Pesticide residue detection method and system for pesticide residue detector

Abstract

The invention provides a pesticide residue detection method and system for a pesticide residue detector, and the method comprises the steps: collecting environment temperature data and matrix type information of fruit and vegetable samples, and combining historical detection data to construct a dynamic control database; a double-shaft movement mechanism is used for synchronously controlling a cutting blade to rotationally cut a sample and a linkage stirrer to mix a detection reagent so as to generate a uniformly mixed detection solution. Inputting the mixed solution and environment temperature data into a detector to obtain an initial detection value, finally performing temperature compensation and deviation correction on the initial value based on a dynamic contrast database, and outputting a target pesticide residue detection value. According to the invention, the pesticide residue detection precision and efficiency of multiple batches of fruit and vegetable samples in a variable-temperature environment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of agricultural product quality and safety detection, and particularly to a pesticide residue detection method and system for a pesticide residue detector. Background Art

[0002] In the circulation link of agricultural products, especially in places such as farmers' markets and supermarkets, it is necessary to quickly and accurately detect pesticide residues in fruits and vegetables. Due to temperature changes in different seasons and storage environments, as well as differences in fruit and vegetable matrices, the detection results are easily interfered by environmental factors. Therefore, there is an urgent need for a portable detection method that can automatically compensate for the influence of temperature and adapt to various fruit and vegetable matrices to improve the stability and reliability of detection results.

[0003] Currently, there is a pesticide residue detection scheme that uses a fixed temperature compensation coefficient. This scheme corrects the detection results by presetting a single temperature compensation parameter and combines spectral analysis technology to identify pesticide characteristic peaks. During detection, the system calls the preset compensation value according to the current environmental temperature and directly adjusts the detection signal output, thereby reducing the influence of temperature fluctuations on the results.

[0004] The fixed temperature compensation coefficient is only effective for specific fruit and vegetable matrices and a narrow temperature range. When the temperature span of the detection environment is large or the sample matrix type changes, the compensation accuracy decreases. In addition, this method relies on manual pre-calibration of compensation parameters and cannot dynamically adapt to the actual detection conditions of different batches of samples, resulting in large deviations in detection results in complex scenarios. Summary of the Invention

[0005] The present application provides a pesticide residue detection method and system for a pesticide residue detector to solve the problems of low detection accuracy and poor efficiency of pesticide residues in multi-batch fruit and vegetable samples in a variable temperature environment in the prior art.

[0006] In a first aspect, the present application provides a pesticide residue detection method for a pesticide residue detector, including: Obtaining environmental temperature data, sample matrix type information, and chemical detection reagents where the fruit and vegetable sample is located; Based on the environmental temperature data and the sample matrix type information, and combined with historical detection data, establishing a dynamic control database; Controlling a cutting blade to perform a rotary cutting operation on the fruit and vegetable sample through a biaxial motion mechanism, and driving a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection solution; Inputting the mixed detection solution and the environmental temperature data into a pesticide residue detector to output an initial pesticide residue detection value; Performing dynamic compensation on the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0007] Optionally, based on the ambient temperature data and the sample matrix type information, and in combination with historical detection data, a dynamic control database is established, including: According to the ambient temperature data, historical ambient temperature data of fruit and vegetable samples at different time points and pesticide residue detection values detected at the corresponding historical ambient temperatures are extracted from the historical detection data; The historical ambient temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample is grouped according to a preset temperature interval to generate temperature data groups, and each temperature data group contains multiple temperature ranges; Calculate the average value of all pesticide residue detection values within each temperature data group, and use the average value as the historical control value for the corresponding temperature range; Calculate the parameter influence factor according to the temperature data group and the historical control value; Generate a dynamic control database based on the sample matrix type information, the temperature range, the historical control value, and the parameter influence factor.

[0008] Optionally, the calculating the parameter influence factor according to the temperature data group and the historical control value includes: Determine the median point of the temperature range corresponding to each temperature data group as the representative temperature; Form a set of numerical pairs according to the corresponding relationship between the representative temperature and the historical control value; Calculate the average change rate of the historical control value with respect to the representative temperature according to the set of numerical pairs; Multiply the absolute value of the average change rate by a preset adjustment coefficient to generate the parameter influence factor.

[0009] Optionally, the inputting the mixed detection solution and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value includes: Place the mixed detection solution under the light source of the pesticide residue detector to obtain a detection signal of the transmitted light intensity changing with time; Adjust the signal acquisition parameters of the pesticide residue detector according to the ambient temperature data, and optimize the amplitude of the detection signal based on the adjusted signal acquisition parameters; Perform time-frequency conversion on the optimized detection signal to generate spectrum data; Screen out the frequency points whose amplitudes exceed a preset amplitude threshold from the spectrum data; Match the frequency points with the frequency point characteristic data corresponding to the current sample matrix type information in the dynamic control database to generate an initial pesticide residue detection value.

[0010] Optionally, the matching of the frequency point with the frequency point feature data corresponding to the current sample matrix type information in the dynamic control database to generate an initial pesticide residue detection value includes: Calling the frequency point feature data corresponding to the current sample matrix type information from the dynamic control database, where the frequency point feature data includes feature frequency positions and associated concentration values; Calculating the frequency deviation values between the frequency point and each feature frequency position in the frequency point feature data; Selecting, from the frequency point feature data, multiple associated concentration values corresponding to the feature frequency positions where the frequency deviation values are less than or equal to a preset matching threshold; Taking the average value of all the associated concentration values as the initial pesticide residue detection value.

[0011] Optionally, the dynamic compensation of the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value includes: Comparing the current environmental temperature data with the representative temperatures of each temperature range of the same sample matrix type information in the dynamic control database, and calculating the temperature deviation amount according to the comparison result; Multiplying the parameter influence factor by the temperature deviation amount to obtain a temperature compensation coefficient; Adjusting the historical control value corresponding to the current sample matrix type information and the temperature range to which the current environmental temperature data belongs in the dynamic control database by using the temperature compensation coefficient to obtain a reference reference value; Performing linear correction on the initial pesticide residue detection value based on the reference reference value and the temperature compensation coefficient to generate a target pesticide residue detection value.

[0012] Optionally, controlling the cutting blade by a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable sample, and driving a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection liquid, includes: Driving the cutting blade through the first rotating shaft of the biaxial motion mechanism to perform rotary cutting on the fruit and vegetable sample to generate a shredded sample; Driving the stirrer through the second rotating shaft of the biaxial motion mechanism to stir the chemical detection reagent and the shredded sample in a container to form a uniform mixture; When it is detected that the stirring duration of the uniform mixture reaches a preset duration corresponding to the sample matrix type information, stop stirring to generate a mixed detection liquid.

[0013] In a second aspect, the present application provides a pesticide residue detection system for a pesticide residue detector, including: An acquisition module for acquiring the ambient temperature data of the fruit and vegetable sample, the sample matrix type information, and the chemical detection reagent; A database building module for building a dynamic control database based on the ambient temperature data and the sample matrix type information, in combination with historical detection data; A first generation module for controlling a cutting blade to perform a rotary cutting operation on the fruit and vegetable sample through a biaxial motion mechanism, and driving a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection solution; An output module for inputting the mixed detection solution and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value; A second generation module for dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0014] In a third aspect, the present application provides a computing device including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the methods for detecting pesticide residues in a pesticide residue detector according to the first aspect.

