Multi-target green low-toxicity insect repellent formula optimization system

Abstract

The application discloses a multi-target green low-toxicity insect repellent formula optimization system and belongs to the technical field of insect repellent formula optimization. The system comprises the following steps: acquiring multi-target fly, mosquito and cockroach repellent activity data, mammal oral toxicity data and environmental degradation half-life data of a plurality of insect repellent active ingredients, constructing a continuous repellent response surface of each active ingredient in the multi-dimensional space of target flies, mosquitoes and cockroaches, and extracting a target coverage index; determining the multi-target synergistic repellent effect according to the target coverage index, generating a candidate component combination set; performing low-toxicity screening and degradation matching processing on the candidate combination to obtain a green low-toxicity candidate proportioning scheme set; finally, constructing a formula optimization fitness function based on a multi-target repellent broad-spectrum index and a toxicity comprehensive score, and outputting an optimal insect repellent proportioning scheme through iterative optimization processing. The system can quantitatively describe the multi-target coverage capability of the active ingredients and output a formula that takes into account the broad-spectrum repellent performance, low toxicity and degradation adaptability.

Description

Technical Field

[0001] This invention relates to the field of insect repellent formulation optimization technology, specifically to a multi-target green and low-toxicity insect repellent formulation optimization system. Background Technology

[0002] In insect repellent formulation design, balancing broad-spectrum repellency against multiple targets (flies, mosquitoes, and cockroaches) with mammalian toxicity and environmental degradation characteristics is a key challenge in formulation optimization. Existing insect repellent formulation screening methods largely rely on repellent activity test data for single targets (flies, mosquitoes, and cockroaches), evaluating synergistic effects through simple combination of different active ingredients and co-toxicity coefficient measurements. In multi-target scenarios, these methods struggle to systematically quantify the overall coverage of a single active ingredient against multiple targets (flies, mosquitoes, and cockroaches), and fail to intuitively reflect the repellent distribution characteristics of the ingredient in a multidimensional space composed of multiple targets, resulting in a lack of quantitative basis for identifying high-potential broad-spectrum ingredients. In the synergistic effect determination stage, existing methods typically require the individual measurement of numerous binary and multi-component combinations at each target, leading to a highly subjective screening process and a heavy experimental burden. In low-toxicity screening, most existing technologies only set a single toxicity threshold to eliminate highly toxic components, failing to incorporate the differences in degradation half-lives of different components in the environment into the system matching. This results in components that degrade too quickly and too slowly entering the same formulation, affecting the environmental compatibility and duration of action of the formulation. In formulation optimization, existing methods often focus only on the maximum repellent activity or the lowest toxicity as their objective function, lacking the technical means to integrate multi-target broad-spectrum repellency indicators and comprehensive toxicity scores into a single fitness function and to automatically search for the optimal formulation using iterative optimization. Facing the development needs of green and low-toxicity insect repellents, how to accurately construct a broad-spectrum repellent response characterization of insecticidal active ingredients in a multi-target space, and based on this, achieve efficient screening of synergistic combinations, and how to simultaneously drive the three constraints of multi-target broad-spectrum repellency, low toxicity, and degradation consistency in formulation optimization, have become urgent problems to be solved by formulation optimization systems. Summary of the Invention

[0003] This invention provides a multi-target green and low-toxicity insect repellent formulation optimization system. By constructing a continuous repellency response surface in a multi-target space and extracting a target coverage index, the system quantitatively characterizes the broad-spectrum repellency ability of each active ingredient. Based on this index, the system efficiently determines the synergistic effect of multiple targets. Simultaneously, it combines mammalian toxicity data and environmental degradation half-life data for low-toxicity screening and degradation matching. Finally, it uses a fitness function composed of a multi-target repellency broad-spectrum index and a comprehensive toxicity score for iterative optimization, outputting an insect repellent formulation scheme that comprehensively optimizes broad-spectrum repellency, low mammalian toxicity, and environmental degradation coordination.

[0004] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a multi-target green and low-toxicity insect repellent formulation optimization system. This system works collaboratively through specific functional modules, aiming to solve the technical problems of low screening efficiency, incomplete toxicity evaluation, and difficulty in simultaneously achieving environmental friendliness in existing insect repellent formulations.

[0005] In one technical solution of the present invention, the system includes a data acquisition module for acquiring repellent activity data of various insecticidal active ingredients against multiple target flies, mosquitoes, and cockroaches, as well as mammalian oral toxicity data and environmental degradation half-life data of each insecticidal active ingredient. Preferably, when acquiring repellent activity data, each target fly, mosquito, and cockroach is placed in a test environment with a concentration gradient distribution, and the repellent response rate at each concentration gradient is recorded. The concentration value corresponding to the repellent response rate reaching a preset repellency rate threshold is used as the repellent activity data. Outlier detection and smoothing filtering are performed on the acquired repellent activity data to improve data quality. Mammal oral toxicity data and environmental degradation half-life data are retrieved from relevant professional databases and compared with preset confidence thresholds. After removing low-confidence data, they are stored in association with the names of insecticidal active ingredients, providing a reliable data foundation for subsequent processing.

[0006] The system also includes a surface construction module, which constructs repellent response surfaces for each insect-repellent active ingredient in a multi-dimensional space targeting flies, mosquitoes, and cockroaches based on the acquired repellent activity data. As a preferred construction method, the repellent response concentration thresholds for each of the various target flies, mosquitoes, and cockroaches are used as coordinate axes in the multi-dimensional space, and the activity response points of each insect-repellent active ingredient are marked in the multi-dimensional space. A continuous and smooth repellent response surface is generated by performing radial basis function interpolation on these activity response points. This interpolation preferably uses a Gaussian kernel function as the radial basis function, and the width parameter of each central node is positively correlated with its spatial distance to its nearest neighbor central node. Based on the generated surface, the target coverage index of each insect-repellent active ingredient is extracted. This index is defined as the proportion of the volume of the repellent response surface above a preset repellent activity threshold to the total volume of the multi-dimensional space.

