Insecticide screening recommendation method and system

Abstract

The invention relates to the technical field of chemical screening, in particular to an insecticide screening recommendation method and system.The insecticide screening recommendation method comprises the following steps that on the basis of molecular structure parameters, environment temperature parameters, pH value parameters and soil type parameters of a compound, according to the relevance of the molecular decomposition rate, the environment dynamic stability and the biodegradation behavior, the biodegradation behavior of the compound is determined; and extracting characteristic variables associated with the decomposition rate in the molecular structure, and generating an environment decomposition dynamic data set. According to the method, through correlation analysis of the exposure dose, the exposure time and the survival rate of the non-target organism, key variables of toxicity data are mainly extracted, more accurate evaluation of the risk of the non-target organism is achieved, the probability that the non-target organism is injured is effectively reduced, and the risk of the non-target organism is evaluated by analyzing the cross relation between the action time and the experimental period. The high-precision prediction of the pesticide experiment effect and the optimization of the screening list are realized, and the precision and practicability of the screening result are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical screening, and in particular to a method and system for recommending pesticide screening. Background Art

[0002] The field of chemical screening technology includes a wide range of chemical substance evaluation and selection processes aimed at identifying compounds with specific functions. This technology field covers a variety of applications from drug development to agricultural chemicals, including the construction of chemical libraries, high-throughput screening technology, the identification of active substances and the analysis of their mechanisms of action. In the field of chemical screening, various chemical analysis and biological evaluation methods are used to systematically evaluate the effects of compounds on specific biological systems in order to quickly and effectively screen out potential active chemicals.

[0003] Among them, the pesticide screening recommendation method refers to a specific chemical screening process used to identify and recommend chemical substances with insecticidal effects. The patent subject involves screening and evaluating the lethal or repellent effects of compounds on specific pests. By applying biological tests, chemical structure analysis and activity comparison, it is determined which compounds are effective pesticides. The process does not involve complex signal processing or data processing algorithms, but is based on direct chemical and biological experimental methods to evaluate and screen compounds.

[0004] The existing technology relies on basic chemical analysis of compounds and direct biological experimental evaluation. The dynamic consideration of environmental factors in the screening process is relatively simple, and it can only focus on the stability of chemical substances under static conditions, which is difficult to reflect the decomposition behavior and action dynamics of compounds in complex environments. This evaluation mode easily leads to some compounds showing uncontrollable decomposition characteristics in the actual environment, posing a higher risk to non-target organisms. The existing technology uses a single toxicity experimental data for toxicity assessment of non-target organisms, lacks a comprehensive analysis of the complex relationship between exposure dose and exposure time, resulting in inaccurate toxicity results and weakening the ability to control non-target biological risks. In the screening process, the existing technology lacks a unified quantitative standard for the trade-off between compound efficacy and toxicity, resulting in insufficient reliability of the screening results. In terms of experimental design, due to the lack of in-depth research on the dynamic correlation between the experimental cycle and the action concentration, the experimental optimization ability is insufficient, and there are problems of waste of experimental resources and low efficiency. Insufficient restrictions on the accuracy, environmental protection and resource utilization efficiency of the pesticide screening process affect the practical value and generalizability of the screening results. Summary of the invention

[0005] In order to solve the problem that the dynamic consideration of environmental influencing factors in the prior art is relatively single, it can only focus on the stability of chemical substances under static conditions, and it is difficult to reflect the decomposition behavior and action dynamics of compounds in complex environments. This evaluation mode easily leads to some compounds showing uncontrollable decomposition characteristics in the actual environment, posing a higher risk to non-target organisms. The prior art uses a single toxicity experimental data for toxicity assessment of non-target organisms, lacks a comprehensive analysis of the complex relationship between exposure dose and exposure time, resulting in inaccurate toxicity results and weakening the ability to control non-target biological risks. In the screening link, the prior art lacks a unified quantitative standard for the trade-off between compound efficacy and toxicity, resulting in insufficient reliability of the screening results. In terms of experimental design, due to the lack of in-depth research on the dynamic correlation between the experimental cycle and the action concentration, the experimental optimization capability is insufficient, and there are problems of waste of experimental resources and low efficiency. In order to solve the technical problems of insufficiently limiting the accuracy, environmental protection and resource utilization efficiency of the insecticide screening process, and affecting the practical value and extensibility of the screening results, the embodiment of the present invention provides an insecticide screening recommendation method and system. The technical solution is as follows:

[0006] In one aspect, a method for recommending pesticide screening is provided, the method comprising:

[0007] S1: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, according to the correlation between molecular decomposition rate, environmental dynamic stability and biodegradation behavior, the characteristic variables associated with the decomposition rate in the molecular structure are extracted to generate an environmental decomposition dynamic data set;

[0008] S2: using the environmental decomposition dynamic data set, identifying the non-target biological exposure dose, exposure time and survival rate parameters, extracting key variables associated with the non-target biological impact in the toxicity data, and obtaining the non-target biological toxicity assessment results;

[0009] S3: using the non-target organism toxicity assessment results, setting weights according to the ratio between the insecticide decomposition rate and the toxicity assessment value, extracting the inhibitory dose in the compound inhibitory effect data, and performing normalization processing to obtain a compound scoring table;

[0010] S4: According to the compound scoring table, an experiment of the insecticide screening cycle is performed, the dynamic response relationship between the inhibition rate and the experimental duration is extracted, the correlation parameters between the insecticide concentration and the number of experiments are adjusted, and the experimental parameter optimization record is obtained;

[0011] S5: Based on the experimental parameter optimization record, call the pesticide action concentration and non-target biological toxicity parameters, extract the action time, experimental cycle and inhibition rate threshold data, analyze the cross-operation relationship between the pesticide action time and the experimental cycle, and obtain a recommended list of pesticide screening.

