Agricultural product safety risk assessment method based on pesticide residue risk amount integral model and computer program product

Abstract

The invention discloses an agricultural product safety risk assessment method based on a pesticide residue risk amount integral model and a computer program product, and relates to the technical field of risk assessment of pesticide residues. The agricultural product safety risk assessment method based on the pesticide residue risk amount integral model comprises the following steps: acquiring sample detection data, and determining pesticide residue types in the sample detection data and detection values corresponding to the pesticide residue types; and calculating a residual coefficient according to the sample detection data. Calculating the residual risk amount of the pesticide variety based on a quantitative evaluation model, and recording the residual risk amount as # imgabs0 #; wherein F represents the pesticide variety residual risk amount, K represents the risk amount reference, H represents the hazard coefficient, and B represents the residual coefficient; and calculating the risk coefficient of the corresponding area according to the pesticide variety residual risk amount. By adopting the technology provided by the invention, the residual coefficient is introduced, and the correlation between the risk quantity and the safety can be reflected by utilizing the integral area of the hazard degradation curve.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment of pesticide residues, and particularly to a method for assessing the safety risk of agricultural products based on a pesticide residue risk quantity integration model and a computer program product. Background Art

[0002] The risk assessment of pesticide residues plays a crucial role in ensuring public health, protecting the ecological environment, maintaining food safety, and promoting the sustainable development of agriculture. In the assessment process, the relationship between the risk quantity and safety is particularly important, which requires us to adopt scientific and precise methods to evaluate the safety of pesticide residues to human health and the environment. At present, the maximum residue limit (MRL) is the main benchmark for evaluating the safety of pesticide residues in agricultural products, that is, as long as the residue amount of any pesticide in GB 2763 (National Food Safety Standard - Maximum Residue Limits of Pesticides in Foods) does not exceed the maximum residue limit, the agricultural product is considered safe. Its limitations cannot be ignored. Especially when there are multiple pesticide residues in agricultural products but none of them exceed the standard, the cumulative effect of these pesticides may pose a potential risk to human health. The current pass rate statistics are used to describe the safety risk situation of agricultural products in a certain area over a period of time, but this method is a rough qualitative evaluation method that relies on the subjective experience and intuition of experts rather than detailed data quantitative analysis, and cannot fully reflect the hazard risk of pesticide residues. Therefore, we need to explore more comprehensive and detailed assessment methods.

[0003] Based on this, the Chinese invention patent document (CN111667122B) discloses a method for assessing the risk of pesticide residues in agricultural product varieties. By analyzing the distribution change law of the pesticide residue content in agricultural product varieties within a preset time period, it is obtained whether the pesticide residue content values of each agricultural product variety have statistical laws. For agricultural product varieties with and without statistical laws, a pesticide residue risk prediction model and a pesticide residue risk knowledge base based on risk levels are established respectively. For agricultural product varieties with statistical laws, the mean value of the pesticide residue content values in the previous time period is used as input sample data and input into the pesticide residue risk prediction model to obtain the mean value of the pesticide residue content value of the agricultural product variety in the next time period. The mean value of the predicted pesticide residue content value of the agricultural product variety is matched and compared with the preset list of risk level assessments for agricultural product residues to obtain the agricultural product risk level of the agricultural product variety in the next time period, and the risk pre-assessment of pesticide residues in agricultural product varieties is completed.

[0004] However, in the invention patent document (CN111667122B), it is based on probability distribution research and analysis, separately dealing with regular and irregular agricultural product varieties for independent risk assessment, focusing on evaluating risks from the perspective of the statistical laws of pesticide residue content, relying on a large amount of pesticide residue content detection data and agricultural product detection data, and this method involves multiple models, and the optimization and adjustment of each model require a large amount of time and computing resources, which are difficult for some small agricultural product testing institutions or regulatory departments to bear the technical costs.

