AFW-BRS model-based agricultural ecosystem stability evaluation method

Abstract

The invention provides an agricultural ecosystem stability assessment method based on an AFW-BRS model, and belongs to the technical field of ecological assessment, and the method comprises the steps: employing a first model to simulate the change of biomass of each species before and after the use of chemical substances in an agricultural cycle, the first model being an agricultural food web model; a second model is adopted to simulate and evaluate the stability of the first ecological system before and after the chemical substances are used, and the second model is a BRS evaluation model; the first model is used for simulating and analyzing the change condition of the ecological system after the species are introduced, and the second model is used for simulating and evaluating the stability of the second ecological system after the species are introduced; acquiring a factor set influencing the stability of the agricultural ecosystem, and determining an organic agriculture strategy in combination with simulation results of the first model and the second model; when the first model does not meet the set standard, the organic agriculture strategy is optimized. Chain reactions generated by introducing other species into the ecological system are analyzed, and the necessity and influence of various chemical substances in the ecological system are evaluated.

Description

Technical Field

[0001] The invention relates to the technical field of ecological assessment, and in particular to an agricultural ecosystem stability assessment method based on an AFW-BRS model. Background Art

[0002] As the global population grows and the demand for resources such as food increases, forest land is often converted to farmland, which leads to dramatic changes in ecosystems. Agricultural yields may be high at first, but over time, soil fertility decreases, pests and diseases increase, and reliance on chemical pesticides and fertilizers increases. Agricultural activities simplify ecosystems and reduce species diversity. Over time, marginal habitats may gradually regain some of their ecological functions. In this process, the adoption of sustainable agricultural practices directly affects ecological restoration. Using mathematical modeling to analyze ecological changes and explore agricultural management practices can help achieve a balance between agricultural output and ecological sustainability.

[0003] Therefore, the present invention proposes an agricultural ecosystem stability assessment method based on the AFW-BRS model. Summary of the invention

[0004] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, which is used to construct an agricultural food web model (AFW) and a BRS assessment model to understand the changes in the ecosystem and promote the sustainable development of agriculture. The relative changes in the biomass of producer species before and after the use of chemical substances can be simulated; the BRS assessment model is used to calculate the Shannon-Wiener index and stability index S of the agricultural ecosystem after the application of chemical substances. The AFW model can reveal the intricate ecological relationships between different species and accurately present the impact of human intervention on the ecosystem. At the same time, the model can also visualize the relationships between species in the agricultural ecosystem; the model is flexible and can not only analyze the chain reactions caused by introducing other species into the ecosystem, but also evaluate the necessity and impact of various chemical substances in the ecosystem. It can comprehensively evaluate the possible impacts of these interference factors on the ecosystem.

[0005] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, comprising: Step 1: using model 1 to simulate the changes in biomass of various species before and after the use of chemical substances in the agricultural cycle, wherein the model 1 is an agricultural food web model, and the agricultural food web model is: a competition model and a LotkaVolterra model; Step 2: using Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of the chemical substance, wherein the Model 2 is a BRS evaluation model; Step 3: Use Model 1 to simulate and analyze the changes in the ecosystem after the introduction of the species. At the same time, use Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of the species. Step 4: Obtain the set of factors that affect the stability of agricultural ecosystems, and determine the organic agricultural strategy by combining the simulation results of Model 1 and Model 2; Step 5: Perform sensitivity analysis on the model one. When the model one meets the set standard, the organic agriculture strategy is sent to the agricultural end. Otherwise, the organic agriculture strategy is optimized. The AFW-BRS model is model one and model two.

[0006] Preferably, the competition model is composed of a differential equation of producer ecological quantity, a differential equation of weed biomass, and an equation of the effect of chemical substances on species biomass; The LotkaVolterra model is composed of the Lotka-Kolmogorov equation, wherein the Lotka-Kolmogorov equation is used to model the variation pattern between pest and bird biomass.