[0012] As a further scheme of the present invention, the environmental decomposition dynamic data set includes molecular degradation rate time series data, an environmental stability parameter set, and biodegradation dynamic behavior data; the non-target biological toxicity assessment results include toxic effect classification parameters, environmental exposure influencing factors, and toxicity prediction model output values; the compound scoring table includes decomposition rate weight scores, toxicity assessment normalized scores, and inhibition effect rankings; the experimental parameter optimization records include inhibition rate dynamic response curves, experimental error ranges, and optimized test condition tables; the screening recommendation list includes the environmental safety level of pesticides, non-target biological toxicity indicators, and degradation dynamic trend classification information.

[0013] As a further solution of the present invention, based on the molecular structure parameters, environmental temperature parameters, pH parameters and soil type parameters of the compound, according to the correlation between the molecular decomposition rate, environmental dynamic stability and biodegradation behavior, the characteristic variables associated with the decomposition rate in the molecular structure are extracted, and the steps of generating the environmental decomposition dynamic data set are specifically as follows:

[0014] S101: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, the basic physical and chemical properties of the molecular structure are extracted, the molecular polarity, charge distribution and chemical bond type are extracted, and the physical and chemical properties are quantified and characterized item by item. Multivariate cross analysis is performed in combination with environmental parameters to obtain a molecular key structure feature table;

[0015] S102: using the molecular key structure feature table, combining the environmental temperature parameter, pH value parameter and soil type parameter, calling the temperature change sequence, pH range and soil particle composition characteristics, mapping the molecular decomposition rate influencing variables item by item, performing multi-parameter cross-coupling calculation, summarizing the dynamic correlation and coupling relationship between the variables, and obtaining correlation feature data;

[0016] S103: Utilizing the associated characteristic data, analyzing the dynamic data of the molecular decomposition rate and the associated variables, calculating the rate change in the time series, and generating an environmental decomposition dynamic data set.

[0017] As a further solution of the present invention, the formula for calculating the rate change in the time series is as follows:

[0018]

[0019] Among them, R(t) is the rate change in the time series, α represents the influence coefficient of molecular concentration on the rate, and C t represents the molecular concentration at time t, β represents the weight coefficient of temperature, B t represents the ambient temperature at time t, γ represents the pH adjustment factor, pH t represents the pH value at time t, Dt and D t-1 They represent the decomposed dynamic data at time t and t-1 respectively, and e is a natural constant.

[0020] As a further solution of the present invention, the steps of using the environmental decomposition dynamic data set to identify non-target biological exposure dose, exposure time and survival rate parameters, extracting key variables associated with non-target biological impacts in toxicity data, and obtaining non-target biological toxicity assessment results are specifically as follows:

[0021] S201: using the environmental decomposition dynamic data set, extracting non-target biological exposure dose and exposure time parameters item by item, calculating the cumulative dose within the exposure duration by matching the dynamic change relationship in the dose and time series, and generating a non-target biological exposure data table;

[0022] S202: According to the non-target organism exposure data table, call the survival rate parameters of the non-target organisms, combine the exposure time and cumulative dose data, calculate the survival rate change curve and perform data segmentation analysis, extract the key variables corresponding to the survival rate change points, and obtain the non-target organism exposure-related feature data;

[0023] S203: By combining the non-target biological exposure-related characteristic data with the toxicity data, the dynamic characteristics of exposure dose, exposure time and survival rate are matched item by item, the variables associated with the impact on non-target organisms are extracted, the toxic effect values ​​of multiple parameters are evaluated, and the non-target biological toxicity assessment results are obtained.

[0024] As a further solution of the present invention, the steps of using the non-target biological toxicity assessment results, setting weights according to the ratio between the insecticide decomposition rate and the toxicity assessment value, extracting the inhibitory dose in the compound inhibition data, and performing normalization processing to obtain the compound score table are specifically as follows:

[0025] S301: Based on the non-target organism toxicity assessment results, extract the pesticide decomposition rate and toxicity assessment value item by item, and gradually screen the inhibitory effect of the compound by calculating the ratio relationship between the two to obtain compound inhibitory dose data;

[0026] S302: using the inhibitory dose data of the compound, normalizing the inhibitory dose parameter, calculating the normalized value by calling the maximum and minimum values ​​of the inhibitory dose, and obtaining a normalized value record;

[0027] S303: Using the normalized value records and the ratio weights calculated in the toxicity assessment results, the compounds are scored one by one according to the scoring rules of the normalized inhibitory dose and the ratio weight to obtain a compound scoring table.

[0028] As a further solution of the present invention, according to the compound scoring table, an experiment of the pesticide screening cycle is conducted, the dynamic response relationship between the inhibition rate and the experimental duration is extracted, and the correlation parameters between the pesticide concentration and the number of experiments are adjusted to obtain the experimental parameter optimization record. Specifically, the steps are as follows:

[0029] S401: According to the compound scoring table, select pesticides with priority scores as experimental objects, set the initial experimental period and action concentration, analyze the relationship between the pesticide inhibition rate and the compound properties, calculate the inhibition rate prediction value, and obtain the inhibition rate dynamic response record;

[0030] S402: Analyze the dynamic response data of the pesticide inhibition rate and the experimental duration through the inhibition rate dynamic response record, adjust the pesticide action concentration parameter, evaluate the effect of the pesticide concentration on the inhibition rate by resetting the concentration range and repeating the experiment to obtain the pesticide action concentration analysis result;

[0031] S403: Based on the analysis results of the insecticide action concentration and the number of experiments within the experimental cycle, by adjusting the correlation parameters between the number of experimental repetitions and the action concentration, recording the dynamic relationship between the optimized experimental cycle and the number of experiments, analyzing the experimental effect of the concentration adjustment, and obtaining the experimental parameter optimization record.

[0032] As a further embodiment of the present invention, the formula for calculating the predicted value of the inhibition rate is as follows:

[0033]

[0034] Among them, RQ is the predicted value of inhibition rate, P represents the polarity of the compound, F represents the concentration of the compound, T represents the time of the experimental cycle, D represents the dose, and a 1 、a 2 、a 3 、a 4 is the weight parameter.