[0015] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method for detecting pesticide residues in a pesticide residue detector according to any one of the first aspect is implemented.

[0016] In the present application, a method for detecting pesticide residues in a pesticide residue detector is provided. The method includes: acquiring the ambient temperature data of the fruit and vegetable sample, the sample matrix type information, and the chemical detection reagent; building a dynamic control database based on the ambient temperature data and the sample matrix type information, in combination with historical detection data; controlling a cutting blade to perform a rotary cutting operation on the fruit and vegetable sample through a biaxial motion mechanism, and driving a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection solution; inputting the mixed detection solution and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value; dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value The technical solution provided by the present application has the following beneficial effects: This application provides basic parameter input for subsequent detections, ensuring that the detection conditions match the actual environment. Through the accumulation and analysis of historical detection data, a dynamically adjustable reference benchmark is formed to improve the adaptability of detections. The automation and standardization of sample pretreatment are realized to ensure the uniformity and consistency of the mixed detection liquid. Optimized detection samples and compensation parameters are provided for the detector to ensure the stability of detection signals. The detection results are adjusted according to real-time environmental conditions and sample characteristics to improve the accuracy and reliability of detections.

[0017] Furthermore, this application also collects the environmental temperature and pesticide residue values in historical detection data, groups them according to the sample matrix type and temperature range, calculates the average pesticide residue value in each temperature range as the historical control value, and calculates the parameter influence factor in combination with the relationship between the temperature data group and the historical control value. Finally, a dynamic control database including the sample matrix type, temperature range, historical control value, and parameter influence factor is constructed.

[0018] Moreover, by establishing a dynamic control database, the systematic management of pesticide residue detection values under different environmental temperatures and sample matrix conditions is realized, providing a dynamically adjustable reference basis for real-time detections and improving the consistency and comparability of detection results.

[0019] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a flowchart of a pesticide residue detection method for a pesticide residue detector provided by an embodiment of this application; Figure 2 It is a schematic structural diagram of a pesticide residue detection system for a pesticide residue detector provided by an embodiment of this application; Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of this application. Detailed Embodiments

[0022] To enable those skilled in the art to better understand the solutions of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application.

[0023] In some of the processes described in the specification, claims, and the above-mentioned drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0024] In the existing pesticide residue detection technology, the scheme of using a fixed temperature compensation coefficient has obvious limitations. This scheme only presets compensation parameters for specific fruit and vegetable matrices and a narrow temperature range. When the detection environment temperature fluctuates greatly or the sample matrix type changes, the compensation accuracy decreases. In addition, this method relies on the static compensation value calibrated manually and cannot be dynamically adjusted according to the actual detection conditions, resulting in a large deviation in the detection results in complex scenarios and making it difficult to meet the requirements of rapid screening of multi-batch fruit and vegetable samples.

[0025] To address the above problems, the present application proposes a pesticide residue detection method for a pesticide residue detector. The core lies in constructing a dynamic control database through the collaborative analysis of environmental temperature data, sample matrix type information, and historical detection data. Specifically, during the detection process, the biaxial motion mechanism synchronously controls the cutting blade to perform a rotary cutting operation on the fruit and vegetable sample, and drives the linkage stirrer to uniformly mix the chemical detection reagent with the sample to generate a mixed detection liquid; subsequently, the mixed detection liquid and the environmental temperature data are input into the pesticide residue detector to obtain an initial pesticide residue detection value, and real-time compensation is performed based on the parameter influence factors and historical reference values in the dynamic control database to output the target pesticide residue detection value. This method solves the problem of poor adaptability of the fixed compensation scheme to temperature changes and matrix differences through the adaptive adjustment of the dynamic control database, realizes the accuracy and stability of the detection results under different environmental conditions, and improves the detection efficiency and reliability of multi-batch fruit and vegetable samples.

[0026] Next, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0027] Figure 1 The flowchart of a pesticide residue detection method for a pesticide residue detector provided by an embodiment of the present application is asFigure 1 As shown in Figure 1 , the method includes: Step 101: Obtain the environmental temperature data of the fruit and vegetable sample, the sample matrix type information, and the chemical detection reagent.

[0028] In step 101, the environmental temperature data refers to the real-time temperature measurement value of the environment where the fruit and vegetable sample is located, which is used to reflect the environmental conditions during detection. The sample matrix type information represents the physical property data describing the fruit and vegetable sample, including characteristics such as variety and texture. The chemical detection reagent refers to the solution used to undergo a specific chemical reaction with the fruit and vegetable sample, which is mixed with the sample in pesticide residue detection, and makes the pesticide residue recognizable by the instrument through a specific chemical reaction.

[0029] In the embodiment of the present application, first, the real-time temperature data of the environment where the fruit and vegetable sample is located is collected by a temperature sensor, and at the same time, the matrix type information such as the variety and texture of the sample is recorded, and a dedicated chemical detection reagent is prepared. The temperature sensor transmits the collected environmental temperature data to the processing unit. The sample matrix type information is obtained through manual input or an automatic recognition system, and the chemical detection reagent is pre-configured according to the standard ratio. These three types of data together constitute the initial input parameters for detection.

[0030] For example, in a certain agricultural product testing center, an operator places a to-be-tested apple sample in the detection area. The temperature sensor collects the current environmental temperature as 25°C in real time. The system records the sample matrix type as "apple-like fruits", and at the same time, uses the standard-configured organophosphorus pesticide detection reagent to complete the data collection work.

[0031] Step 102: Based on the environmental temperature data and the sample matrix type information, and in combination with historical detection data, establish a dynamic control database.

[0032] In step 102, the historical detection data represents a data set accumulated in the past, including environmental temperature, sample matrix type, corresponding pesticide residue detection values, and detection results. The dynamic control database represents a reference database storing standard detection results under various conditions.

[0033] In the embodiment of the present application, the system retrieves the historical detection data, screens out the historical records that match the current environmental temperature data and sample matrix type information, groups and statistics them according to temperature intervals, calculates the average value of the pesticide residue detection results in each temperature interval as the historical control value, then analyzes the influence degree of temperature change on the detection results and calculates the parameter influence factor, and finally establishes a dynamic control database including sample matrix type, temperature interval, historical control value, and parameter influence factor.

[0034] For example, the system retrieves the detection records of apple samples within the temperature range of 20 - 30°C in the past three months from the database, calculates that the average value of pesticide residues in this temperature range is 0.5 mg / kg, and determines that the influence coefficient of the detection result for every 1°C change in temperature is 0.02, and stores these data in the dynamic control database.

[0035] Step 103: Control the cutting blade to perform a rotary cutting operation on the fruit and vegetable sample through a biaxial motion mechanism, and drive a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection solution.

[0036] In step 103, the biaxial motion mechanism refers to a mechanical device with two independent rotating shafts. The rotary cutting operation refers to a sample processing method of rotary cutting. The mixed detection solution refers to the solution to be detected after the sample and the reagent are fully mixed.