[0007] The synergistic determination module assesses the multi-target synergistic repellency effect of each insecticidal active ingredient based on the extracted target coverage index, generating a set of candidate ingredient combinations that meet the multi-target synergistic conditions. In a preferred embodiment of the invention, the target coverage index is compared with a preset coverage threshold to distinguish between high-coverage and low-coverage ingredients. Using the high-coverage ingredient as a benchmark, binary combinations are formed with each low-coverage ingredient, and the synergistic repellency coefficient of each combination on multiple target points (flies, mosquitoes, and cockroaches) is measured. The determination of the synergistic repellency coefficient considers the single-target synergistic coefficient and performs weighted averaging based on the economic hazard level of each target point (flies, mosquitoes, and cockroaches). Combinations with synergistic repellency coefficients greater than the preset synergistic threshold are marked as synergistically effective combinations, and all insecticidal active ingredients involved in these combinations are merged to form a set of synergistic candidate ingredient combinations.

[0008] The low-toxicity screening module receives a set of candidate component combinations and performs dual screening based on mammalian oral toxicity data and environmental degradation half-life data for each component. This module uses the maximum toxicity value of the anthelmintic active ingredient in each candidate combination as the representative toxicity value for that combination, eliminating combinations whose representative toxicity value exceeds a preset toxicity threshold. For the retained combinations, the module further calculates the maximum difference between the environmental degradation half-life data of each component within the combination, and retains combinations whose maximum half-life difference is less than or equal to a preset half-life difference threshold, thus obtaining a set of green, low-toxicity candidate formulations that demonstrate a balance in ecotoxicology and environmental fate.

[0009] The iterative optimization module is responsible for selecting the optimal insecticide formulation from a set of green and low-toxicity candidate formulations through optimization calculations. This module first constructs a formulation optimization fitness function, which uses the broad-spectrum repellency index of each formulation as a positive incentive factor. This index is determined by the weighted sum of the target coverage indices of each component within the formulation. Simultaneously, the reciprocal of the comprehensive toxicity score of each formulation is used as a negative constraint factor. This score is obtained by normalizing and summing the mammalian oral toxicity data of each component. The fitness function value is the product of the positive incentive factor and the negative constraint factor. During iterative optimization, the mass percentage of the insecticidal active ingredient in each formulation is used as the optimization variable, and the recommended field concentration range is used as the boundary constraint. A multi-starting point search method is used to initialize the population. In each iteration, individuals undergo crossover mutation processing. By comparing the changes in the objective function value over multiple generations, when the change in the optimal individual is less than a preset convergence threshold, the mass percentage of the insecticidal active ingredient corresponding to that individual is output as the final optimal insecticide formulation.

[0010] The technical effects and advantages provided by the present invention in the above technical solution are as follows: In the optimization of multi-target insect repellent formulations, a radial basis function interpolation method is used to construct a continuous repellency response surface in a multi-dimensional space of flies, mosquitoes, and cockroaches from the discrete repellency activity data of each insect repellent active ingredient against multiple target insects (flies, mosquitoes, and cockroaches). The volume percentage of this surface above a preset repellency activity threshold is extracted as the target coverage index. This index condenses the repellency efficacy of the ingredient against multiple targets into a single value, intuitively reflecting the broad-spectrum coverage of the ingredient in the multi-target space. Based on this, ingredients with a target coverage index higher than a preset coverage threshold are marked as high-coverage ingredients, and those below the threshold are marked as low-coverage ingredients. Then, a baseline ingredient is selected from the high-coverage ingredients, and a limited number of binary compound combinations are constructed with each low-coverage ingredient. By simply measuring the synergistic repellency coefficient of these combinations at multiple targets, effective combinations with significant multi-target synergistic effects can be identified, and a candidate ingredient combination set can be generated accordingly. Compared to exhaustive testing of all possible combinations, this process significantly reduces the experimental scale of synergistic screening while ensuring systematic capture of multi-target synergistic potential combinations and avoiding omission of component pairings with complementary coverage properties.

[0011] After obtaining a set of green and low-toxicity candidate formulations, a fitness function for formulation optimization was constructed using the multi-target broad-spectrum repellency index and the comprehensive toxicity score of each formulation. The multi-target broad-spectrum repellency index was obtained by weighted summation of the target coverage indices of each insecticidal active ingredient within the formulation, reflecting the overall multi-target coverage capability of the formulation; the comprehensive toxicity score was the normalized summation of mammalian oral toxicity data for each ingredient within the formulation. The fitness function used the multi-target broad-spectrum repellency index as a positive incentive factor and the reciprocal of the comprehensive toxicity score as a negative constraint factor. The product of the two factors simultaneously amplified the broad-spectrum repellency efficacy and suppressed the weight of highly toxic ingredients during the optimization process. Through multi-starting point search, crossover mutation, and population iteration based on objective function value comparison, the combination of ingredient mass percentages with the highest fitness was automatically searched under the boundary constraints of the recommended field concentration range. The optimization process incorporates a degradation matching screening mechanism that compares the maximum half-life difference with a preset half-life difference threshold. This eliminates combinations with excessively large degradation rate differences, ensuring that the final optimal formulation not only achieves a balance between broad-spectrum repellent activity and low mammalian toxicity, but also ensures that the environmental degradation half-lives of each component are consistent. This is beneficial for coordinating the overall degradation rhythm of the formulation after field application and reducing the risk of environmental residues. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0013] Figure 1 This is a flowchart of a system for optimizing multi-target, green, and low-toxicity insect repellent formulations. Figure 2 This is a flowchart of the construction of the avoidance response surface and the calculation of the target coverage index; Figure 3 This is a flowchart for determining the multi-target synergistic avoidance effect; Figure 4 This is a flowchart of the iterative optimization process for insect repellent formulation. Figure 5 This is a flowchart of data acquisition and preprocessing. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] See Figure 1 This invention provides a multi-target green low-toxicity insect repellent formulation optimization system, including a data acquisition module, a surface construction module, a synergistic judgment module, a low-toxicity screening module, and an iterative optimization module. In practical applications, the target insects—flies, mosquitoes, and cockroaches—are preferably sanitary flies, mosquitoes, and cockroaches. The data acquisition module acquires repellent activity data of various insect repellent active ingredients against multiple target insects—flies, mosquitoes, and cockroaches—as well as mammalian oral toxicity data and environmental degradation half-life data for each insect repellent active ingredient. Based on the repellent activity data, the surface construction module constructs repellent response surfaces of each insect repellent active ingredient in a multi-dimensional space of target insects—flies, mosquitoes, and cockroaches, and extracts the target coverage index of each insect repellent active ingredient on the repellent response surface. The synergistic judgment module evaluates the multi-target synergistic repellent effect of each insect repellent active ingredient based on the target coverage index. The system should determine and generate a set of candidate ingredient combinations that meet the conditions for multi-target synergy. The low-toxicity screening module should perform low-toxicity screening and degradation matching on each candidate ingredient combination based on the mammalian oral toxicity data and environmental degradation half-life data of each insecticidal active ingredient in the set of candidate ingredient combinations to obtain a set of green and low-toxicity candidate formulation schemes. The iterative optimization module should construct a formulation optimization fitness function based on the multi-target repellency broad-spectrum index and comprehensive toxicity score of each scheme in the set of green and low-toxicity candidate formulation schemes, and output the optimal insecticide formulation scheme through iterative optimization.