[0005] In addition, the invention patent document (CN111667122B) focuses on evaluating risks from the perspective of the statistical laws of pesticide residue content, and it is easy to ignore the toxicological characteristics of pesticides themselves and the potential hazards after human ingestion. Summary of the Invention

[0006] The present invention provides a method for evaluating the safety risk of agricultural products based on a pesticide residue risk quantity integration model and a computer program product, to solve the problems in the prior art that the cost of the pesticide residue prediction model is relatively high and the evaluation of the correlation between the pesticide residue risk quantity and safety is lacking.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is to provide a method for evaluating the safety risk of agricultural products based on a pesticide residue risk quantity integration model. The method for evaluating the safety risk of agricultural products based on the pesticide residue risk quantity integration model includes: Obtain sample detection data, and determine the types of pesticide residues in the sample detection data and the detection values corresponding to the types of pesticide residues; calculate a residue coefficient according to the sample detection data, where the residue coefficient is the ratio of the detection value of the pesticide residue in the sample detection data to the preset residue limit.

[0008] Calculate the residue risk quantity of the pesticide variety based on a quantitative evaluation model, denoted as:

[0009] where F represents the residue risk quantity of the pesticide variety, K represents the risk quantity benchmark, H represents the hazard coefficient, and B represents the residue coefficient; calculate the risk coefficient within a preset time for the corresponding region according to the residue risk quantity of the pesticide variety, where the risk coefficient is the ratio of the residue risk quantity of the pesticide variety to the number of detection samples.

[0010] The beneficial effects brought by the technical solution provided by the present invention compared with the prior art are: By introducing data such as the residual coefficient, the ADI value (Acceptable Daily Intake) of pesticides, the pre-harvest interval, and the integral area of the degradation curve, the safe intake threshold of pesticides for humans is combined with the actual intake to quantitatively evaluate the pesticide residue risk in agricultural products. Compared with the traditional pass rate statistical method, it can more comprehensively reflect the hazard coefficient and residual coefficient of pesticide residues, and combine their actual residue amounts and degradation characteristics in agricultural products to comprehensively evaluate the risk coefficients of multiple pesticide varieties in a corresponding area over a period of time.

[0011] Among them, by reflecting the safe intake threshold of pesticides for humans and combining this threshold with the actual dietary intake of residents, the potential hazards of pesticide residues to human health can be more comprehensively considered, reflecting the correlation between the risk amount and safety. The provided quantitative assessment model enables regulators to more accurately identify high-risk agricultural products or high-risk pesticide varieties based on the quantitative analysis results, thereby improving the regulatory efficiency without increasing the regulatory cost.

[0012] In addition, taking the integral amount of the pesticide degradation curve on the last day of the pre-harvest interval (i.e., the number of days after pesticide application) as the reference for the risk amount benchmark. Specifically, taking the integral value at this time as the benchmark can more scientifically and accurately reflect the cumulative risk of pesticide residues over time and the actual hazard risk of pesticide residues to humans.

[0013] In some embodiments, calculating the residue risk amount of a pesticide variety based on the quantitative assessment model includes: Constructing a degradation curve based on the Y-axis mirror image of the Sigmoid function, denoted as:

[0014] Wherein, represents the number of days of the pesticide pre-harvest interval, which refers to the minimum number of days from the last pesticide application to the safe harvest of agricultural products, and the unit can be days; x represents the number of days after pesticide application, and the unit can be days.

[0015] Taking the integral area of the degradation curve to represent the cumulative amount of risk. When , that is, the number of days after pesticide application reaches the pre-harvest interval specified for this pesticide, calculate the risk amount benchmark K, denoted as:

[0016] Wherein, the value of the risk amount benchmark K is , approximately taking the value of 0.692.

[0017] In some embodiments, calculating the residue risk amount of a pesticide variety based on the quantitative evaluation model further includes: obtaining the ADI value corresponding to the pesticide variety; calculating the hazard coefficient H according to the ADI value corresponding to the pesticide variety, denoted as:

[0018] where 0 ≤ H ≤ 6, when the hazard coefficient H increases as the ADI value decreases; such that when the ADI value is 1 mg / Kg, the hazard coefficient H is 0; when ADI to avoid interference from extreme values, the hazard coefficient H is taken as 6.

[0019] In some embodiments, calculating the residue coefficient according to the sample test data includes: obtaining the MRL value of the corresponding pesticide variety in the sample, and denoting the MRL value as the preset residue limit of the corresponding pesticide variety, then the residue coefficient B of the corresponding pesticide variety is denoted as:

[0020] where t represents the detected value of the pesticide residue of the corresponding variety; then when t > MRL, B > 1, showing an increasing function, and the risk contribution of the residue coefficient B to the residue risk amount F of the pesticide variety increases; when t < MRL, B < 1, showing a decreasing function, and the risk contribution of the residue coefficient B to the residue risk amount F of the pesticide variety decreases; when t = 0, B = 0.