[0035] As a further solution of the present invention, based on the experimental parameter optimization record, calling the pesticide action concentration and non-target biological toxicity parameters, extracting the action time, experimental cycle and inhibition rate threshold data, analyzing the cross-calculation relationship between the pesticide action time and the experimental cycle, and obtaining the recommended list of pesticide screening steps are specifically as follows:

[0036] S501: Based on the experimental parameter optimization record, the pesticide action concentration and non-target biological toxicity parameters are called item by item, and the pesticide action time and toxicity data are obtained by screening and comparing the action time, experimental cycle and inhibition rate threshold data;

[0037] S502: using the pesticide action time and toxicity data, cross-calculating the pesticide action time and experimental cycle data, and calculating the dynamic matching relationship between the two, screening the corresponding action time interval and inhibition rate threshold in the experimental cycle, and obtaining the pesticide screening cycle matching result;

[0038] S503: The screening results are verified step by step through the insecticide screening cycle matching results, and the matching insecticide data are screened according to the inhibition rate threshold and toxicity parameter requirements to obtain an insecticide screening recommendation list.

[0039] On the other hand, an electric vehicle state monitoring system is provided, the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, the system comprises:

[0040] The cross-analysis module calculates the dynamic relationship between the molecular decomposition rate and environmental parameters based on the molecular structure parameters, environmental temperature parameters, pH parameters and soil type parameters of the compound, and cross-analyzes the dynamic impact of the molecular decomposition rate and environmental conditions to generate an environmental decomposition dynamic data set;

[0041] The toxicity assessment module uses the environmental decomposition dynamic data set to analyze the dynamic changes of non-target biological exposure dose, exposure time and survival rate parameters to obtain non-target biological toxicity assessment results;

[0042] The inhibitory dose normalization module performs normalized calculation on the ratio of the inhibitory dose to the decomposition rate of the compound according to the non-target biological toxicity assessment result, and generates a compound inhibition score table;

[0043] The insecticide action time analysis module adjusts the dynamic response relationship between the inhibition rate and the experimental duration in the experiment through the compound inhibition score table, analyzes the insecticide action time and experimental concentration change parameters in the experimental cycle, and obtains the experimental parameter optimization record;

[0044] The insecticide selection module uses the experimental parameter optimization records to organize the dynamic matching relationship between the insecticide action time, experimental cycle and inhibition rate threshold data to obtain an insecticide screening recommendation list.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By extracting the molecular structure parameters, environmental conditions and decomposition dynamic related variables of the compounds, and combining the variables with the molecular decomposition rate, environmental stability and biodegradation behavior, the initial screening process in pesticide screening is optimized, so that the environmental adaptability and dynamic decomposition characteristics of the compounds can be fully considered in the screening stage, and the comprehensive assessment ability of environmental impact in the screening process is improved. Based on the correlation analysis of the exposure dose, exposure time and survival rate of non-target organisms, the key variables of toxicity data are extracted to achieve a more accurate assessment of the risk of non-target organisms and effectively reduce the probability of non-target organisms being harmed. A standardized scoring system between compounds is established through weight setting and normalization processing to improve the objectivity and comparability of screening results. Through the dynamic response analysis of inhibition rate and experimental duration, the relationship between experimental concentration and experimental number is reasonably adjusted, which greatly improves the experimental efficiency and reduces unnecessary waste of resources. By analyzing the cross-relationship between action time and experimental cycle, high-precision prediction of the experimental effect of pesticides and optimization of the screening list are achieved, which significantly improves the accuracy and practicality of screening results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0048] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0049] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0050] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0051] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0052] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0053] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] See also Figure 1 The embodiment of the present invention provides a method for screening and recommending pesticides. The processing flow of the method may include the following steps:

[0058] S1: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, according to the correlation between molecular decomposition rate, environmental dynamic stability and biodegradation behavior, the characteristic variables associated with the decomposition rate in the molecular structure are extracted to generate an environmental decomposition dynamic data set;

[0059] S2: Use environmental decomposition dynamic data sets to identify non-target biological exposure dose, exposure time and survival rate parameters, calculate the curve integral value between exposure dose and survival rate, extract key variables associated with non-target biological effects in toxicity data based on the integral value, and obtain non-target biological toxicity assessment results;

[0060] S3: Using the non-target biological toxicity assessment results, weights are set according to the ratio between the decomposition rate of the pesticide and the toxicity assessment value, the inhibitory dose in the compound inhibition data is extracted, and normalized to obtain a compound score table;

[0061] S4: According to the compound scoring table, conduct experiments on the pesticide screening cycle, extract the dynamic response relationship between the inhibition rate and the experimental duration, adjust the correlation parameters between the pesticide concentration and the number of experiments, and obtain the experimental parameter optimization record;

[0062] S5: Based on the experimental parameter optimization records, call the pesticide action concentration and non-target biological toxicity parameters, extract the action time, experimental cycle and inhibition rate threshold data, analyze the cross-operation relationship between the pesticide action time and the experimental cycle, and obtain the recommended list of pesticide screening.

[0063] The environmental decomposition dynamic data set includes molecular degradation rate time series data, environmental stability parameter set, and biodegradation dynamic behavior data. The non-target biological toxicity assessment results include toxic effect classification parameters, environmental exposure influencing factors, and toxicity prediction model output values. The compound scoring table includes decomposition rate weight score, toxicity assessment normalized score, and inhibition effect ranking. The experimental parameter optimization record includes the inhibition rate dynamic response curve, experimental error range, and optimized test condition table. The screening recommendation list includes the environmental safety level of pesticides, non-target biological toxicity indicators, and degradation dynamic trend classification information.

