Pesticide residue detection method and system for pesticide residue detector

Abstract

The present application provides a pesticide residue detection method and system for a pesticide residue detector, wherein the method comprises: collecting ambient temperature data and matrix type information of fruit and vegetable samples, and building a dynamic control database in combination with historical detection data. A dual-axis motion mechanism is used to synchronously control the cutting blade to peel the sample and the linkage stirrer to mix the detection reagent to generate a uniformly mixed detection liquid. The mixed liquid and the ambient temperature data are input into the detector to obtain an initial detection value, and finally the initial value is temperature compensated and bias corrected based on the dynamic control database to output the target pesticide residue detection value. The present application improves the accuracy and efficiency of pesticide residue detection of multiple batches of fruit and vegetable samples in a variable temperature environment.

Description

Technical Field

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

[0002] Rapid and accurate detection of pesticide residues in fruits and vegetables is essential for the distribution of agricultural products, particularly in farmers' markets and supermarkets. Due to temperature fluctuations in different seasons and storage environments, as well as differences in fruit and vegetable matrices, test results are susceptible to environmental interference. Therefore, a portable detection method that can automatically compensate for temperature effects and adapt to a variety of fruit and vegetable matrices is urgently needed to improve the stability and reliability of test results.

[0003] Currently, pesticide residue detection solutions employ fixed temperature compensation coefficients. These solutions calibrate test results using a single, preset temperature compensation parameter and combine it with spectral analysis technology to identify characteristic pesticide peaks. During testing, the system applies the preset compensation value based on the current ambient temperature and directly adjusts the test signal output, thereby minimizing the impact of temperature fluctuations on the results.

[0004] Fixed temperature compensation coefficients are only effective for specific fruit and vegetable matrices and within a narrow temperature range. Compensation accuracy decreases when the ambient temperature spans a wide range or when the sample matrix type varies. Furthermore, this method relies on manual pre-calibration of compensation parameters and cannot dynamically adapt to the actual testing conditions of different sample batches, leading to significant deviations in test results under complex scenarios. Summary of the Invention

[0005] The present application provides a pesticide residue detection method and system for a pesticide residue detector, which is used to solve the problems of low accuracy and poor efficiency in pesticide residue detection of multiple batches of 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, comprising:

[0007] Obtaining the ambient temperature data, sample matrix type information, and chemical detection reagents of the fruit and vegetable samples;

[0008] 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;

[0009] The cutting blade is controlled by a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable sample, and a stirrer mechanically linked to the cutting blade is driven to perform a stirring operation on the chemical detection reagent and the rotary cut fruit and vegetable sample to generate a mixed detection liquid;

[0010] Inputting the mixed test liquid and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue test value;

[0011] The initial pesticide residue detection value is dynamically compensated based on the dynamic control database to generate a target pesticide residue detection value.

[0012] Optionally, the establishing of a dynamic control database based on the ambient temperature data and the sample matrix type information, in combination with historical detection data, includes:

[0013] Extracting historical environmental temperature data of fruit and vegetable samples at different time points and pesticide residue detection values ​​detected at corresponding historical environmental temperatures from historical detection data based on the environmental temperature data;

[0014] Grouping the historical environmental temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample according to a preset temperature interval to generate a temperature data group, wherein the temperature data group includes multiple temperature intervals;

[0015] Calculating the average value of all pesticide residue detection values ​​in each temperature data group, and using the average value as the historical control value for the corresponding temperature range;

[0016] Calculating a parameter influencing factor based on the temperature data set and the historical control value;

[0017] A dynamic control database is generated based on the sample matrix type information, the temperature range, the historical control value, and the parameter influencing factor.

[0018] Optionally, calculating the parameter influencing factor based on the temperature data group and the historical comparison value includes:

[0019] Determine the median point of the temperature interval corresponding to each temperature data group as the representative temperature;

[0020] forming a value pair set according to the correspondence between the representative temperature and the historical reference value;

[0021] Calculating, based on the set of value pairs, a mean of the rates of change of the historical control values ​​relative to the representative temperature;

[0022] The absolute value of the mean value of the change rate is multiplied by a preset adjustment coefficient to generate a parameter impact factor.

[0023] Optionally, inputting the mixed test liquid and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue detection value includes:

[0024] Placing the mixed test liquid under the light source of the pesticide residue detector to obtain a detection signal of the intensity of the transmitted light changing with time;

[0025] 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;

[0026] Performing time-frequency conversion on the optimized detection signal to generate spectrum data;

[0027] Filtering frequency points whose amplitudes exceed a preset amplitude threshold from the spectrum data;

[0028] The frequency point is matched 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.

[0029] Optionally, matching 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:

[0030] Retrieving frequency feature data corresponding to the matrix type information of the current sample from the dynamic control database, wherein the frequency feature data includes a characteristic frequency position and an associated concentration value;

[0031] Calculating a frequency deviation value between the frequency point and each characteristic frequency position in the frequency point characteristic data;

[0032] In the frequency point feature data, a plurality of associated concentration values ​​corresponding to the characteristic frequency positions where the frequency deviation value is less than or equal to a preset matching threshold are selected;

[0033] The average value of all the associated concentration values ​​was taken as the initial pesticide residue detection value.

[0034] Optionally, dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value includes:

[0035] Comparing the current ambient temperature data with the representative temperatures of each temperature interval of the same sample matrix type information in the dynamic control database, and calculating the temperature deviation according to the comparison result;

[0036] Multiplying the parameter influence factor by the temperature deviation to obtain a temperature compensation coefficient;

[0037] Using the temperature compensation coefficient, adjusting the historical control value in the dynamic control database corresponding to the temperature interval to which the current sample matrix type information and the current ambient temperature data belong, to obtain a benchmark reference value;

[0038] The initial pesticide residue detection value is linearly corrected based on the benchmark reference value and the temperature compensation coefficient to generate a target pesticide residue detection value.

[0039] Optionally, the biaxial motion mechanism is used to control the cutting blade to perform a rotary cutting operation on the fruit and vegetable sample, and the stirring device mechanically linked to the cutting blade is driven to stir the chemical detection reagent and the rotary cut fruit and vegetable sample to generate a mixed detection solution, including:

[0040] The cutting blade is driven by the first rotating shaft of the biaxial motion mechanism to perform rotary cutting on the fruit and vegetable sample to generate a minced sample;

[0041] The stirrer is driven by 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;

[0042] 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.