[0021] In some embodiments, calculating the residue coefficient according to the sample test data includes: when a single sample in the sample test data contains pesticide residues of multiple pesticide varieties, the residue coefficient of the corresponding residual pesticide variety in the single sample is denoted as:

[0022] where i is an index variable representing the number of each pesticide variety.

[0023] In some embodiments, calculating the residue risk amount of a pesticide variety based on the quantitative evaluation model further includes: when the single sample test data contains pesticide residues of multiple pesticide varieties, calculating the hazard coefficient sequentially according to the ADI value corresponding to the pesticide variety, denoted as:

[0024] where 0 ≤ H ≤ 6, when the hazard coefficient H increases as the ADI value decreases; then the total hazard coefficient of multiple pesticide varieties in a single sample can be denoted as:

[0025] Among them, represents the total hazard coefficient, represents the hazard coefficient of the i-th pesticide variety.

[0026] In some embodiments, calculating the residue risk amount of pesticide varieties based on the quantitative evaluation model includes: The residue risk amount of a single sample of pesticide variety , denoted as:

[0027] Among them, when B > 1, the residue risk amount of the pesticide variety has an increasing functional relationship with the square of the residue coefficient B. When B < 1, the residue risk amount F of the pesticide variety has a decreasing functional relationship with the square of the residue coefficient B.

[0028] In some embodiments, when a single sample contains multiple pesticide residues, the total risk amount is the superposition value of the individual risk amounts of each pesticide variety, denoted as:

[0029] Among them, n represents the detection number of the pesticide variety, represents the hazard coefficient of the i-th pesticide; represents the residue coefficient of the i-th pesticide.

[0030] When the detection data of a certain batch of samples includes multiple detection samples, calculate the residue risk amount of this batch of detection samples , denoted as:

[0031] Among them, m represents the detection sample number, represents the risk amount of the pesticide in the m-th sample.

[0032] Then the risk coefficient F′ of the corresponding detection batch is:

[0033] Among them, n represents the number of samples in the sample detection data.

[0034] In some embodiments, the method for evaluating the safety risk of agricultural products based on the pesticide residue risk amount integral model further includes visual display.

[0035] In some embodiments, the present application further provides a computer program product, which includes computer programs / instructions. When the computer programs / instructions are executed by a processor, the above-mentioned method for evaluating the safety risk of agricultural products based on the pesticide residue risk amount integral model is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings, where: Figure 1 is a schematic flowchart of an embodiment of a method for assessing the safety risk of agricultural products based on a pesticide residue risk quantity integration model provided by the present invention; Figure 2 is a simulated curve graph of pesticide degradation in an embodiment of a method for assessing the safety risk of agricultural products based on a pesticide residue risk quantity integration model provided by the present invention; Figure 3 is Figure 2 a partial schematic diagram of; Figure 4 is a simulated curve graph of the pesticide toxicity ADI hazard index in an embodiment of a method for assessing the safety risk of agricultural products based on a pesticide residue risk quantity integration model provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Referring to Figure 1 as shown in Figure 1 shows a schematic flowchart of an embodiment of a method for assessing the safety risk of agricultural products based on a pesticide residue risk quantity integration model provided by the present application.

[0039] In some embodiments, the method for assessing the safety risk of agricultural products based on a pesticide residue risk quantity integration model includes: Step S100, obtaining sample detection data and determining the types of pesticide residues in the sample detection data and the detection values corresponding to the types of pesticide residues.

[0040] Exemplarily, the way to obtain sample detection data can be to obtain the sample detection results from the risk monitoring data of government functional departments, or to obtain the sample detection results from the detection tests independently established by laboratories, etc. Among them, the sample detection data includes but is not limited to: detection area, sampling time, sample name, sample number, types of pesticide residues, and the detection values corresponding to the types of pesticide residues. For example, the sample detection data is: the detection value of the residue of pesticide variety B in the agricultural product with sample number N001 in area A is 0.15 mg / kg.

