Pesticide toxicological effect intelligent simulation method and system based on big data
By analyzing the binding free energy and interaction relationship between pesticides and target organisms through big data analysis, the combined dosage of pesticides can be optimized, solving the problems of pesticide interactions and residues, and achieving efficient and safe pesticide use.
Patent Information
- Application Number
- CN202511979941.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-04-07
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing technologies fail to effectively consider pesticide interactions and residues when applying pesticides, affecting control efficacy and crop safety.
Using a big data-based intelligent simulation method for pesticide toxicology, the binding free energy between pesticides and target organisms is simulated through molecular dynamics. This allows for the screening of pesticide combinations with strong binding capacity and high efficacy. The interaction relationships and action sites between pesticides are analyzed to optimize pesticide dosage.
It significantly improves the effectiveness of pest and disease control, reduces pesticide usage and residues, and ensures the safety and reliability of crop efficacy.
Smart Images
Figure CN121811954A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of pesticide toxicology effect analysis, and particularly relates to a pesticide toxicology effect intelligent simulation method and system based on big data. BACKGROUND
[0002] In the growth cycle of crops, diseases and pests can directly damage the roots, stems, leaves, flowers and fruits of crops, and further cause the growth of crops to be hindered and the yield to be reduced. However, when pesticides are used to control diseases and pests, the pesticides themselves can also have an impact on crops. Therefore, carrying out pesticide toxicology effect analysis can effectively ensure the effectiveness and safety of pesticide control of diseases and pests.
[0003] The prior art still has some deficiencies in pesticide toxicology effect analysis, which are specifically embodied in the following aspects: (1) the prior art mainly analyzes the dosage and control effect of pesticides when the pesticides are used alone, thereby ignoring the interaction of various pesticides when used due to the long decomposition period of the pesticides or the intensive time of disease and pest control, which further affects the control effect of the pesticides and even causes harm to crops.
[0004] (2) the prior art mostly focuses on the analysis of the synergistic effect of various pesticides, thereby ensuring the control effect while reducing the use amount of pesticides, but ignores that the synergistic effect of various pesticides in the pesticide spraying process can increase the pesticide residue on parts that do not need to be controlled, thereby affecting the safety of crops. SUMMARY
[0005] In view of the above technical deficiencies, the present application aims to provide a pesticide toxicology effect intelligent simulation method and system based on big data.
[0006] To solve the above technical problems, the present application adopts the following technical solution: in a first aspect, the present application provides a pesticide toxicology effect intelligent simulation method based on big data, comprising: step one, obtaining historical crop information of a target planting site, and analyzing to obtain various types of pesticides required in a planting cycle under the target planting site, performing efficacy testing on the various types of pesticides required in the planting cycle under the target planting site, and further screening to obtain various alternative pesticide groups.
[0007] Step two, performing toxicology effect simulation testing on each alternative pesticide group, thereby obtaining the interaction relationship between each pesticide in each alternative pesticide group, and then according to the interaction relationship between each pesticide and the action site of the corresponding crop, analyzing to obtain the target pesticide corresponding to each target organism from each alternative pesticide group, and analyzing to obtain the dosage of each target pesticide.
[0008] Step three, according to the corresponding target pesticide of each target organism and the dosage of each target pesticide, the complete growth cycle of the crop is simulated and tested, and then the use effect of each target pesticide is fed back.
[0009] The application provides an intelligent pesticide toxicological effect simulation system based on big data, which comprises a pesticide analysis module, a pesticide toxicological effect test module, and a feedback module.
[0010] The pesticide analysis module is used for obtaining historical crop information of a target planting site and analyzing required pesticides in a planting cycle of the target planting site, performing pesticide efficacy testing on the required pesticides in the planting cycle of the target planting site, and screening each alternative pesticide group.
[0011] The pesticide toxicological effect test module is used for performing toxicological effect simulation testing on each alternative pesticide group, obtaining the action relationship between each pesticide in each alternative pesticide group, and analyzing the corresponding target pesticide of each target organism and the dosage of each target pesticide from each alternative pesticide group according to the action relationship between each pesticide and the action site of the corresponding crop.