[0043] In a second aspect, the present application provides a pesticide residue detection system for a pesticide residue detector, comprising:

[0044] An acquisition module is used to obtain the ambient temperature data of the fruit and vegetable samples, sample matrix type information, and chemical detection reagents;

[0045] An establishment module is used to establish a dynamic control database based on the ambient temperature data and the sample matrix type information, in combination with historical detection data;

[0046] a first generating module, configured to control a cutting blade through 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 solution;

[0047] An output module, configured to input the mixed test liquid and the ambient temperature data into the pesticide residue detector and output an initial pesticide residue detection value;

[0048] The second generating module is used to dynamically compensate the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0049] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a pesticide residue detection method for a pesticide residue detector as described in any one of the first aspects.

[0050] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a pesticide residue detection method for a pesticide residue detector as described in any one of the first aspects.

[0051] The present application provides a pesticide residue detection method for a pesticide residue detector, the method comprising: obtaining ambient temperature data of fruit and vegetable samples, sample matrix type information, and chemical detection reagents; establishing a dynamic control database based on the ambient temperature data and the sample matrix type information, and in combination with historical detection data; controlling a cutting blade through a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable samples, 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 samples to generate a mixed detection liquid; inputting the mixed detection liquid and the ambient temperature data into the pesticide residue detector, and outputting 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.

[0052] The technical solution provided by this application has the following beneficial effects:

[0053] This application provides basic parameter input for subsequent testing to ensure that the test conditions match the actual environment. By accumulating and analyzing historical test data, a dynamically adjustable reference benchmark is formed to improve the adaptability of the test. This enables automation and standardization of sample pre-processing to ensure the uniformity and consistency of the mixed test fluid. It provides the detector with optimized test samples and compensation parameters to ensure the stability of the test signal. The test results are adjusted according to real-time environmental conditions and sample characteristics to improve the accuracy and reliability of the test.

[0054] Furthermore, the present application also collects the ambient temperature and pesticide residue values ​​in the 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 influencing factor based on the relationship between the temperature data group and the historical control value, and finally constructs a dynamic control database containing sample matrix type, temperature range, historical control value and parameter influencing factor.

[0055] In addition, by establishing a dynamic control database, systematic management of pesticide residue detection values ​​under different ambient temperatures and sample matrix conditions is achieved, providing a dynamically adjustable reference basis for real-time detection and improving the consistency and comparability of test results.

[0056] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 A flowchart of a pesticide residue detection method for a pesticide residue detector provided in an embodiment of the present application;

[0059] 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;

[0060] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0062] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between 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 of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0063] Existing pesticide residue detection technologies that use fixed temperature compensation coefficients have significant limitations. This approach only presets compensation parameters for specific fruit and vegetable matrices and within a narrow temperature range. This accuracy decreases when the ambient temperature fluctuates significantly or when the sample matrix type changes. Furthermore, this method relies on manually calibrated static compensation values ​​and cannot be dynamically adjusted based on actual testing conditions. This leads to significant deviations in test results in complex scenarios, making it difficult to meet the needs of rapid screening of multiple batches of fruit and vegetable samples.

[0064] In response to the above problems, the present application proposes a pesticide residue detection method for a pesticide residue detector, the core of which is to construct a dynamic control database through the collaborative analysis of ambient temperature data, sample matrix type information and historical detection data. Specifically, during the detection process, the dual-axis motion mechanism synchronously controls the cutting blade to perform a rotary cutting operation on the fruit and vegetable samples, and drives the linkage agitator to evenly mix the chemical detection reagent with the sample to generate a mixed detection liquid; then, the mixed detection liquid and the ambient temperature data are input into the pesticide residue detector to obtain the initial pesticide residue detection value, and real-time compensation is performed based on the parameter influencing factors and historical control 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 adaptive adjustment of the dynamic control database, achieves the accuracy and stability of the detection results under different environmental conditions, and improves the detection efficiency and reliability of multiple batches of fruit and vegetable samples.

[0065] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0066] Figure 1 A flowchart of a pesticide residue detection method for a pesticide residue detector provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0067] Step 101: Obtaining the ambient temperature data of the fruit and vegetable samples, sample matrix type information, and chemical detection reagents.

[0068] In step 101, ambient temperature data refers to the real-time temperature measurement of the environment surrounding the fruit and vegetable samples, reflecting the environmental conditions during testing. Sample matrix type information describes the physical properties of the fruit and vegetable samples, including characteristics such as type and texture. Chemical testing reagents are solutions designed to undergo specific chemical reactions with the fruit and vegetable samples. During pesticide residue testing, these reagents are mixed with the samples to enable the detection of pesticide residues through a specific chemical reaction.

[0069] In this embodiment, a temperature sensor first collects real-time temperature data from the environment in which the fruit and vegetable samples are located. This data also records information about the sample's substrate type, such as type and texture, and prepares specialized chemical detection reagents. The temperature sensor transmits the collected ambient temperature data to a processing unit. Sample substrate type information is acquired through manual input or an automated recognition system, and chemical detection reagents are pre-configured according to a standard ratio. These three types of data together constitute the initial input parameters for the test.

[0070] For example, at an agricultural product testing center, operators place apple samples to be tested in the testing area. The temperature sensor collects the current ambient temperature of 25°C in real time, and the system records the sample matrix type as "apple fruit." At the same time, standard organophosphorus pesticide detection reagents are used to complete data collection.

[0071] Step 102: 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.

[0072] In step 102, the historical test data represents a data set that has been accumulated in the past and includes ambient temperature, sample matrix type, corresponding pesticide residue test values, and test results. The dynamic control database represents a reference database that stores standard test results under various conditions.

[0073] In an embodiment of the present application, the system retrieves historical detection data, filters out historical records that match the current ambient temperature data and sample matrix type information, performs group statistics according to temperature ranges, calculates the average value of the pesticide residue detection results in each temperature range as the historical control value, then analyzes the degree of influence of temperature changes on the detection results and calculates the parameter influencing factors, and finally establishes a dynamic control database containing sample matrix types, temperature ranges, historical control values ​​and parameter influencing factors.

[0074] For example, the system retrieves the test records of apple samples in the temperature range of 20-30℃ in the past three months from the database, calculates that the average pesticide residue in this temperature range is 0.5mg / kg, and determines that the influence coefficient of each 1℃ temperature change on the test result is 0.02, and stores these data in the dynamic control database.

[0075] Step 103: The cutting blade is controlled by a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable sample, and the stirrer mechanically linked to the cutting blade is driven to perform a stirring operation on the chemical detection reagent and the rotary cut fruit and vegetable sample to generate a mixed detection solution.