[0041] Step S200, calculate the residue coefficient according to the sample detection data.

[0042] Exemplarily, obtain the maximum residue limit (MRL) value of each pesticide according to the national food safety standard (such as GB2763-2021), and use the MRL value of the corresponding pesticide variety in the corresponding agricultural product as the preset residue limit value. By introducing the MRL value and the following safety interval period after pesticide application, the correlation between the risk amount and safety is reflected in the risk calculation.

[0043] When there is only one kind of pesticide residue, the residue coefficient is the ratio of the detection value of the pesticide residue in the sample detection data to the preset residue limit, and the residue coefficient can be recorded as:

[0044] Among them, t represents the detection value of the pesticide residue in the corresponding sample (i.e., a single agricultural product). When the detection value t of the corresponding pesticide variety is greater than the MRL value of the corresponding pesticide variety, B is greater than 1 to indicate that there is a risk of exceeding the standard for this pesticide. When the detection value t of the corresponding pesticide variety is less than the MRL value of the corresponding pesticide variety, B is less than 1 to indicate that there is no risk of exceeding the standard for this pesticide in this agricultural product. When the detection value t of the corresponding pesticide variety is equal to the MRL value of the corresponding pesticide variety, B is equal to 1 to indicate that the pesticide residue is at the critical risk value of the pesticide residue in this agricultural product.

[0045] Step S300, calculate the residue risk amount of the pesticide variety based on the quantitative evaluation model, recorded as:

[0046] Among them, F represents the residue risk amount of the pesticide variety, K represents the risk amount benchmark, H represents the hazard coefficient, and B represents the residue coefficient.

[0047] See Figures 2 to 3 shown in Figure 2 shows a pesticide degradation simulation curve graph of an embodiment of an agricultural product safety risk assessment method based on a pesticide residue risk amount integral model provided by the present application. Among them, Figure 2 the interval period after pesticide application shown in is 5 days; Figure 3is Figure 2 a partial schematic diagram of Figure 3 The midpoint a represents the end date of the safety interval after pesticide application.

[0048] Pesticide degradation is the mirror process of the S-shaped growth curve. The testing agency detects residues at the sampling time points of agricultural products. Since agricultural products have a harvesting duration and a marketing duration, the residual hazard intensity decreases over time, but the hazard is cumulative during the duration. Therefore, the integral area of the degradation curve can better reflect the risk quantity. That is, the integral area of the curve represents the cumulative amount of risk. When is satisfied, the risk quantity benchmark K is calculated and denoted as:

[0049] Among them, the degradation curve is a mirror model based on the Sigmoid function, which is used to describe the degradation process of pesticide residues over time. The degradation curve is constructed based on the Y-axis mirror image of the Sigmoid function, and its mathematical expression is: . represents the date of the pesticide safety interval, specifically referring to the minimum number of days between the last application of the pesticide and the safe harvesting of agricultural products, with the unit of days; x represents the interval after pesticide application, with the unit of days, indicating that the formulation of the safety interval after pesticide application is based on the time when the pesticide degrades below the maximum residue limit MRL. Then, combined with Figure 3 as shown, the right part of the end date (a) of the safety interval represents the integral area of the residual hazard risk.

[0050] There is a corresponding relationship between the residual amount value of the pesticide on agricultural products and the degradation curve. By separately obtaining the integral areas of the residual detection value t and the MRL on the curve, that is, the ratio of the integral area at the t point to the integral area at the MRL point can be approximately regarded as , that is . Among them, when B > 1, the residual risk quantity of the pesticide variety has an increasing functional relationship with the square of the residue coefficient B, increasing the correlation between the risk quantity F and the residue coefficient B. In other words, it increases the risk contribution of the residue coefficient B to the residual risk quantity F of this pesticide variety; thus, a slight increase in the residue coefficient can significantly change the value of the risk quantity. Using this non-linear relationship (F and ) can improve the evaluation sensitivity of the model, more sensitively capture high-risk situations, and enable the staff to give early warnings and take measures.