[0037] In the embodiment of the present application, the first rotating shaft of the biaxial motion mechanism drives the cutting blade to rotate at high speed to crush the fruit and vegetable sample. At the same time, the second rotating shaft drives the stirrer to rotate in the opposite direction to fully mix the chemical detection reagent with the crushed sample. The movements of the two rotating shafts are synchronized and coordinated to ensure the mixing uniformity, and finally generate a mixed detection solution that meets the detection requirements.

[0038] For example, after the detection system is started, the first shaft of the biaxial mechanism drives the blade to cut the apple sample into pieces at a set speed. At the same time, the second shaft drives the stirrer to mix the detection reagent with the shredded sample. After a set stirring time, a uniform mixed detection solution is formed.

[0039] Step 104: Input the mixed detection solution and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value.

[0040] In step 104, the pesticide residue detector refers to a dedicated device for measuring the content of pesticide residues. The initial pesticide residue detection value refers to the original detection result without compensation processing.

[0041] In the embodiment of the present application, the mixed detection solution is placed in the sample chamber of the detector. Under the irradiation of a light source with a specific wavelength, the detection solution generates a characteristic optical signal. The photoelectric sensor converts the optical signal into an electrical signal, preliminarily processes the signal in combination with the current ambient temperature data, and outputs an initial pesticide residue detection value by analyzing the matching degree between the signal characteristics and the preset standard.

[0042] For example, after the mixed detection solution is injected into the detector, the instrument measures an absorbance value of 0.3 at a specific wavelength. Combining the current ambient temperature of 25°C, the initial detection value is calculated to be 0.6 mg / kg through preliminary calculation.

[0043] Step 105: Dynamically compensate the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0044] In step 105, the dynamic compensation process represents a correction process for adjusting the detection result according to environmental conditions. The target pesticide residue detection value represents the final detection result after environmental compensation processing. In the embodiments of the present application, the system retrieves the historical control value and parameter influence factor matching the current sample matrix type and environmental temperature from the dynamic control database, calculates the influence degree of temperature deviation on the detection result, and accordingly compensates and corrects the initial pesticide residue detection value, and finally outputs an accurate target pesticide residue detection value.

[0045] For example, according to the current environmental temperature of 25°C, the system calls the historical control value of 0.5 mg / kg and the influence coefficient of 0.02 for apple-like samples in this temperature range from the database, performs compensation calculation on the initial detection value of 0.6 mg / kg, and finally outputs a target detection value of 0.55 mg / kg.

[0046] This method realizes the intelligent compensation of the detection result by establishing a dynamic control database, ensures the quality of sample preparation by adopting dual-axis collaborative processing, and conducts precise analysis in combination with environmental parameters, effectively improving the accuracy and reliability of pesticide residue detection under different environmental conditions, and providing a scientific basis for the supervision of agricultural product quality and safety.

[0047] To solve the problem of the influence of environmental temperature changes on the detection result in pesticide residue detection and further improve the detection accuracy, in some embodiments, step 102: Based on the environmental temperature data and the sample matrix type information, and in combination with historical detection data, establish a dynamic control database, including: Step 201: According to the environmental temperature data, extract the historical environmental temperature data of fruit and vegetable samples at different time points and the corresponding pesticide residue detection values detected under the corresponding historical environmental temperatures from the historical detection data.

[0048] In step 201, the historical environmental temperature data refers to the environmental temperature values recorded during past detections. The pesticide residue detection value is the result of the pesticide content measured under the corresponding historical temperature.

[0049] In the embodiments of the present application, the system screens out the detection data with the same matrix as the current sample from the stored historical detection records, extracts the environmental temperature values recorded at the time of these detections and the corresponding pesticide residue detection results, providing basic data for subsequent analysis.

[0050] Step 202: Group the historical environmental temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample at a preset temperature interval to generate temperature data groups, and each temperature data group contains multiple temperature ranges.

[0051] In step 202, the preset temperature interval refers to the fixed span value used when dividing the historical ambient temperature data into continuous temperature ranges. For example, every 5°C is an interval (20 - 25°C, 25 - 30°C, etc.). This interval is preset by analyzing the characteristics of the historical temperature distribution to ensure that there is a sufficient amount of data in each interval and it can reflect the temperature change trend. Usually, it is selected according to the actual detection requirements and data distribution uniformity. For example, a 5°C interval is commonly used in agricultural product detection to balance accuracy and data coverage. The temperature data group refers to the data set divided according to a certain temperature range. The temperature interval is the divided temperature range segment.

[0052] In the embodiment of the present application, the system groups the extracted historical temperature data according to the set temperature span. For example, every 5 degrees is an interval, and the detection data under similar temperatures are grouped together to form a data set of multiple temperature intervals.

[0053] Step 203: Calculate the average value of all pesticide residue detection values within each of the temperature data groups, and use the average value as the historical control value for the corresponding temperature interval.

[0054] In step 203, the average value refers to the arithmetic mean of all pesticide residue detection values within the same temperature interval, which is used to represent the typical residue level in this interval. When calculating, add up all the detection values within the interval and then divide by the number of data. For example, if an interval contains 60 groups of data with a total of 30 mg / kg, then the average value is 30÷60 = 0.5 mg / kg, which reflects the central tendency of pesticide residues under this temperature condition. The historical control value refers to the representative value of the pesticide residue detection results within a certain temperature interval.

[0055] In the embodiment of the present application, the system performs an average calculation on all pesticide residue detection values within each temperature interval, and the obtained average value is used as the standard reference value for this temperature interval, reflecting the typical pesticide residue level under this temperature condition.

[0056] Step 204: Calculate the parameter influence factor according to the temperature data group and the historical control value.

[0057] In step 204, the parameter influence factor is a coefficient reflecting the degree of influence of temperature change on the detection result.

[0058] In the embodiment of the present application, the system analyzes the law of change of the historical control value in each temperature interval with temperature change, calculates the change amount of the detection value when the temperature changes by one unit, and uses this as the basis coefficient for compensation adjustment.

[0059] Step 205: Generate a dynamic control database based on the sample matrix type information, the temperature interval, the historical control value, and the parameter influence factor.

[0060] In the embodiments of the present application, the system uses the sample matrix type as the main classification basis, associates and integrates the temperature range under each matrix type, the corresponding historical control values in this range, and the parameter influence factors, and constructs a structured data storage format. Each data record completely contains the corresponding relationships of the four key parameters: sample matrix type, temperature range, historical control value, and parameter influence factor, and is stored in the database for real-time calling. For example, for the matrix type of apple fruits, the system will record a complete set of data that the historical control value in the temperature range of 20 - 25°C is 0.5 mg / kg, and the parameter influence factor is 0.02 mg / kg / °C. Through this organizational form, the corresponding compensation parameters can be quickly queried and obtained according to different matrix types and ambient temperatures.