[0076] In step 103, the dual-axis motion mechanism refers to a mechanical device with two independent rotation axes. The rotary cutting operation refers to a sample processing method of rotary cutting. The mixed test solution refers to the solution to be tested after the sample and the reagent are fully mixed.

[0077] In this embodiment, the first axis of the dual-axis motion mechanism drives the cutting blades to rotate at high speed, pulverizing the fruit and vegetable samples. Simultaneously, the second axis drives the agitator to rotate in the opposite direction, thoroughly mixing the chemical testing reagents with the pulverized samples. The synchronized movement of the two axes ensures uniform mixing, ultimately producing a mixed test solution that meets testing requirements.

[0078] For example, after the detection system is started, the first axis of the dual-axis mechanism drives the blade to chop the apple sample at a set speed, while the second axis drives the agitator to mix the detection reagent with the chopped sample. After the set stirring time, a uniform mixed detection liquid is formed.

[0079] Step 104: Input the mixed test liquid and the ambient temperature data into a pesticide residue detector, and output an initial pesticide residue detection value.

[0080] In step 104, the pesticide residue detector represents a dedicated device for measuring the pesticide residue content. The initial pesticide residue detection value represents the original detection result without compensation processing.

[0081] In an embodiment of the present application, a mixed test liquid is placed in the sample chamber of the detector. Under the irradiation of a light source of a specific wavelength, the test liquid generates a characteristic light signal. The photoelectric sensor converts the light signal into an electrical signal. The signal is preliminarily processed in combination with the current ambient temperature data. By analyzing the degree of matching between the signal characteristics and the preset standards, the initial pesticide residue detection value is output.

[0082] For example, after the mixed test liquid is injected into the detector, the instrument measures an absorbance value of 0.3 at a specific wavelength. Combined with the current ambient temperature of 25°C, a preliminary calculation shows that the initial detection value is 0.6 mg / kg.

[0083] Step 105: Dynamically compensate the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0084] In step 105, the dynamic compensation process represents the correction process of adjusting the test results according to environmental conditions. The target pesticide residue test value represents the final test result after environmental compensation processing.

[0085] In an embodiment of the present application, the system retrieves historical control values ​​and parameter influencing factors that match the current sample matrix type and ambient temperature from the dynamic control database, calculates the degree of influence of temperature deviation on the test results, and compensates and corrects the initial pesticide residue detection value accordingly, ultimately outputting an accurate target pesticide residue detection value.

[0086] For example, based on the current ambient temperature of 25°C, the system calls the historical control value of 0.5mg / kg and the influence coefficient of 0.02 for apple samples in this temperature range from the database, performs compensation calculation on the initial detection value of 0.6mg / kg, and finally outputs the target detection value of 0.55mg / kg.

[0087] This method achieves intelligent compensation of test results by establishing a dynamic control database, adopts dual-axis collaborative processing to ensure sample preparation quality, and combines environmental parameters for precise analysis, effectively improving the accuracy and reliability of pesticide residue detection under different environmental conditions, and providing a scientific basis for the quality and safety supervision of agricultural products.

[0088] In order to address the issue of the impact of ambient temperature changes on detection results during pesticide residue detection and further improve detection accuracy, in some embodiments, step 102: establishing a dynamic control database based on the ambient temperature data and the sample matrix type information, combined with historical detection data, includes:

[0089] Step 201: extracting historical environmental temperature data of fruit and vegetable samples at different time points and pesticide residue detection values ​​detected at corresponding historical environmental temperatures from historical detection data based on the environmental temperature data.

[0090] In step 201, the historical ambient temperature data refers to the ambient temperature values ​​recorded during past tests. The pesticide residue test value is the pesticide content result measured at the corresponding historical temperature.

[0091] In an embodiment of the present application, the system filters out test data of the same matrix as the current sample from the stored historical test records, extracts the ambient temperature values ​​recorded when these tests occurred and the corresponding pesticide residue test results, and provides basic data for subsequent analysis.

[0092] Step 202: grouping the historical environmental temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample according to a preset temperature interval to generate a temperature data group, wherein the temperature data group includes multiple temperature intervals.

[0093] In step 202, the preset temperature interval refers to the fixed span used to divide historical ambient temperature data into continuous temperature ranges. For example, each 5°C interval (20-25°C, 25-30°C, etc.) is pre-set by analyzing historical temperature distribution characteristics to ensure sufficient data within each interval and reflect temperature trends. This interval is typically selected based on actual testing needs and data distribution uniformity. For example, a 5°C interval is often used in agricultural product testing to balance accuracy and data coverage. A temperature data group is a collection of data divided into a specific temperature range. A temperature interval is a segment of the divided temperature range.

[0094] In an embodiment of the present application, the system groups the extracted historical temperature data according to a set temperature span, for example, every 5 degrees is an interval, and the detection data at similar temperatures are grouped together to form a data set of multiple temperature intervals.

[0095] Step 203: Calculate the average value of all pesticide residue detection values ​​in each temperature data group, and use the average value as the historical control value of the corresponding temperature range.

[0096] In step 203, the average value is the arithmetic mean of all pesticide residue test values ​​within the same temperature range. It represents the typical residue level within that range. This calculation is performed by adding all test values ​​within the range and dividing by the number of data points. For example, if a range contains 60 data points with a total of 30 mg / kg, the average value is 30 ÷ 60 = 0.5 mg / kg, reflecting the concentration trend of pesticide residues under those temperature conditions. The historical reference value is a representative value of the pesticide residue test results within a certain temperature range.

[0097] In the embodiment of the present application, the system averages all pesticide residue detection values ​​within each temperature range, and the obtained average value serves as the standard reference value for the temperature range, reflecting the typical pesticide residue level under the temperature conditions.

[0098] Step 204: Calculate parameter impact factors based on the temperature data set and the historical comparison values.

[0099] In step 204 , the parameter impact factor is a coefficient reflecting the degree of influence of temperature change on the detection result.

[0100] In the embodiment of the present application, the system analyzes the pattern of changes in the historical control values ​​of each temperature interval with temperature, calculates the change in the detection value when the temperature changes by one unit, and uses this as the basis coefficient for compensation adjustment.

[0101] Step 205: Generate a dynamic control database based on the sample matrix type information, the temperature range, the historical control value, and the parameter influencing factor.