[0016] Example 1:

[0017] In specific implementation, please refer to Figure 2When constructing the repellent response surfaces of various insecticidal active ingredients in the multidimensional space of target mosquitoes and flies using the surface construction module, the repellent response concentration thresholds of various target mosquitoes and flies are used as the coordinate axes of each dimension of the multidimensional space. The number of target mosquitoes and flies determines the number of dimensions of the multidimensional space. If the number of target mosquito and fly species is N, then the multidimensional space is an N-dimensional Euclidean space. The scale range of each coordinate axis is determined by the repellent response concentration threshold range of the corresponding target mosquitoes and flies. The lower limit of the range is the lowest measured concentration that induces a repellent response in the target mosquitoes and flies during the repellent activity assay, and the upper limit is the highest concentration used in the repellent activity assay.

[0018] The surface construction module determines the repellent activity measurement values ​​of each insecticidal active ingredient on each coordinate axis. The repellent activity measurement value refers to the lowest concentration value obtained through a concentration gradient repellent experiment for a specific target mosquito or fly species, which enables the mosquito or fly species to achieve a preset repellency rate threshold. The preset repellency rate threshold is a pre-defined criterion for judging the repellent response rate; in practice, the preset repellency rate threshold is set to 90%. For each insecticidal active ingredient, N repellent activity measurement values ​​are obtained for all N target mosquitoes and flies species, and these N repellent activity measurement values ​​constitute an N-dimensional coordinate vector. The surface construction module marks the activity response points of each insecticidal active ingredient in multidimensional space according to the repellent activity measurement values ​​of each insecticidal active ingredient for each target mosquito or fly species. Each activity response point corresponds to one insecticidal active ingredient, and its coordinate vector is the N-dimensional vector composed of the aforementioned N repellent activity measurement values. The distribution of the active response sites of all insecticidal active ingredients in multidimensional space characterizes the overall repellent activity of different insecticidal active ingredients against mosquitoes and flies at multiple target sites.

[0019] The surface construction module performs radial basis function interpolation on the active response points to generate continuous repellent response surfaces for each insecticidal active ingredient in the multidimensional space of mosquitoes and flies at the target site. In the radial basis function interpolation, the surface construction module uses each active response point as the central node of the radial basis function, and the coordinates of the central node are the coordinate vectors of the active response point. The repellent activity measurement value of each central node is set as the response amplitude of the radial basis function. Specifically, the response amplitude refers to the target output value that the radial basis function needs to match during the interpolation process; that is, the function value of the interpolation fitting function at that central node must be equal to the repellent activity measurement value of the corresponding insecticidal active ingredient.

[0020] The surface construction module uses the spatial distance between multiple center nodes as the input variables of the radial basis functions, and weights and sums the response amplitudes of multiple radial basis functions to generate an interpolation fitting function covering all active response points. The specific expression for the interpolation fitting function S(X) is as follows:

[0021] Where X represents the coordinate vector of any point in the multidimensional space, and K represents the total number of active response points. This represents the coordinate vector of the k-th active response point. Indicate X and The Euclidean distance between them This represents the superposition weights of the k-th radial basis function. This represents the width parameter of the k-th radial basis function. (Superposition weights) By solving the interpolation condition S( )= The system of linear equations formed is obtained. This represents the measured repellency activity at the k-th active response site. Width parameter. The initial value is according to =η× Settings, in which Let η represent the spatial distance from the k-th central node to its nearest neighbor central node, and let η be a scaling factor ranging from 0.5 to 2.0. In this implementation, the value of η is determined based on the cross-validation results and is initially set to 1.0, so that the width parameter of each central node is equal to the spatial distance of its nearest neighbor central node, thus adaptively reflecting the local density of active response points.

[0022] The surface construction module adjusts the width parameters of each radial basis function through cross-validation to ensure that the fitting error of the interpolation function at each active response point is less than a preset fitting accuracy threshold. The specific operation of cross-validation is as follows: all active response points are divided into Q mutually exclusive validation subsets. Each time, one validation subset is selected as the test set, and the remaining Q-1 subsets are used as the training set. The training set is used to construct the interpolation function, and the fitting error at each active response point in the test set is calculated. The fitting error is the absolute difference between the output value of the interpolation function at the test point and the measured value of the repelling activity. This process is repeated for all validation subsets to obtain the average fitting error at all active response points. If the average fitting error is greater than the preset fitting accuracy threshold, the scaling factor η is adjusted according to a preset step size, the interpolation function is reconstructed, and cross-validation is repeated until the average fitting error is less than the preset fitting accuracy threshold. The preset fitting accuracy threshold is set to 5% of the fluctuation range of the measured repelling activity value. The adjusted width parameter σ_k is obtained by multiplying the finally determined scaling factor η by d_k_min, thus forming a different width parameter at each central node.

[0023] The continuous distribution of the interpolation fitting function in multidimensional space after cross-validation adjustment is the repellency response surface of a certain insecticidal active ingredient. The surface construction module performs the above radial basis function interpolation process on each insecticidal active ingredient to generate the repellency response surface corresponding to each insecticidal active ingredient.

[0024] The surface construction module extracts the target coverage index of each insect-repelling active ingredient on the repellency response surface. The target coverage index is defined as the proportion of the volume of the repellency response surface above a preset repellency activity threshold to the total volume of the multidimensional space. The preset repellency activity threshold is a pre-defined threshold value for repellency activity judgment; in this implementation, the preset repellency activity threshold is set to 50% of the average repellency activity measurement value. The total volume of the multidimensional space is the volume of the hypercube formed by the concentration range covered by each coordinate axis. The surface construction module uses a grid sampling method to calculate the volume: a uniformly distributed dense sampling grid is generated in the multidimensional space, with a total of M sampling grid points. The function value of the repellency response surface at each sampling grid point is calculated, and the number U of grid points with function values ​​greater than the preset repellency activity threshold is counted. Then, the target coverage index TCI = UM. For each insect-repelling active ingredient, a target coverage index value is obtained for subsequent multi-target synergistic repellency effect determination.

