interval and then accumulating the values ​​to obtain the interval cumulative thermal damage potential within each time interval; the interval cumulative thermal damage potential is used to characterize the effective heat load accumulation that can cause irreversible denaturation of the enzyme within the corresponding time interval; and generating the interval cumulative thermal damage potential vector based on the interval cumulative thermal damage potential of each time interval.

[0007] In one possible implementation, competitive diagnosis is performed based on the interval cumulative thermal damage potential vector and the initial blank enzyme activity and the endpoint blank enzyme activity to determine the optimal loss allocation order for non-linearly distributing the activity loss between the initial blank enzyme activity and the endpoint blank enzyme activity to each time interval. This includes: determining the total activity loss during the detection session based on the initial blank enzyme activity and the endpoint blank enzyme activity; constructing an early damage attribution cost matrix and a late damage attribution cost matrix based on the total activity loss and the interval cumulative thermal damage potential vector; the early damage attribution cost matrix is ​​used to characterize the time asymmetric cost of attributing each loss unit in the total activity loss to the early stage of the detection session, and the late damage attribution cost matrix is ​​used to characterize the time asymmetric cost of attributing each loss unit in the total activity loss to the later stage of the detection session; competitively solving the early damage attribution cost matrix and the late damage attribution cost matrix based on a preset allocation problem solving algorithm, and determining the optimal loss allocation order based on the solution results; the optimal loss allocation order is used to characterize the optimal one-to-one mapping relationship for non-linearly redistributing the total activity loss to each time interval of the detection session.

[0008] In one possible implementation, an early damage attribution cost matrix and a late damage attribution cost matrix are constructed based on the total activity loss and the interval cumulative thermal damage potential vector, respectively. This includes: determining the dimensions of the early and late damage attribution cost matrices based on the total number of time intervals in the interval cumulative thermal damage potential vector; for each cost element in the early damage attribution cost matrix, calculating the absolute value of the difference between the interval cumulative thermal damage potential of the target time interval and the source time interval as the base cost; determining a first time weight factor that increases with the increase of the target time interval's time sequence index based on the ratio between the time sequence index of the target time interval and the total number of time intervals, and a preset time bias weight coefficient; and then combining the base cost with the first time weight factor. Factor multiplication yields the cost element at the corresponding position in the early damage attribution cost matrix; where the source time interval is the original time interval corresponding to the proportional allocation of thermal damage potential loss, and the target time interval refers to the time interval after the redistribution of activity loss; for each cost element in the later damage attribution cost matrix, the absolute value of the difference between the cumulative thermal damage potential of the target time interval and the source time interval is calculated as the base cost; based on the difference between the total number of time intervals and the time series index of the target time interval, and the preset time bias weight coefficient, a second time weight factor that decreases as the time series index of the target time interval increases is determined; the base cost is multiplied by the second time weight factor to obtain the cost element at the corresponding position in the later damage attribution cost matrix.

[0009] In one possible implementation, a pre-defined allocation problem solving algorithm is used to competitively solve the early damage attribution cost matrix and the late damage attribution cost matrix. The optimal loss allocation order is determined based on the solution results, including: solving the early damage attribution cost matrix using the pre-defined allocation problem solving algorithm to determine a first minimum total cost and its corresponding first loss allocation arrangement; solving the late damage attribution cost matrix using the pre-defined allocation problem solving algorithm to determine a second minimum total cost and its corresponding second loss allocation arrangement; determining the decay process time bias index based on the first and second minimum total costs; the decay process time bias index is used to quantitatively diagnose the relative fit between the early concentrated damage pattern and the late cumulative fatigue pattern; and determining the optimal loss allocation order from the first and second loss allocation arrangements based on the sign of the decay process time bias index.

[0010] In one possible implementation, the dynamic baseline enzyme activity corresponding to each sample detection time is determined according to the optimal loss allocation order, including: determining a diagnostic feature set; the diagnostic feature set includes: a decay process time bias index, the optimal loss allocation order, and a process irregularity score; the process irregularity score is used to quantify the unexplainable randomness of the decay process in the detection session under the optimal interpretation model; according to the optimal loss allocation order in the diagnostic feature set, the proportional allocation loss of each thermal damage potential corresponding to the total activity loss is remapped to each time interval to determine the reconstructed interval activity loss of each time interval; the reconstructed interval activity loss is used to characterize the actual amount of activity loss that occurs in the corresponding time interval after nonlinear path redistribution; for each sample, the target time interval in which its sample detection time is located is determined, and the initial blank enzyme activity is subtracted from the sum of all reconstructed interval activity losses from the first time interval to the previous time interval before the target time interval to determine the dynamic baseline enzyme activity corresponding to that sample.

[0011] In one possible implementation, determining the diagnostic feature set includes: determining a process irregularity score based on the ratio between the smaller of a first minimum total cost and a second minimum total cost and the sum of the interval cumulative thermal damage potential vectors; and encapsulating the decay process time bias index, the process irregularity score, and the optimal loss allocation order into a diagnostic feature set.

[0012] In one possible implementation, the compensated pesticide residue detection result is calculated based on each dynamic benchmark enzyme activity and its corresponding sample enzyme activity, including: determining the compensated inhibition rate based on the dynamic benchmark enzyme activity and the corresponding sample enzyme activity; the compensated inhibition rate is used to characterize the sample inhibition rate calculated using the dynamic benchmark enzyme activity; determining process diagnostic information based on the sign of the decay process time bias index in the diagnostic feature set; the process diagnostic information is used to characterize the time bias pattern of the enzyme activity decay process in this detection session; determining the result reliability assessment level based on the relationship between the process irregularity score in the diagnostic feature set and the preset first reliability threshold and second reliability threshold; the result reliability assessment level is used to characterize the credibility of the compensated inhibition rate; and generating a three-stage detection result based on the compensated inhibition rate, process diagnostic information, and result reliability assessment level.