[0051] When B < 1, the residual risk quantity F of the pesticide variety has a decreasing functional relationship with the square of the residual coefficient B. While increasing the correlation between the risk quantity F and the residual coefficient B, the high-risk phenomenon (B > 1) and the low-risk phenomenon (B < 1) can be significantly distinguished, and it is ensured that at low risk, the risk contribution of the residual coefficient B to the residual risk quantity F of the pesticide variety is reduced, so as to avoid excessive intervention by staff in low-risk situations.

[0052] Among them, the value of the above risk quantity benchmark K is , taking an approximate value of 0.692 for substitution and calculation, so that both the risk quantity benchmark K and the hazard coefficient H in the above quantitative evaluation model are constants that can be looked up, thereby enabling the risk quantity to be simplified into a quadratic function with the residual coefficient B as the variable. While improving the correlation between the pesticide residue amount and safety, the relevant relationship is simplified, facilitating decision-makers to more quickly judge the risk level.

[0053] Step S400, calculate the risk coefficient of the corresponding area according to the residual risk quantity of the pesticide variety, where the risk coefficient is the ratio of the residual risk quantity of the pesticide variety to the number of detection samples.

[0054] Exemplarily, the risk coefficient is determined by the ratio of the risk quantity to the number of detection samples in the area, thereby facilitating staff to intuitively compare the residual risks of the same pesticide variety in different areas or compare the residual risks of different pesticide varieties in the same area.

[0055] Step S500, visual display.

[0056] Exemplarily, a data visualization tool can be used to visually display the risk assessment results of pesticide residues to visually display the assessment results to staff. For example, the degradation process of pesticide residues over time can be displayed by drawing a chart, or the distribution of pesticide residues in different areas can be displayed by a map, etc. Specifically, Figures 1 to 2 It can be expressed as an embodiment of the visual display of pesticide degradation. However, in some application scenarios, the form of visual display can be adjusted according to the specific needs of users, and this application will not elaborate too much here.

[0057] In the embodiments of the present application, by introducing data such as the residual coefficient, the ADI value (acceptable daily intake) of the pesticide, the safety interval period, and the integral area of the degradation curve, the safe intake threshold of the pesticide by the human body is combined with the actual intake, so as to quantitatively evaluate the pesticide residue risk in agricultural products. Compared with the traditional pass rate statistical method, it can more comprehensively reflect the hazard coefficient and residual coefficient of pesticide residues, and combines the actual residue amount and degradation characteristics of pesticides in agricultural products to comprehensively evaluate the risk coefficients of multiple pesticide varieties in the corresponding area.

[0058] Among them, by reflecting the safe intake threshold of pesticides for the human body and combining this threshold with the actual dietary intake of residents, the potential harm of pesticide residues to human health can be considered more comprehensively, reflecting the correlation between the risk quantity and safety. And a quantitative assessment model is given so that regulators can more accurately identify high-risk agricultural products or high-risk pesticide varieties based on the results of quantitative analysis, thereby improving the regulatory efficiency without increasing the regulatory cost.

[0059] In addition, taking the integral amount of the pesticide degradation curve on the last day of the safety interval as the risk quantity benchmark reference. Specifically, taking the integral value at

[0060] as the benchmark can more scientifically reflect the cumulative risk of pesticide residues over time and more accurately reflect the actual harm risk of pesticide residues to the human body. Figure 4 As shown in Figure 4 FIG.

[0061] shows a simulated curve graph of the pesticide toxicity ADI hazard index of an embodiment of an agricultural product safety risk assessment method provided by this application based on a pesticide residue risk quantity integral model.

[0062] where 0 ≤ H ≤ 6. When , the hazard coefficient H increases as the ADI value decreases, such that when the ADI value is 1 mg / kg, the hazard coefficient H is 0. To avoid obtaining a hazard coefficient of 1 when ADI = 0.1 mg / kg or ADI = 10 mg / kg, resulting in pesticides with significantly different toxicities being assigned the same hazard coefficient, which violates the risk assessment logic. Therefore, when ADI , the value of H is 6.

[0063] In the embodiments of this application, the ADI (Acceptable Daily Intake) value represents the daily allowable intake amount, which refers to the daily intake of a certain chemical substance (such as food additives, pesticides, etc.) by humans or animals. It is an important food safety parameter used to evaluate the safety of chemical substances in food. Among them, the smaller the ADI value, the greater the toxicity. Currently, the ADI of pesticides is generally between 10^-1 mg / Kg and 10^-6 mg / Kg, showing a certain exponential order relationship.