[0061] The following is a specific example: In a certain agricultural product testing center, the operator places the apple sample to be tested in the testing area. The temperature sensor collects the current ambient temperature of 25°C in real time. The system records that the sample matrix type is apple fruits, and at the same time uses the standard-configured organophosphorus pesticide detection reagent to complete the data collection work. The system retrieves 100 sets of detection records of apple samples in the two temperature ranges of 20 - 25°C and 25 - 30°C within the past three months from the database. Among them, the 20 - 25°C range contains 60 sets of data, and the total sum of pesticide residue detection values is 30 mg / kg. Through the arithmetic mean formula, the average value of this range is calculated as the total sum of 30 mg / kg divided by the data volume of 60 sets, which is equal to 0.5 mg / kg; the 25 - 30°C range contains 40 sets of data, and the total sum is 28 mg / kg. Similarly, the average value is calculated as 28 mg / kg divided by 40 sets, which is equal to 0.7 mg / kg. The system analyzes the relationship between the representative temperatures of 22.5°C and 27.5°C in the two ranges and the corresponding average values through linear regression. The representative temperature takes the midpoint value of each temperature range. For the 20 - 25°C range, the representative temperature is (20 + 25) divided by 2, which is equal to 22.5°C; for the 25 - 30°C range, the representative temperature is (25 + 30) divided by 2, which is equal to 27.5°C. Using the least squares method to fit, it is obtained that for every 1°C increase in temperature, the average decrease in the pesticide residue detection value is 0.02 mg / kg, that is, the parameter influence factor is 0.02 mg / kg / °C. This value reflects the unit influence amount of temperature change on the detection result. Subsequently, the system classifies and stores these data according to the matrix type of apple fruits, and establishes a data record including the historical control value of 0.5 mg / kg and the parameter influence factor of 0.02 mg / kg / °C corresponding to the temperature range of 20 - 25°C, and the historical control value of 0.7 mg / kg and the parameter influence factor of 0.02 mg / kg / °C corresponding to the temperature range of 25 - 30°C, and finally generates a complete dynamic control database for subsequent testing.

[0062] In the embodiments of the present application, by establishing a dynamic control database, the intelligent association between the detection parameters and the temperature change is realized, providing a reliable compensation basis for subsequent detections, effectively improving the accuracy and comparability of the detection results under different ambient temperatures, and solving the detection deviation problem caused by temperature fluctuations.

[0063] In order to further improve the compensation accuracy for temperature changes in pesticide residue detection, in some embodiments, step 204: calculating the parameter influence factor according to the temperature data group and the historical control value includes: Step 301: Determine the median point of the temperature range corresponding to each temperature data group as the representative temperature.

[0064] In step 301, the representative temperature refers to the intermediate value of the temperature range and is used to represent the typical temperature of the range.

[0065] In the embodiments of the present application, the system takes the intermediate value of the upper and lower limits of each temperature range as the representative temperature of the range. For example, the representative temperature of the 20 - 25 °C range is 22.5 °C.

[0066] Step 302: Form a set of numerical pairs according to the corresponding relationship between the representative temperature and the historical control value.

[0067] In step 302, the corresponding relationship refers to the quantitative association relationship established between the representative temperature of each temperature range and its corresponding historical control value. Specifically, it is manifested as pairing the median point temperature (representative temperature) of each temperature range with the average value of pesticide residues (historical control value) calculated for that range one by one to form a mapping combination of temperature - residue values. For example, the representative temperature of 22.5 °C corresponds to the historical control value of 0.5 mg / kg, and the representative temperature of 27.5 °C corresponds to the historical control value of 0.7 mg / kg. These paired temperature and residue value data together constitute a set of numerical pairs for subsequent analysis of the influence law of temperature changes on pesticide residue detection results. The set of numerical pairs refers to the paired data group of the representative temperature and its corresponding historical control value, and numerical pair examples are (20 °C, 5.2 ppm), (30 °C, 4.8 ppm).

[0068] In the embodiments of the present application, the system pairs the representative temperature of each temperature range with the historical control value calculated for that range to form a series of corresponding relationship data groups of temperature - residue values.

[0069] Step 303: Calculate the average change rate of the historical control value with respect to the representative temperature according to the set of numerical pairs.

[0070] In step 303, the average change rate refers to the average ratio of the historical control value changing with the representative temperature.

[0071] In the embodiments of the present application, the system analyzes the change trends of each data point in the set of numerical pairs, calculates the average ratio of the historical control value with respect to the representative temperature change, and reflects the overall influence degree of temperature on the detection result.

[0072] Step 304: Multiply the absolute value of the average change rate by a preset adjustment coefficient to generate a parameter influence factor.

[0073] In step 304, the preset adjustment coefficient is a proportional factor set according to actual requirements.

[0074] In the embodiments of the present application, after taking the absolute value of the calculated average change rate, the system multiplies it by a preset adjustment coefficient, and finally generates a parameter influence factor for actual compensation calculation.

[0075] The following is a specific example: In a certain agricultural product testing center, after the system retrieves the historical test data of apple samples, it first determines that the median point 22.5°C in the temperature range of 20 - 25°C is the representative temperature, and the corresponding historical control value is 0.5 mg / kg; the median point 27.5°C in the range of 25 - 30°C is the representative temperature, and the corresponding historical control value is 0.7 mg / kg. The system forms a set of numerical pairs {22.5°C, 0.5 mg / kg} and {27.5°C, 0.7 mg / kg} with these two representative temperatures and historical control values. By analyzing these two numerical pairs, the system calculates that when the temperature rises from 22.5°C to 27.5°C, the temperature change amount is 5°C, and the historical control value change amount is 0.2 mg / kg. Therefore, the average change rate is 0.04 mg / kg / °C. The system multiplies the absolute value 0.04 of this average change rate by a preset adjustment coefficient 0.5, and finally generates a parameter influence factor of 0.02 mg / kg / °C. The change rate calculation uses the formula Δy / Δx, where Δy represents the historical control value change amount of 0.2 mg / kg, and Δx represents the temperature change amount of 5°C; the parameter influence factor calculation uses the formula k = a×|b|, where k is the parameter influence factor 0.02, a is the adjustment coefficient 0.5, and b is the average change rate 0.04.

[0076] In the embodiments of the present application, through a systematic temperature influence analysis process, the influence degree of temperature change on the detection result is accurately quantified, providing a scientific basis for dynamic temperature compensation and effectively improving the adaptability of the detection system to environmental temperature changes.

[0077] In order to further improve the accuracy and stability of pesticide residue detection, in some embodiments, step 104: The inputting the mixed detection solution and the environmental temperature data into a pesticide residue detector to output an initial pesticide residue detection value includes: Step 401: Place the mixed detection solution under the light source of the pesticide residue detector to obtain a detection signal of the transmitted light intensity varying with time.

[0078] In Step 401, the transmitted light intensity refers to the intensity of the light signal that is not absorbed and transmitted when the mixed detection solution is irradiated by a light source of a specific wavelength. Its source is the light energy value measured in real time by the photoelectric sensor of the detector. When the light source passes through the mixed detection solution, the pesticide molecules will selectively absorb light of a specific wavelength, resulting in a decrease in the transmitted light intensity. The sensor records this intensity change at a fixed frequency (such as 100 times per second) to form a time-series signal reflecting the characteristics of the pesticide components. The detection signal refers to the data of the transmitted light intensity of the mixed detection solution fluctuating with time under the light source irradiation.