[0013] This application also provides a rapid detection device for pesticide residues, the device comprising: The data acquisition unit is used to acquire the detection data of the detection session; the detection session includes a complete pesticide residue detection process; the detection data includes the initial blank enzyme activity measured at the start of the detection session, the endpoint blank enzyme activity measured at the end of the detection session, the sample enzyme activity and the corresponding sample detection time when each sample detection is completed, and the ambient temperature sequence during the detection session. The data processing unit is used to determine the interval cumulative thermal damage potential vector based on the ambient temperature sequence; the interval cumulative thermal damage potential vector is used to characterize the effective heat load borne by the enzyme reagent in each time interval during the detection session. The competitive diagnostic unit is used to perform competitive diagnostics based on the cumulative thermal damage potential vector of the interval and the initial blank enzyme activity and the final blank enzyme activity, and to determine the optimal loss allocation order for nonlinearly distributing the activity loss between the initial blank enzyme activity and the final blank enzyme activity to each time interval. The compensation calculation unit is used to determine the dynamic baseline enzyme activity corresponding to each sample detection time according to the optimal loss allocation order; and to calculate the compensated pesticide residue detection results based on each dynamic baseline enzyme activity and its corresponding sample enzyme activity.

[0014] This application offers the following advantages: By acquiring detection data throughout the entire detection session, a complete data chain from start to finish is established; by calculating the cumulative thermal damage potential vector within each time interval, the cumulative thermal damage is effectively quantified; through a competitive diagnostic mechanism, the time-biased pattern of enzyme activity decay is accurately identified; by determining the dynamic baseline enzyme activity through the optimal loss allocation order, personalized baseline compensation for each sample is achieved; and finally, accurate pesticide residue detection results are obtained through compensation calculations. Based on this, this application solves the technical problem of existing technologies' difficulty in accurately compensating for the asymmetric decay of enzyme activity caused by temperature effects, significantly improving the accuracy of rapid pesticide residue detection. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a rapid detection method for pesticide residues provided in one embodiment of this application. Figure 2 This is a schematic diagram of the structure of a rapid pesticide residue detection device provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a rapid detection method for pesticide residues proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The specific scheme of a rapid detection method for pesticide residues provided in this application is described below with reference to the accompanying drawings.

[0020] Please see Figure 1 It shows a flowchart of a rapid detection method for pesticide residues provided in one embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps: Step 101: Obtain the detection data of the detection session.

[0021] The detection session encompasses a complete pesticide residue detection process, from the first blank calibration to the end of the second. The detection data includes the initial blank enzyme activity measured at the start of the session, the endpoint blank enzyme activity measured at the end, the sample enzyme activity at the completion of each sample test and the corresponding test time, as well as the ambient temperature sequence during the session. These data collectively constitute a complete dataset characterizing the entire detection session, providing a data foundation for subsequent enzyme activity attenuation diagnosis and compensation calculations.

[0022] It should be noted that the enzyme activity in the embodiments of this application can also be referred to as enzyme activity.

[0023] Step 102: Determine the interval cumulative thermal damage potential vector based on the ambient temperature sequence.

[0024] Among them, the interval cumulative thermal damage potential vector is used to characterize the effective thermal load borne by the enzyme reagent in each time interval during the detection session.

[0025] Optionally, this application discretizes continuous temperature monitoring data into several time intervals and calculates the cumulative effective heat load exceeding the enzyme thermal inactivation reference temperature in each interval to obtain the interval cumulative thermal damage potential vector. This index quantifies the time-varying damage effect of temperature on enzyme activity.

[0026] Step 103: Based on the cumulative thermal damage potential vector of the interval and the initial blank enzyme activity and the final blank enzyme activity, perform competitive diagnosis to determine the optimal loss allocation order for nonlinearly distributing the activity loss between the initial blank enzyme activity and the final blank enzyme activity to each time interval.

[0027] The optimal loss allocation order is used to characterize the optimal one-to-one mapping relationship for nonlinearly redistributing the total activity loss between the initial blank enzyme activity and the final blank enzyme activity to each time interval of the detection session.

[0028] Optionally, this application constructs cost matrices representing two attenuation modes—early concentrated damage and late cumulative fatigue—through a competitive diagnostic mechanism, and solves them competitively to identify the optimal allocation path that best matches the actual attenuation process of this detection session.

[0029] Step 104: Determine the dynamic baseline enzyme activity corresponding to each sample detection time according to the optimal loss allocation order.

[0030] Among them, dynamic baseline enzyme activity is an enzyme activity baseline value reconstructed for each sample at its specific detection time. It fully considers the asymmetric activity loss that has occurred from the start of the detection session to the detection time of the sample, and can accurately represent the true activity state of the enzyme at that time.

[0031] Step 105: Calculate the compensated pesticide residue detection results based on the dynamic baseline enzyme activity and its corresponding sample enzyme activity.

[0032] Optionally, this application uses dynamic baseline enzyme activity instead of fixed initial blank enzyme activity to calculate the inhibition rate, eliminating the systematic error caused by enzyme activity decay during the detection process, and obtaining a compensated pesticide residue detection result.

[0033] Based on the above technical solution, this application establishes a complete data chain from start to finish by acquiring detection data throughout the entire detection session; effectively quantifies the cumulative thermal damage within each time interval by calculating the cumulative thermal damage potential vector; accurately identifies the time-biased pattern of enzyme activity decay through a competitive diagnostic mechanism; determines the dynamic baseline enzyme activity by determining the optimal loss allocation order, achieving personalized baseline compensation for each sample; and finally obtains accurate pesticide residue detection results through compensation calculation. Based on this, this application solves the technical problem of existing technologies' difficulty in accurately compensating for the asymmetric decay of enzyme activity caused by temperature effects, significantly improving the accuracy of rapid pesticide residue detection.

[0034] In one possible implementation, the process of obtaining detection data for the detection session in step 101 above specifically includes: Step 201: At the start of the detection session, record the initial blank enzyme activity.

[0035] Optionally, the start time is the moment when the first blank calibration occurs in the detection session, i.e., the point at which the operator begins the current detection session and completes the first blank calibration measurement. At this moment, the device records the absolute value of the enzyme activity obtained from this measurement as the initial blank enzyme activity. This serves as an anchor point for detecting the start state of a session.