[0064] The logarithm value taken through ADI is expressed as a hazard coefficient, and this hazard coefficient is introduced into the calculation of the risk quantity, that is, the ADI value is introduced into the above quantitative evaluation model to reflect the hazards of different residual pesticides.

[0065] In some embodiments, step S100, calculating the residue coefficient according to the sample detection data, includes: When a single sample in the sample detection data includes pesticide residues of multiple pesticide varieties, the residue coefficient is denoted as:

[0066] where i is an index variable representing the number of each pesticide variety.

[0067] In the embodiments of the present application, the sum of the residue coefficients of pesticides of different varieties is substituted into the above quantitative evaluation model for calculation, comprehensively considering that the residue amount of each pesticide may be individually lower than its maximum residue limit, and the cumulative state of multiple pesticides. That is, by calculating the residue coefficient of each pesticide variety of a single variety, the cumulative effect of pesticides can be comprehensively considered, and the residue coefficients of each pesticide are superimposed, so as to quantify the co-residue state of multiple pesticides. Thus, it makes up for the problem that only single-type pesticides meeting the safety limit are considered while ignoring the superposition effect of multiple pesticide residues when evaluating pesticide residue risks currently.

[0068] In addition, the above calculation method of the total residue coefficient can simplify the calculation process of the risk assessment model, and at the same time maintain the evaluation effect of risk prediction. It has high maneuverability in practical applications and can provide data support for food safety supervision.

[0069] In some embodiments, step S300, calculating the pesticide variety residue risk quantity based on the quantitative evaluation model, further includes: When the single sample detection data includes pesticide residues of multiple pesticide varieties, the hazard coefficients are calculated sequentially according to the ADI values of the corresponding pesticide varieties, denoted as:

[0070] where 0 ≤ H ≤ 6, when the hazard coefficient H increases with the decrease of the ADI value.

[0071] In the embodiments of the present application, according to the ADI (Acceptable Daily Intake) value of the pesticide variety, the hazard coefficients can be calculated sequentially, specifically: when the ADI value is greater than or equal to 1 mg / kg, the hazard coefficient is defined as 0.

[0072] When the ADI value is between 10−6 mg / kg and 1 mg / kg, the hazard coefficient It is defined as -lg(ADI), and the hazard coefficient H increases as the ADI value decreases. When the ADI value is less than 10−6 mg / kg, the hazard coefficient is defined as 6.

[0073] When conducting a risk assessment for a single pesticide variety, using the logarithm of the ADI value as the calculation method for the hazard coefficient can more scientifically and intuitively reflect the toxicity level of the pesticide. When conducting a risk assessment for multiple pesticide varieties, the hazard coefficient is calculated based on the proportion of the pesticide residue coefficient in the test sample. The total hazard coefficient of multiple pesticide varieties in a single sample can be denoted as:

[0074] Among them, among them, represents the total hazard coefficient, represents the hazard coefficient of the i-th pesticide variety.

[0075] When a single sample contains multiple pesticide residues, the total risk amount is denoted as:

[0076] Among them, n represents the number of the pesticide variety detected, represents the hazard coefficient of the i-th pesticide; represents the residue coefficient of the i-th pesticide.

[0077] In practical applications, the hazard coefficient of each corresponding pesticide variety can be calculated by looking up a table in advance for subsequent calculations. The total hazard coefficient of a single sample is the sum of the hazard coefficients of each pesticide variety in the single sample. It is related to the residue coefficient and can obtain the contribution of each pesticide to the total hazard of the sample, thus taking into account the differences in the pesticide residue amounts of each pesticide variety and more accurately reflecting the actual risk of pesticide residues in the sample.

[0078] In some embodiments, step S300, calculating the residue risk amount of the pesticide variety based on the quantitative evaluation model, includes: When the test data of a certain batch of samples includes multiple test samples, calculate the residue risk amount of this batch of test samples, denoted as:

[0079] Among them, m represents the test sample number, represents the risk amount of the pesticide in the m-th sample; Then the risk coefficient F′ of the corresponding test batch is:

[0080] Among them, n represents the number of samples in the sample test data.