[0102] In the embodiment of the present application, the system uses the sample matrix type as the main classification basis, associates and integrates the temperature range range under each matrix type, the historical control value corresponding to the interval, and the parameter influencing factor to construct a structured data storage format. Each data record completely contains the correspondence between the four key parameters of sample matrix type, temperature range, historical control value and parameter influencing factor, and is stored in the database for real-time access. For example, for the matrix type of apple fruit, the system will record its historical control value in the temperature range of 20-25°C as 0.5mg / kg and the parameter influencing factor as 0.02mg / kg / °C. This complete set of data can be used to quickly query and obtain the corresponding compensation parameters according to different matrix types and ambient temperatures.

[0103] Here's a specific example:

[0104] At an agricultural product testing center, an operator placed an apple sample in the testing area. A temperature sensor recorded the current ambient temperature as 25°C in real time, and the system recorded the sample matrix as apple. Standard organophosphorus pesticide detection reagents were then used to complete data collection. The system retrieved 100 sets of data from the database covering the past three months, including apple samples tested at two temperature ranges: 20-25°C and 25-30°C. Sixty sets of data were collected for the 20-25°C range, with a total pesticide residue value of 30 mg / kg. Using the arithmetic mean formula, the average for this range was calculated as 30 mg / kg divided by 60 data sets, equaling 0.5 mg / kg. Similarly, 40 sets of data were collected for the 25-30°C range, with a total value of 28 mg / kg. Similarly, the average was calculated as 28 mg / kg divided by 40 data sets, equaling 0.7 mg / kg. The system used linear regression to analyze the relationship between two representative temperatures (22.5°C and 27.5°C) and their corresponding average values. The representative temperatures were the midpoints of each temperature interval: 20+25 divided by 2 (22.5°C) for the 20-25°C interval, and 25+30 divided by 2 (27.5°C) for the 25-30°C interval. Least squares fitting revealed that the average pesticide residue value decreased by 0.02 mg / kg for every 1°C increase in temperature, resulting in a parameter impact factor of 0.02 mg / kg / °C, reflecting the unit effect of temperature change on the test results. The system then categorized and stored these data by apple fruit matrix type, establishing a data set containing a historical control value of 0.5 mg / kg and a parameter impact factor of 0.02 mg / kg / °C for the 20-25°C temperature interval, and a historical control value of 0.7 mg / kg and a parameter impact factor of 0.02 mg / kg / °C for the 25-30°C temperature interval. This ultimately generated a complete dynamic control database for subsequent testing.

[0105] In the embodiment of the present application, by establishing a dynamic control database, an intelligent association between detection parameters and temperature changes is achieved, which provides a reliable compensation basis for subsequent detection, effectively improves the accuracy and comparability of detection results under different ambient temperatures, and solves the problem of detection deviation caused by temperature fluctuations.

[0106] To further improve the accuracy of temperature compensation in pesticide residue detection, in some embodiments, step 204: calculating the parameter influencing factor based on the temperature data set and the historical control value includes:

[0107] Step 301: Determine the median point of the temperature interval corresponding to each temperature data group as the representative temperature.

[0108] In step 301 , the representative temperature refers to the middle value of the temperature range and is used to represent the typical temperature of the range.

[0109] In the embodiment of the present application, the system takes the middle value between the upper limit and the lower limit of each temperature interval as the representative temperature of the interval, for example, the representative temperature of the 20-25°C interval is 22.5°C.

[0110] Step 302: forming a value pair set according to the corresponding relationship between the representative temperature and the historical control value.

[0111] In step 302, the correspondence is a quantitative relationship established between the representative temperature of each temperature interval and its corresponding historical control value. Specifically, the median temperature (representative temperature) of each temperature interval is paired with the average pesticide residue value (historical control value) calculated for that interval, forming a temperature-residue value mapping combination. For example, a representative temperature of 22.5°C corresponds to a historical control value of 0.5 mg / kg, and a representative temperature of 27.5°C corresponds to a historical control value of 0.7 mg / kg. These paired temperature and residue value data together constitute a value pair set, which is used for subsequent analysis of the impact of temperature changes on pesticide residue test results. A value pair set is a data set of paired representative temperatures and their corresponding historical control values. Examples of value pairs include (20°C, 5.2 ppm) and (30°C, 4.8 ppm).

[0112] In the embodiment of the present application, the system pairs the representative temperature of each temperature interval with the historical control value calculated for the interval to form a series of temperature-residual value correspondence relationship data sets.

[0113] Step 303: Calculate the average rate of change of the historical control value relative to the representative temperature based on the value pair set.

[0114] In step 303 , the average change rate refers to the average ratio of the historical control value to the representative temperature.

[0115] In the embodiment of the present application, the system analyzes the changing trend of each data point in the value pair set, calculates the average ratio of the historical control value to the representative temperature change, and reflects the overall impact of temperature on the test results.

[0116] Step 304: Multiply the absolute value of the mean change rate by a preset adjustment coefficient to generate a parameter impact factor.

[0117] In step 304 , the preset adjustment coefficient is a proportional factor set according to actual needs.

[0118] In the embodiment of the present application, the system takes the absolute value of the calculated mean value of the rate of change and multiplies it by a preset adjustment coefficient to ultimately generate a parameter influencing factor for actual compensation calculation.

[0119] Here's a specific example:

[0120] At a certain agricultural product testing center, the system retrieved historical test data for apple samples and first determined the median temperature of 22.5°C in the 20-25°C temperature range as the representative temperature, corresponding to a historical control value of 0.5 mg / kg; and the median temperature of 27.5°C in the 25-30°C range as the representative temperature, corresponding to a historical control value of 0.7 mg / kg. The system combined these two representative temperatures with the historical control values ​​to form a set of value pairs: 22.5°C, 0.5 mg / kg and 27.5°C, 0.7 mg / kg. By analyzing these two value pairs, the system calculated that when the temperature increases from 22.5°C to 27.5°C, the temperature changes by 5°C, while the historical control value changes by 0.2 mg / kg, resulting in a mean rate of change of 0.04 mg / kg / °C. The system then multiplied the absolute value of this mean rate of change, 0.04, by a preset adjustment factor of 0.5 to generate a parameter impact factor of 0.02 mg / kg / °C. The change rate is calculated using the Δy / Δx formula, where Δy represents the change in the historical control value of 0.2 mg / kg, and Δx represents the temperature change of 5°C; the parameter influence factor is calculated using the k=a×|b| formula, where k is the parameter influence factor 0.02, a is the adjustment coefficient 0.5, and b is the mean change rate 0.04.