[0036] Step 202: Between the start and end times, record the enzyme activity of the sample and the corresponding sample detection time each time the sample detection is completed.

[0037] Optionally, for each sample detected between the start and end times, the device automatically records the moment the measurement is completed as the sample detection moment. The absolute value of the measured enzyme activity of the sample was recorded as the sample enzyme activity. Subscript This represents the sample's detection sequence index within this testing session. A one-to-one correspondence exists between the sample detection time and the sample enzyme activity, used for subsequent compensation calculations specific to that sample.

[0038] Step 203: At the end time, record the endpoint blank enzyme activity.

[0039] The end time is the moment when the second blank calibration occurs during the testing session, i.e., the point at which the operator completes the current testing session and performs the second blank calibration measurement. At this moment, the device records the absolute value of the enzyme activity obtained from this measurement as the endpoint blank enzyme activity. This serves as the anchor point for detecting the endpoint state of the session. The difference between the initial blank enzyme activity and the endpoint blank enzyme activity represents the total activity loss that occurred during the detection session.

[0040] Step 204: Continuously collect the raw temperature sequence during the detection session using a temperature sensor, and smooth the raw temperature sequence to obtain the ambient temperature sequence.

[0041] Optionally, the device uses an NTC temperature sensor located near the reaction module to detect the temperature from the start time. Start to End Time The process ends by continuously acquiring raw temperature readings at a frequency no lower than a preset frequency (e.g., 1 Hz) to form a raw temperature sequence.

[0042] As one possible implementation, since the original temperature sequence may contain high-frequency electrical noise, the device smooths it to eliminate noise interference, resulting in an ambient temperature sequence, denoted as . For example, a first-order low-pass filter can be used to smooth the original temperature sequence, and the resulting ambient temperature sequence is denoted as... Satisfy the following formula: in, Indicates at time The smoothed ambient temperature value is a key parameter characterizing the temperature of the microenvironment in which the enzyme is located at that moment. Indicates the temperature sensor at time The raw temperature readings collected contain the superposition of the real temperature signal and electrical noise; This represents the smoothing coefficient, with a value range of (0,1). It is used to adjust the weight ratio of the current raw reading and the historical smoothed value in the output result. In this embodiment, the value is 0.2. Represents a discrete time index used to identify the order in which samples were taken; Indicates the first The smoothed temperature value at each sampling time.

[0043] Based on the above technical solution, this application establishes a complete detection session data recording mechanism by refining the steps for acquiring detection data. By recording the start and end blank enzyme activities, the boundary conditions for enzyme activity decay are determined; by recording the detection time and enzyme activity of each sample, the original data of the object to be compensated is established; and through continuous temperature acquisition and smoothing, accurate time-series temperature data is obtained. This solution provides a high-quality data foundation for subsequent thermal damage quantification and decay diagnosis, ensuring the integrity of the detection process and the consistency of the data.

[0044] In one possible implementation, the process of determining the interval cumulative thermal damage potential vector based on the ambient temperature sequence in step 102 above specifically includes: Step 301: Divide the duration of the detection session into multiple consecutive time intervals.

[0045] To facilitate subsequent diagnostic calculations, the device will calculate the total duration of the detection session. Divided equally on the timeline a continuous time interval , where the range index Total number of intervals These are engineering parameters preset based on the device's computing power and the required time resolution. In this embodiment, we take... Each time interval represents a discrete time period in the detection session, and the total number of intervals is [number missing]. The time resolution is determined based on the average duration of the detected session history and the required time resolution, usually set to 60, so that each time interval lasts 1-2 minutes.

[0046] Step 302: Based on the ambient temperature sequence within each time interval, determine the difference between the temperature at each sampling moment within each time interval and the reference temperature for enzyme thermal inactivation.

[0047] For each time interval Each sampling time within The device calculates the ambient temperature at that moment. Compared with the preset enzyme thermal inactivation reference temperature The difference between the two values. This difference reflects the portion of the temperature above which the enzyme begins to undergo significant thermal inactivation; only the portion exceeding this threshold will cause irreversible thermal damage to the enzyme.

[0048] Step 303: Perform a nonlinear transformation on the difference in each time interval and then sum them up to obtain the interval cumulative thermal damage potential in each time interval.

[0049] In this design, considering that the thermal inactivation process of enzymes follows chemical kinetics and that the inactivation rate has a nonlinear positive correlation with the superthreshold temperature, the device first performs a nonlinear transformation (e.g., power function transformation) on the superthreshold temperature, and then performs time accumulation. For example, the interval cumulative thermal damage potential... Satisfy the following formula: in, Indicates the first The cumulative thermal damage potential of a time interval is the core parameter characterizing the cumulative effective heat load borne by the enzyme within that time interval. Indicates at time The smoothed ambient temperature value is derived from the smoothing result of the aforementioned steps. The reference temperature for enzyme thermal inactivation is a temperature threshold obtained through experimental calibration based on the biochemical characteristics of the acetylcholinesterase reagent used. It represents the critical temperature at which the enzyme begins to undergo significant irreversible thermal conformational changes. In this example, the value is taken as 35°C. represents the thermal stress power exponent, a dimensionless parameter used to simulate the chemical kinetics of the nonlinear increase in enzyme inactivation rate with temperature exceeding a threshold, reflecting the nonlinear relationship that higher temperatures cause more severe molecular damage per unit time. In this embodiment, the value is taken as 1.5; max() is the maximum value function, used to ensure that the contribution of thermal damage is calculated only when the ambient temperature exceeds the reference temperature. If the contribution is zero at that moment, then the contribution is zero.

[0050] Step 304: Generate the interval cumulative thermal damage potential vector based on the interval cumulative thermal damage potential of each time interval.

[0051] Among them, the device is for all Repeat the above calculation for each time interval to generate a final result. dimensional interval cumulative thermal damage potential vector This vector quantifies the distribution of thermal load borne by the enzyme during the entire detection session in a time-ordered manner, laying the foundation for subsequent identification of the temporal distribution characteristics of thermal load.