[0025] Example 2:

[0026] In specific implementation, please refer to Figure 3 The synergistic determination module assesses the multi-target synergistic repellency effect of each anthelmintic active ingredient based on the target coverage index. The module sorts the target coverage indices of all anthelmintic active ingredients by value, calculates the median value of all target coverage indices, and sets this median value as a preset coverage threshold. This preset coverage threshold is used to distinguish between high-coverage and low-coverage ingredients.

[0027] The collaborative determination module marks insecticidal active ingredients with a target coverage index greater than a preset coverage threshold as high-coverage ingredients, and insecticidal active ingredients with a target coverage index less than or equal to the preset coverage threshold as low-coverage ingredients. High-coverage ingredients indicate insecticidal active ingredients with a wide coverage range against multiple target mosquitoes and flies, while low-coverage ingredients indicate insecticidal active ingredients with a relatively narrow coverage range against multiple target mosquitoes and flies.

[0028] The synergistic determination module selects a high-coverage component from the high-coverage component set as the baseline component. The baseline component is selected either arbitrarily from the high-coverage component set or as the high-coverage component with the highest target coverage index. In one implementation, the high-coverage component with the highest target coverage index is selected as the baseline component to explore synergistic effects based on the component with the broadest coverage. The synergistic determination module then forms binary combination combinations with each low-coverage component in the low-coverage component set and the baseline component. If the low-coverage component set contains L low-coverage components, a total of L binary combination combinations are generated. Each binary combination consists of a baseline component and a low-coverage component.

[0029] The synergistic repellency module determines the synergistic repellency coefficients of each binary compound combination on multiple target mosquitoes and flies. The steps for determining the synergistic repellency coefficients are as follows: The individual repellent activity value of each single insecticidal active ingredient in each binary compound combination against each target mosquito and fly is determined, as well as the combined repellent activity value of the binary compound combination against each target mosquito and fly. For determining the individual repellent activity value of an insecticidal active ingredient, a concentration gradient repellency test method is used: A target mosquito or fly is placed in a test environment with a concentration gradient distribution of the insecticidal active ingredient. The repellency response rate of the target mosquito or fly at each concentration gradient is recorded. The concentration value corresponding to the repellency response rate reaching a preset repellency rate threshold is taken as the individual repellent activity value of the insecticidal active ingredient against that target mosquito or fly. The preset repellency rate threshold is set to 90%. To determine the repellent activity value of binary compound formulations, the reference component and the low-coverage component are mixed at a mass ratio of 1:1 to prepare a mixed formulation. For the same target mosquito and fly, the same concentration gradient repellent test method is used to determine the concentration value at which the mixed formulation causes the target mosquito and fly to reach the preset repellency threshold. This concentration value is taken as the repellent activity value of the binary compound formulation for the target mosquito and fly.

[0030] For each target mosquito / fly, the single-target synergy coefficient was calculated. The single-target synergy coefficient was calculated as follows: the difference between the combined repellent activity value and the sum of the two individual repellent activity values ​​was calculated, and then the difference was divided by the sum of the two individual repellent activity values. The single-target synergy coefficient is represented by the symbol... The calculation formula is as follows:

[0031] in, Indicates the index number of the target mosquito / fly. The value range is 1 to , The total number of mosquito and fly species targeting the target; Indicates the reference component relative to the first Individual repellent activity values ​​of mosquitoes and flies targeting specific species; Indicates the low-coverage component for the first Individual repellent activity values ​​of mosquitoes and flies targeting specific species; This indicates the effect of a binary compound consisting of a baseline component and a low-coverage component on the first... The repellent activity value of the combination targeting mosquitoes and flies. When A value greater than 0 indicates that the two insecticidal active ingredients have a synergistic effect on mosquitoes and flies at that target site; when... When equal to 0, it indicates an additive effect; when A value less than 0 indicates an antagonistic effect.

[0032] After obtaining the single-target synergy coefficients on all target mosquitoes and flies, the synergy determination module performs a weighted average of these coefficients to obtain the synergistic avoidance coefficients for each binary combination. The synergistic avoidance coefficients are represented by the symbol... The weighted average processing method is as follows: the single-target synergy coefficient of mosquitoes and flies at each target point is calculated. Weights corresponding to the target mosquitoes and flies Multiply the results, sum them, and then divide by the sum of all weights. Weights of mosquitoes and flies at each target site. The economic damage level of the target mosquito / fly is positively correlated with the proportion of the annual crop damage area caused by the target mosquito / fly to the total annual crop damage area caused by all target mosquito / fly species. In practical implementation, the weighting... The calculation method is as follows: obtain the economic harm level score of mosquitoes and flies at each target point. The economic hazard rating is determined based on the mosquito and fly economic hazard classification standards issued by the agricultural department, with values ​​ranging from 1 to 5 (integers). The annual crop damage area caused by mosquitoes and flies at each target site is obtained. The unit is 10,000 mu; weighting is calculated. Based on the above weighting settings, the higher the economic hazard level and the larger the hazard area of ​​the target mosquitoes and flies, the greater their contribution to the synergistic repellency coefficient.

[0033] The synergy determination module marks binary compound combinations with a synergistic repellency coefficient greater than a preset synergistic threshold as synergistically effective combinations. The preset synergistic threshold is a pre-defined threshold value for determining whether the synergistic effect is significant. In one embodiment, the preset synergistic threshold is set to 0.2, based on the following: when the synergistic repellency coefficient is greater than 0.2, it indicates that the binary compound combination has an overall repellency activity of more than 20% compared to using either component alone on mosquitoes and flies at multiple target sites. Excluding slight fluctuations caused by experimental errors, this indicates a substantial synergistic effect.

[0034] The synergistic determination module performs a union and processing of high-coverage and low-coverage components involved in all synergistically effective combinations to generate a candidate component combination set that meets the multi-target synergistic condition. The specific method of union and processing is as follows: extract the baseline component from all binary compound combinations marked as synergistically effective combinations to form a high-coverage component subset; extract the low-coverage components from all synergistically effective combinations to form a low-coverage component subset; merge all anthelmintic active ingredients from the high-coverage and low-coverage component subsets, remove duplicate component names, and form the candidate component combination set. Each anthelmintic active ingredient in the candidate component combination set is verified to have a multi-target synergistic repellent effect with at least one other anthelmintic active ingredient, thus serving as a candidate for subsequent low-toxicity screening and formulation optimization.