[0121] In the embodiment of the present application, through a systematic temperature impact analysis process, the degree of influence of temperature changes on the detection results is accurately quantified, providing a scientific basis for dynamic temperature compensation and effectively improving the detection system's adaptability to ambient temperature changes.

[0122] To further improve the accuracy and stability of pesticide residue detection, in some embodiments, step 104: inputting the mixed detection liquid and the ambient temperature data into the pesticide residue detector and outputting the initial pesticide residue detection value includes:

[0123] Step 401: placing the mixed test liquid under the illumination of the light source of the pesticide residue detector to obtain a detection signal of the intensity of the transmitted light changing with time.

[0124] In step 401, transmitted light intensity refers to the intensity of the light signal transmitted through the mixed test liquid without being absorbed when illuminated by a light source of a specific wavelength. This intensity is derived from the light energy value measured in real time by the detector's photoelectric sensor. When the light source passes through the mixed test liquid, pesticide molecules selectively absorb light of specific wavelengths, causing the transmitted light intensity to decrease. The sensor records this intensity change at a fixed frequency (e.g., 100 times per second), forming a time-series signal reflecting the characteristics of the pesticide composition. The detection signal is the data indicating the time-dependent fluctuation of the transmitted light intensity of the mixed test liquid under illumination.

[0125] In an embodiment of the present application, the system places the mixed detection liquid under a light source of a specific wavelength, and continuously records the intensity changes of the transmitted light through a photoelectric sensor to form a timing signal reflecting the characteristics of the detection liquid.

[0126] Step 402: 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.

[0127] In step 402, the signal acquisition parameters refer to the gain, sensitivity, and other settings used by the detector when acquiring signals. The optimization process involves first automatically adjusting the detector's signal amplifier gain and sampling sensitivity based on the current ambient temperature data. For example, the gain is appropriately reduced at higher temperatures to avoid signal saturation, while the gain is increased at lower temperatures to enhance signal strength. The transmitted light signal of the mixed test fluid is then re-acquired based on the adjusted parameters, stabilizing the signal's maximum amplitude within the ideal range of the detector's measuring range (e.g., 60%-80% of the range). This optimization process avoids signal overload distortion while ensuring effective capture of weak signals, providing reliable time-domain signals for subsequent spectrum analysis. This optimization is achieved through a preset temperature-gain comparison table and a real-time feedback adjustment mechanism, ensuring that the detection signal amplitude under different temperature conditions remains within the optimal analysis range.

[0128] 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, so that the intensity of the detection signal is within an appropriate range, avoiding the influence of the analysis result caused by the signal being too strong or too weak.

[0129] Step 403: Perform time-frequency conversion on the optimized detection signal to generate spectrum data.

[0130] In step 403, the time-to-frequency conversion process involves mathematically converting the optimized time-domain detection signal (a waveform showing light intensity varying over time) into frequency-domain spectrum data (a graph showing the distribution of light intensity over frequency). Specifically, the system discretely samples the continuous time-domain signal and then uses a fast Fourier transform (FFT) algorithm to decompose the signal into sinusoidal components of different frequencies. The system then calculates the amplitude and phase information for each frequency component, ultimately generating a spectrum plot with frequency on the horizontal axis and amplitude on the vertical axis. Spectral data refers to the characteristic distribution data resulting from the conversion of the time-domain signal into the frequency domain.

[0131] 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 diagram that can reflect the characteristic frequency of the pesticide.

[0132] Step 404: Filter out frequency points whose amplitudes exceed a preset amplitude threshold from the spectrum data.

[0133] In step 404, the preset amplitude threshold refers to the minimum intensity standard for determining valid characteristic frequency points. The upper limit of the preset range is determined through factory calibration of the detector, so that the signal amplitude of common pesticide residue concentrations is within this range (e.g., ±5V). The amplitude refers to the signal intensity of each frequency component in the spectrum data, which is obtained by performing time-frequency conversion (e.g., 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 signal in the time domain, reflecting the instantaneous change in light 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 frequency of pesticides. The system screens frequency points whose amplitude exceeds the preset threshold, which is essentially extracting the characteristic peaks of energy in the spectrum. These peaks correspond to the specific absorption frequencies of pesticide molecules.

[0134] In the embodiment of the present application, the system screens out frequency points whose intensities exceed a set standard in the spectrum data. These frequency points often correspond to characteristic absorption frequencies of specific pesticide molecules.

[0135] Step 405: Match 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.

[0136] In step 405, the frequency feature data refers to a pre-stored database containing data on the relationship between characteristic frequencies and concentrations corresponding to different pesticide residues. The matching process involves first retrieving the standard characteristic frequencies and their corresponding concentrations for all pesticides in the current sample matrix type (e.g., apples) from the dynamic control database. The system then calculates the frequency difference between the characteristic frequencies selected in the detection spectrum and the standard frequencies in the database (e.g., the deviation between the detected 12.5Hz frequency and the standard 12.4Hz frequency is 0.1Hz). The system then selects standard frequencies with frequency deviations less than a preset tolerance threshold (e.g., 0.5Hz) and obtains the associated concentrations corresponding to these successfully matched standard frequencies (e.g., 0.6mg / kg and 0.4mg / kg). Finally, the arithmetic mean of all successfully matched associated concentrations is calculated (e.g., (0.6 + 0.4) / 2 = 0.5mg / kg). This result is used as the initial pesticide residue detection value for the sample, completing the frequency matching process.

[0137] 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.

[0138] Here's a specific example:

[0139] At an agricultural product testing center, an operator injected a prepared test solution mixture from an apple sample into the sample chamber of a pesticide residue detector, which initiated the testing process. First, the detector's built-in light source of a specific wavelength illuminated the test solution. A photoelectric sensor recorded the intensity changes of the transmitted light 100 times per second, continuously collecting data for 10 seconds to generate a test signal consisting of 1000 light intensity data points. Based on the current ambient temperature of 25°C, the system automatically adjusted the signal amplifier gain to a mid-range level, maintaining the maximum amplitude of the test signal at approximately 70% of the range to avoid signal saturation or excessive attenuation. The optimized test signal was then fed into a processor for mathematical transformation, converting it into a spectrum containing different frequency components, with a frequency axis ranging from 0 to 50 Hz and an amplitude axis ranging from 0 to 5V. The system identified two peaks in the spectrum, located at 12.5Hz and 28.3Hz, with amplitudes of 3.2V and 2.8V, respectively, exceeding the preset threshold of 2.5V. The detector retrieved characteristic frequency data for apples from the dynamic comparison database and found that the 12.5Hz frequency was closest to the standard characteristic frequency of 12.4Hz for organophosphorus pesticides, with a frequency deviation of 0.1Hz, which is less than the allowed matching threshold of 0.5Hz. Based on the frequency-concentration relationship stored in the database, the 12.4Hz frequency corresponds to a pesticide residue concentration of 0.6mg / kg. Therefore, the system determined the initial pesticide residue value for the current sample to be 0.6mg / kg.