[0052] Based on the above technical solution, this application embodiment effectively solves the problem of inaccurate characterization of time-varying thermal stress in the prior art by discretizing continuous temperature data into time intervals and calculating the cumulative thermal damage potential of each interval. By introducing an enzyme thermal inactivation reference temperature and a thermal stress power exponent, the nonlinear chemical kinetic characteristics of enzyme thermal inactivation are considered, ensuring the accuracy of thermal damage quantification. This scheme provides key input parameters for subsequent competitive degradation diagnosis, enabling the diagnostic process to be based on real heat load history rather than a simple time linearity assumption, thereby improving the accuracy of degradation pattern recognition.

[0053] In one possible implementation, step 103 above, which involves competitive diagnosis based on the interval cumulative thermal damage potential vector and the initial blank enzyme activity and the endpoint blank enzyme activity, to determine the optimal loss allocation order for non-linearly distributing the activity loss between the initial blank enzyme activity and the endpoint blank enzyme activity to each time interval, specifically includes: Step 401: Determine the total activity loss during the detection session based on the initial blank enzyme activity and the endpoint blank enzyme activity.

[0054] The device first checks the boundary conditions: total activity loss. It must be greater than zero, and the sum of the cumulative thermal damage potential over the interval Is it greater than zero? If any condition is not met, it indicates that no measurable thermal decay occurred during the detection session, and the device will skip subsequent diagnostic steps and use default linear compensation or directly output the raw result. If the condition is met, the device calculates the total net loss of enzyme activity that occurred and is measurable throughout the entire detection session. For example, total activity loss... Satisfy the following formula: in, It represents the total activity loss during the detection session, and the unit is enzyme activity unit (such as absorbance change rate, fluorescence intensity, etc., depending on the specific detection method). It is a key parameter characterizing the net reduction in enzyme activity during the entire detection process. This represents the initial blank enzyme activity measured at the start of the detection session, and represents the absolute value of the enzyme activity at the start of the detection. This represents the endpoint blank enzyme activity measured at the end of the detection session, and indicates the absolute value of enzyme activity at the end of the detection.

[0055] Step 402: Based on the total activity loss and the interval cumulative thermal damage potential vector, construct the early damage attribution cost matrix and the late damage attribution cost matrix respectively.

[0056] To diagnose the time-biased pattern of enzyme activity decay, the device constructs two mathematical models representing the hypotheses of early concentrated damage and late cumulative fatigue, respectively. These two models, by introducing different time weighting factors, reflect the cost difference in attributing activity loss to early or late detection. Specifically, they include: First, based on the total activity loss and the interval cumulative thermal damage potential vector, the proportional distribution loss of thermal damage potential is calculated, and a linear reference system is established. For example, the proportional distribution loss of thermal damage potential... Satisfy the following formula: in, This indicates the first... under the linear decay assumption The proportion of thermal damage potential loss that should be allocated to each time interval, in units of enzyme activity, is the basic unit for subsequent nonlinear redistribution. This represents the total activity loss calculated above; Indicates the first The cumulative thermal damage potential of each time interval; This represents the sum of the cumulative thermal damage potential across all time intervals, i.e., the total heat load. For the summation index, the range is from 1 to... It should be noted that if (Total heat load is zero). To avoid division by zero errors, all... Setting all values ​​to 0 indicates that no thermal damage has occurred and no further allocation is required.

[0057] Following this, based on the total number of time intervals in the cumulative thermal damage potential vector... Determine the early damage attribution cost matrix and the later damage attribution cost matrix All dimensions are .

[0058] For the early damage attribution cost matrix Each cost element Calculate the target time interval The absolute value of the difference between the cumulative thermal damage potential in the time interval k from the source time interval. As the base cost; based on the time series index of the target time interval. The ratio between the total number of time intervals M and the total number of time intervals M and preset time bias weighting coefficient Determine the first time weight factor that increases with the time series index of the target time interval. Multiply the base cost by the first-time weighting factor to obtain the cost element at the corresponding position in the early damage attribution cost matrix. The source time interval is then considered. The proportional distribution of thermal damage potential loss The corresponding original time interval (i.e., the first) (intervals), target time interval The time interval after the redistribution of activity loss (i.e., the first) (Intervals).

[0059] For example, the elements of the early damage attribution cost matrix satisfy the following formula: in, In the early damage attribution cost matrix, the first... Line number The elements of the column represent the source range. The activity loss is redistributed to the target region A quantified value of the required cost; Indicates the target time interval and source time interval The absolute value of the difference between the cumulative thermal damage potentials in the intervals is used as the basic cost to ensure that the cost is allocated more reasonably between intervals with similar thermal damage potentials. This represents the preset time bias weighting coefficient, which is a dimensionless parameter with a value greater than or equal to 0. In this embodiment, the value is 1.0, which is used to adjust the influence intensity of the time weight. The time series index representing the target time interval, with values ​​ranging from 1 to... ; Indicates the total number of time intervals; This represents the first-time weighting factor, which is a dimensionless parameter that varies with time. It increases linearly with increasing volume.

[0060] For the later damage attribution cost matrix Each cost element Calculate the target time interval and source time interval The absolute value of the difference between the cumulative thermal damage potentials in the interval As the base cost; based on the total number of time intervals Time series index with target time interval Add one to the difference between them. ), and preset time bias weighting coefficient Determine the second time weighting factor that decreases as the time series index of the target time interval increases. Multiply the base cost by the second time weighting factor to obtain the cost element at the corresponding position in the later damage attribution cost matrix.

[0061] For example, the elements of the later damage attribution cost matrix satisfy the following formula: in, In the later damage attribution cost matrix, the first... Line number The elements of the column; the remaining parameters , , , The meaning is the same as in the earlier cost matrix; This represents the time distance from the current position to the end of the process (including the current interval); This represents the second time weighting factor, which decreases linearly as j increases.

[0062] Step 403: Competitively solve the early damage attribution cost matrix and the late damage attribution cost matrix based on the preset allocation problem solving algorithm, and determine the optimal loss allocation order based on the solution results.