[0079] In the embodiment of the present application, the system places the mixed detection solution under a light source of a specific wavelength and continuously records the change in the transmitted light intensity through the photoelectric sensor to form a time-series signal reflecting the characteristics of the detection solution.

[0080] Step 402: Adjust the signal acquisition parameters of the pesticide residue detector according to the ambient temperature data, and optimize the amplitude of the detection signal based on the adjusted signal acquisition parameters.

[0081] In Step 402, the signal acquisition parameters refer to the setting parameters such as the gain and sensitivity when the detector acquires signals. The optimization process means that first, the gain of the signal amplifier and the sampling sensitivity of the detector are automatically adjusted according to the current ambient temperature data. For example, the gain is appropriately reduced at a higher temperature to avoid signal saturation, and the gain is increased at a lower temperature to enhance the signal intensity. Then, based on the adjusted parameters, the transmitted light signal of the mixed detection solution is re-acquired to make the maximum amplitude of the signal stable within the ideal range of the detector range (such as 60%-80% of the range). This optimization process not only avoids signal overload distortion but also ensures the effective capture of weak signals, providing a time-domain signal with reliable quality for subsequent spectrum analysis, which is specifically achieved through a temperature-gain preset comparison table and a real-time feedback adjustment mechanism, so that the amplitudes of the detection signals under different temperature conditions are all within the optimal analysis range.

[0082] In the embodiment of the present application, the system automatically adjusts parameters such as the signal amplification factor of the detector according to the current ambient temperature to make the intensity of the detection signal within a suitable range, avoiding the influence of too strong or too weak signals on the analysis results.

[0083] Step 403: Perform time-frequency conversion on the optimized detection signal to generate spectrum data.

[0084] In step 403, the time-frequency conversion process refers to the mathematical processing process of converting the optimized time-domain detection signal (the waveform of the optical intensity varying with time) into frequency-domain spectrum data (the spectrum map of the optical intensity distributed with frequency). The specific conversion process is as follows: After the system discretely samples the continuous time-domain signal, it decomposes the signal into sine wave components of different frequencies through the fast Fourier transform (FFT) algorithm, calculates the amplitude and phase information of each frequency component, and finally generates a spectrum map with frequency on the horizontal axis and amplitude on the vertical axis. Spectrum data refers to the characteristic distribution data after converting the time-domain signal into the frequency domain.

[0085] In the embodiment of the present application, the system performs a mathematical transformation on the optimized time-domain signal, decomposes it into a set of different frequency components, and forms a spectrum map that can reflect the characteristic frequencies of pesticides.

[0086] Step 404: Screen out the frequency points in the spectrum data whose amplitudes exceed the preset amplitude threshold.

[0087] In step 404, the preset amplitude threshold refers to the minimum intensity standard for determining effective characteristic frequency points. The upper limit of the preset range is determined by the factory calibration of the detector, so that the signal amplitudes of common pesticide residue concentrations are within this range (such as ±5V). Amplitude refers to the signal intensity of each frequency component in the spectrum data, which is obtained by performing time-frequency conversion (such as Fourier transform) on the optimized detection signal. The amplitude in the spectrum data and the amplitude of the detection signal are not the same concept: the amplitude of the detection signal refers to the voltage amplitude of the original time-domain signal, reflecting the instantaneous change of the optical intensity; the spectrum amplitude refers to the energy intensity of each frequency component in the frequency domain, which is converted from the time-domain signal and is used to identify the characteristic frequencies of pesticides; the system screens out the frequency points whose amplitudes exceed the preset threshold, and essentially extracts the characteristic peaks of the energy in the spectrum, and these peaks correspond to the specific absorption frequencies of pesticide molecules.

[0088] In the embodiment of the present application, the system screens out the frequency points in the spectrum data whose intensities exceed the set standard, and these frequency points often correspond to the characteristic absorption frequencies of specific pesticide molecules.

[0089] Step 405: Match the frequency points with the frequency point characteristic data corresponding to the current sample matrix type information in the dynamic control database to generate an initial pesticide residue detection value.

[0090] In step 405, the frequency point characteristic data refers to a set of data stored in advance in the database, which is the relationship between the characteristic frequencies corresponding to different pesticide residues and their concentrations. The matching process means that first, the standard characteristic frequency points of all pesticides and their corresponding concentration values under the current sample matrix type (such as apple fruits) are retrieved from the dynamic control database; then, the frequency difference between the characteristic frequency points selected from the detected spectrum and the standard frequency points in the database is calculated (for example, the deviation between the detected 12.5 Hz frequency point and the standard 12.4 Hz frequency point is 0.1 Hz); then the system will select the standard frequency points with a frequency deviation less than the preset tolerance threshold (such as 0.5 Hz), and obtain the associated concentration values corresponding to these successfully matched standard frequency points (such as 0.6 mg / kg and 0.4 mg / kg); finally, the arithmetic mean of all successfully matched associated concentration values is taken (such as (0.6 + 0.4) / 2 = 0.5 mg / kg), and the calculation result is used as the initial pesticide residue detection value of the sample, completing the entire frequency point matching process.

[0091] In the embodiment of the present application, the system compares the selected characteristic frequency points with the standard frequency point data corresponding to the current sample matrix type in the database to find the most matching pesticide type and its concentration reference value.

[0092] The following is a specific example: In a certain agricultural product testing center, after the operator injects the prepared apple sample mixed detection solution into the sample chamber of the pesticide residue detector, the system starts the detection process. First, the specific wavelength light source built into the detector irradiates the mixed detection solution, and the photoelectric sensor records the change in the transmitted light intensity at a frequency of 100 times per second, continuously collecting for 10 seconds to obtain a detection signal composed of 1000 light intensity data points. The system automatically adjusts the gain of the signal amplifier to the medium level according to the current ambient temperature of 25°C, so that the maximum amplitude of the detection signal is about 70% of the range, avoiding signal saturation or being too weak. The optimized detection signal is sent to the processor for mathematical transformation processing to convert it into a spectrogram containing different frequency components, where the frequency axis range is 0 - 50 Hz and the amplitude axis range is 0 - 5 V. The system identifies two peaks in the spectrogram, located at 12.5 Hz and 28.3 Hz respectively, with amplitudes of 3.2 V and 2.8 V, exceeding the preset threshold of 2.5 V. The detector retrieves the characteristic frequency point data of apple fruits from the dynamic control database and finds that the 12.5 Hz frequency point is closest to the standard characteristic frequency of organophosphorus pesticides, which is 12.4 Hz, and the frequency deviation is 0.1 Hz, less than the allowed matching threshold of 0.5 Hz. According to the frequency point-concentration correspondence relationship stored in the database, the pesticide residue concentration corresponding to the 12.4 Hz frequency point is 0.6 mg / kg, so the system determines that the initial pesticide residue detection value of the current sample is 0.6 mg / kg.

[0093] In the embodiments of the present application, through an intelligent signal acquisition and analysis method, accurate identification of pesticide components in the mixed detection solution is achieved, providing a reliable initial detection value for subsequent temperature compensation and effectively improving the overall performance of the detection system.