[0035] Example 3:

[0036] In practice, the low-toxicity screening module acquires a set of candidate ingredient combinations, which contains several combinations, each consisting of at least two anthelmintic active ingredients. The low-toxicity screening module retrieves mammalian oral toxicity data and environmental degradation half-life data for each anthelmintic active ingredient from the associated storage data. The associated storage data structure uses the name of the anthelmintic active ingredient as the primary key, and stores the mammalian oral toxicity data and environmental degradation half-life data as attribute values.

[0037] The low-toxicity screening module performs low-toxicity screening on each candidate ingredient combination. The module iterates through each candidate ingredient combination, extracting mammalian oral toxicity data for all anthelmintic active ingredients in the current candidate ingredient combination. Mammal oral toxicity data is the acute oral median lethal dose (LD50) value in rats, expressed in milligrams per kilogram (mg / kg). The module arranges the mammalian oral toxicity data of all anthelmintic active ingredients in the current candidate ingredient combination in descending order of numerical value, forming a descending toxicity data sequence. The mammalian oral toxicity data value ranking first in the descending toxicity data sequence is selected as the representative toxicity value for the current candidate ingredient combination, reflecting the strongest mammalian toxicity level in the current candidate ingredient combination.

[0038] The low-toxicity screening module compares a representative toxicity value with a preset toxicity threshold. The preset toxicity threshold is a pre-defined low-toxicity determination limit; in one embodiment, it is set to 500 mg / kg. When the representative toxicity value is greater than the preset toxicity threshold, it indicates that the current candidate ingredient combination contains an anthelmintic active ingredient whose acute oral toxicity exceeds the low-toxicity standard, and the candidate ingredient combination is eliminated. When the representative toxicity value is less than or equal to the preset toxicity threshold, it indicates that the mammalian oral toxicity of all anthelmintic active ingredients in the current candidate ingredient combination meets the low-toxicity standard, and the candidate ingredient combination is retained. After the above screening, all the retained candidate ingredient combinations constitute the low-toxicity initial screening combination set.

[0039] The low-toxicity screening module performs degradation matching processing on each combination in the low-toxicity initial screening combination set. The module iterates through each combination in the set, extracting the environmental degradation half-life data of all insecticidal active ingredients in the current combination. The environmental degradation half-life data represents the number of days required for each insecticidal active ingredient to degrade by half under standard soil conditions. The module calculates the half-life difference between any two insecticidal active ingredients in the current combination, using the following method: taking the environmental degradation half-life data of the r-th insecticidal active ingredient in the current combination... Environmental degradation half-life data of the sth insecticidal active ingredient Calculate the half-life difference ,in Iterate through all pairs of anthelmintic active ingredients in the current combination to obtain several half-life differences. Select the maximum value from these half-life differences as the maximum half-life difference of the current combination.

[0040] The low-toxicity screening module compares the maximum half-life difference with a preset half-life difference threshold. The preset half-life difference threshold is a pre-defined threshold for determining degradation synchronicity. In one embodiment, the preset half-life difference threshold is set to 30 days. The basis for this setting is that when the environmental degradation half-lives of two insecticidal active ingredients differ by more than 30 days, the difference in their residual periods in the soil after field application is too large. One ingredient may have degraded to an ineffective concentration while the other continues to remain, resulting in a mismatch in environmental exposure windows and failing to meet the degradation matching requirements for green and low-toxicity formulations. When the maximum half-life difference is greater than the preset half-life difference threshold, it indicates that there are component pairs with excessively large degradation half-lives in the current combination, and this combination is screened out. When the maximum half-life difference is less than or equal to the preset half-life difference threshold, it indicates that the environmental degradation half-life differences of all insecticidal active ingredients in the current combination are within an acceptable range, demonstrating good degradation synchronicity, and this combination is retained. After the above degradation matching process, all the retained combinations constitute a set of green and low-toxicity candidate formulation schemes. Each of the green, low-toxicity candidate formulation schemes in the collection consists of a combination of insect-repellent active ingredients that have passed both low-toxicity screening and degradation matching verification, serving as a set of input schemes for subsequent formulation optimization.

[0041] Example 4:

[0042] In specific implementation, please refer to Figure 4 The iterative optimization module obtains a set of green, low-toxicity candidate formulation schemes from the low-toxicity screening module. Each scheme in the green, low-toxicity candidate formulation scheme set contains at least two insecticidal active ingredients, each present in the scheme at a specific mass percentage. The iterative optimization module constructs a formulation optimization fitness function and performs iterative optimization based on the formulation optimization fitness function.

[0043] When constructing the fitness function for formula optimization, the iterative optimization module obtains the target coverage index of each insecticidal active ingredient on the repellency response surface in each green and low-toxicity candidate formulation. The target coverage index, calculated in the surface construction module, represents the proportion of the volume of a single insecticidal active ingredient's repellency response surface above a preset repellency activity threshold to the total volume of the multidimensional space. The iterative optimization module also obtains the mass percentage of each insecticidal active ingredient in each green and low-toxicity candidate formulation. This mass percentage represents the proportion of the mass of one insecticidal active ingredient to the total mass of all insecticidal active ingredients in the corresponding formulation; the sum of the mass percentages of all insecticidal active ingredients within the same formulation is 1.

[0044] The iterative optimization module weights and sums the target coverage indices of all anthelmintic active ingredients within each scheme according to the mass percentage of each anthelmintic active ingredient in the corresponding scheme, thus obtaining the multi-target broad-spectrum repellency index for each scheme. The multi-target broad-spectrum repellency index is represented by the symbol... This indicates that the green and low-toxicity candidate formulations have a comprehensive coverage capability against multiple target mosquitoes and flies. The larger the multi-target repellency broad spectrum index, the broader the overall repellency spectrum of the corresponding formulation.

[0045] The iterative optimization module obtains mammalian oral toxicity data for each anthelmintic active ingredient in each green, low-toxicity candidate formulation. Mammal oral toxicity data refers to the acute oral median lethal dose (LD50) in rats, expressed in milligrams per kilogram (mg / kg); a higher value indicates lower toxicity. The iterative optimization module normalizes and sums the mammalian oral toxicity data of all anthelmintic active ingredients in each formulation to obtain a comprehensive toxicity score for each formulation. The specific steps of the normalization and summation process are as follows: First, the mammalian oral toxicity data is normalized by dividing the data by a normalization baseline value, which is set to the maximum value among all candidate anthelmintic active ingredients, ensuring the normalized data falls between 0 and 1. Then, the normalized mammalian oral toxicity data is weighted and summed according to the mass percentage of each anthelmintic active ingredient in the corresponding formulation to obtain the comprehensive toxicity score. The comprehensive toxicity score is represented by the symbol... The higher the overall toxicity score, the higher the mammalian toxicity risk of the corresponding regimen.