[0064] See also Figure 2Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, according to the correlation between molecular decomposition rate, environmental dynamic stability and biodegradation behavior, the characteristic variables associated with the decomposition rate in the molecular structure are extracted. The steps to generate the environmental decomposition dynamic data set are as follows:

[0065] S101: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, the basic physical and chemical properties of the molecular structure are extracted, the molecular polarity, charge distribution and chemical bond type are extracted, and the physical and chemical properties are quantified and characterized item by item. Multivariate cross analysis is performed in combination with environmental parameters to obtain a molecular key structure feature table;

[0066] Analyze and extract basic physicochemical properties of compounds such as molecular polarity, charge distribution and chemical bond type. The process includes using advanced computational chemistry tools to simulate and predict molecular behavior and interactions, quantifying properties item by item, and performing multivariate cross-analysis with environmental parameters such as temperature, pH value and soil properties. The purpose is to reveal how environmental variables affect the stability and reactivity of compounds. This comprehensive analysis method can effectively identify key features in molecular structure, record in detail the relationship between various physicochemical properties and environmental conditions, provide basic data for subsequent chemical evaluation and environmental adaptability analysis, and obtain a table of key molecular structural features.

[0067] S102: Using the molecular key structure feature table, combined with the environmental temperature parameters, pH value parameters and soil type parameters, calling the temperature change sequence, pH range and soil particle composition characteristics, mapping the molecular decomposition rate influencing variables item by item, performing multi-parameter cross-coupling calculations, summarizing the dynamic association and coupling relationship between variables, and obtaining the associated feature data;

[0068] Combined with environmental temperature parameters, pH value parameters and soil type parameters, multi-parameter cross-coupling calculations are further performed, which includes calling the temperature change sequence, pH range and soil particle composition characteristics, and mapping the influence of environmental parameters on the molecular decomposition rate item by item through complex mathematical models and statistical analysis methods. This process not only focuses on the impact of a single parameter, but also reveals the dynamic correlation between them by coupling multiple parameters, showing the changes in molecular behavior under different environmental conditions, providing a scientific basis for the environmental risk assessment and application of compounds, ensuring the safety of chemical use and environmental adaptability, and obtaining correlation characteristic data.

[0069] S103: Analyze the dynamic data of the molecular decomposition rate and the associated variables by using the associated feature data, calculate the rate change in the time series, and generate the environmental decomposition dynamic data set. The execution flow is as follows;

[0070] The formula for calculating the rate change in a time series is as follows:

[0071]

[0072] Among them, R(t) is the rate change in the time series, α represents the influence coefficient of molecular concentration on the rate, and C t represents the molecular concentration at time t, β represents the weight coefficient of temperature, B t represents the ambient temperature at time t, γ represents the pH adjustment factor, pH t represents the pH value at time t, D t and D t-1 represent the decomposed dynamic data at time t and t-1 respectively, and e is a natural constant;

[0073] Parameter meaning and setting value:

[0074] α is the influence coefficient of molecular concentration, which is set to 0.05. This coefficient reflects the sensitivity of molecular concentration in the decomposition rate;

[0075] β is the weight coefficient of temperature, which is set to 0.03. This coefficient indicates the influence of temperature change on the decomposition rate;

[0076] γ is the adjustment coefficient of pH value, which is set to 0.02. This coefficient indicates the role of pH value in controlling the biodegradation rate;

[0077] C t , B t , pH t are the molecular concentration, temperature and pH at time t, respectively, and set C t =100mg / L,

[0078] T t =20℃, pH t =7;

[0079] D t and D t-1 are the decomposed dynamic data at time t and t-1 respectively, and set D t =0.4 and D t-1 =0.3,

[0080] Substitute the parameters into the formula for calculation:

[0081]

[0082] The results show that under given environmental conditions, the biodegradation rate of the molecules is very low, reflecting that the efficiency of the biodegradation process under such conditions is low, and further condition optimization or technical improvement is needed to improve the degradation efficiency.

[0083] See also Figure 3, using the environmental decomposition dynamic data set, identifying the non-target biological exposure dose, exposure time and survival rate parameters, extracting the key variables associated with the non-target biological impact in the toxicity data, and obtaining the non-target biological toxicity assessment results are as follows:

[0084] S201: Using the environmental decomposition dynamic data set, extract the non-target biological exposure dose and exposure time parameters item by item, calculate the cumulative dose within the exposure duration by matching the dynamic change relationship in the dose and time series, and generate the non-target biological exposure data table. The execution process is as follows;

[0085] Set the key parameters contained in the environmental data set, such as temperature, humidity, chemical concentration, etc., decompose the environmental data through advanced data processing technology, conduct in-depth analysis of the data points, extract each data point related to non-target biological exposure, such as concentration threshold, duration, etc., and use dynamic simulation algorithms based on the data to calculate the exposure dose and time relationship of each organism. Such decomposition and extraction ensure the real-time update and accuracy of the data through precise data matching and time series analysis. This process not only involves complex data processing, but also needs to ensure the integrity and accuracy of the data. Each step must be precisely controlled to truly reflect the environmental conditions and biological exposure, provide a scientific basis for further toxicity assessment and environmental protection measures, and generate non-target biological exposure data tables.

[0086] S202: According to the non-target organism exposure data table, the survival rate parameters of the non-target organisms are called, and the survival rate change curve is calculated and the data is segmented and analyzed in combination with the exposure time and cumulative dose data, and the key variables corresponding to the survival rate change points are extracted. The execution process of obtaining the non-target organism exposure-related characteristic data is as follows;

[0087] By calling the non-target organism survival rate parameter, according to the formula:

[0088] S(t)=S 0 exp(-k·Q·t);

[0089] Calculate the survival rate change curve, where S(t) represents the survival rate at time t, S 0 represents the initial survival rate, k represents the dose-related decay constant, Q represents the cumulative dose, t represents the exposure time, and exp represents the natural exponential function;

[0090] Set the initial survival rate S 0 The cumulative dose Q is 100%, the exposure time t is 72 hours, and the attenuation constant k is set to 0.05h / mg according to the experimental data. Substitute it into the formula for calculation:

[0091] S(72)=100%·exp(-0.05·50·72)=100%·exp(-180)≈0%;

[0092] The results show that at this dose and time, the expected survival rate of non-target organisms is close to zero, indicating that dose and time have a great impact on the toxicity of organisms. This formula reflects the relationship between the survival rate of non-target organisms and the exposure dose and time. The accuracy of the model is verified by actual data, and the changes in survival rate under different conditions are further analyzed to provide a basis for scientific evaluation.