[0007] Preferably, the construction of the differential equation of the producer's ecological quantity includes: According to the relative utilization space of producers and farmland areas in the ecosystem, and combined with the competition relationship between producers and weeds, a blocked growth equation for the producer growth part is constructed; Use the sine function to construct an equation for the effect of seasonal factors on producer growth rate; According to the predation relationship between producers and pests, the influence of producers on seeds at the sowing stage, and the accumulation of pesticide pollution on soil, the loss equation of producers' biomass is constructed. Based on the random disturbance term of biomass, and combined with the blocked growth equation, the growth rate influence equation and the loss equation, the differential equation of the producer's ecological quantity is constructed.

[0008] Preferably, the construction of the differential equation of weed biomass includes: According to the competition between producers and weeds, the growth equation of weed biomass was constructed; Construct a weed growth equation based on the effects of seasonal climate; According to the inhibitory relationship between pesticides and weeds, the direct effect equation of pesticides on weeds is constructed; Based on the growth part equation, the weed growth equation and the direct impact equation, a differential equation for the weed biomass is constructed.

[0009] Preferably, the construction of the equation for the effect of chemical substances on species biomass includes: Construct a cumulative equation for soil pollution caused by the number of times the agent is used; Based on the impulse function, the equation of biological change of weeds and pests after using pesticides was constructed; Based on the one-dimensional linear function, the equation of the effect of the drug on the biomass of birds was constructed; Based on the accumulation equation, the biological change equation and the equation for the impact on bird biomass, an equation for the impact of chemical substances on species biomass is constructed.

[0010] Preferably, after constructing the effect equation of chemical substances on species biomass, the method further includes: Retrieving soil pollution concentrations of several detection areas at different agricultural stages in the agricultural cycle from a historical storage database, wherein the soil pollution concentration includes a first detection concentration after each spraying of the pesticide and a second detection concentration of the spraying pesticide at the end of a corresponding preset volatilization cycle; Marking the concentration curve according to the spraying time of the pesticide, determining the light intensity of the marked point every day within the preset volatilization cycle, and determining the set volatilization concentration of the corresponding pesticide type under the agricultural stage of the corresponding marked point based on the light intensity set according to the stage-pharmaceutical type-intensity set-volatilization comparison table; Based on the set volatilization concentration of each marked point and the concentration difference between the first detection concentration and the second detection concentration, a concentration difference array of the same detection area in the corresponding agricultural stage is constructed. ,in, represents the set volatilization concentration of the ith annotation point in the uth agricultural stage; represents the first detected concentration of the i-th annotation point in the u-th agricultural stage; represents the second detected concentration of the i-th annotation point in the u-th agricultural stage; represents the concentration difference coefficient of the ith annotation point in the uth agricultural stage; Indicates the number of times the corresponding detection area is sprayed with pesticides in the u-th agricultural stage; Construct a concentration-volatilization difference function based on the concentration difference arrays under different agricultural stages; Retrieve from the historical storage database the first growth density of the producer after the corresponding drug spraying time and the second growth density of the producer before the corresponding drug spraying time corresponding to the drug type corresponding to each marked point; Obtaining the historical final growth state of the producer at each agricultural stage from the historical storage database, and determining the growth loss function in combination with the first growth density and the second growth density involved; ; in, represents the historical final growth state of the p-th detection area under the u-th agricultural stage; represents the state judgment value of the p-th detection area in the u-th agricultural stage; represents the first growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the second growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the density judgment function, and ; represents the growth loss function of all detected areas under the u-th agricultural stage; Indicates the total number of detection areas involved; Based on the growth loss function and the concentration volatilization difference function, a pollution fine-tuning function is generated, and based on the pollution fine-tuning function, an equation for the influence of chemical substances on species biomass is adjusted.

[0011] Preferably, a concentration-volatilization difference function is constructed according to the concentration difference arrays under different agricultural stages, including: Construct the phase volatilization difference function based on all the detection areas under the corresponding agricultural stages; ; according to The phase volatility difference function under each agricultural stage is adjusted, where: represents the variance based on all Ru; ;

[0012] in, Indicates the p-th detection area under the u-th agricultural stage based on the corresponding concentration difference array The variance of the coefficient of difference; Indicates that the pth detection area exists in the uth agricultural stage the number of represents the stage volatility difference function under the u-th agricultural stage determined based on all detection areas; Indicates all The minimum value in ; It represents the concentration volatilization difference function under the u-th agricultural stage after adjusting the corresponding Ru; Represents the variance threshold.