[0094] In order to further improve the matching accuracy and reliability of pesticide residue detection, in some embodiments, step 405: matching the frequency points with the frequency point feature data corresponding to the current sample matrix type information in the dynamic control database to generate an initial pesticide residue detection value, includes: Step 501: Call the frequency point feature data corresponding to the current sample matrix type information from the dynamic control database, where the frequency point feature data includes characteristic frequency positions and associated concentration values.

[0095] In step 502, the characteristic frequency position refers to the characteristic absorption frequency values exhibited by various pesticide molecules pre-calibrated in the dynamic control database under specific detection conditions. These frequency values are determined through a large number of standard experiments and form a one-to-one correspondence with the pesticide molecular structure. For example, organophosphorus pesticides exhibit stable characteristic peaks at 12.4 Hz, and pyrethroids exhibit stable characteristic peaks at 28.5 Hz. These characteristic frequency positions are stored in the database as the "fingerprints" for pesticide identification and are used to match the frequency points found in actual detections. The associated concentration value refers to the standard pesticide residue concentration reference data bound to the characteristic frequency position, which is obtained by establishing a calibration curve of the characteristic peak intensity and concentration of standard samples with different concentrations. For example, 0.6 mg / kg corresponding to the characteristic frequency of 12.4 Hz means that when the detection system identifies this characteristic frequency, the pesticide residue amount reflected by its signal intensity is equivalent to the level of 0.6 mg / kg of the standard sample. These concentration values are used as a quantization benchmark to convert the signal intensity of the successfully matched frequency points into specific pesticide residue detection values.

[0096] In the embodiments of the present application, the system retrieves the characteristic frequencies of various pesticides that may be contained in the current detected fruit and vegetable sample type and their corresponding concentration reference values from the database.

[0097] Step 502: Calculate the frequency deviation values between the frequency points and each characteristic frequency position in the frequency point feature data.

[0098] In step 502, the frequency deviation value refers to the frequency difference between the detected characteristic frequency point and the standard frequency point in the database.

[0099] In the embodiments of the present application, the system calculates the frequency differences between each characteristic frequency point selected from the detection signal and all relevant standard frequency points in the database to evaluate the matching degree.

[0100] Step 503: Among the frequency characteristic data, select multiple associated concentration values corresponding to the characteristic frequency positions where the frequency deviation value is less than or equal to a preset matching threshold.

[0101] In step 503, the preset matching threshold is the maximum allowable frequency difference for determining whether two frequency points match.

[0102] In the embodiment of the present application, the system filters out all standard frequency points with a frequency deviation less than this threshold, and the pesticide types corresponding to these frequency points are considered to be the pesticides that may exist in the sample.

[0103] Step 504: Take the average value of all the associated concentration values as the initial pesticide residue detection value.

[0104] In step 504, the average value of the associated concentration values refers to the result obtained by performing an arithmetic average calculation on the associated concentration values corresponding to all the filtered matching frequency points. Its mathematical expression is: Initial pesticide residue detection value = (∑ associated concentration values) / n, where n is the number of frequency points with successful matching. For example, when two frequency points are matched and correspond to 0.6 mg / kg and 0.4 mg / kg respectively, the average value is calculated as (0.6 + 0.4) / 2 = 0.5 mg / kg. This processing method effectively reduces the random error of single-frequency point detection by integrating the concentration information of multiple characteristic frequencies, improves the representativeness and reliability of the initial detection value, and provides reference data for subsequent temperature compensation.

[0105] In the embodiment of the present application, the system takes the average of the concentration reference values corresponding to all successfully matched standard frequency points as the initial pesticide residue detection value of the current sample.

[0106] The following is a specific example: In a certain agricultural product testing center, after the system completes the spectral analysis of the mixed detection solution of apple samples, two characteristic peaks located at 12.5 Hz and 28.3 Hz are identified from the spectrogram. The detector retrieves the frequency point characteristic data of apple-like fruits from the dynamic control database, and this data includes records such as 0.6 mg / kg corresponding to organophosphorus pesticides at 12.4 Hz and 0.4 mg / kg corresponding to pyrethroids at 28.5 Hz. The system first calculates the deviation value between the detection frequency point of 12.5 Hz and the standard frequency point of 12.4 Hz as 0.1 Hz, and then calculates the deviation value between the detection frequency point of 28.3 Hz and the standard frequency point of 28.5 Hz as 0.2 Hz, both of which are less than the preset matching threshold of 0.5 Hz. According to the matching rule, the system selects the concentration values of 0.6 mg / kg and 0.4 mg / kg corresponding to these two successfully matched standard frequency points, and calculates the initial pesticide residue detection value as 0.5 mg / kg through arithmetic mean. Among them, the frequency point matching adopts the nearest neighbor principle, the frequency deviation calculation adopts the absolute difference method, and the concentration average calculation adopts the arithmetic mean formula C = (C1 + C2) / 2, where C represents the final concentration, and C1 and C2 respectively represent the concentration values corresponding to the two matched frequency points.

[0107] In the embodiment of the present application, through the intelligent frequency point matching and concentration calculation process, the accurate identification and quantitative analysis of pesticide components in the mixed detection solution are realized, providing a scientific and reliable initial data basis for subsequent temperature compensation and correction, and effectively improving the overall accuracy of the detection system.

[0108] In order to further improve the environmental adaptability of the pesticide residue detection results, in some embodiments, step 105: dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value, includes: Step 601: Compare the current environmental temperature data with the representative temperatures of each temperature range of the same sample matrix type information in the dynamic control database, and calculate the temperature deviation amount according to the comparison result.

[0109] In step 601, the current ambient temperature data refers to the instant temperature value of the detection environment where the mixed detection liquid is located, which is collected in real time by a temperature sensor. Its "currency" is ensured by the clock module and timestamp mechanism of the detection system. When the detector starts to analyze the mixed detection liquid, the system automatically records the latest temperature data measured by the sensor at that moment, ensuring strict synchronization with the detection operation and avoiding the use of historical or lagged temperature data. The specific implementation of the comparison process is that the system compares the current ambient temperature with the representative temperatures of all temperature ranges under the same sample matrix type (such as apple-like fruits) in the database one by one. First, calculate the absolute value of the difference between the current temperature and the representative temperature of each range (for example, the deviation between the current 25°C and the representative temperature of 22.5°C in the range of 20 - 25°C is 2.5°C). Then, select the range with the smallest difference as the matching result (if there are multiple ranges with equal and close distances, preferentially select the lower temperature range). This process is automatically completed through traversing the database records and numerical comparison algorithms. The comparison result contains two key pieces of information: one is to determine the nearest temperature range to which the current temperature belongs (such as 25°C belongs to the range of 20 - 25°C), and the other is to calculate the specific deviation between the current temperature and the representative temperature of this range (such as the deviation of +2.5°C between 25°C and 22.5°C). The temperature deviation is the difference value between the current ambient temperature and the representative temperature of the closest temperature range in the database.

[0110] In the embodiment of the present application, the system first determines the current detection ambient temperature, then retrieves the representative temperatures of all temperature ranges under the same sample matrix type from the database, calculates the difference between the current temperature and each representative temperature one by one, and selects the smallest difference as the temperature deviation, which reflects the temperature difference degree between the current detection conditions and the standard conditions.

[0111] Step 602: Multiply the parameter influence factor by the temperature deviation to obtain a temperature compensation coefficient.