[0093] S203: Through the non-target biological exposure-related characteristic data, combined with the toxicity data, the exposure dose, exposure time and survival rate dynamic characteristics are matched item by item, the variables associated with the non-target biological impact are extracted, and the toxic effect values ​​of multiple parameters are evaluated to obtain the non-target biological toxicity assessment results. The execution process is as follows;

[0094] Toxicity assessment is performed through non-target biological exposure-related characteristic data, and variables associated with non-target biological impacts, such as exposure dose and survival rate, are extracted. Detailed data analysis of the variables is performed, and the toxicity data of non-target organisms is compared with the exposure data. Advanced statistical models and toxicological methods are used to compare the exposure dose, time and the response of the organisms item by item, and key variables, such as the intensity of associated toxicity and the rate of decline in survival rate, are extracted. Through comprehensive variables, a multi-parameter toxicity model is established to calculate the toxicity value, which reflects the comprehensive impact of different doses and times on organisms and provides a scientific basis for environmental protection. This process covers data induction, model construction, variable extraction and analysis, and effect prediction, ensuring that each step can actually reflect the direct relationship between toxicity data and biological responses, and obtain non-target biological toxicity assessment results.

[0095] See also Figure 4 , using the non-target biological toxicity assessment results, setting weights according to the ratio between the insecticide decomposition rate and the toxicity assessment value, extracting the inhibitory dose from the compound inhibition data, and performing normalization to obtain the compound score table are as follows:

[0096] S301: Based on the non-target biological toxicity assessment results, the pesticide decomposition rate and toxicity assessment value are extracted item by item, and the inhibitory effect of the compound is screened step by step by calculating the ratio relationship between the two. The execution process of obtaining the compound inhibitory dose data is as follows;

[0097] By calculating the ratio of the pesticide decomposition rate to the toxicity assessment value, according to the formula:

[0098]

[0099] Get the compound inhibitory dose data, where JR represents the compound inhibitory dose ratio, Drate represents the decomposition rate of pesticides, T value represents the toxicity assessment value;

[0100] Set the pesticide decomposition rate D rate The toxicity assessment value is 0.8 mg / L / h. value is 50mg / L, substitute into the formula to calculate:

[0101]

[0102] The results reflect the inhibitory efficiency of the pesticide under given conditions, with a lower ratio indicating a higher toxic effect. This data is used to further screen the inhibitory efficiency of compounds and provide a basis for environmental safety.

[0103] S302: using the compound inhibitory dose data, normalizing the inhibitory dose parameter, and calculating the normalized value by calling the maximum and minimum values ​​of the inhibitory dose, and obtaining the normalized value record. The execution process is as follows;

[0104] The compound inhibitory dose data was normalized, and the maximum and minimum values ​​of the compound inhibitory dose were set. Based on the extreme value parameters, normalization calculations were performed using mathematical formulas to determine the normalized inhibitory dose value of each compound. This calculation not only depended on the specific maximum and minimum values, but also depended on the distribution of the inhibitory dose data, ensuring that the normalized value of each compound could reflect the inhibitory ability relative to the remaining compounds, and serving as the basic data for scoring and further analysis, providing a quantitative way to evaluate the efficacy of different compounds and obtain normalized value records.

[0105] S303: Using the normalized value record, combined with the ratio weight calculated in the toxicity assessment result, and according to the scoring rules of the normalized inhibitory dose and the ratio weight, the compounds are scored one by one to obtain the compound scoring table. The execution process is as follows;

[0106] Combined with the ratio weight calculated in the toxicity assessment results, each compound is scored in detail according to specific scoring rules. This process involves the use of normalized inhibitory dose in combination with ratio weight to ensure that the score of each compound can accurately reflect the toxicity and efficacy in the actual environment. It not only provides researchers with an intuitive comparison tool, but also provides a scientific basis for future compound screening and use. The entire scoring process is strictly based on normalized data and calculated weights to ensure the scientificity and practicality of the scoring results, and obtain a compound scoring table.

[0107] See also Figure 5According to the compound scoring table, the experiment of the pesticide screening cycle is carried out, the dynamic response relationship between the inhibition rate and the experimental duration is extracted, and the correlation parameters between the pesticide concentration and the number of experiments are adjusted. The specific steps for obtaining the experimental parameter optimization record are as follows:

[0108] S401: According to the compound scoring table, the pesticides with priority scores are screened as experimental objects, the initial experimental cycle and action concentration are set, the relationship between the pesticide inhibition rate and the compound properties is analyzed, the inhibition rate prediction value is calculated, and the execution process of obtaining the inhibition rate dynamic response record is as follows;

[0109] The formula for calculating the predicted value of inhibition rate is as follows:

[0110]

[0111] Among them, RQ is the predicted value of inhibition rate, P represents the polarity of the compound, F represents the concentration of the compound, T represents the time of the experimental cycle, D represents the dose, and a 1 、a 2 、a 3 、a 4 is the weight parameter;

[0112] Quantitative acquisition and calculation process of parameters:

[0113] P (compound polarity) is obtained through chromatographic analysis techniques in the laboratory, and the value is converted from the retention time of the chromatographic peak;

[0114] F (compound concentration) was measured by mass spectrometry or UV-visible spectrometry and was expressed in milligrams per liter (mg / L);

[0115] T (experimental cycle time) is the number of days of experimental observation, recorded by calendar time;

[0116] D (dose) is the total amount of compound applied in the experiment, in milligrams (mg);

[0117] Weight parameter a 1 、a 2 、a 3 、a 4 Setting: a 1 and a 2 The historical experimental data were fitted using the least square method to reflect the relative effects of polarity and concentration on the inhibition rate;

[0118] a 3 and a 4 Adjustments were also made using historical data to maximize the model's sensitivity to response over time and dose;

[0119] Example of setting parameter values:

[0120] a 1 =0.5, a 2 =0.3, based on experimental data showing that polarity has a greater impact on inhibition rate;

[0121] a 3 =0.2, a 4 =0.1, reflecting that the inhibitory effect increases with time but the increase rate decreases, and the rate at which the inhibitory effect increases when the dose increases.