[0012] The feedback module is used for performing simulation testing on the complete growth cycle of the crop according to the corresponding target pesticide of each target organism and the dosage of each target pesticide, and feeding back the use effect of each target pesticide.
[0013] The application has the following beneficial effects: (1) the application directly quantifies the action strength of the pesticide on the target by simulating the binding free energy of the pesticide and the key protein of the target organism, selects each pesticide with strong binding capacity and high prevention effect, avoids the blindness of traditional field selection of pesticides by experience, guarantees the significance of the disease and pest inhibition effect, and guarantees the reliability of the drug efficacy from the molecular level. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.
[0015] Figure 1 It is a method implementation step flowchart of the application.
[0016] Figure 2 The system structure connection diagram of the present application is shown in the figure. DETAILED DESCRIPTION
[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0018] Referring to Figure 1 As shown in the figure, the present application provides, in a first aspect, a pesticide toxicological effect intelligent simulation method based on big data, comprising: step one, obtaining historical crop information of a target planting site, and analyzing to obtain various types of pesticides required in a planting period under the target planting site, performing efficacy testing on the various types of pesticides required in the planting period under the target planting site, and then screening to obtain various alternative pesticide groups.
[0019] It should be noted that the crop information includes crop type, growth period, disease and pest information corresponding to each time period in the growth period, etc., wherein the crops planted in the various planting periods of the target planting site are the same.
[0020] In a specific example, the analysis to obtain the various types of pesticides required in the planting period under the target planting site is specifically as follows: obtaining the crop type planted in the planting period under the target planting site from the planting plan, and then matching to obtain the various types of pesticides required in the next planting period according to the crop type, and dividing the various types of pesticides into various pesticide groups according to the utility of the various types of pesticides, wherein the utilities of the various types of pesticides in the same pesticide group are the same.
[0021] It should be noted that the matching to obtain the various types of pesticides required in the next planting period according to the crop type is specifically represented as: obtaining the growth period of the crop according to the crop information of the previous planting period, and then obtaining the disease and pest types corresponding to each time period in the growth period, so as to screen to obtain the corresponding various types of pesticides according to the disease and pest types, for example, for aphids, the corresponding various types of pesticides thereof can be screened to be imidacloprid, thiamethoxam, acetamiprid and thiamethoxam, etc.
[0022] In one specific example, the efficacy of each type of pesticide required in the planting period of the target planting field is tested, and the specific testing process is as follows: using molecular dynamics simulation software to simulate the efficacy of each pesticide on the corresponding target organism, first inputting the three-dimensional structure of the key protein of the target organism and the molecular structure of each pesticide in the corresponding pesticide group, virtually docking the three-dimensional structure of the key protein of the target organism with the molecular structure of each pesticide in the corresponding pesticide group, and then obtaining the binding free energy of the three-dimensional structure of the key protein of the target organism with the molecular structure of each pesticide in the corresponding pesticide group. The binding free energy of the key protein of each target organism and the molecular structure of each pesticide in the corresponding pesticide group is obtained by testing in this way.
[0023] According to the crop information of the target planting field in the planting period, the type of crop is obtained, and then the pesticide residue of each pesticide in the pesticide group on each part of the crop after the action of each pesticide in the corresponding pesticide group of the target organism on the type of crop for a predetermined period of time is simulated. According to this, the pesticide residue of each pesticide in each pesticide group on each part of the crop is tested.
[0024] In one specific example, each alternative pesticide group is screened, and the specific process is as follows: comparing the binding free energy of the key protein of the target organism with the molecular structure of each pesticide in the corresponding pesticide group with the set binding free energy threshold, and recording each pesticide with a binding free energy less than or equal to the set binding free energy threshold as each binding free energy qualified pesticide.
[0025] Then, the pesticide residue of each binding free energy qualified pesticide on each part of the crop is compared with the set pesticide residue threshold, and each binding free energy qualified pesticide with a pesticide residue on each part of the crop less than the set pesticide residue threshold is recorded as each alternative pesticide, and then each alternative pesticide in each pesticide group is retained, and each non-alternative pesticide in each pesticide group is deleted, thereby obtaining each alternative pesticide group.