[0013] Preferably, the organic farming strategy is optimized, including: Determine the sensitivity index of the model 1, and extract the index to be optimized whose sensitivity value does not meet the corresponding set standard from all the sensitivity indexes; Determine the first influencing factor based on each indicator to be optimized from the indicator-factor mapping table; Conduct a global strategic analysis of the organic farming strategy to determine the strategic layout factors involved; Projecting the strategic layout factor and the first influencing factor into a factor mapping table respectively, obtaining a second influencing factor mapped with each strategic layout factor, and determining a sub-adjustment suggestion based on the mapped second influencing factor according to a layout implementation rule; The organic farming strategy is optimized based on all sub-adjustment suggestions, and an optimized farming strategy is obtained and issued.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The Agricultural Food Web Model (AFW) and the BRS assessment model are constructed to understand the changes in the ecosystem and promote the sustainable development of agriculture. The relative changes in the biomass of producer species before and after the use of chemicals can be simulated; the BRS assessment model is used to calculate the Shannon-Wiener index and stability index S of the agricultural ecosystem after the application of chemicals. The AFW model can reveal the intricate ecological relationships between different species and accurately present the impact of human intervention on the ecosystem. At the same time, the model can also visualize the relationships between species in the agricultural ecosystem; the model is flexible and can not only analyze the chain reactions caused by the introduction of other species into the ecosystem, but also evaluate the necessity and impact of various chemicals in the ecosystem. It can comprehensively evaluate the possible impacts of these interference factors on the ecosystem.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0016] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model in an embodiment of the present invention; Figure 2 is a curve diagram of stability change in an embodiment of the present invention; Figure 3 A comparison diagram of producer biomass before and after applying chemical substances in an embodiment of the present invention; Figure 4 A comparison diagram of insect biomass before and after application of insecticides in an embodiment of the present invention; Figure 5 A comparison chart of bird biomass in the case of applying and not applying pesticides in an embodiment of the present invention; Figure 6 A comparison diagram of the total biomass when the chemical substance is applied and when the chemical substance is not applied in the embodiment of the present invention; Figure 7 A comparison chart of bird biomass before and after the introduction of new consumers in an embodiment of the present invention; Figure 8 This is a comparison chart of insect biomass before and after the introduction of new consumers in the examples of the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, such as Figure 1 As shown, including: Step 1: using model 1 to simulate the changes in biomass of various species before and after the use of chemical substances in the agricultural cycle, wherein the model 1 is an agricultural food web model, and the agricultural food web model is: a competition model and a LotkaVolterra model; Step 2: using Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of the chemical substance, wherein the Model 2 is a BRS evaluation model; Step 3: Use Model 1 to simulate and analyze the changes in the ecosystem after the introduction of the species. At the same time, use Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of the species. Step 4: Obtain the set of factors that affect the stability of agricultural ecosystems, and determine the organic agricultural strategy by combining the simulation results of Model 1 and Model 2; Step 5: Perform sensitivity analysis on the model one. When the model one meets the set standard, the organic agriculture strategy is sent to the agricultural end. Otherwise, the organic agriculture strategy is optimized. The AFW-BRS model is model one and model two.

[0020] Preferably, the competition model is composed of a differential equation of producer ecological quantity, a differential equation of weed biomass, and an equation of the effect of chemical substances on species biomass; The LotkaVolterra model is composed of the Lotka-Kolmogorov equation, wherein the Lotka-Kolmogorov equation is used to model the variation pattern between pest and bird biomass.

[0021] In this embodiment, the ecosystem is a wheat-corn rotation agricultural system.

[0022] In this embodiment, the chemical substance is the agricultural chemical used, and the agricultural chemical is herbicide, insecticide, etc.