[0122] Substitute the parameters into the formula for calculation:

[0123] Assume P = 0.8, F = 200 mg / L, T = 14 days, D = 50 mg, substitute into the formula:

[0124]

[0125] The results showed that the predicted inhibition rate was 60.2%, which expressed that under given conditions, the insecticide had a higher effect and could reflect the combined influence of the properties of the compound and the experimental conditions on the inhibition effect.

[0126] S402: Analyze the dynamic response data of the pesticide inhibition rate and the experimental duration through the inhibition rate dynamic response record, adjust the pesticide action concentration parameter, and evaluate the effect of the pesticide concentration on the inhibition rate by resetting the concentration range and repeating the experiment to obtain the pesticide action concentration analysis result. The execution process is as follows;

[0127] A detailed analysis of the dynamic response data of the pesticide inhibition rate and the experimental duration was conducted. This process involved adjusting the pesticide concentration parameters to more accurately evaluate the effect of concentration changes on the inhibition rate. The effectiveness of the adjustment was verified by setting a new concentration range and repeating the experiment. This not only included collecting and analyzing data, but also involved using statistical and data analysis methods to determine the optimal concentration setting, helping researchers identify the most effective pesticide concentration, optimize use, and reduce potential impacts on the environment. During the experiment, the biological response after each concentration adjustment was recorded in detail, including the trend of inhibition rate changes and biological survival data at different concentrations, which affected the setting of future experiments and the use strategy of pesticides, ensuring that the use of pesticides effectively controlled pests while reducing the impact on non-target organisms, and improving the economic benefits and environmental sustainability of pesticides, and obtaining the results of the pesticide concentration analysis.

[0128] S403: Based on the analysis results of the pesticide action concentration, combined with the number of experiments in the experimental cycle, by adjusting the correlation parameters between the number of experimental repetitions and the action concentration, recording the optimized dynamic relationship between the experimental cycle and the number of experiments, analyzing the experimental effect of the concentration adjustment, and obtaining the execution process of the experimental parameter optimization record is as follows;

[0129] Further combined with the number of experiments within the experimental cycle, the correlation parameters between the number of experimental repetitions and the effective concentration are adjusted. In this way, the dynamic relationship between the optimized experimental cycle and the number of experiments is recorded, and the impact of this concentration adjustment on the experimental effect is analyzed. In this process, researchers use advanced data recording and analysis tools to ensure the accurate entry and analysis of each data. The purpose of optimizing the experimental cycle and number is to achieve the best experimental efficiency and result accuracy, reduce resource waste, and test the specific effects of different parameter settings on the results through scientific methods. The optimization of experimental parameters is based on comprehensive consideration of experimental cost, time and accuracy, to ensure scientific and valid data, support the decision-making process of pesticide use, and describe in detail the reasons and results of adjustments after each round of experiments. The records will be used to guide future experimental design and pesticide application, ensure the close integration of scientific research and practical application, improve the effectiveness and safety of pesticide use, and obtain optimized records of experimental parameters.

[0130] See also Figure 6 Based on the experimental parameter optimization records, the pesticide concentration and non-target biological toxicity parameters are called, the action time, experimental cycle and inhibition rate threshold data are extracted, and the cross-operation relationship between the pesticide action time and the experimental cycle is analyzed. The specific steps to obtain the recommended list of pesticide screening are as follows:

[0131] S501: Based on the experimental parameter optimization record, the pesticide concentration and non-target biological toxicity parameters are called item by item, and the execution process of obtaining the pesticide effect time and toxicity data is as follows by screening and comparing the action time, experimental cycle and inhibition rate threshold data;

[0132] The pesticide concentration and non-target biological toxicity parameters are called up, and the data are screened and compared, especially the data of action time, experimental cycle and inhibition rate threshold are carefully analyzed. This process includes the classification, screening and comparative analysis of experimental data to ensure that the obtained pesticide action time and toxicity data are not only accurate but also representative. The data will directly affect the application effect and safety assessment of pesticides. In this way, it can be ensured that only pesticides that meet safety standards and efficacy requirements will be included in further consideration and use, thereby improving the safety and effectiveness of pesticide use. Through the data screening and analysis at this stage, some pesticides that show high efficiency and low toxicity under specific conditions can be preliminarily determined, which lays the foundation for the next step of experimental design and product development. Data screening is not only based on statistical analysis, but also relies on the application of historical data and cutting-edge technologies, such as the application of artificial intelligence and machine learning in data analysis, to enhance the accuracy and efficiency of data processing and obtain pesticide action time and toxicity data.

[0133] S502: Through the pesticide effect and toxicity data, the pesticide action time and experimental cycle data are cross-calculated, and the corresponding action time interval and inhibition rate threshold in the experimental cycle are screened by calculating the dynamic matching relationship between the two, and the execution process of obtaining the pesticide screening cycle matching result is as follows;

[0134] The cross-calculation method is used to analyze the pesticide action time and experimental cycle data. The calculation includes the dynamic matching relationship between the two to determine the corresponding action time interval and inhibition rate threshold within the experimental cycle. The process involves complex data analysis and mathematical models, and the purpose is to screen out the action time interval that can achieve the best inhibition effect within a given experimental cycle. It can also ensure that the application of pesticides achieves the best balance between effectiveness and economy, directly affecting the market recommendation and use strategy of pesticides. It is the key to evaluating the performance of pesticides. It involves meticulous data verification and model calculation, and uses various algorithms to process data, such as time series analysis, regression analysis, etc., to ensure the scientificity and practicality of the results obtained, provide decision makers with detailed information on when and how to use pesticides, optimize the allocation and use of resources, reduce agricultural production costs, and reduce environmental burdens, and obtain pesticide screening cycle matching results.