[0026] It should be noted that the binding free energy threshold and the pesticide residue threshold are set by the staff according to relevant information documents, and are not specifically limited herein. The smaller the binding free energy, the higher the effectiveness of the pesticide on the target. Therefore, the smaller the binding free energy threshold, the higher the requirement for the efficacy of the pesticide. Similarly, the smaller the pesticide residue, the safer the pesticide for the crop. Therefore, the smaller the pesticide residue, the higher the safety requirement for the crop.
[0027] Step two, toxicological efficacy simulation test is performed on each alternative pesticide group, and the action relationship between each pesticide in each alternative pesticide group is obtained, and then the target pesticide corresponding to each target organism is analyzed from each alternative pesticide group according to the action relationship between each pesticide and the action site of the corresponding crop, and the dosage of each target pesticide is analyzed.
[0028] In one specific example, the toxicological effect simulation test is performed on each alternative pesticide group, and the interaction relationship between each pesticide in each alternative pesticide group is obtained, and the specific test process is as follows: first, the curve of the inhibition rate of each alternative pesticide in each alternative pesticide group on the corresponding target organism is obtained by fitting the Logistic model, and the control effect of a single pesticide on the target organism is determined accordingly.
[0029] It should be noted that the Logistic model is a prior art model technique, and thus will not be described again.
[0035] According to each growth time period of each target organism in the corresponding crop growth period, any alternative pesticide in each alternative pesticide group corresponding to different target organisms in the same time period or adjacent time period is obtained, combined according to a predetermined proportion, and each combined pesticide is obtained, and a curve of the inhibition rate of each combined pesticide on the target organism is fitted. The curve of the inhibition rate of each combined pesticide on the target organism is compared with the curve of the inhibition rate of the corresponding single pesticide on the target organism. If the inhibition rate decreases, it indicates that each pesticide in each combined pesticide has an inhibition relationship. If the inhibition rate increases, it indicates that each pesticide in each combined pesticide has a synergistic relationship. If the inhibition rate does not change, it indicates that each pesticide in each combined pesticide has no correlation.
[0031] Any alternative pesticide in each alternative pesticide group corresponding to different target organisms in the same time period or adjacent time period is obtained, and combined according to a predetermined proportion, which is specifically represented as: one alternative pesticide is selected from each alternative pesticide group, and combined according to a predetermined proportion, thereby ensuring that each target organism is controlled by the corresponding pesticide.
[0032] It should be noted that the curve of the inhibition rate of each combined pesticide on the target organism is compared with the curve of the inhibition rate of the corresponding single pesticide on the target organism, and the relationship between the single pesticide and the combined pesticide is that the single pesticide is a subset of the combined pesticide.
[0033] It should be noted that for each pesticide in the same time period or adjacent time period, if each pesticide binds to the same action site of the target organism, it may cause inhibition due to target site occupation, or one pesticide may induce or inhibit the metabolic enzyme activity of another pesticide, thereby exhibiting a synergistic or inhibitory relationship. If each pesticide binds to different sites of the target organism, it may cause synergistic effect because it blocks the physiological pathway of the target organism, or it may induce or inhibit the metabolic enzyme activity of another pesticide, thereby exhibiting a synergistic or inhibitory effect.
[0034] In one specific example, the target pesticides corresponding to each target organism are analyzed from each alternative pesticide group according to the action relationship between each pesticide and the action site of the corresponding crop, and the use amount of each target pesticide is analyzed. The specific analysis process is as follows: for each target organism with different action sites in the same time period or adjacent time period, each pesticide with an inhibitory relationship is obtained, a simulation test of the toxicological inhibitory relationship between each pesticide is performed, and then each target inhibitory pesticide corresponding to each target organism and the pesticide use amount are analyzed.
[0035] For each target organism with the same action site in the same time period or adjacent time period, each pesticide with a synergistic relationship is obtained, a simulation test of the toxicological synergistic relationship between each pesticide is performed, and then each target synergistic pesticide corresponding to each target organism and the pesticide use amount are analyzed.
[0036] It should be noted that the target pesticide is a target inhibitory pesticide or a target synergistic pesticide.