[0023] In this embodiment, based on the competition model and the LotkaVolterra model, the state of the ecosystem is presented from the perspective of biomass, and the relationship between producers and consumers is analyzed. A sine function is used to simulate the growth rate of producers to reflect seasonal changes. Taking into account the agricultural cycle, herbicides are applied on the 40th, 85th, 210th and 339th days, and insecticides are applied on the 20th, 70th, 150th, 235th and 331st days, with an application rate of 50 ml per acre.

[0024] In this embodiment, a BRS evaluation model is developed based on biomass, diversity and stability, in which the Shannon-Wiener diversity index is introduced to measure ecosystem diversity, and a variance-based stability metric S is used. The biomass calculated in Model 1 is used to solve the diversity index and stability metric.

[0025] In this embodiment, in order to study the impact of chemical substances on the ecosystem, model I was used to simulate the changes in the biomass of various species during the agricultural cycle and evaluate the health of plants, and model II was used to evaluate the stability of the ecosystem. The results showed that when chemical substances were used, the stress indexes of wheat and corn were 0.2 and 0.167, respectively, and the plants were healthy; the average biomass of insects, bats and birds were 27.3, 12 and 3, respectively, and the stability index S was 142.9 (<150), indicating that the ecosystem was stable. When chemical substances were not used, the biomass of insects, bats and birds increased to 212.3 and 70.2, respectively, and the stability index was 140.2, indicating that the ecosystem was more stable.

[0026] In order to explore the effects of introducing rabbits and eagles and replacing chemicals with bats or frogs on ecosystem stability, Model 1 was used to analyze the changes in the ecosystem after the introduction of species, and Model 2 was used to calculate the stability index, and the results were 139.8, 136.6, and 139.2, respectively. The results showed that the introduction of rabbits and eagles significantly improved the stability of the ecosystem; in terms of biological control, the stability of the ecosystem introduced by introducing bats was better than that of introducing frogs.

[0027] In this embodiment, the set of factors affecting the stability of the agricultural ecosystem includes: factors affecting the ecosystem's changes (environment, weather), factors affecting organic agricultural practices (pest control, producer health, plant reproduction, biodiversity, cost-effectiveness), etc.

[0028] In this embodiment, the sensitivity analysis is related to the stability index of the ecosystem. For example, when the growth rates of wheat and corn are 0.3 and 0.2 respectively, the stability index of model one is calculated to be 142.9. When the growth rates of wheat and corn are adjusted from 0.3 and 0.2 to 0.4 and 0.3 respectively, the stability index of model one is calculated to be 137.6. When the growth rates of wheat and corn are adjusted from 0.3 and 0.2 to 0.4 and 0.1 respectively, the stability index of model one is calculated to be 140.2. When the growth rates of wheat and corn are adjusted from 0.3 and 0.2 to 0.1 and 0.1 respectively, the stability index of model one is calculated to be 143.4. When the growth rate fluctuates within a certain range, the total biomass and stability of the ecosystem do not change much, and the trends are basically the same, which indicates that the stability of the model is very good. Figure 2 shown.

[0029] In this example, the agricultural food web model (AFW) can simulate the relative changes in the biomass of producer species before and after the application of chemicals, such as Figure 3 As shown in the figure, under the action of herbicides and pesticides, the biomass of the two producers fluctuated during the agricultural cycle and then stabilized at around 1000 kg / m2 and 800 kg / m2, respectively. Due to seasonal factors, wheat grows slowly in winter and its overall growth rate is lower than that of corn. Due to factors such as weeds, pests, birds and accumulated soil pollution, wheat has a complex relationship with producers, so its biomass did not reach its maximum value. In addition, after the application of herbicides, the number of weeds dropped sharply to a certain level and then slowly recovered.

[0030] To study plant health status, a stress index is introduced: ; Wherein, SI stands for stress index; is the biomass of producers under normal conditions; is the biomass of the producer under stress conditions, i.e. the biomass of the producer at harvest.

[0031] The stress index (SI) reflects the extent of biomass loss when producers are stressed. The larger the SI value, the more severe the stress, the greater the biomass loss, and the worse the health of the producer. At that time, the index indicated that the producer was not affected by stress and was in a healthy growth state. The safety index (SI index) of corn and wheat was calculated separately: 0.167 vs. 0.2, which shows that corn and wheat are in a relatively healthy growth state under the influence of herbicides.