[0063] The device will assign the early damage attribution cost matrix to each of the following components: and the later damage attribution cost matrix As input, a pre-defined allocation problem-solving algorithm (e.g., the Hungarian algorithm) is invoked for solution. The Hungarian algorithm is a standard optimization algorithm for solving this type of one-to-one allocation problem, guaranteeing a globally optimal solution. Optionally, the Hungarian algorithm transforms the early damage attribution cost matrix or the late damage attribution cost matrix into a minimum weight matching problem in a bipartite graph, using labeling and augmented path search to determine the optimal allocation permutation.

[0064] Specifically, the early damage attribution cost matrix is ​​solved based on a pre-defined allocation problem-solving algorithm to determine the first minimum total cost. and its corresponding first loss allocation arrangement The second minimum total cost is determined by solving the later damage attribution cost matrix based on a pre-defined allocation problem-solving algorithm. and its corresponding second loss allocation arrangement One of the optimal permutations It is a set of range indices The one-to-one mapping (bijective) to itself defines the optimal correspondence between the loss unit and the target interval, i.e. Indicates the first The loss from the first source interval is allocated to the first... One target interval.

[0065] Next, based on the first minimum total cost and the second minimum total cost, the time bias index of the decay process is determined ( For example, the decay process is time-biased exponential. Satisfy the following formula: Among them, DAI represents the time bias index of the decay process, which is a dimensionless parameter with a value range of [−1,1]. It is the core indicator for quantitatively diagnosing the relative fit between the early concentrated damage pattern and the later cumulative fatigue pattern. This represents the first minimum total cost obtained by solving the early damage attribution cost matrix; This represents the second minimum total cost obtained based on the later damage attribution cost matrix. It should be noted that if... If the value is zero, then set DAI to 0; if it is not zero, then calculate according to the above formula.

[0066] The DAI calculated by this formula is a quantitative diagnostic index characterizing the time-biased pattern of enzyme activity decay during this testing session. When DAI < 0, it indicates that the early concentrated damage model has a lower minimum total cost and is a better interpretation, meaning that the enzyme experienced more severe activity loss in the early stages of the testing session (possibly due to initial thermal shock or conformational relaxation). When DAI > 0, it indicates that the later cumulative fatigue model is better, meaning that the enzyme experienced more severe activity loss in the later stages of the testing session (possibly due to the cumulative heat effect). When DAI is close to 0, it indicates that the two models are fairly well-fitted, and the decay process is relatively symmetrical. This index provides a decision-making basis for subsequently determining the optimal loss allocation order.

[0067] Finally, based on the sign of the time bias exponent in the decay process, the optimal loss allocation order is determined from the first and second loss allocation arrangements. .

[0068] Specifically, if DAI > 0, it indicates that the later model is better, so the second loss assignment arrangement is selected. This serves as the optimal order for loss allocation.

[0069] If DAI ≤ 0, it indicates that the earlier model is better or both are comparable, then the first loss assignment arrangement is selected. This serves as the optimal order for loss allocation.

[0070] This sequence represents the damage allocation path that best reflects the actual process of this detection session. It defines the optimal one-to-one mapping relationship for nonlinearly redistributing the total activity loss to each time interval, providing a nonlinear compensation path basis for subsequent calculation of dynamic baseline enzyme activity.

[0071] Based on the above technical solution, this application embodiment achieves quantitative diagnosis of enzyme activity decay patterns by constructing and solving a competitive cost matrix. Unlike the linear interpolation method in the prior art, this solution considers the temporal asymmetry of the decay process. By comparing the goodness of fit between early concentrated damage and late cumulative fatigue modes, it automatically identifies the decay mode that best matches the actual process. This solution provides the correct allocation path for subsequent nonlinear compensation, avoids systematic compensation errors caused by using an incorrect decay model, and significantly improves the accuracy of compensation.

[0072] In one possible implementation, step 104 above, which involves determining the dynamic baseline enzyme activity at each sample detection time based on the optimal loss allocation order, specifically includes: Step 501: Determine the diagnostic feature set.

[0073] The diagnostic feature set includes: the time bias index of the decay process (DAI), the optimal loss allocation order (… The process irregularity score (PIS) is used to quantify the unexplained randomness in detecting the decay process of a session under the optimal interpretation model, reflecting the degree of fit between the data and the model.

[0074] Specifically, the process irregularity score is determined based on the ratio between the smaller of the first and second minimum total costs and the sum of the cumulative thermal damage potential vectors over the interval. For example, the process irregularity score PIS satisfies the following formula: in, This represents the smaller of the first and second minimum total costs, i.e., the minimum total cost of the winning model. This represents the sum of the cumulative thermal damage potential vectors over the interval, i.e., the total heat load, expressed in units of thermal damage potential. It should be noted that if... If the process irregularity score (PIS) is set to 0, then the linear compensation mode is used or the original result is output directly.

[0075] After calculating the Process Irregularity Score (PIS), the decay process time bias index, the process irregularity score, and the optimal loss allocation order are encapsulated into a diagnostic feature set, serving as a comprehensive basis for subsequent compensation calculations and result output. This feature set is derived from pattern qualitative analysis (DAI), intensity quantitative analysis (PIS), and path definition (…). The three dimensions (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ...

[0076] Step 502: Based on the optimal loss allocation order in the diagnostic feature set, remap the proportional allocation loss of each thermal damage potential corresponding to the total active loss to each time interval, and determine the reconstructed interval active loss for each time interval.

[0077] The device utilizes the optimal loss allocation order from the diagnostic feature set. Distribute the thermal damage potential proportionally to the loss vector Remap back to the original time series. For example, reconstruct the interval activity loss. Satisfy the following formula: in, Indicates the first The reconstructed interval activity loss of each time interval, in units of enzyme activity, is a characterization of the actual amount of activity loss that occurs in the corresponding time interval after nonlinear path redistribution. Indicates the optimal loss allocation order Under the mapping, assigned to the first The thermal damage potential of each target interval is proportionally allocated to the loss.