[0112] In step 602, the temperature compensation coefficient refers to the correction amount after comprehensively considering the temperature deviation and the parameter influence factor.

[0113] In the embodiment of the present application, the system multiplies the pre-calculated parameter influence factor by the temperature deviation to obtain a compensation coefficient that reflects the degree of adjustment required under the current temperature conditions.

[0114] Step 603: Use the temperature compensation coefficient to adjust the historical control value in the dynamic control database corresponding to the current sample matrix type information and the temperature range to which the current ambient temperature data belongs, to obtain a reference reference value.

[0115] In step 603, the reference reference value is the standard reference value after temperature compensation adjustment.

[0116] In the embodiments of the present application, the system adjusts the historical reference values in the database for the corresponding temperature ranges using a temperature compensation coefficient to obtain corrected reference values adapted to the current ambient temperature conditions.

[0117] Step 604: Perform linear correction on the initial pesticide residue detection value based on the reference reference value and the temperature compensation coefficient to generate a target pesticide residue detection value.

[0118] In step 604, linear correction is a process of standardizing the initial detection value according to the reference reference value and the temperature compensation coefficient.

[0119] In the embodiments of the present application, the system compares the initial detection value with the reference reference value, calculates the adjustment amount based on the difference between the two and the temperature compensation coefficient, and finally outputs a target detection value that takes into account both the actual measured value and temperature compensation, ensuring the accuracy and reliability of the result.

[0120] The following is a specific example: At a certain agricultural product testing center, the operator places the apple sample to be tested in the detection area. The temperature sensor collects the current ambient temperature of 25°C in real time. The system records that the sample matrix type is apple-like fruits. At the same time, a standard-configured organophosphorus pesticide detection reagent is used to complete the data collection work. The system retrieves the data of apple-like samples in the temperature range of 20 - 25°C from the database. The representative temperature of 22.5°C in this range is calculated by adding the lower limit of the range 20°C to the upper limit 25°C and then dividing by 2. The corresponding historical reference value of 0.5 mg / kg is the average value of all detection data in this range. The deviation between the current temperature of 25°C and the representative temperature of 22.5°C is 2.5°C, which is obtained by subtracting 22.5°C from 25°C. The known parameter influence factor is 0.02 mg / kg / °C, which is the influence amount of the detection result per 1°C change in temperature determined through previous experiments. Multiply the parameter influence factor of 0.02 by the temperature deviation of 2.5°C to obtain a temperature compensation coefficient of 0.05 mg / kg. The calculation formula is that the temperature compensation coefficient is equal to the parameter influence factor multiplied by the temperature deviation. The system adds the historical reference value of 0.5 mg / kg to the temperature compensation coefficient of 0.05 mg / kg to obtain a reference reference value of 0.55 mg / kg. The initial detection value of 0.6 mg / kg is obtained by converting the absorbance value of 0.3 of the mixed detection solution according to the standard curve. The final target pesticide residue detection value of 0.55 mg / kg is obtained by performing a weighted average calculation on the initial value of 0.6 mg / kg and the reference reference value of 0.55 mg / kg.

[0121] In the embodiments of the present application, through the dynamic temperature compensation and linear correction mechanisms, the influence of ambient temperature fluctuations on the detection results is effectively eliminated, making the final detection value more accurate and reliable, improving the adaptability and stability of the detection system under different environmental conditions, and providing a scientific basis for the supervision of agricultural product quality and safety.

[0122] To further improve the degree of automation and mixing uniformity of sample pre-processing, in some embodiments, step 103: controlling the cutting blades through the biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable samples, and driving a stirrer mechanically linked to the cutting blades to perform a stirring operation on the chemical detection reagent and the rotary cut fruit and vegetable samples to generate a mixed detection solution, includes: Step 701: The cutting blade is driven by the first rotating shaft of the dual-axis motion mechanism to perform rotary cutting on the fruit and vegetable sample to generate a minced sample.

[0123] In step 701, the minced sample refers to a fruit and vegetable sample that has been cut to an ideal fineness.

[0124] In the embodiment of the present application, the system drives the special cutting blade to rotate at high speed through the first rotating shaft, and uses centrifugal cutting to crush the fruit and vegetable samples into fine particles, ensuring that the samples have sufficient specific surface area for subsequent sufficient reaction.

[0125] Step 702: driving a stirrer via the second rotating shaft of the biaxial motion mechanism to stir the chemical detection reagent and the crushed sample in the container to form a uniform mixture.

[0126] In step 702, the container is a special mixing container integrated into the sample processing module of the pesticide residue detector, which is directly connected to the dual-axis motion mechanism through a mechanical fixing device. The container is designed as an open-top structure, and the bottom is connected to the second rotating shaft of the dual-axis motion mechanism by a detachable coupling to ensure that the agitator can operate deep inside the container; at the same time, the side wall of the container is provided with a positioning slot, which is engaged and fixed with the guide rail on the dual-axis motion mechanism frame, so that the container remains stable and does not move during the stirring process. This structural design allows the dual-axis motion mechanism to drive the cutting blade to perform the rotary cutting operation while synchronously driving the agitator in the container through the second rotating shaft, thereby realizing continuous automated processing of sample crushing and reagent mixing, wherein the container serves as both a receiving device for sample cutting and a reaction chamber for subsequent mixing and stirring, and is a key component connecting the two process links of rotary cutting and stirring. A uniform mixture refers to a mixed liquid after the detection reagent and the sample fragments are fully integrated.

[0127] In the embodiment of the present application, the second rotating shaft drives the agitator to rotate in the opposite direction to the cutting blade, forming a bidirectional vortex in the closed container, so that the detection reagent and the sample fragments are mixed in three dimensions.

[0128] Step 703: When it is detected that the stirring duration of the uniform mixture reaches the preset duration corresponding to the sample matrix type information, the stirring is stopped to generate a mixed detection liquid.

[0129] In step 703, the preset duration is the optimal stirring time set according to the characteristics of different fruit and vegetable matrices.

[0130] In the embodiment of the present application, the system automatically calls the corresponding stirring time parameters according to the type of sample currently being processed, and controls the stirring process through a timer to ensure that samples of different textures can achieve the ideal mixing effect.

[0131] Here's a specific example: At an agricultural product testing center, an operator places an apple sample for testing in the testing area. The system first registers the sample type as apple and records the current ambient temperature as 25°C. The testing system then activates a dual-axis motion mechanism. The first axis drives a stainless steel cutting blade at 3000 rpm to shred the apple sample. After 30 seconds of shredding, the apple sample is reduced to approximately 2 mm in diameter. Simultaneously, a second axis drives a polytetrafluoroethylene stirrer at a reverse speed of 500 rpm, mixing the pre-prepared organophosphorus pesticide testing reagent with the apple sample in a sealed stainless steel container. The system sets the stirring time to 3 minutes based on pre-set parameters for apples in the database. When the stirring time reaches 3 minutes, the system automatically stops stirring, and the mixture in the container becomes a uniform light brown suspension, indicating that the preparation of the mixed test solution is complete.