[0032] In this example, the agricultural food web model (AFW) can simulate the relative changes in the biomass of consumer species before and after the application of chemicals, such as Figure 4Shown is a comparison of insect biomass before and after application of insecticides, e.g. Figure 5 Shown is a comparison of bird biomass with and without pesticide application.

[0033] Due to the predator-prey relationship between insects and birds, biomass changes dynamically. After the application of pesticides, newly hatched insects are quickly killed, some eggs survive, and insect biomass plummets to zero and remains constant for a period of time. Due to food shortages, the number of birds then decreases. After the eggs hatch, insect biomass rebounds and the number of birds gradually increases. However, as the frequency and dosage of chemical applications increase, the enrichment of chemicals in birds intensifies, and even if the insect population recovers, the bird biomass eventually still shows a downward trend.

[0034] After the use of chemicals, the period average biomass of insects, bats and birds were 27.3 and 12.3, respectively, while without the use of chemicals, the biomass of insects, bats and birds increased to 212.3 and 70.2, respectively, indicating that the use of chemicals significantly reduced the biomass of insects as well as bats and birds.

[0035] In this example, the Shannon-Wiener index and the stability index S of the agricultural ecosystem after the application of chemical substances were calculated using the BRS evaluation model.

[0036] like Figure 6 As shown in the figure, the overall fluctuation of ecosystem species is on an upward trend. When chemicals are used, the image values ​​drop sharply because weeds and pests are eliminated, but over time, the values ​​gradually rise again due to the stability of the ecosystem's resistance. This shows that chemicals have a temporary inhibitory effect on ecosystem stability, but eventually the ecosystem can recover on its own.

[0037] The stability index S calculated by using herbicides and pesticides was 142.9 (less than 150), indicating that the ecosystem was relatively stable; when herbicides and pesticides were not used, the stability index was 140.2, which also indicated that the ecosystem was relatively stable.

[0038] In this embodiment, the BRS evaluation model was used to calculate the stability index of the agricultural ecosystem after the introduction of rabbits and hawks, and a new ecological balance could be gradually formed.

[0039] like Figure 7 As shown and Figure 8 As shown in Figure 1, the introduction of rabbits and hawks changed the biomass of the ecosystem. Rabbits preyed on producers, causing their biomass to decrease; rabbits competed with insects for resources, resulting in a decrease in insect numbers. Bird numbers also declined due to a decrease in prey insects and predation by hawks.

[0040] The beneficial effects of the above technical solution are: constructing an agricultural food web model (AFW) and a BRS assessment model to understand changes in the ecosystem and promote sustainable agricultural development, which can simulate the relative changes in the biomass of producer species before and after the use of chemicals; using the BRS assessment model to calculate the Shannon-Wiener index and stability index S of the agricultural ecosystem after the application of chemicals. The AFW model can reveal the intricate ecological relationships between different species and accurately present the impact of human intervention on the ecosystem. At the same time, the model can also visualize the relationships between species in the agricultural ecosystem; the model is flexible and can not only analyze the chain reactions caused by the introduction of other species into the ecosystem, but also evaluate the necessity and impact of various chemicals in the ecosystem. It can comprehensively evaluate the possible impacts of these interference factors on the ecosystem.

[0041] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, and the construction of a differential equation of producer ecological quantity, including: According to the relative utilization space of producers and farmland areas in the ecosystem, and combined with the competition relationship between producers and weeds, a blocked growth equation for the producer growth part is constructed; Use the sine function to construct an equation for the effect of seasonal factors on producer growth rate; According to the predation relationship between producers and pests, the influence of producers on seeds at the sowing stage, and the accumulation of pesticide pollution on soil, the loss equation of producers' biomass is constructed. Based on the random disturbance term of biomass, and combined with the blocked growth equation, the growth rate influence equation and the loss equation, the differential equation of the producer's ecological quantity is constructed.