[0078] The formula is used to calculate... It is a characterization of the first Within a given time interval, the most likely actual amount of activity loss determined through competitive diagnostics. Thermal damage potential proportionally allocates the loss vector. This constitutes a reconstructed, time-ordered true loss sequence, providing a basis for interval-by-interval loss subtraction to calculate the dynamic baseline enzyme activity of each sample, enabling the baseline reconstruction to reflect the true asymmetric decay path.

[0079] Step 503: For each sample, determine the target time interval in which the sample detection time is located, and subtract the sum of all reconstructed interval activity losses from the first time interval to the previous time interval from the first time interval to the target time interval from the initial blank enzyme activity to determine the dynamic benchmark enzyme activity corresponding to the sample.

[0080] Among them, for in The first time detection For each sample, first determine the target time interval index where the sample detection time falls. Then, the initial blank enzyme activity was... Subtract from the first time interval to the second The sum of activity losses across all reconstructed time intervals within a given time interval is used to obtain the dynamic baseline enzyme activity of the sample. For example, dynamic benchmark enzyme activity Satisfy the following formula: in, Indicates the initial blank enzyme activity; Indicates from the 1st to the 1st The sum of the activity loss across the reconstructed time intervals represents the total activity loss that has occurred completely before the sample was detected. Indicates the first The samples were in the target time interval The relative time position coefficient within the target time interval k is determined by the start and end times of the sample detection time and the target time interval k, satisfying 0 ≤ k. <1.

[0081] The formula is calculated to obtain Characterization in the The dynamic baseline activity value of the enzyme reagent at the moment of sample detection, after asymmetric decay compensation. This value comprehensively considers the thermal load history throughout the detection session, the decay time bias pattern identified by competitive diagnostics (early concentration or late accumulation), and interval cumulative loss, and is the most reasonable estimate of the enzyme activity at the moment of sample detection. Using this dynamic baseline instead of a fixed initial blank enzyme activity for inhibition rate calculation can effectively eliminate the systematic error introduced by the asymmetric decay of enzyme activity during the detection session, thus improving detection accuracy.

[0082] Based on the above technical solution, this application embodiment achieves personalized compensation for each sample through the generation of diagnostic feature sets, nonlinear remapping of losses, and calculation of dynamic benchmarks. Unlike existing technologies that use fixed benchmarks, this solution reconstructs the enzyme activity benchmark that each sample should have at the detection time, fully considering the asymmetric decay of enzyme activity during the detection process. This solution significantly improves the accuracy of inhibition rate calculation, especially for scenarios with long detection time spans or large environmental temperature fluctuations, effectively avoiding misjudgments caused by outdated benchmark values.

[0083] In one possible implementation, step 105 above, which calculates the compensated pesticide residue detection result based on each dynamic baseline enzyme activity and its corresponding sample enzyme activity, specifically includes: Step 601: Determine the compensation inhibition rate based on the dynamic baseline enzyme activity and the corresponding sample enzyme activity.

[0084] Among them, for the first One sample, using its dynamic benchmark enzyme activity And the measured enzyme activity of the samples Calculate the suppression rate after compensation. For example, the inhibition rate after compensation. Satisfy the following formula: The formula calculates It is a representation of the first The final quantitative index for pesticide residue content in each sample indicates the pesticide's inhibitory effect on acetylcholinesterase. A higher value indicates a stronger inhibitory effect and higher pesticide residue content; a lower value indicates a weaker inhibitory effect and lower pesticide residue content. This is achieved by using a dynamic baseline enzyme activity level. Rather than fixed This inhibition rate eliminates systematic errors caused by asymmetric attenuation during the detection process (such as misclassifying qualified products as unqualified products), offering higher accuracy and reliability compared to traditional methods, and can be directly used for sample qualification determination. It should be noted that... (The specific value can be determined based on the noise baseline or minimum effective resolution of the enzyme activity detection of this device.) If the sample detection is invalid, it indicates that the enzyme activity is depleted and needs to be detected again.

[0085] Step 602: Determine the process diagnostic information based on the sign of the decay process time bias index in the diagnostic feature set.

[0086] Specifically, based on the symbol of DAI, a clear textual conclusion is output as process diagnostic information. If DAI < 0, the output decay mode is: early concentrated damage, indicating that the enzyme experienced a relatively severe loss of activity in the early stage of the detection session. The possible reasons are: the equipment was taken out of the low temperature environment and used immediately, and the enzyme conformation was not yet stable before being subjected to thermal shock.

[0087] If DAI>0, the output decay mode is: late-stage cumulative fatigue, indicating that the enzyme activity loss is more severe in the later stages of detection. The possible reason is that long-term continuous detection leads to heat accumulation in the equipment and reagents.

[0088] If DAI is close to 0 (e.g., absolute value less than 0.1), the output decay mode is: symmetrical process, indicating a relatively uniform decay process. This information helps users understand the temporal characteristics and possible causes of enzyme activity decay during this test.

[0089] Step 603: Determine the reliability assessment level of the result based on the relationship between the process irregularity score in the diagnostic feature set and the preset first reliability threshold and second reliability threshold.

[0090] Among them, the process irregularity score (PIS) is compared with the first reliability threshold preset by the equipment. Second reliability threshold (in For example, the first reliability threshold Set to 0.2, the second reliability threshold. A value of 0.5 (which can be determined through experimental calibration before the equipment leaves the factory) is used to compare the results and determine the reliability assessment level (RCS). If... Then the RCS rating is high; if Then the RCS rating is medium; if If the RCS level is low, then the RCS level is considered low. This level characterizes the reliability of the suppression rate after compensation; a higher level indicates a more stable detection process, a better fit of the compensation model, and more reliable results.

[0091] Step 604: Generate a three-stage detection result based on the post-compensation inhibition rate, process diagnostic information, and result reliability assessment level.

[0092] The device is for each sample. Generate and output the final detection results, which include three dimensions: the first segment is the quantitative result, namely the suppression rate after compensation. The second segment is process diagnosis, which is a description of the attenuation pattern based on DAI symbols, such as attenuation pattern: early concentrated damage; the third segment is reliability assessment, which is a reliability level based on PIS, such as high reliability of the result. This three-segment output not only provides accurate quantitative detection results, but also provides qualitative diagnosis of the detection process and result reliability assessment, enabling users to make sample disposal decisions based on more comprehensive information. For example, when the reliability level is low, it prompts the user to retest or check environmental conditions.