[0132] In the embodiment of the present application, the standardization and automation of sample pretreatment are achieved through the integrated design of dual-axis coordinated rotary cutting and stirring, ensuring that the sample preparation conditions for each test are consistent, laying a solid foundation for subsequent accurate testing, and improving detection efficiency.

[0133] Figure 2 A schematic diagram of the structure of a pesticide residue detection system for a pesticide residue detector provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system includes: The acquisition module 21 is used to obtain the ambient temperature data of the fruit and vegetable samples, the sample matrix type information, and the chemical detection reagents.

[0134] The establishing module 22 is configured to establish a dynamic control database based on the ambient temperature data and the sample matrix type information in combination with historical detection data.

[0135] The first generating module 23 is used to control the cutting blade to perform a rotary cutting operation on the fruit and vegetable samples through a dual-axis motion mechanism, and drive the stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary cut fruit and vegetable samples to generate a mixed detection liquid.

[0136] An output module 24 is configured to input the mixed detection liquid and the ambient temperature data into a pesticide residue detector, and output an initial pesticide residue detection value.

[0137] A second generation module 25 is configured to perform dynamic compensation on the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0138] Figure 2 The described pesticide residue detection system for a pesticide residue detector can execute Figure 1 the pesticide residue detection method for a pesticide residue detector described in the illustrated embodiment. Its implementation principle and technical effects will not be elaborated further. For the pesticide residue detection system for a pesticide residue detector in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0139] In a possible design, Figure 2 the pesticide residue detection system for a pesticide residue detector in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0140] The processing component 32 is configured to execute the Figure 1 pesticide residue detection method for a pesticide residue detector in the above

[0141] embodiment(s). Wherein, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0142] The storage component 31 is configured to store various types of data to support the operations of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic memory, flash memory, magnetic disks or optical discs.

[0143] Of course, the computing device may necessarily further include other components, such as an input / output interface, a display component, a communication component, etc.

[0144] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0145] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0146] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above-mentioned processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0147] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the Figure 1 pesticide residue detection method for a pesticide residue detector shown in the above-mentioned

[0148] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0149] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0150] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A pesticide residue detection method for a pesticide residue detector, characterized in that, Comprising: Obtaining the ambient temperature data of the fruit and vegetable sample, the sample matrix type information, and the chemical detection reagent; Based on the ambient temperature data and the sample matrix type information, and in combination with historical detection data, establishing a dynamic control database; Controlling a cutting blade to perform a rotary cutting operation on the fruit and vegetable sample through a biaxial motion mechanism, and driving a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection solution; Inputting the mixed detection solution and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value; Performing dynamic compensation on the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

2. The method according to claim 1, wherein The establishing a dynamic control database based on the ambient temperature data and the sample matrix type information, and in combination with historical detection data, includes: According to the ambient temperature data, extracting the historical ambient temperature data of the fruit and vegetable sample at different time points and the pesticide residue detection values detected at the corresponding historical ambient temperatures from the historical detection data; Grouping the historical ambient temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample at a preset temperature interval to generate temperature data groups, and the temperature data groups include multiple temperature ranges; Calculating the average value of all pesticide residue detection values within each temperature data group, and taking the average value as the historical control value corresponding to the temperature range; Calculating a parameter influence factor according to the temperature data group and the historical control value; Generating a dynamic control database based on the sample matrix type information, the temperature range, the historical control value, and the parameter influence factor.

3. The method according to claim 2, wherein The calculating a parameter influence factor according to the temperature data group and the historical control value includes: Determining the median point of the temperature range corresponding to each temperature data group as the representative temperature; Forming a set of numerical pairs according to the corresponding relationship between the representative temperature and the historical control value; Calculating the average change rate of the historical control value relative to the representative temperature according to the set of numerical pairs; Multiplying the absolute value of the average change rate by a preset adjustment coefficient to generate a parameter influence factor.

4. The method according to claim 1, wherein The inputting the mixed detection solution and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value includes: Placing the mixed detection solution under the light source irradiation of the pesticide residue detector to obtain a detection signal of the transmitted light intensity changing with time; Adjusting the signal acquisition parameters of the pesticide residue detector according to the ambient temperature data, and optimizing the amplitude of the detection signal based on the adjusted signal acquisition parameters; Performing time-frequency conversion on the optimized detection signal to generate spectrum data; Selecting the frequency points whose amplitudes exceed a preset amplitude threshold from the spectrum data; Matching the frequency points with the frequency point characteristic data corresponding to the current sample matrix type information in the dynamic control database to generate an initial pesticide residue detection value.

5. The method according to claim 4, wherein The matching the frequency points with the frequency point characteristic data corresponding to the current sample matrix type information in the dynamic control database to generate an initial pesticide residue detection value includes: Call the frequency point feature data corresponding to the current sample matrix type information from the dynamic control database, where the frequency point feature data includes the characteristic frequency positions and associated concentration values; Calculate the frequency deviation values between the frequency points and each characteristic frequency position in the frequency point feature data; Select, from the frequency point feature data, multiple associated concentration values corresponding to the characteristic frequency positions where the frequency deviation values are less than or equal to a preset matching threshold; Take the average value of all the associated concentration values as the initial pesticide residue detection value.

6. The method according to claim 1, wherein The dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value includes: Compare the current environmental temperature data with the representative temperatures of each temperature range of the same sample matrix type information in the dynamic control database, and calculate the temperature deviation amount according to the comparison result; Multiply the parameter influence factor by the temperature deviation amount to obtain a temperature compensation coefficient; Use the temperature compensation coefficient to adjust the historical control value corresponding to the current sample matrix type information and the temperature range to which the current environmental temperature data belongs in the dynamic control database to obtain a reference reference value; Based on the reference reference value and the temperature compensation coefficient, perform linear correction on the initial pesticide residue detection value to generate a target pesticide residue detection value.

7. The method according to claim 1, characterized in that The controlling a cutting blade by a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable sample, and driving a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection liquid includes: Drive the cutting blade through the first rotating shaft of the biaxial motion mechanism to perform rotary cutting on the fruit and vegetable sample to generate a shredded sample; Drive the stirrer through the second rotating shaft of the biaxial motion mechanism to stir the chemical detection reagent and the shredded sample in a container to form a uniform mixture; When it is detected that the stirring duration of the uniform mixture reaches a preset duration corresponding to the sample matrix type information, stop stirring to generate a mixed detection liquid.

8. A pesticide residue detection system for a pesticide residue detector, characterized in that, Includes: An acquisition module, configured to acquire the environmental temperature data, sample matrix type information, and chemical detection reagent where the fruit and vegetable sample is located; A building module, configured to build a dynamic control database based on the environmental temperature data and the sample matrix type information, in combination with historical detection data; A first generation module, configured to control a cutting blade by a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable sample, and drive a stirrer mechanically linked to the cutting blade to perform a stirring operation on the chemical detection reagent and the rotary-cut fruit and vegetable sample to generate a mixed detection liquid; An output module, configured to input the mixed detection liquid and the environmental temperature data into a pesticide residue detector to output an initial pesticide residue detection value; A second generation module, configured to dynamically compensate the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a pesticide residue detection method for a pesticide residue detector as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, it implements a pesticide residue detection method for a pesticide residue detector as described in any one of claims 1 to 7.

Images