[0042] Preferably, the construction of the differential equation of weed biomass includes: According to the competition between producers and weeds, the growth equation of weed biomass was constructed; Construct a weed growth equation based on the effects of seasonal climate; According to the inhibitory relationship between pesticides and weeds, the direct effect equation of pesticides on weeds is constructed; Based on the growth part equation, the weed growth equation and the direct impact equation, a differential equation for the weed biomass is constructed.

[0043] Preferably, the construction of the equation for the effect of chemical substances on species biomass includes: Construct a cumulative equation for soil pollution caused by the number of times the agent is used; Based on the impulse function, the equation of biological change of weeds and pests after using pesticides was constructed; Based on the one-dimensional linear function, the equation of the effect of the drug on the biomass of birds was constructed; Based on the accumulation equation, the biological change equation and the equation for the impact on bird biomass, an equation for the impact of chemical substances on species biomass is constructed.

[0044] In this embodiment, the biomass differential model based on the random disturbance term is: ; in, is the biomass of the ith population at time t; is the growth fraction of the biomass of the ith population; It is a Gaussian white noise with a mean of 0 and a variance of a2; is the proportion of biomass loss of the i-th species; The retarded growth equation is: ; in, denote the growth rates of wheat and corn biomass, respectively; represent the biomass of wheat, corn and weeds, respectively; represent the maximum biomass of wheat, corn and weeds, respectively; Represent the competition coefficients of weeds to wheat and corn, respectively.

[0045] The growth rate equation is: ; in, represent the maximum growth rates of wheat and corn, respectively; The biomass loss equation is: ; in, It indicates the intensity of pest predation on producers; represents the intensity of bird predation on producer seeds; represent the biomass of pests and birds, respectively; Represents the impact of soil pollution accumulation on biomass.

[0046] The differential equation of the producer's ecological quantity is: .

[0047] The growth equation for weed biomass is: ; in, Indicates the growth rate of weeds; represents the competition coefficient of producers against weeds; The weed growth equation is: ; in, Indicates the maximum rate of weed growth; Direct effect equation of pesticide on weeds: ; in, It is the inhibition coefficient of the pesticide on weeds.

[0048] In this embodiment, the formula for modeling the variation pattern between pest and bird biomass based on the Lotka-Kolmogorov equation (LotkaVolterra equation) is as follows: ; in, are the natural growth rates of pests and birds, respectively; Indicates the intensity of predation on pests; Indicates the intensity of predation on birds; Indicates the intensity of bird predation on crop seeds; Represent the effects of pesticides and herbicides on pest and bird biomass, respectively.

[0049] In this embodiment, the accumulation equation for soil pollution is: ; in, Indicates the accumulated amount of soil pollution; They represent conversion coefficients respectively; They represent the number of times and amount of pesticides used, respectively; Respectively represent the number of times and amount of herbicide used.

[0050] The equation for the biomass change of weeds and pests after using pesticides is: ; in, Respectively, in time The pulse intensity; represents the impulse function, and ; The effect equation of the drug on bird biomass is: ; in, is the scaling factor.

[0051] The equation for the effect of chemical substances on species biomass is: .

[0052] The beneficial effects of the above technical solution are: constructing an agricultural food web model (AFW) based on the differential equation of producer ecological quantity, the differential equation of weed biomass, the equation of the influence of chemical substances on species biomass, and the Lotka-Kolmogorov equation, providing a basis for subsequent decision-making.