[0093] Based on the above technical solution, this application embodiment obtains the final quantitative result of eliminating systematic errors by calculating the suppression rate after compensation; provides a qualitative understanding of the attenuation mode of the detection process by generating process diagnostic information; and gives a quantitative evaluation of the reliability of the results by classifying the reliability assessment level. This three-stage detection result output mechanism enables users not only to obtain accurate detection values, but also to understand the stability of the detection process and the reliability of the results, providing comprehensive information support for on-site decision-making and significantly improving the practicality and reliability of rapid pesticide residue detection.

[0094] Please see Figure 2 The diagram illustrates a structural schematic of a rapid pesticide residue detection device according to an embodiment of the present invention. The device includes a data acquisition unit 201, a data processing unit 202, a competitive diagnostic unit 203, and a compensation calculation unit 204. The units communicate bidirectionally via a communication link, ensuring real-time interaction of collected data and analysis results. The communication link can employ wired or wireless transmission methods to meet the communication needs of different monitoring scenarios.

[0095] The data acquisition unit 201 is used to acquire the detection data of the detection session; the detection session includes a complete pesticide residue detection process; the detection data includes the initial blank enzyme activity measured at the start time of the detection session, the endpoint blank enzyme activity measured at the end time, the sample enzyme activity and the corresponding sample detection time when each sample detection is completed, and the ambient temperature sequence during the detection session.

[0096] The data processing unit 202 is used to determine the interval cumulative thermal damage potential vector based on the ambient temperature sequence; the interval cumulative thermal damage potential vector is used to characterize the effective heat load borne by the enzyme reagent in each time interval during the detection session.

[0097] The competitive diagnostic unit 203 is used to perform competitive diagnostics based on the interval cumulative thermal damage potential vector and the initial blank enzyme activity and the endpoint blank enzyme activity, and to determine the optimal loss allocation order for nonlinearly allocating the activity loss between the initial blank enzyme activity and the endpoint blank enzyme activity to each time interval.

[0098] The compensation calculation unit 204 is used to determine the dynamic baseline enzyme activity corresponding to each sample detection time according to the optimal loss allocation order; and to calculate the compensated pesticide residue detection results based on each dynamic baseline enzyme activity and its corresponding sample enzyme activity.

[0099] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A rapid detection method for pesticide residues, characterized in that, The method includes: Acquire detection data from the detection session; the detection session includes a complete pesticide residue detection process; the detection data includes the initial blank enzyme activity measured at the start time of the detection session, the endpoint blank enzyme activity measured at the end time, the sample enzyme activity and corresponding sample detection time at the completion of each sample detection, and the ambient temperature sequence during the detection session. Based on the ambient temperature sequence, a cumulative thermal damage potential vector for each interval is determined; the cumulative thermal damage potential vector for each interval is used to characterize the effective heat load borne by the enzyme reagent in each time interval during the detection session. Based on the cumulative thermal damage potential vector of the interval and the initial blank enzyme activity and the endpoint blank enzyme activity, competitive diagnosis is performed to determine the optimal loss allocation order for nonlinearly distributing the activity loss between the initial blank enzyme activity and the endpoint blank enzyme activity to each time interval. Based on the optimal loss allocation order, determine the dynamic baseline enzyme activity corresponding to each sample detection time. The compensated pesticide residue detection results are calculated based on the dynamic benchmark enzyme activity and the corresponding sample enzyme activity.

2. The rapid detection method for pesticide residues according to claim 1, characterized in that, Obtain detection data from the detection session, including: At the start of the detection session, the initial blank enzyme activity is recorded; the start time is the time when the first blank calibration occurs in the detection session. Between the start and end times, each time a sample test is completed, the enzyme activity of the sample and its corresponding sample test time are recorded. At the end time, the endpoint blank enzyme activity is recorded; the end time is the time when the second blank calibration occurs in the detection session; The ambient temperature sequence is obtained by continuously acquiring the raw temperature sequence during the detection session using a temperature sensor and smoothing the raw temperature sequence; the smoothing process is used to eliminate high-frequency electrical noise in the raw temperature sequence.

3. The rapid detection method for pesticide residues according to claim 1, characterized in that, Based on the ambient temperature sequence, the interval cumulative thermal damage potential vector is determined, including: The duration of the detection session is divided into multiple consecutive time intervals; Based on the ambient temperature sequence within each time interval, determine the difference between the temperature at each sampling moment within each time interval and the enzyme thermal inactivation reference temperature; The difference values ​​within each time interval are nonlinearly transformed and then accumulated to obtain the interval cumulative thermal damage potential within each time interval; the interval cumulative thermal damage potential is used to characterize the effective heat load accumulation that can cause irreversible denaturation of the enzyme within the corresponding time interval. The interval cumulative thermal damage potential vector is generated based on the interval cumulative thermal damage potential of each time interval.

4. The rapid detection method for pesticide residues according to claim 1, characterized in that, Based on the cumulative thermal damage potential vector of the interval and the initial blank enzyme activity and the endpoint blank enzyme activity, competitive diagnosis is performed to determine the optimal loss allocation order for non-linearly distributing the activity loss between the initial blank enzyme activity and the endpoint blank enzyme activity to each time interval, including: The total activity loss during the detection session is determined based on the initial blank enzyme activity and the endpoint blank enzyme activity. Based on the total activity loss and the interval cumulative thermal damage potential vector, an early damage attribution cost matrix and a late damage attribution cost matrix are constructed respectively. The early damage attribution cost matrix is ​​used to characterize the time asymmetric cost of attributing each loss unit in the total activity loss to the early stage of the detection session, and the late damage attribution cost matrix is ​​used to characterize the time asymmetric cost of attributing each loss unit in the total activity loss to the late stage of the detection session. The early damage attribution cost matrix and the late damage attribution cost matrix are competitively solved based on a preset allocation problem solving algorithm, and the optimal loss allocation order is determined according to the solution results; the optimal loss allocation order is used to characterize the optimal one-to-one mapping relationship for nonlinearly redistributing the total activity loss to each time interval of the detection session.