[0140] In the embodiment of the present application, an intelligent signal acquisition and analysis method is used to accurately identify the pesticide components in the mixed test liquid, providing a reliable initial detection value for subsequent temperature compensation and effectively improving the overall performance of the detection system.

[0141] To further improve the matching accuracy and reliability of pesticide residue detection, in some embodiments, step 405: matching 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:

[0142] Step 501: Retrieve frequency feature data corresponding to the matrix type information of the current sample from the dynamic control database, wherein the frequency feature data includes a characteristic frequency position and an associated concentration value.

[0143] In step 502, characteristic frequency positions refer to the characteristic absorption frequency values ​​exhibited by various pesticide molecules under specific detection conditions, pre-calibrated in the dynamic reference database. These frequency values ​​are determined through extensive 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 "fingerprints" for pesticide identification and are used to match frequency points detected during actual testing. Associated concentration values ​​refer to reference data for standard pesticide residue concentrations associated with characteristic frequency positions. These values ​​are obtained by establishing a calibration curve between the characteristic peak intensity and concentration of standard samples of varying concentrations. For example, a characteristic frequency of 12.4 Hz corresponding to 0.6 mg / kg indicates that when the detection system recognizes this characteristic frequency, its signal intensity reflects a pesticide residue equivalent to the 0.6 mg / kg level in the standard sample. These concentration values ​​serve as quantitative benchmarks for converting the signal intensity of successfully matched frequency points into specific pesticide residue detection values.

[0144] In the embodiment of the present application, the system retrieves the characteristic frequencies and corresponding concentration reference values ​​of various types of pesticides that may be contained in the sample of the type of fruit and vegetable currently being tested from the database according to the type of the sample.

[0145] Step 502: Calculate the frequency deviation value between the frequency point and each characteristic frequency position in the frequency point characteristic data.

[0146] 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.

[0147] In the embodiment of the present application, the system calculates the frequency difference between each characteristic frequency point screened out in the detection signal and all relevant standard frequency points in the database to evaluate the degree of matching.

[0148] Step 503: Selecting, from the frequency point feature data, a plurality of associated concentration values ​​corresponding to the feature frequency positions where the frequency deviation value is less than or equal to a preset matching threshold.

[0149] In step 503, the preset matching threshold is the maximum allowable frequency difference for determining whether two frequency points match.

[0150] In the embodiment of the present application, the system screens out all standard frequency points whose frequency deviation is less than the threshold value, and the types of pesticides corresponding to these frequency points are considered to be pesticides that may exist in the sample.

[0151] Step 504: taking the average value of all the associated concentration values ​​as the initial pesticide residue detection value.

[0152] In step 504, the average value of the associated concentration value refers to the result obtained by arithmetic averaging the associated concentration values ​​corresponding to all the matched frequency points screened out. Its mathematical expression is: initial pesticide residue detection value = (∑ associated concentration value) / n, where n is the number of successfully matched frequency points. For example, when two frequency points corresponding to 0.6 mg / kg and 0.4 mg / kg are matched, 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 benchmark data for subsequent temperature compensation.

[0153] In the embodiment of the present application, the system averages the concentration reference values ​​corresponding to all successfully matched standard frequency points as the initial pesticide residue detection value of the current sample.

[0154] Here's a specific example:

[0155] At an agricultural product testing center, after completing spectral analysis of a mixed test solution of apple samples, the system identified two characteristic peaks at 12.5Hz and 28.3Hz. The detector retrieved frequency characteristic data for apples from a dynamic comparison database, including records of organophosphorus pesticides at 12.4Hz corresponding to 0.6mg / kg and pyrethroids at 28.5Hz corresponding to 0.4mg / kg. The system first calculated the deviation between the 12.5Hz test frequency and the 12.4Hz standard frequency, which was 0.1Hz. It then calculated the deviation between the 28.3Hz test frequency and the 28.5Hz standard frequency, which was 0.2Hz. Both values ​​were less than the preset matching threshold of 0.5Hz. Based on the matching rules, the system selected the concentration values ​​corresponding to the two successfully matched standard frequencies, 0.6mg / kg and 0.4mg / kg, and calculated the initial pesticide residue value as 0.5mg / kg through arithmetic average. The frequency matching adopts the nearest neighbor principle, the frequency deviation is calculated using the absolute difference method, and the concentration average is calculated using the arithmetic average formula C=(C1+C2) / 2, where C represents the final concentration, and C1 and C2 represent the concentration values ​​corresponding to the two matching frequency points, respectively.

[0156] In the embodiment of the present application, accurate identification and quantitative analysis of pesticide components in the mixed test liquid are achieved through intelligent frequency matching and concentration calculation processes, providing a scientific and reliable initial data basis for subsequent temperature compensation correction, and effectively improving the overall accuracy of the detection system.

[0157] 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:

[0158] Step 601: Compare the current ambient temperature data with the representative temperatures of each temperature interval of the same sample matrix type information in the dynamic control database, and calculate the temperature deviation according to the comparison result.

[0159] In step 601, the current ambient temperature data refers to the instantaneous temperature value of the test environment in which the mixed test fluid is being tested, collected in real time by a temperature sensor. This "currentness" is ensured by the testing system's clock module and timestamp mechanism. When the instrument begins analyzing the mixed test fluid, the system automatically records the most recent temperature data measured by the sensor at that moment, ensuring strict synchronization with the test operation and avoiding the use of historical or lagged temperature data. The comparison process is implemented by comparing the current ambient temperature with the representative temperatures of all temperature intervals in the database for the same sample matrix type (e.g., apples). First, the system calculates the absolute difference between the current temperature and the representative temperature of each interval (e.g., the difference between the current temperature of 25°C and the representative temperature of 22.5°C for the interval 20-25°C is 2.5°C). The interval with the smallest difference is then selected as the matching result (if multiple intervals are equally spaced, the lower temperature interval is prioritized). This process is automated by traversing the database records and using a numerical comparison algorithm. The comparison results contain two key pieces of information: the determination of the nearest temperature interval to which the current temperature belongs (e.g., 25°C belongs to the 20-25°C interval); and the calculation of the specific deviation between the current temperature and the representative temperature of that interval (e.g., the deviation between 25°C and 22.5°C is +2.5°C). The temperature deviation is the difference between the current ambient temperature and the nearest representative temperature in the database.