[0053] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model. After constructing an equation for the influence of chemical substances on species biomass, the method further includes: Retrieving soil pollution concentrations of several detection areas at different agricultural stages in the agricultural cycle from a historical storage database, wherein the soil pollution concentration includes a first detection concentration after each spraying of the pesticide and a second detection concentration of the spraying pesticide at the end of a corresponding preset volatilization cycle; Marking the concentration curve according to the spraying time of the pesticide, determining the light intensity of the marked point every day within the preset volatilization cycle, and determining the set volatilization concentration of the corresponding pesticide type under the agricultural stage of the corresponding marked point based on the light intensity set according to the stage-pharmaceutical type-intensity set-volatilization comparison table; Based on the set volatilization concentration of each marked point and the concentration difference between the first detection concentration and the second detection concentration, a concentration difference array of the same detection area in the corresponding agricultural stage is constructed. ,in, represents the set volatilization concentration of the ith annotation point in the uth agricultural stage; represents the first detected concentration of the i-th annotation point in the u-th agricultural stage; represents the second detection concentration of the i-th annotation point in the u-th agricultural stage; represents the concentration difference coefficient of the ith annotation point in the uth agricultural stage; Indicates the number of times the corresponding detection area is sprayed with pesticides in the u-th agricultural stage; Construct a concentration-volatilization difference function based on the concentration difference arrays under different agricultural stages; Retrieve from the historical storage database the first growth density of the producer after the corresponding drug spraying time and the second growth density of the producer before the corresponding drug spraying time corresponding to the drug type corresponding to each marked point; Obtaining the historical final growth state of the producer at each agricultural stage from the historical storage database, and determining the growth loss function in combination with the first growth density and the second growth density involved;

[0054] in, represents the historical final growth state of the p-th detection area under the u-th agricultural stage; represents the state judgment value of the p-th detection area in the u-th agricultural stage; represents the first growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the second growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the density judgment function, and ; represents the growth loss function of all detected areas under the u-th agricultural stage; Indicates the total number of detection areas involved; Based on the growth loss function and the concentration volatilization difference function, a pollution fine-tuning function is generated, and based on the pollution fine-tuning function, an equation for the influence of chemical substances on species biomass is adjusted.

[0055] In this embodiment, the historical database contains the soil pollution concentration, growth density, historical final growth state, standard growth state in each stage, etc. of different detection areas before and after each spraying in different agricultural stages, which are all pre-stored, and the detection area can be different farmland areas, for example, farmland k1, farmland k2, farmland k3, etc. in Village A, and the value of M1 is greater than 20.

[0056] In this embodiment, the agricultural stages include: sowing stage, seedling growth stage, pollination and maturity stage, etc.

[0057] In this embodiment, the three stages of the wheat agricultural cycle correspond to September to October, October to May, and May to June, while the three stages of the corn agricultural cycle correspond to June to July, July to August, and August to September.

[0058] In this embodiment, the detection of soil concentration is based on the measurement by the concentration sensor to determine the concentration of pesticide residues in the soil.

[0059] In this embodiment, the preset volatilization periods corresponding to different medicines are 3 days for some medicines and 7 days for some medicines, and are clearly recorded in the instructions of different medicines.

[0060] In this embodiment, the concentration curve is obtained by plotting all measured concentrations in the corresponding detection area at different agricultural stages according to the time sequence, and the pesticide spraying time point refers to the time when the pesticide spraying starts.

[0061] In this embodiment, the stage-agent type-intensity set-volatilization comparison table includes different agent types sprayed in different agricultural stages, and the residual concentration of the agent type after volatilization under continuous light intensity combinations, that is, the set concentration after volatilization, is pre-stored.

[0062] In this embodiment, different agricultural stages involve are different, but in the same agricultural stage set in different detection areas are the same.

[0063] In this embodiment, there is a concentration volatilization difference function and a growth loss function in each agricultural stage.

[0064] In this example, the producer refers to wheat or corn.

[0065] In this embodiment, the historical final growth state refers to the historical actual growth state at the last moment of the corresponding agricultural stage, which is collected and stored in advance.

[0066] In this embodiment, the pollution fine-tuning function = (1-growth loss function corresponding to the agricultural stage / )×concentration-volatilization difference function.

[0067] In this embodiment, the value combination of the fine-tuning function under different agricultural stages is obtained from the fine-tuning-adjustment comparison table to obtain the biological adjustment amount based on the comparison table. The table contains the value combination of fine-tuning under different stages and the biological adjustment amount matching the combination, which is pre-set, and the biological adjustment amount is generally between -2%×Sw and 2%×Sw, and Sw is the biomass after the agent.

[0068] In this embodiment, the adjustment is: the effect equation of the chemical substance on the species biomass + the biological adjustment amount.