[0037] In one specific example, the toxicological inhibitory relationship between each pesticide is simulated and tested, and then each target inhibitory pesticide corresponding to each target organism and the pesticide use amount are analyzed. The specific analysis process is as follows: for each target organism with different action sites in the same time period or adjacent time period, each pesticide with an inhibitory relationship is matched arbitrarily to obtain each inhibitory pesticide test group, the growth of the crops is simulated according to the crop information of one planting period on the target planting site, and each type of disease and pest corresponding to each target organism is simulated in the growth period in the corresponding growth cycle. According to the actual pesticide spraying method, each inhibitory pesticide test group is sprayed in turn on each action site corresponding to the crops. After a preset time period, the target organism inhibition rate of each action site and the pesticide residue amount of the non-action site are collected. According to a preset gradient, the proportion of each pesticide in each inhibitory pesticide test group is changed, and then the target organism inhibition rate of each action site and the pesticide residue amount of the non-action site corresponding to each inhibitory pesticide test group at each proportion are tested.
[0038] When the pesticide residue amount of the non-action site is the lowest and the target organism inhibition rate of the action site meets the standard, the corresponding inhibitory pesticide test group is obtained, each pesticide in the inhibitory pesticide test group is recorded as the target inhibitory pesticide corresponding to each target organism, and the target use amount of each target inhibitory pesticide corresponding to the target organism is calculated based on the proportion of each pesticide in the inhibitory pesticide test group and the number of target organisms.
[0039] It should be noted that when the pesticide residue amount of the non-action site is the lowest and the target organism inhibition rate of the action site meets the standard, the corresponding inhibitory pesticide test group is obtained, and the target organism inhibition rate meeting the standard indicates that the inhibition rate is ≥80%.
[0040] For example, the pesticide thiamethoxam acting on the root-knot nematode of the tomato root system is in an inhibitory relationship with the pesticide clothianidin acting on the whitefly of the tomato leaf. In the same time period or adjacent time period, thiamethoxam and clothianidin are sprayed on the tomato root system and the leaf, respectively. Under the interaction of the two, the root-knot nematode control efficiency reaches 91%, the thiamethoxam residue in the parts other than the root system decreases from 0.7 mg / kg to 0.2 mg / kg, the whitefly control efficiency on the leaf reaches 89%, and the clothianidin residue in the parts other than the leaf decreases from 0.6 mg / kg to 0.1 mg / kg, greatly reducing the pesticide residue in the non-acting site.
[0041] In a specific example, the toxicological synergistic relationship between each pesticide is simulated and tested, and then the target synergistic pesticide and the pesticide dosage corresponding to each target organism are analyzed. The specific process is as follows: for each target organism in the same time period or adjacent time period and the same acting site, each candidate pesticide with synergistic relationship is matched arbitrarily, and then each synergistic pesticide test group is obtained. According to the crop information of one planting period on the target planting site, the crop growth is simulated, and in the growth period in the corresponding growth cycle, each type of disease and pest corresponding to each target organism is simulated. According to the actual pesticide spraying mode, each pesticide in each synergistic pesticide test group is sprayed in turn on the corresponding acting site of the crop. After a preset time period, the target organism inhibition rate of the acting site and the pesticide residue of the non-acting site are collected. According to a preset gradient, the proportion of each pesticide in each synergistic pesticide test group is changed, and then the target organism inhibition rate of the corresponding acting site and the pesticide residue of the non-acting site of each synergistic pesticide test group under each proportion are tested.
[0042] The synergistic pesticide test group corresponding to the highest inhibition rate of the acting site and the pesticide residue of the non-acting site meeting the standard is obtained, each pesticide in the synergistic pesticide test group is recorded as the target synergistic pesticide corresponding to each target organism, and based on the proportion of each pesticide in the synergistic pesticide test group and the number of target organisms, the target dosage of each target synergistic pesticide corresponding to the target organism is calculated.
[0043] It should be noted that the synergistic pesticide test group corresponding to the highest inhibition rate of the acting site and the pesticide residue of the non-acting site meeting the standard is obtained, and the pesticide residue of the non-acting site meeting the standard means that the pesticide residue is less than or equal to the residue threshold value of the corresponding pesticide.