[0160] In the embodiment of the present application, the system first determines the current detection environment temperature, then retrieves the representative temperatures of all temperature intervals 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 minimum difference as the temperature deviation, which reflects the degree of temperature difference between the current detection conditions and the standard conditions.

[0161] Step 602: Multiply the parameter influence factor by the temperature deviation to obtain a temperature compensation coefficient.

[0162] In step 602 , the temperature compensation coefficient refers to a correction value after comprehensively considering temperature deviation and parameter influencing factors.

[0163] 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.

[0164] Step 603: Using the temperature compensation coefficient, adjust the historical control value in the dynamic control database corresponding to the temperature range to which the current sample matrix type information and the current ambient temperature data belong to, to obtain a baseline reference value.

[0165] In step 603 , the baseline reference value is a standard reference value adjusted by temperature compensation.

[0166] In the embodiment of the present application, the system uses a temperature compensation coefficient to adjust the historical reference value of the corresponding temperature range in the database to obtain a corrected reference value that adapts to the current ambient temperature conditions.

[0167] Step 604: Performing linear correction on the initial pesticide residue detection value based on the baseline reference value and the temperature compensation coefficient to generate a target pesticide residue detection value.

[0168] In step 604 , linearity correction is a process of normalizing the initial detection value according to the reference value and the temperature compensation coefficient.

[0169] In the embodiment of the present application, the system compares the initial detection value with the benchmark reference value, calculates the adjustment amount based on the difference between the two and the temperature compensation coefficient, and finally outputs the target detection value that takes into account both the actual measurement value and the temperature compensation, ensuring that the results are accurate and reliable.

[0170] Here's a specific example:

[0171] At an agricultural product testing center, an operator placed an apple sample for testing in the testing area. A temperature sensor recorded the current ambient temperature as 25°C in real time, and the system recorded the sample matrix type as apple. Standard organophosphorus pesticide detection reagents were used to complete data collection. The system retrieved data from the database for apple samples in the 20-25°C temperature range. The representative temperature for this range, 22.5°C, was calculated by adding the lower limit of the range, 20°C, to the upper limit, 25°C, and dividing by 2. The corresponding historical control value, 0.5 mg / kg, was the average of all test data within this range. The deviation between the current temperature of 25°C and the representative temperature of 22.5°C was 2.5°C, calculated by subtracting 22.5°C from 25°C. The parameter influence factor, 0.02 mg / kg / °C, was determined through preliminary experiments to indicate the effect of a 1°C temperature change on the test result. Multiplying the parameter influence factor of 0.02 by the temperature deviation of 2.5°C yielded a temperature compensation coefficient of 0.05 mg / kg. The temperature compensation coefficient is calculated as the parameter influence factor multiplied by the temperature deviation. The system adds a temperature compensation factor of 0.05 mg / kg to the historical control value of 0.5 mg / kg to obtain a baseline reference value of 0.55 mg / kg. The initial detection value of 0.6 mg / kg is calculated by converting the absorbance value of the mixed test solution to 0.3 according to the standard curve. The final target pesticide residue detection value of 0.55 mg / kg is calculated by taking the weighted average of the initial value of 0.6 mg / kg and the baseline reference value of 0.55 mg / kg.

[0172] In the embodiment of the present application, through dynamic temperature compensation and linear correction mechanism, the influence of ambient temperature fluctuations on the test results is effectively eliminated, making the final test 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 quality and safety supervision of agricultural products.

[0173] 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:

[0174] 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.

[0175] In step 701, the minced sample refers to a fruit and vegetable sample that has been cut to an ideal fineness.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] In step 703, the preset duration is the optimal stirring time set according to the characteristics of different fruit and vegetable matrices.

[0182] 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.

[0183] Here's a specific example:

[0184] 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.

[0185] 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.

[0186] 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:

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The output module 24 is used to input the mixed detection liquid and the ambient temperature data into the pesticide residue detector and output an initial pesticide residue detection value.

[0191] The second generating module 25 is configured to dynamically compensate the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value.

[0192] Figure 2 The pesticide residue detection system for a pesticide residue detector can perform Figure 1 The implementation principles and technical effects of the pesticide residue detection method for a pesticide residue detector described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the pesticide residue detection system for a pesticide residue detector in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0193] In one possible design, Figure 2 The pesticide residue detection system for a pesticide residue detector of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0194] 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 .

[0195] The processing component 32 is used to perform the above Figure 1 The embodiment provides a pesticide residue detection method for a pesticide residue detector.

[0196] 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 as 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 to perform the above method.

[0197] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile memory 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 disk, or optical disk.

[0198] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0199] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0200] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0201] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0202] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment provides a pesticide residue detection method for a pesticide residue detector.