5. The rapid detection method for pesticide residues according to claim 4, characterized in that, Based on the total activity loss and the cumulative thermal damage potential vector over the interval, early damage attribution cost matrices and late damage attribution cost matrices are constructed, including: The dimensions of the early damage attribution cost matrix and the late damage attribution cost matrix are determined based on the total number of time intervals in the cumulative thermal damage potential vector. For each cost element in the early damage attribution cost matrix, the absolute value of the difference between the cumulative thermal damage potential of the target time interval and the source time interval is calculated as the base cost; based on the ratio between the time series index of the target time interval and the total number of time intervals, and a preset time bias weighting coefficient, a first time weighting factor that increases with the time series index of the target time interval is determined; the base cost is multiplied by the first time weighting factor to obtain the cost element at the corresponding position in the early damage attribution cost matrix; wherein, the source time interval is the original time interval corresponding to the proportional allocation loss of thermal damage potential, and the target time interval refers to the time interval after the redistribution of activity loss; For each cost element in the late-stage damage attribution cost matrix, the absolute value of the difference between the cumulative thermal damage potential of the target time interval and the source time interval is calculated as the base cost; based on the difference between the total number of time intervals and the time series index of the target time interval, and the preset time bias weighting coefficient, a second time weighting factor that decreases as the time series index of the target time interval increases is determined; the base cost is multiplied by the second time weighting factor to obtain the cost element at the corresponding position in the late-stage damage attribution cost matrix.

6. The rapid detection method for pesticide residues according to claim 4, characterized in that, The early damage attribution cost matrix and the late damage attribution cost matrix are competitively solved based on a preset allocation problem-solving algorithm. The optimal loss allocation order is determined based on the solution results, including: The early damage attribution cost matrix is ​​solved based on the preset allocation problem solving algorithm to determine the first minimum total cost and its corresponding first loss allocation arrangement; The pre-defined allocation problem solving algorithm is used to solve the later damage attribution cost matrix to determine the second minimum total cost and its corresponding second loss allocation arrangement. The decay process time bias index is determined based on the first minimum total cost and the second minimum total cost; the decay process time bias index is used to quantitatively diagnose the relative fit between the early concentrated damage mode and the later cumulative fatigue mode. The optimal loss allocation order is determined from the first loss allocation arrangement and the second loss allocation arrangement based on the sign of the time bias index of the decay process.

7. The rapid detection method for pesticide residues according to claim 6, characterized in that, Based on the optimal loss allocation order, the dynamic baseline enzyme activity corresponding to each sample detection time is determined, including: A diagnostic feature set is determined; the diagnostic feature set includes: the decay process time bias index, the optimal loss allocation order, and the process irregularity score; the process irregularity score is used to quantify the unexplainable randomness of the decay process of the detection session under the optimal interpretation model; Based on the optimal loss allocation order in the diagnostic feature set, the proportional allocation loss of each thermal damage potential corresponding to the total active loss is remapped to each time interval to determine the reconstructed interval active loss of each time interval; the reconstructed interval active loss is used to characterize the actual amount of active loss that occurs in the corresponding time interval after nonlinear path redistribution. For each sample, the target time interval in which the sample detection time is located is determined. The dynamic baseline enzyme activity corresponding to the sample is determined by subtracting the sum of the activity losses of all reconstructed intervals from the first time interval to the time interval preceding the target time interval from the initial blank enzyme activity.

8. The rapid detection method for pesticide residues according to claim 7, characterized in that, Determine the diagnostic feature set, including: The process irregularity score is determined based on the ratio between the smaller of the first minimum total cost and the second minimum total cost and the sum of the cumulative thermal damage potential vectors in the interval. The decay process time bias index, the process irregularity score, and the optimal loss allocation order are encapsulated into the diagnostic feature set.

9. The rapid detection method for pesticide residues according to claim 8, characterized in that, Based on the dynamic baseline enzyme activity and the corresponding sample enzyme activity, the compensated pesticide residue detection results are calculated, including: The post-compensation inhibition rate is determined based on the dynamic baseline enzyme activity and the corresponding sample enzyme activity; the post-compensation inhibition rate is used to characterize the sample inhibition rate calculated using the dynamic baseline enzyme activity. Based on the sign of the decay process time bias index in the diagnostic feature set, process diagnostic information is determined; the process diagnostic information is used to characterize the time bias pattern of the enzyme activity decay process in this detection session. The reliability assessment level of the result is determined based on the relationship between the process irregularity score in the diagnostic feature set and the preset first reliability threshold and second reliability threshold; the reliability assessment level of the result is used to characterize the credibility of the post-compensation inhibition rate. Based on the post-compensation inhibition rate, the process diagnostic information, and the result reliability assessment level, a three-stage detection result is generated.

10. A rapid detection device for pesticide residues, characterized in that, The device includes: The data acquisition unit is used to acquire detection data of the detection session; the detection session includes a complete pesticide residue detection process; the detection data includes the initial blank enzyme activity measured at the start time of the detection session, the endpoint blank enzyme activity measured at the end time, the sample enzyme activity and the corresponding sample detection time when each sample detection is completed, and the ambient temperature sequence during the detection session. The data processing unit is used to determine the interval cumulative thermal damage potential vector based on the ambient temperature sequence; the interval cumulative thermal damage potential vector is used to characterize the effective heat load borne by the enzyme reagent in each time interval during the detection session; A competitive diagnostic unit is used to perform competitive diagnostics based on the cumulative thermal damage potential vector of the interval and the initial blank enzyme activity and the endpoint blank enzyme activity to determine the optimal loss allocation order for nonlinearly allocating the activity loss between the initial blank enzyme activity and the endpoint blank enzyme activity to each time interval. The compensation calculation unit is used to determine the dynamic benchmark enzyme activity corresponding to each sample detection time according to the optimal loss allocation order; and to calculate the compensated pesticide residue detection result according to each dynamic benchmark enzyme activity and its corresponding sample enzyme activity.

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