[0081] In the embodiments of the present application, the risk quantity calculation of multiple risk assessment factors (pesticide varieties) in multiple agricultural products (numbered m) in a certain region within a set time period can be expressed by the above formula as the average value of the cumulative total and the number of samples.

[0082] When the batches are different, n can also represent the batch of agricultural product samples, so as to facilitate the horizontal comparison and evaluation of the risk coefficients of agricultural product samples in different batches. In some application scenarios, when evaluating the pesticide residue risk in a specific region, the risk assessment factors of pesticide residues in this region can be determined according to the risk monitoring plan or scientific research evaluation plan of local government functional departments, etc. (that is, select pesticide varieties for evaluation). Exemplarily, Table 1 shows the numbers of 25 pesticide residue risk assessment factors and their corresponding ADI values (mg / kg BW), and Table 2 shows the numbers of 25 pesticide residue risk assessment factors and their corresponding MRL values (mg / kg).

[0083] Table 1 ADI values corresponding to pesticide residue risk assessment factors

[0084] Table 2 MRL values of pesticide residue risk assessment factors in corresponding Chinese cabbage agricultural products

[0085] Execute step S100, and construct a test data result table according to the regional area, sampling time, sample name, sample number, pesticide name, test value data, etc.

[0086] Exemplarily, taking the above Chinese cabbage agricultural products as an example, 100 samples are sequentially tested, and the test values and related data are filled into the following table, so as to facilitate subsequent substitution of only numerical values for calculation in the risk quantitative assessment model.

[0087] Table 3 Pesticide residue test results (t) table of Chinese cabbage agricultural products

[0088] Among them, the value of 0 in Table 3 indicates that the corresponding pesticide variety residue is not detected. When all the test values (pesticide varieties) in the test samples of the corresponding Chinese cabbage agricultural products are lower than the MRL value, the product can be considered qualified. Exemplarily, the above Tables 1 to 3 can be pre-stored in the database for easy retrieval and calculation during subsequent calculations.

[0089] Calculate the hazard coefficients of the corresponding varieties according to Table 1, and the results are shown in Table 4 below: Table 4 Pesticide hazard coefficient (H) table of Chinese cabbage agricultural products

[0090] Based on the detection results in Table 4, the residue coefficients in corresponding agricultural products (such as Chinese cabbages) can be calculated, and the residue coefficient table is shown in Table 5: Table 5 Pesticide Detection Residue Coefficient (B) Table of Chinese Cabbage Agricultural Products

[0091] According to the above table values, for example, when calculating the risk amount of multiple pesticide residues in a single Chinese cabbage agricultural product with sample number N001, it can be expressed as: 1.34 Among them, the value of K is 0.692.

[0092] For example, when calculating the risk amount of a single variety of pesticide residue, such as imidacloprid, 100 samples (numbered N001 to N100) are sampled in Area A, then the risk amount of imidacloprid can be expressed as:

[0093] According to the above table values, it is known that a total of two samples are detected among all the detection samples (i.e., sample N001 and sample N002), then the risk amount of imidacloprid can be expressed as:

[0094] Then, the risk coefficient of imidacloprid in 100 batches of samples in this area is:

[0095] In some application scenarios, food safety supervision is usually exercised by local governments at or above the county level. In other words, the local monitoring plan is formulated by county-level units, and their sampling requirements are generally monthly plans. Therefore, when taking the county-level administrative region as the risk assessment area, it is preferable to use the month as the minimum assessment period to effectively meet local needs.

[0096] In some implementation scenarios, the present application also provides a computer program product. The computer program product includes computer programs / instructions, and when the computer programs / instructions are executed by a processor, they implement the above-mentioned agricultural product safety risk assessment method based on the pesticide residue risk amount integration model.

[0097] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Those skilled in the art can clearly understand that each implementation can be realized by means of software plus a necessary general hardware platform, and of course, it can also be realized by hardware. Based on such an understanding, the above technical solution, in essence, or the part 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, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0098] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall fall within the protection scope of the present invention.