[0203] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0204] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0205] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting pesticide residues in a pesticide residue detector, characterized in that: include: Obtaining the ambient temperature data, sample matrix type information, and chemical detection reagents of the fruit and vegetable samples; 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; The cutting blade is controlled by a biaxial motion mechanism to perform a rotary cutting operation on the fruit and vegetable sample, and an agitator mechanically linked to the cutting blade is driven to perform a stirring operation on the chemical detection reagent and the rotary cut fruit and vegetable sample by a centrifugal cutting method to generate a mixed detection liquid. The intensity of the transmitted light of the mixed detection liquid fluctuates over time under the illumination of a light source; Inputting the mixed test liquid and the ambient temperature data into a pesticide residue detector to output an initial pesticide residue test value; Dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value; The step of 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: Extracting historical environmental temperature data of fruit and vegetable samples at different time points and pesticide residue detection values ​​detected at corresponding historical environmental temperatures from historical detection data based on the environmental temperature data; Grouping the historical environmental temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample according to a preset temperature interval to generate a temperature data group, wherein the temperature data group includes multiple temperature intervals; Calculating the average value of all pesticide residue detection values ​​in each temperature data group, and using the average value as the historical control value for the corresponding temperature range; Calculating a parameter influencing factor based on the temperature data set 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 influencing factor; The step of inputting the mixed test liquid and the ambient temperature data into the pesticide residue detector and outputting an initial pesticide residue detection value comprises: Placing the mixed test liquid under the light source of the pesticide residue detector to obtain a detection signal of the intensity of the transmitted light changing with time; Adjusting the signal acquisition parameters of the pesticide residue detector according to the ambient temperature data and the temperature-gain preset comparison table, and optimizing the amplitude of the detection signal based on the adjusted signal acquisition parameters, wherein the signal acquisition parameters refer to the gain parameters and sensitivity parameters of the pesticide residue detector when acquiring signals; Performing time-frequency conversion on the optimized detection signal to generate spectrum data; Filtering frequency points whose amplitudes exceed a preset amplitude threshold from the spectrum data, wherein the frequency points refer to characteristic peaks of energy in the spectrum data, and the characteristic peaks of energy correspond to specific absorption frequencies of pesticide molecules; Matching 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; The dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value includes: Comparing the current ambient temperature data with the representative temperatures of each temperature interval of the same sample matrix type information in the dynamic control database, and calculating the temperature deviation amount according to the comparison result, wherein the temperature deviation amount is the minimum difference; Multiplying the parameter influence factor by the temperature deviation to obtain a temperature compensation coefficient; Using the temperature compensation coefficient, adjusting the historical control value in the dynamic control database corresponding to the temperature interval to which the current sample matrix type information and the current ambient temperature data belong, to obtain a benchmark reference value; The initial pesticide residue detection value is linearly corrected based on the benchmark reference value and the temperature compensation coefficient to generate a target pesticide residue detection value, wherein the target pesticide residue detection value is obtained by weighted averaging the initial pesticide residue detection value and the benchmark reference value.

2. The method according to claim 1, characterized in that Calculating the parameter influencing factor based on the temperature data set and the historical comparison value includes: Determine the median point of the temperature interval corresponding to each temperature data group as the representative temperature; forming a value pair set according to the correspondence between the representative temperature and the historical reference value; Calculating, based on the set of value pairs, a mean of the rates of change of the historical control values ​​relative to the representative temperature; The absolute value of the mean value of the change rate is multiplied by a preset adjustment coefficient to generate a parameter impact factor.

3. The method according to claim 1, characterized in that 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: Retrieving frequency feature data corresponding to the matrix type information of the current sample from the dynamic control database, wherein the frequency feature data includes a characteristic frequency position and an associated concentration value; Calculating a frequency deviation value between the frequency point and each characteristic frequency position in the frequency point characteristic data; In the frequency point feature data, a plurality of associated concentration values ​​corresponding to the characteristic frequency positions where the frequency deviation value is less than or equal to a preset matching threshold are selected; The average value of all the associated concentration values ​​was taken as the initial pesticide residue detection value.

4. The method according to claim 1, wherein The biaxial motion mechanism is used to control the cutting blade to perform a rotary cutting operation on the fruit and vegetable sample, and the stirring device mechanically linked to the cutting blade is driven to stir the chemical detection reagent and the rotary cut fruit and vegetable sample to generate a mixed detection liquid, including: The cutting blade is driven by the first rotating shaft of the biaxial motion mechanism to perform rotary cutting on the fruit and vegetable sample to generate a minced sample; The stirrer is driven by 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; 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.

5. A pesticide residue detection system for a pesticide residue detector, characterized in that: include: An acquisition module is used to obtain the ambient temperature data of the fruit and vegetable samples, sample matrix type information, and chemical detection reagents; An establishment module is used to establish a dynamic control database based on the ambient temperature data and the sample matrix type information, in combination with historical detection data; a first generating module, configured to control a cutting blade via 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 stir the chemical detection reagent and the rotary cut fruit and vegetable sample via a centrifugal cutting method to generate a mixed detection solution, wherein the intensity of the transmitted light of the mixed detection solution fluctuates over time under illumination by a light source; An output module, configured to input the mixed test liquid and the ambient temperature data into the pesticide residue detector and output an initial pesticide residue detection value; A second generating module is used to dynamically compensate the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value; The step of 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: Extracting historical environmental temperature data of fruit and vegetable samples at different time points and pesticide residue detection values ​​detected at corresponding historical environmental temperatures from historical detection data based on the environmental temperature data; Grouping the historical environmental temperature data corresponding to the sample matrix type information of the same fruit and vegetable sample according to a preset temperature interval to generate a temperature data group, wherein the temperature data group includes multiple temperature intervals; Calculating the average value of all pesticide residue detection values ​​in each temperature data group, and using the average value as the historical control value for the corresponding temperature range; Calculating a parameter influencing factor based on the temperature data set 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 influencing factor; The step of inputting the mixed test liquid and the ambient temperature data into the pesticide residue detector and outputting an initial pesticide residue detection value comprises: Placing the mixed test liquid under the light source of the pesticide residue detector to obtain a detection signal of the intensity of the transmitted light changing with time; Adjusting the signal acquisition parameters of the pesticide residue detector according to the ambient temperature data and the temperature-gain preset comparison table, and optimizing the amplitude of the detection signal based on the adjusted signal acquisition parameters, wherein the signal acquisition parameters refer to the gain parameters and sensitivity parameters of the pesticide residue detector when acquiring signals; Performing time-frequency conversion on the optimized detection signal to generate spectrum data; Filtering frequency points whose amplitudes exceed a preset amplitude threshold from the spectrum data, wherein the frequency points refer to characteristic peaks of energy in the spectrum data, and the characteristic peaks of energy correspond to specific absorption frequencies of pesticide molecules; Matching 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; The dynamically compensating the initial pesticide residue detection value based on the dynamic control database to generate a target pesticide residue detection value includes: Comparing the current ambient temperature data with the representative temperatures of each temperature interval of the same sample matrix type information in the dynamic control database, and calculating the temperature deviation amount according to the comparison result, wherein the temperature deviation amount is the minimum difference; Multiplying the parameter influence factor by the temperature deviation to obtain a temperature compensation coefficient; Using the temperature compensation coefficient, adjusting the historical control value in the dynamic control database corresponding to the temperature interval to which the current sample matrix type information and the current ambient temperature data belong, to obtain a benchmark reference value; The initial pesticide residue detection value is linearly corrected based on the benchmark reference value and the temperature compensation coefficient to generate a target pesticide residue detection value, wherein the target pesticide residue detection value is obtained by weighted averaging the initial pesticide residue detection value and the benchmark reference value.

6. A computing device, characterized in that It comprises 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 4.

7. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the pesticide residue detection method for a pesticide residue detector according to any one of claims 1 to 4 is implemented.

Images