[0046] The iterative optimization module uses the multi-target repulsion broad-spectrum index as the positive incentive factor for the fitness function and the reciprocal of the comprehensive toxicity score as the negative constraint factor. The product of the positive incentive factor and the negative constraint factor is used as the fitness function value for each green and low-toxicity candidate formulation. The fitness function is represented by the symbol... The formula for calculating the fitness function value is:

[0047] in, This represents the fitness function value. This indicates a broad-spectrum index for multi-target evasion. This indicates the overall toxicity score. When... The larger and The smaller, The larger the value, the better the corresponding scheme achieves a balance between broad-spectrum repellency and low toxicity. In the calculation, if... If it is 0, then Replace it with a very small positive number, which is set to 0. To avoid division by zero errors.

[0048] The iterative optimization module outputs the optimal insecticide formulation through iterative optimization. The module uses the mass percentage of each insecticidal active ingredient in each green, low-toxicity candidate formulation as the optimization variable. The dimension of the optimization variable is equal to the total number of insecticidal active ingredients in the green, low-toxicity candidate formulation set. If the green, low-toxicity candidate formulation set involves D different insecticidal active ingredients, then the optimization variable is a D-dimensional vector, and the d-th component of the vector... This represents the mass percentage of the d-th anthelmintic active ingredient, satisfying the following conditions: and .

[0049] The iterative optimization module uses the field-recommended concentration range for each insecticidal active ingredient as the boundary constraint condition for the optimization variable. The field-recommended concentration range is obtained from pesticide registration information or agricultural technology extension manuals. For the d-th insecticidal active ingredient, the lower limit of the field-recommended concentration range is... Indicates that the upper limit is used The unit is milligrams per liter. The mass percentage of each anthelmintic active ingredient in the optimization variables must satisfy the corresponding field-recommended concentration range. That is, in the optimal formulation, the concentration of the d-th anthelmintic active ingredient should be within [range missing]. to Within the range.

[0050] The iterative optimization module will use the fitness function value The objective function value is used for iterative optimization. The iterative optimization module uses a multi-starting-point search method to initialize the initial population. The implementation method of the multi-starting-point search method is as follows: within the boundary constraints of the optimization variables, R initial individuals are generated, each representing a mass percentage allocation scheme of an insecticidal active ingredient. The value of R is 100, set to balance the search space coverage and computational efficiency. Each component of each initial individual is randomly generated under the constraints of satisfying the boundary constraints and the constraint that the sum is 1. Specifically, for each initial individual, a random concentration value satisfying its boundary constraints is generated for each of the D insecticidal active ingredients. The generated D concentration values ​​are normalized so that the sum of the concentration values ​​is 1, thus obtaining the component values ​​of the optimization variables for that initial individual. The R initial individuals constitute the initial population.

[0051] In each iteration, the iterative optimization module performs crossover and mutation processing on the optimization variables of each individual in the current population to generate a new generation of the population. The crossover process involves randomly selecting two individuals from the current population as parents, generating a random crossover point, and swapping the components of the two parent individuals after the crossover point to obtain two offspring individuals. The mutation process involves randomly perturbing a component of the optimization variable of the crossover offspring individuals with a preset mutation probability. The perturbation method involves adding or removing a random variable from the current component, ensuring that the perturbed component still satisfies the boundary constraints. The preset mutation probability is set to 0.05, based on a commonly recommended mutation rate range in evolutionary computation, allowing the population to maintain convergence while possessing a certain degree of exploratory ability. All offspring individuals generated after the crossover and mutation processes are combined to form a new generation of the population.

[0052] The iterative optimization module compares the individual with the highest objective function value in the new generation with the individual with the highest objective function value in the previous generation. The objective function value is the fitness function value. The iterative optimization module records the changes in the objective function value of the individual with the largest objective function value during continuous iterations. The iteration process terminates when the change in the objective function value of the individual with the largest objective function value is less than a preset convergence threshold across multiple iterations. The preset convergence threshold is set to 0.001, based on the following: when the relative change in the fitness function value is less than 0.1%, it indicates that the optimization process has essentially converged to a stable solution, and further iterations have negligible impact on the optimization effect of the matching scheme. The number of iterations in "multiple iterations" is set to 10, based on the following: if the change in the objective function value of the individual with the largest objective function value is less than the preset convergence threshold across 10 consecutive iterations, false convergence caused by local fluctuations can be ruled out, confirming that the optimization process has reached global convergence.

[0053] The iterative optimization module outputs the optimal insecticide formulation as the optimal insecticide ratio, based on the mass percentage of the anthelmintic active ingredient corresponding to the individual with the highest objective function value in the current population at the end of the iteration. The optimal insecticide formulation includes the names of all anthelmintic active ingredients involved in the formulation and their respective mass percentages, as well as the multi-target broad-spectrum repellency index and comprehensive toxicity score corresponding to the formulation.

[0054] Example 5:

[0055] In specific implementation, please refer to Figure 5The data acquisition module obtains repellent activity data of various insecticidal active ingredients against mosquitoes and flies at multiple target sites. The module places mosquitoes and flies at each target site in a test environment with a concentration gradient distribution of the repellent active ingredient. The concentration gradient distribution is set as follows: based on the results of preliminary experiments, the effective concentration range of each insecticidal active ingredient for each target mosquito and fly is determined. Within the effective concentration range, several concentration gradients are set at logarithmic intervals, with a total of 7 gradients. The logarithmic interval setting is based on the fact that the repellent response rate and the logarithm of the repellent active ingredient concentration usually have a linear response relationship, and using logarithmic intervals can evenly cover the response range from low to high repellent response rate. The test environment is a closed Y-shaped olfactory instrument or a feeding selection experimental device. Filter paper containing the repellent active ingredient solution and filter paper containing blank solvent are placed at opposite ends of the test environment. Twenty adult mosquitoes and flies at the target sites are placed in the test environment, and after a certain period of time, the distribution position of the mosquitoes and flies at each target site is recorded, and the repellent response rate is calculated. The repulsion response rate is defined as the ratio of the number of mosquitoes and flies at the repulsion end to the total number of mosquitoes and flies in the test, expressed as a percentage.