[0135] S503: The screening results are gradually verified through the insecticide screening cycle matching results, and the matching insecticide data are screened according to the requirements of the inhibition rate threshold and the toxicity parameter to obtain the execution process of the insecticide screening recommendation list as follows;

[0136] The screening results are verified step by step. The verification process is carried out by comparing the inhibition rate threshold and toxicity parameter requirements to ensure that each pesticide meets the established safety and efficacy standards. The process involves not only data verification and analysis, but also field testing and simulation experiments to verify the accuracy and reliability of the data. Through this rigorous screening and verification process, it is provided to agricultural producers and relevant departments to guide the selection and use of pesticides, ensuring crop protection while also protecting environmental safety and the health of non-target organisms. The process includes complex logical judgment and decision analysis, and adopts multi-level verification mechanisms such as repeated experiments and cross-validation to ensure the repeatability and reliability of the results. The importance of this stage lies in its direct relationship to the market performance and user acceptance of pesticide products. The performance of each pesticide is verified through scientific and rigorous methods to ensure that the expected effect can be achieved in actual use, improve the market competitiveness of the product and user satisfaction, and obtain a recommended list of pesticide screening.

[0137] See also Figure 7 On the other hand, an electric vehicle state monitoring system is provided, and the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, and the system includes:

[0138] The cross-analysis module calculates the dynamic relationship between the molecular decomposition rate and environmental parameters based on the molecular structure parameters, environmental temperature parameters, pH parameters and soil type parameters of the compound, and cross-analyzes the dynamic impact of the molecular decomposition rate and environmental conditions to generate an environmental decomposition dynamic data set;

[0139] The toxicity assessment module uses the environmental decomposition dynamic data set to analyze the dynamic changes of non-target biological exposure dose, exposure time and survival rate parameters to obtain non-target biological toxicity assessment results;

[0140] The inhibitory dose normalization module normalizes the ratio of the inhibitory dose to the decomposition rate of the compound according to the non-target biological toxicity assessment results and generates a compound inhibition score table;

[0141] The insecticide action time analysis module adjusts the dynamic response relationship between the inhibition rate and the experimental duration in the experiment through the compound inhibition score table, analyzes the insecticide action time and experimental concentration change parameters during the experimental cycle, and obtains the experimental parameter optimization record;

[0142] The insecticide selection module uses experimental parameter optimization records to organize the dynamic matching relationship between insecticide action time, experimental cycle and inhibition rate threshold data to obtain a recommended list of insecticide screening.

[0143] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for screening and recommending pesticides, characterized in that: The following steps are involved: S1: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, according to the correlation between molecular decomposition rate, environmental dynamic stability and biodegradation behavior, the characteristic variables associated with the decomposition rate in the molecular structure are extracted to generate an environmental decomposition dynamic data set; S2: using the environmental decomposition dynamic data set, identifying the non-target biological exposure dose, exposure time and survival rate parameters, extracting key variables associated with the non-target biological impact in the toxicity data, and obtaining the non-target biological toxicity assessment results; S3: using the non-target organism toxicity assessment results, setting weights according to the ratio between the insecticide decomposition rate and the toxicity assessment value, extracting the inhibitory dose in the compound inhibitory effect data, and performing normalization processing to obtain a compound scoring table; S4: According to the compound scoring table, an experiment of the insecticide screening cycle is performed, the dynamic response relationship between the inhibition rate and the experimental duration is extracted, the correlation parameters between the insecticide concentration and the number of experiments are adjusted, and the experimental parameter optimization record is obtained; S5: Based on the experimental parameter optimization record, call the pesticide action concentration and non-target biological toxicity parameters, extract the action time, experimental cycle and inhibition rate threshold data, analyze the cross-operation relationship between the pesticide action time and the experimental cycle, and obtain a recommended list of pesticide screening.

2. The method for selecting and recommending pesticides according to claim 1, characterized in that: The environmental decomposition dynamic data set includes molecular degradation rate time series data, environmental stability parameter set, and biodegradation dynamic behavior data; the non-target biological toxicity assessment results include toxic effect classification parameters, environmental exposure influencing factors, and toxicity prediction model output values; the compound scoring table includes decomposition rate weight scores, toxicity assessment normalized scores, and inhibition effect rankings; the experimental parameter optimization records include inhibition rate dynamic response curves, experimental error ranges, and optimized test condition tables; the screening recommendation list includes the environmental safety level of pesticides, non-target biological toxicity indicators, and degradation dynamic trend classification information.

3. The method for selecting and recommending pesticides according to claim 1, characterized in that: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, according to the correlation between molecular decomposition rate, environmental dynamic stability and biodegradation behavior, the characteristic variables associated with the decomposition rate in the molecular structure are extracted. The specific steps for generating the environmental decomposition dynamic data set are as follows: S101: Based on the molecular structure parameters, environmental temperature parameters, pH value parameters and soil type parameters of the compound, the basic physical and chemical properties of the molecular structure are extracted, the molecular polarity, charge distribution and chemical bond type are extracted, and the physical and chemical properties are quantified and characterized item by item. Multivariate cross analysis is performed in combination with environmental parameters to obtain a molecular key structure feature table; S102: using the molecular key structure feature table, combining the environmental temperature parameter, pH value parameter and soil type parameter, calling the temperature change sequence, pH range and soil particle composition characteristics, mapping the molecular decomposition rate influencing variables item by item, performing multi-parameter cross-coupling calculation, summarizing the dynamic correlation and coupling relationship between the variables, and obtaining correlation feature data; S103: Utilizing the associated characteristic data, analyzing the dynamic data of the molecular decomposition rate and the associated variables, calculating the rate change in the time series, and generating an environmental decomposition dynamic data set.

4. The method for selecting and recommending pesticides according to claim 3, characterized in that: The formula for calculating the rate change in the time series is as follows: Among them, R(t) is the rate change in the time series, α represents the influence coefficient of molecular concentration on the rate, and C t represents the molecular concentration at time t, β represents the weight coefficient of temperature, B t represents the ambient temperature at time t, γ represents the pH adjustment factor, pH t represents the pH value at time t, D t and D t-1 They represent the decomposed dynamic data at time t and t-1 respectively, and e is a natural constant.