Claims

1. A method for agricultural product safety risk assessment based on a pesticide residue risk integral model, characterized in that: include: Acquire sample test data, and determine the types of pesticide residues in the sample test data and the test values ​​of the corresponding types of pesticide residues; Calculating a residue coefficient according to the sample test data, wherein the residue coefficient is a ratio of a detection value of the pesticide residue in the sample test data to a preset residue limit; The residual risk of pesticide varieties is calculated based on the quantitative assessment model, which is expressed as: Among them, F represents the residual risk of pesticide varieties, K represents the risk benchmark, H represents the hazard coefficient, and B represents the residual coefficient; The risk coefficient of the corresponding area within a preset time is calculated according to the residual risk amount of the pesticide variety, wherein the risk coefficient is the ratio of the residual risk amount of the pesticide variety to the number of test samples.

2. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to claim 1 is characterized in that: The calculation of the pesticide residue risk based on the quantitative assessment model includes: The degradation curve is constructed based on the Y-axis mirror image of the Sigmoid function, which is recorded as: in, represents the safe interval period for pesticides; x represents the number of days after pesticide application; The integral of the degradation curve represents the cumulative amount of risk. When , calculate the risk benchmark K, which is recorded as: Among them, the value of the risk benchmark K is , the approximate value is 0.

692.

3. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to claim 1 is characterized in that: The calculation of the pesticide residue risk based on the quantitative assessment model also includes: Get the ADI value of the corresponding pesticide variety; The hazard coefficient H is calculated according to the ADI value of the corresponding pesticide variety and is recorded as: Among them, 0≤H≤6, when When the ADI value decreases, the hazard factor H increases.

4. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to claim 1 is characterized in that: Calculating the residual coefficient according to the sample test data includes: Obtain the MRL value of the corresponding pesticide variety in the sample, and record the MRL value as the preset residue limit of the corresponding pesticide variety. Then the residue coefficient B of the corresponding pesticide variety is recorded as: Wherein, t represents the residual detection value of the corresponding pesticide variety; Then when t>MRL, B>1, It presents an increasing function, and the risk contribution of the residue coefficient B to the residual risk amount F of the pesticide variety increases; When t < MRL, B < 1, showing a decreasing function, the risk contribution of the residue coefficient B to the residue risk amount F of the pesticide variety decreases; When t=0, B=0.

5. A method for agricultural product safety risk assessment based on a pesticide residue risk integral model according to any one of claims 1 or 4, characterized in that: The calculating the residual coefficient according to the sample detection data comprises: When a single sample test data includes pesticide residues of multiple pesticide varieties, the residue coefficient of the single residual pesticide variety is recorded as: Among them, i is the index variable, which represents the number of each pesticide variety.

6. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to claim 5 is characterized in that: The calculation of the pesticide residue risk based on the quantitative assessment model also includes: When a single sample test data includes pesticide residues of multiple pesticide varieties, the hazard coefficients are calculated in sequence according to the ADI values ​​of the corresponding pesticide varieties, and are recorded as: Among them, 0≤H≤6, when When , the hazard factor H increases as the ADI value decreases; The total hazard coefficient of multiple pesticide varieties in a single sample can be recorded as: in, represents the total hazard factor, Represents the hazard coefficient of the i-th pesticide variety.

7. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to claim 6 is characterized in that: The calculation of the pesticide residue risk based on the quantitative assessment model includes: Pesticide residue risk of a single sample , recorded as: Among them, when B>1, the risk of pesticide residues It has an increasing functional relationship with the square of the residue coefficient B. When B<1, the residual risk amount F of the pesticide variety has a decreasing functional relationship with the square of the residue coefficient B.

8. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to claim 7 is characterized in that: When a single sample contains multiple pesticide residues, the total risk is recorded as: Where n represents the number of the pesticide variety tested. represents the hazard coefficient of the i-th pesticide; represents the residual coefficient of the i-th pesticide; When a batch of sample test data includes multiple test samples, calculate the residual risk of the batch of test samples , recorded as: Where m represents the test sample number. represents the risk amount of pesticide in the mth sample; The risk coefficient F′ of the corresponding test batch samples is: Wherein, n represents the number of samples in the sample detection data.

9. The agricultural product safety risk assessment method based on the pesticide residue risk integral model according to any one of claims 1 to 8, characterized in that: The agricultural product safety risk assessment method based on the pesticide residue risk integral model also includes a visual display.

10. A computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the agricultural product safety risk assessment method based on the pesticide residue risk integral model as described in any one of claims 1 to 9.

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