[0056] The data acquisition module records the repellency response rates of mosquitoes and flies at each target site under each concentration gradient, establishing a dataset corresponding to the relationship between concentration gradients and repellency response rates. The data acquisition module uses the concentration value corresponding to the repellency response rate reaching a preset threshold as the repellency activity data of each insecticidal active ingredient against mosquitoes and flies at each target site. The preset repellency response rate threshold is set to 90%, based on the fact that a 90% repellency response rate is considered sufficient in mosquito and fly behavioral experiments, and the corresponding concentration value can stably measure the repellency activity level of the insecticidal active ingredient. In the dataset corresponding to the concentration gradient and repellency response rate, if the repellency response rate at a certain concentration gradient is exactly equal to 90%, then that concentration value is directly taken as the repellency activity data; if there is no exactly corresponding concentration value, then the concentration value at which the repellency response rate is 90% is calculated between the repellency response rates of two adjacent concentration gradients using linear interpolation. The calculation formula is: linear interpolation is performed between two adjacent concentration gradients with repellency response rates below 90% and above 90%.

[0057] The data acquisition module performs outlier detection on the mosquito and fly repellency activity data of each insecticidal active ingredient at each target site. Outlier detection employs a standard deviation-based outlier determination method. The specific steps are as follows: For several repellency activity data points obtained from multiple repeated trials of the same insecticidal active ingredient on the same target mosquito and fly, the mean of these repellency activity data points is calculated. and standard deviation It will deviate from the mean. Avoidance activity data exceeding a preset standard deviation multiple are identified as outliers and removed. The preset standard deviation multiple is set to 2.5, based on the assumption of normal distribution: under this assumption, the probability of data points deviating from 2.5 times the mean is less than 1.2%, representing statistically extreme values. Removing these extreme values ​​eliminates outliers caused by operational errors or equipment malfunctions in a single experiment. The number of repeated experiments is set to 5 to ensure a sufficient sample size for standard deviation calculation.

[0058] The data acquisition module performs smoothing filtering on the repellency activity data after removing outlier repellency activity data. The smoothing filtering uses a moving average smoothing method. Specifically, for each insecticidal active ingredient in five replicate experiments against a single target mosquito / fly, several repellency activity data points retained after outlier detection are arranged according to the experiment number to form a data sequence. A smoothing window size of 3 is set. Starting from the first data point in the sequence, the average value of all data points within the window is taken as the smoothed data point each time. The window slides forward with a step size of 1 until it covers the entire data sequence. The smoothing window size of 3 is set because a window size of 3 can effectively suppress random fluctuations while preserving the overall trend of the data sequence, avoiding the loss of useful information caused by an excessively large window size. The smoothed filtering results are then used as repellency activity data for multiple insecticidal active ingredients against multiple target mosquitoes / flies and are passed to the surface construction module for further processing.

[0059] In practice, the data acquisition module obtains mammalian oral toxicity data and environmental degradation half-life data for each anthelmintic active ingredient. The module retrieves the rat acute oral median lethal dose (LD50) values ​​for each anthelmintic active ingredient from a pesticide toxicity database. The pesticide toxicity database used is an internationally recognized database, and the search method involves using the Chemical Abstracts Service (CISA) number or common name of the anthelmintic active ingredient as the search keyword to extract the rat acute oral LD50 values. The data acquisition module uses the extracted rat acute oral LD50 values ​​as the mammalian oral toxicity data for each anthelmintic active ingredient, expressed in milligrams per kilogram (mg / kg).

[0060] The data acquisition module retrieves the degradation half-life (in days) of each insecticidal active ingredient under standard soil conditions from the environmental fate database. The environmental fate database uses publicly available pesticide environmental fate databases from various national pesticide regulatory agencies. The search method involves using the Chemical Abstracts Service Registry number (CASS) or common name of the insecticidal active ingredient as search keywords to extract the degradation half-life (in days) under standard soil conditions. Standard soil conditions refer to loam conditions with a temperature of 20 degrees Celsius, a soil moisture content of 60% of field capacity, and a soil organic matter content of 2%. The data acquisition module uses the extracted degradation half-life (in days) as the environmental degradation half-life data for each insecticidal active ingredient.

[0061] The data acquisition module compares the retrieved oral toxicity data of mammals with a preset toxicity confidence threshold. The preset toxicity confidence threshold is set according to the reliability level of the data source. In one embodiment, the toxicity confidence threshold is set as follows: 3 points for test data from well-established laboratory practices (GLP), 2 points for test data from publicly available literature but not GLP-certified, and 1 point for data from estimations or model predictions. Oral toxicity data of mammals with a score below 2 are considered below the toxicity confidence threshold and are discarded. Simultaneously, the data acquisition module compares the retrieved environmental degradation half-life data with a preset half-life confidence threshold. The half-life confidence threshold uses the same grading standard as the toxicity confidence threshold; environmental degradation half-life data with a score below 2 are considered below the half-life confidence threshold and are discarded.

[0062] The data acquisition module, after removing data below the corresponding confidence threshold, associates and stores the remaining mammalian oral toxicity data and environmental degradation half-life data according to the name of the anthelmintic active ingredient. The association storage uses a key-value pair structure, where the key is the name of the anthelmintic active ingredient, and the value is a record containing both mammalian oral toxicity data and environmental degradation half-life data. The associated and stored data serves as the basis for subsequent low-toxicity screening and degradation matching processing, and is available for use by the low-toxicity screening module.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A multi-target green and low-toxicity insect repellent formulation optimization system, characterized in that, Includes the following steps: The data acquisition module acquires data on the repellent activity of various insecticidal active ingredients against multiple targets of flies, mosquitoes and cockroaches, as well as mammalian oral toxicity data and environmental degradation half-life data of each insecticidal active ingredient. The surface construction module constructs repellency response surfaces of each insect repellent active ingredient in the multidimensional space of target flies, mosquitoes and cockroaches based on the repellency activity data, and extracts the target coverage index of each insect repellent active ingredient on the repellency response surface. The synergistic determination module, based on the target coverage index, determines the multi-target synergistic repellency effect of each insecticidal active ingredient and generates a set of candidate ingredient combinations that meet the multi-target synergistic conditions. The low-toxicity screening module performs low-toxicity screening and degradation matching on each candidate component combination based on the mammalian oral toxicity data and environmental degradation half-life data of each insecticidal active ingredient in the candidate component combination set, thereby obtaining a set of green low-toxicity candidate formulation schemes. The iterative optimization module constructs a formulation optimization fitness function based on the multi-target repellency broad-spectrum index and comprehensive toxicity score of each scheme in the set of green and low-toxicity candidate formulation schemes, and outputs the optimal insect repellent formulation scheme through iterative optimization.

2. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 1, characterized in that, The construction of the repellent response surfaces of each insect-repellent active ingredient in the multidimensional space of target flies, mosquitoes, and cockroaches includes: The repellent response concentration thresholds of various targets for flies, mosquitoes, and cockroaches were used as coordinate axes in a multidimensional space to determine the repellent activity values ​​of each insecticidal active ingredient on each coordinate axis. Based on the measured values ​​of the repellent activity of each insecticidal active ingredient against flies, mosquitoes, and cockroaches at each target site, the activity response points of each insecticidal active ingredient are marked in multidimensional space. Radial basis function interpolation is performed on the active response sites to generate continuous repellent response surfaces of each insect repellent active ingredient in the multidimensional space of target flies, mosquitoes and cockroaches. The target coverage index is the proportion of the volume of the avoidance response surface above a preset avoidance activity threshold to the total volume of the multidimensional space.

3. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 2, characterized in that, Radial basis function interpolation is performed on the active response sites, including: Each active response point is used as the central node of the radial basis function, and the measured value of the repulsion activity of each central node is set as the response amplitude of the radial basis function. The spatial distance between multiple central nodes is used as the input independent variable of the radial basis function. The response amplitudes of multiple radial basis functions are weighted and superimposed to generate an interpolation fitting function that covers all active response points. The width parameters of each radial basis function are adjusted by cross-validation so that the fitting error of the interpolation fitting function at each active response point is less than the preset fitting accuracy threshold. The continuous distribution of the interpolation fitting function in the multidimensional space of target flies, mosquitoes, and cockroaches is used as the repellency response surface of each insect-repelling active ingredient.

4. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 3, characterized in that, The radial basis function uses a Gaussian kernel function, and the width parameter of each central node is positively correlated with the spatial distance from the central node to its nearest neighbor.

5. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 1, characterized in that, Based on the target coverage index, the multi-target synergistic repellency effect of each insecticidal active ingredient is determined, including: The insect-repellent active ingredients whose target coverage index is greater than the preset coverage threshold are marked as high coverage ingredients, and the insect-repellent active ingredients whose target coverage index is less than or equal to the preset coverage threshold are marked as low coverage ingredients. One high-coverage component is selected from each high-coverage component as the reference component, and each low-coverage component is selected to form a binary compound combination with the reference component. The synergistic repellency coefficients of each binary compound combination on multiple target sites of flies, mosquitoes and cockroaches were determined, and binary compound combinations with synergistic repellency coefficients greater than a preset synergistic threshold were marked as synergistically effective combinations. All high-coverage and low-coverage components involved in the synergistic effective combinations are combined and processed to generate a candidate component combination set that meets the multi-target synergistic conditions.

6. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 5, characterized in that, The preset coverage threshold is the median value of the target coverage index of all insect-repellent active ingredients.

7. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 5, characterized in that, The determination of the synergistic repellency coefficients of each binary compound combination on multiple target sites of flies, mosquitoes, and cockroaches includes: The individual repellent activity values ​​of each binary compound combination against flies, mosquitoes and cockroaches at each target site were determined, as well as the combined repellent activity values ​​of the binary compound combination against flies, mosquitoes and cockroaches at each target site. For each target point of flies, mosquitoes and cockroaches, the difference between the compound repellent activity value and the sum of the two individual repellent activity values ​​is calculated, and the difference is divided by the sum of the two individual repellent activity values ​​to obtain the single target synergy coefficient on each target point of flies, mosquitoes and cockroaches. The synergistic coefficients of single targets on all target flies, mosquitoes, and cockroaches were weighted and averaged. The weighted average result was used as the synergistic repellency coefficient of each binary combination. The weights of each target fly, mosquito, and cockroach were positively correlated with the economic hazard level of each target fly, mosquito, and cockroach.

8. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 7, characterized in that, The weights of flies, mosquitoes, and cockroaches at each target site are positively correlated with the proportion of the annual crop damage area caused by flies, mosquitoes, and cockroaches at that target site to the total annual crop damage area caused by flies, mosquitoes, and cockroaches at all target sites.

9. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 1, characterized in that, The process of screening and degradation matching for each candidate component combination to achieve low toxicity includes: The mammalian oral toxicity data of each anthelmintic active ingredient in each candidate ingredient combination were arranged in descending order. The toxicity data at the top of the list was selected as the representative toxicity value of each candidate ingredient combination. Candidate ingredient combinations with a representative toxicity value greater than the preset toxicity threshold were screened out. Candidate component combinations whose toxicity representative values ​​are less than or equal to the preset toxicity threshold are retained to obtain a low-toxicity initial screening combination set. For each combination in the low-toxicity initial screening combination set, the half-life difference between the environmental degradation half-life data of each insecticidal active ingredient is calculated, and the maximum half-life difference is compared with the preset half-life difference threshold. Combinations with a maximum half-life difference greater than the preset half-life difference threshold are filtered out, while combinations with a maximum half-life difference less than or equal to the preset half-life difference threshold are retained, resulting in a set of green and low-toxicity candidate formulation schemes.

10. The multi-target green and low-toxicity insect repellent formulation optimization system according to claim 1, characterized in that, Based on the multi-target repellency broad-spectrum index and comprehensive toxicity score of each scheme in the set of green and low-toxicity candidate formulations, a formulation optimization fitness function is constructed, including: The target coverage index of each insecticidal active ingredient in each green and low-toxicity candidate formulation is obtained on the repellency response surface. The target coverage index of all insecticidal active ingredients in each formulation is weighted and summed to obtain the multi-target repellency broad-spectrum index of each formulation. The mammalian oral toxicity data of each anthelmintic active ingredient in each green and low-toxicity candidate formulation were obtained, and the mammalian oral toxicity data of all anthelmintic active ingredients in each formulation were normalized and summed to obtain the comprehensive toxicity score of each formulation. The multi-target avoidance broad spectrum index is used as a positive incentive factor for the fitness function, and the reciprocal of the comprehensive toxicity score is used as a negative constraint factor for the fitness function. The product of the positive incentive factor and the negative constraint factor is used as the fitness function value of each green and low-toxicity candidate formulation.

Images