[0044] Exemplary, the test shows that the pesticides imidacloprid and high-efficiency cypermethrin acting on aphids and whiteflies of tomato leaves are in synergistic relationship, wherein the imidacloprid 200 mg / L can achieve a control effect of 85% when used alone; the high-efficiency cypermethrin 150 mg / L can achieve a control effect of 80%; but when the two are used in the same time period or adjacent time period, the imitation of imidacloprid 100 mg / L and high-efficiency cypermethrin 75 mg / L can achieve 90%, greatly reducing the amount of pesticide while improving the efficacy of the pesticide.
[0045] Step three, according to the target pesticides corresponding to each target organism and the use amount of each target pesticide, the complete growth cycle of the crop is simulated and tested, and then the use effect of each target pesticide is fed back.
[0046] In one specific example, the complete growth cycle of the crop is simulated and tested according to the target pesticides corresponding to each target organism and the use amount of each target pesticide, and then the use effect of each target pesticide is fed back, the specific process is as follows: according to the crop information of one planting cycle on the target planting site, the growth of the crop of the next planting cycle is simulated and simulated, and in the simulation process, the crop is prevented and controlled according to the test of each target pesticide corresponding to each target organism and the use amount of each target pesticide, and when the crop is harvested, the crop yield and the pesticide residue are counted, the simulated crop yield and the pesticide residue are compared with the actual crop yield and the pesticide residue of the last planting cycle, and fed back to the data center.
[0047] It should be noted that, in the simulation process, except for the type and amount of pesticide, the rest of the planting conditions are the same as the planting conditions of the target planting site in one planting cycle.
[0048] It should be noted that, in the simulation process, except for the type and amount of pesticide, the rest of the planting conditions are the same as the planting conditions of the target planting site in one planting cycle.
[0049] Referring to Figure 2 The present application provides an intelligent pesticide toxicology effect simulation system based on big data in a second aspect, comprising: a pesticide analysis module: for obtaining the historical crop information of the target planting site, and analyzing to obtain the required various types of pesticides in one planting cycle of the target planting site, and performing efficacy test on the required various types of pesticides in one planting cycle of the target planting site, and then screening to obtain each alternative pesticide group.
[0050] The pesticide toxicology effect test module is used for performing toxicology effect simulation tests on each alternative pesticide group, thereby obtaining the action relationship between each pesticide in each alternative pesticide group, and then analyzing the target pesticides corresponding to each target organism from each alternative pesticide group according to the action relationship between each pesticide and the action site of the corresponding crop, and analyzing the use amount of each target pesticide.
[0051] The feedback module is used for performing simulation tests on the complete growth cycle of the crop according to the target pesticides corresponding to each target organism and the use amount of each target pesticide, thereby feeding back the use effect of each target pesticide.
[0052] The examples in the present application are not limited to the specific modes listed in the above embodiments, and the above examples are only exemplary descriptions provided for the convenience of understanding the present application and do not constitute a limitation on the protection scope of the present application. Any modification, equivalent replacement, etc. made within the spirit and principles of the present application shall be included in the protection scope.
[0053] The above content is only an example and description of the concept of the present application, and those skilled in the art can make various modifications, supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the present application or exceed the range defined in the specification, which shall belong to the protection scope of the present application.
Claims
1. A smart simulation method for pesticide toxicological effects based on big data, characterized in that, Includes the following steps: Step 1: Obtain historical crop information of the target planting site and analyze it to determine the types of pesticides required for the next planting cycle of the target planting site. Conduct efficacy tests on the types of pesticides required for the next planting cycle of the target planting site, and then screen out each candidate pesticide group. Step 2: Conduct toxicological efficacy simulation tests on each candidate pesticide group to obtain the interaction relationship between pesticides in each candidate pesticide group. Then, based on the interaction relationship between pesticides and the corresponding action sites of crops, analyze the target pesticides corresponding to each target organism from each candidate pesticide group and analyze the dosage of each target pesticide. Step 3: Based on the target pesticides corresponding to each target organism and the dosage of each target pesticide, conduct simulation tests on the complete growth cycle of crops, and then provide feedback on the effectiveness of each target pesticide.
2. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 1, characterized in that, The analysis yields the various pesticides required for the target planting site in the next planting cycle. The specific process is as follows: The types of crops to be planted in the next planting cycle at the target planting site are obtained from the planting plan. Then, the types of pesticides required for the next planting cycle are matched according to the crop types. Based on the efficacy of each type of pesticide, the pesticides are divided into pesticide groups, where the pesticides in the same pesticide group have the same efficacy.
3. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 2, characterized in that, The efficacy tests were conducted on various pesticides required for the next planting cycle at the target planting site. The specific testing process is as follows: Molecular dynamics simulation software was used to conduct efficacy simulation tests on each pesticide and its corresponding target organism. First, the three-dimensional structure of the key protein of a target organism was input, and the molecular structures of each pesticide in the corresponding pesticide group were also input. The three-dimensional structure of the key protein of the target organism was then virtually docked with the molecular structures of each pesticide in the corresponding pesticide group to obtain the binding free energy between the three-dimensional structure of the key protein of the target organism and the molecular structures of each pesticide in the corresponding pesticide group. The binding free energy between the key protein of each target organism and the molecular structures of each pesticide in the corresponding pesticide group was tested in this way. Based on the crop information of the target planting site in the next planting cycle, the crop type is obtained. Then, the pesticide residue of each pesticide in the pesticide group corresponding to a certain target organism is simulated after the pesticide is applied to the crop of that type for a preset time period. Based on this, the pesticide residue of each pesticide in each pesticide group corresponding to each part of the crop is obtained.
4. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 3, characterized in that, The screening process for obtaining the candidate pesticide groups is as follows: The binding free energy of the key protein of a target organism and the molecular structure of each pesticide in the corresponding pesticide group is compared with the set binding free energy threshold. Pesticides with binding free energy less than or equal to the set binding free energy threshold are recorded as pesticides that meet the binding free energy standard. Next, the pesticide residue levels of each pesticide that meets the binding free energy standard in each part of the crop are compared with the set pesticide residue threshold. Pesticides that meet the binding free energy standard in each part of the crop with pesticide residue levels less than the set pesticide residue threshold are recorded as candidate pesticides. Then, each candidate pesticide in each pesticide group is retained, and non-candidate pesticides in each pesticide group are deleted, thereby obtaining each candidate pesticide group.
5. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 4, characterized in that, The toxicological efficacy simulation tests were conducted on each candidate pesticide group to obtain the interaction relationships between the pesticides in each candidate pesticide group. The specific test process is as follows: First, the curves of inhibition rates of each candidate pesticide and the corresponding target organism in each candidate pesticide group were obtained by fitting the Logistic model, and the control efficacy of a single pesticide against the target organism was determined accordingly. Then, based on the growth time periods of the crop corresponding to each target organism, any candidate pesticides from each candidate pesticide group corresponding to different target organisms in the same or adjacent time periods are obtained and combined according to a preset ratio to obtain each pesticide combination. Then, the curve of the inhibition rate of each pesticide combination on the target organism is fitted. The curve of the inhibition rate of each pesticide combination on the target organism is compared with the curve of the inhibition rate of the corresponding single pesticide on the target organism. If the inhibition rate decreases, it indicates that the pesticides in each pesticide combination have an inhibitory relationship. If the inhibition rate increases, it indicates that the pesticides in each pesticide combination have a synergistic relationship. If the inhibition rate remains unchanged, it indicates that the pesticides in each pesticide combination have an incoherent relationship.
6. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 5, characterized in that, Based on the interaction relationships between pesticides and the corresponding action sites on crops, the target pesticides corresponding to each target organism were analyzed from each candidate pesticide group, and the dosage of each target pesticide was determined. The specific analysis process is as follows: For each target organism with different sites of action in the same or adjacent time periods, obtain each pesticide with an inhibitory relationship, conduct simulation tests on the toxicological inhibitory relationship between each pesticide, and then analyze and obtain the target inhibitory pesticide and pesticide dosage corresponding to each target organism. For each target organism with the same site of action in the same or adjacent time periods, pesticides with synergistic relationships are obtained, and the toxicological synergistic relationships between pesticides are simulated and tested. Then, the synergistic pesticides and pesticide dosages corresponding to each target organism are analyzed.
7. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 6, characterized in that, The toxicological inhibition relationships among various pesticides were simulated and tested to analyze the corresponding pesticides and pesticide dosages for each target organism. The specific analysis process is as follows: For target organisms at different sites of action within the same or adjacent time periods, pesticides with inhibitory relationships are arbitrarily matched to obtain inhibitory pesticide test groups. Based on crop information from a planting cycle at the target planting site, crop growth is simulated. During the corresponding growth period of the growth cycle, various pests and diseases corresponding to each target organism are simulated. Following the actual pesticide spraying method, each inhibitory pesticide test group is sprayed sequentially onto the corresponding sites of action on the crop. After a preset time period, the inhibition rate of target organisms at each site of action and the pesticide residue at non-sites of action are collected. The proportion of each pesticide in each inhibitory pesticide test group is changed according to a preset gradient, and then the inhibition rate of target organisms at each site of action and the pesticide residue at non-sites of action are tested at each proportion. Obtain the pesticide test group corresponding to the pesticide with the lowest pesticide residue at the non-action site and the target organism inhibition rate at the action site that meets the standard. Record each pesticide in the pesticide test group as the target inhibitor for each target organism. Based on the proportion of each pesticide in the pesticide test group and the number of target organisms, calculate the target dosage of each target inhibitor for each target organism.
8. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 6, characterized in that, The toxicological synergistic relationships among various pesticides were simulated and tested, and then the corresponding synergistic pesticides and pesticide dosages for each target organism were analyzed. The specific process is as follows: For target organisms at the same site of action within the same or adjacent time periods, synergistic pesticides are arbitrarily matched to obtain synergistic pesticide test groups. Based on crop information from a planting cycle at the target planting site, crop growth is simulated. During the corresponding growth period of the growth cycle, various pests and diseases corresponding to each target organism are simulated. Following actual pesticide spraying methods, each pesticide in the synergistic pesticide test group is sprayed sequentially onto the corresponding site of action on the crop. After a preset time period, the target organism inhibition rate at the site of action and the pesticide residue at the non-site of action are collected. The proportion of each pesticide in each synergistic pesticide test group is changed according to a preset gradient, and the target organism inhibition rate at the site of action and the pesticide residue at the non-site of action are tested at each proportion. The synergistic pesticide test group corresponding to the highest inhibition rate at the site of action and the pesticide residue at the non-site of action meeting the standard is obtained. Each pesticide in the synergistic pesticide test group is recorded as the target synergistic pesticide corresponding to each target organism. Based on the proportion of each pesticide in the synergistic pesticide test group and the number of target organisms, the target dosage of each target synergistic pesticide corresponding to the target organism is calculated.
9. The intelligent simulation method for pesticide toxicological effects based on big data according to claim 8, characterized in that, The process involves simulating the complete growth cycle of crops based on the target pesticides corresponding to each target organism and the dosage of each pesticide, thereby providing feedback on the effectiveness of each pesticide. The specific process is as follows: Based on the crop information of the previous planting cycle at the target planting site, the growth of crops in the next planting cycle is simulated. During the simulation, pest and disease control is carried out on crops according to the target pesticides and pesticide dosages corresponding to each target organism obtained from the test. At the time of crop harvest, the crop yield and pesticide residues are counted. The simulated crop yield and pesticide residues are compared with the actual crop yield and pesticide residues of the previous planting cycle and fed back to the data center.
10. A system executed using the intelligent simulation method for pesticide toxicological effects based on big data as described in any one of claims 1-9, characterized in that, include: Pesticide Analysis Module: Used to obtain historical crop information of the target planting site, analyze and obtain the various pesticides required for the next planting cycle of the target planting site, conduct efficacy tests on the various pesticides required for the next planting cycle of the target planting site, and then screen out each candidate pesticide group; The pesticide toxicology efficacy testing module is used to conduct toxicology efficacy simulation tests on each candidate pesticide group, thereby obtaining the interaction relationship between pesticides in each candidate pesticide group. Based on the interaction relationship between pesticides and the corresponding action sites of crops, the target pesticides corresponding to each target organism are analyzed from each candidate pesticide group, and the dosage of each target pesticide is also analyzed. Feedback module: It is used to simulate the complete growth cycle of crops based on the target pesticides corresponding to each target organism and the dosage of each target pesticide, and then provide feedback on the effect of each target pesticide.