5. The method for selecting and recommending pesticides according to claim 1, characterized in that: The steps of using the environmental decomposition dynamic data set to identify non-target biological exposure dose, exposure time and survival rate parameters, extracting key variables associated with non-target biological impacts in toxicity data, and obtaining non-target biological toxicity assessment results are as follows: S201: using the environmental decomposition dynamic data set, extracting non-target biological exposure dose and exposure time parameters item by item, calculating the cumulative dose within the exposure duration by matching the dynamic change relationship in the dose and time series, and generating a non-target biological exposure data table; S202: According to the non-target organism exposure data table, call the survival rate parameters of the non-target organisms, combine the exposure time and cumulative dose data, calculate the survival rate change curve and perform data segmentation analysis, extract the key variables corresponding to the survival rate change points, and obtain the non-target organism exposure-related feature data; S203: By combining the non-target biological exposure-related characteristic data with the toxicity data, the dynamic characteristics of exposure dose, exposure time and survival rate are matched item by item, the variables associated with the impact on non-target organisms are extracted, the toxic effect values ​​of multiple parameters are evaluated, and the non-target biological toxicity assessment results are obtained.

6. The method for selecting and recommending pesticides according to claim 1, characterized in that: The steps of using the non-target biological toxicity assessment results, setting weights according to the ratio between the insecticide decomposition rate and the toxicity assessment value, extracting the inhibitory dose in the compound inhibition data, and performing normalization processing to obtain the compound score table are specifically as follows: S301: Based on the non-target organism toxicity assessment results, extract the pesticide decomposition rate and toxicity assessment value item by item, and gradually screen the inhibitory effect of the compound by calculating the ratio relationship between the two to obtain compound inhibitory dose data; S302: using the inhibitory dose data of the compound, normalizing the inhibitory dose parameter, calculating the normalized value by calling the maximum and minimum values ​​of the inhibitory dose, and obtaining a normalized value record; S303: Using the normalized value records and the ratio weights calculated in the toxicity assessment results, the compounds are scored one by one according to the scoring rules of the normalized inhibitory dose and the ratio weight to obtain a compound scoring table.

7. The method for selecting and recommending pesticides according to claim 1, characterized in that: According to the compound scoring table, the experiment of the pesticide screening cycle is carried out, the dynamic response relationship between the inhibition rate and the experimental time is extracted, and the correlation parameters between the pesticide concentration and the number of experiments are adjusted to obtain the experimental parameter optimization record. Specifically, the steps are as follows: S401: According to the compound scoring table, select pesticides with priority scores as experimental objects, set the initial experimental period and action concentration, analyze the relationship between the pesticide inhibition rate and the compound properties, calculate the inhibition rate prediction value, and obtain the inhibition rate dynamic response record; S402: Analyze the dynamic response data of the pesticide inhibition rate and the experimental duration through the inhibition rate dynamic response record, adjust the pesticide action concentration parameter, evaluate the effect of the pesticide concentration on the inhibition rate by resetting the concentration range and repeating the experiment to obtain the pesticide action concentration analysis result; S403: Based on the analysis results of the insecticide action concentration and the number of experiments within the experimental cycle, by adjusting the correlation parameters between the number of experimental repetitions and the action concentration, recording the dynamic relationship between the optimized experimental cycle and the number of experiments, analyzing the experimental effect of the concentration adjustment, and obtaining the experimental parameter optimization record.

8. The method for selecting and recommending pesticides according to claim 7, characterized in that: The formula for calculating the predicted value of the inhibition rate is as follows: Among them, RQ is the predicted value of inhibition rate, P represents the polarity of the compound, F represents the concentration of the compound, T represents the time of the experimental cycle, D represents the dose, and a1, a2, a3, and a4 are weight parameters.

9. The method for selecting and recommending pesticides according to claim 1, characterized in that: Based on the experimental parameter optimization record, the steps of calling the pesticide action concentration and non-target biological toxicity parameters, extracting the action time, experimental cycle and inhibition rate threshold data, analyzing the cross-calculation relationship between the pesticide action time and the experimental cycle, and obtaining the recommended list of pesticide screening are as follows: S501: Based on the experimental parameter optimization record, the pesticide action concentration and non-target biological toxicity parameters are called item by item, and the pesticide action time and toxicity data are obtained by screening and comparing the action time, experimental cycle and inhibition rate threshold data; S502: using the pesticide action time and toxicity data, cross-calculating the pesticide action time and experimental cycle data, and calculating the dynamic matching relationship between the two, screening the corresponding action time interval and inhibition rate threshold in the experimental cycle, and obtaining the pesticide screening cycle matching result; S503: The screening results are verified step by step through the insecticide screening cycle matching results, and the matching insecticide data are screened according to the inhibition rate threshold and toxicity parameter requirements to obtain an insecticide screening recommendation list.

10. A pesticide screening recommendation system, characterized in that: The method for screening and recommending pesticides according to any one of claims 1 to 9, wherein the system comprises: The cross-analysis module calculates the dynamic relationship between the molecular decomposition rate and environmental parameters based on the molecular structure parameters, environmental temperature parameters, pH parameters and soil type parameters of the compound, and cross-analyzes the dynamic impact of the molecular decomposition rate and environmental conditions to generate an environmental decomposition dynamic data set; The toxicity assessment module uses the environmental decomposition dynamic data set to analyze the dynamic changes of non-target biological exposure dose, exposure time and survival rate parameters to obtain non-target biological toxicity assessment results; The inhibitory dose normalization module performs normalized calculation on the ratio of the inhibitory dose to the decomposition rate of the compound according to the non-target biological toxicity assessment result, and generates a compound inhibition score table; The insecticide action time analysis module adjusts the dynamic response relationship between the inhibition rate and the experimental duration in the experiment through the compound inhibition score table, analyzes the insecticide action time and experimental concentration change parameters in the experimental cycle, and obtains the experimental parameter optimization record; The insecticide selection module uses the experimental parameter optimization records to organize the dynamic matching relationship between the insecticide action time, experimental cycle and inhibition rate threshold data to obtain an insecticide screening recommendation list.

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