[0069] The beneficial effects of the above technical solution are: starting from the soil pollution concentration and combining it with the light intensity, the concentration difference of the agent is determined and then the concentration volatilization difference function is constructed. At the same time, the growth loss function is determined starting from the growth density and growth state of each agricultural stage, and then the two are combined to construct the pollution fine-tuning function, so as to achieve effective adjustment of the equation affecting the species biomass and ensure the accuracy of model one.

[0070] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, which constructs a concentration volatilization difference function according to concentration difference arrays under different agricultural stages, including: Construct the phase volatilization difference function based on all the detection areas under the corresponding agricultural stages;

[0071] according to The phase volatility difference function under each agricultural stage is adjusted, where: represents the variance based on all Ru;

[0072] in, Indicates the p-th detection area under the u-th agricultural stage based on the corresponding concentration difference array The variance of the coefficient of difference; Indicates that the pth detection area exists in the uth agricultural stage the number of represents the stage volatility difference function under the u-th agricultural stage determined based on all detection areas; Indicates all The minimum value in ; It represents the concentration volatilization difference function under the u-th agricultural stage after adjusting the corresponding Ru; represents the variance threshold; In this embodiment, The value of is 0.1.

[0073] The beneficial effect of the above technical solution is: through all the detection areas based on the stage volatility difference function of the same agricultural stage, combined with By adjusting the function, the concentration-volatilization difference function is obtained, which provides a strong support for the adjustment of biomass.

[0074] The present invention provides an agricultural ecosystem stability assessment method based on the AFW-BRS model, which optimizes the organic agricultural strategy, including: Determine the sensitivity index of the model 1, and extract the index to be optimized whose sensitivity value does not meet the corresponding set standard from all the sensitivity indexes; Determine the first influencing factor based on each indicator to be optimized from the indicator-factor mapping table; Conduct a global strategic analysis of the organic farming strategy to determine the strategic layout factors involved; Projecting the strategic layout factor and the first influencing factor into a factor mapping table respectively, and obtaining a second influencing factor mapped to each strategic layout factor; Inputting the same second influencing factor and all strategic local factors involved in the same second influencing factor into the layout implementation model, and determining a sub-adjustment suggestion for the same second influencing factor; The organic farming strategy is optimized based on all sub-adjustment suggestions, and an optimized farming strategy is obtained and issued.

[0075] In this embodiment, the sensitivity index includes not only a stability index but also an accuracy index. The setting standard corresponding to the stability index is that the sensitivity value of the stability index is less than 150.

[0076] The sensitivity value of the accuracy index is greater than 95%.

[0077] In this embodiment, it is assumed that the sensitivity value of the accuracy index is 90%. At this time, the accuracy index is the index to be optimized.

[0078] In this embodiment, the index-factor mapping table includes different sensitivity indexes and influencing factors matching the indexes, such as light factors, humidity factors, temperature factors, etc., which are regarded as the first influencing factors.

[0079] In this embodiment, the global strategy analysis is performed by extracting the parameters of the organic agriculture strategy to obtain the strategy layout parameters, which are regarded as strategy layout factors and can be directly analyzed and extracted from the organic agriculture strategy.

[0080] In this embodiment, the factor mapping table includes the placement positions of different strategic layout factors, the placement positions of the first influencing factors, etc., that is, only the factors need to be placed in the relevant positions of the mapping table to obtain the second influencing factors (part of the first influencing factors) mapped to the strategic layout factors.

[0081] In this embodiment, the layout implementation model is obtained by training the neural network model with different first influencing factors and combinations of different strategic layout factors under the factors and adjustment results as samples. Therefore, sub-adjustment suggestions for the same second influencing factor can be directly obtained.

[0082] In this embodiment, optimization is achieved by adding all sub-adjustment suggestions to the organic farming strategy.

[0083] In this embodiment, the sub-adjustment suggestions are, for example: do not use synthetic fertilizers, pesticides, herbicides and other synthetic substances, or reduce the number of times the required agents are used to half or less of the number of times the agents were used before.

[0084] The beneficial effect of the above technical solution is: by determining the indicator to be optimized, and then combining the indicator-factor mapping table to determine the first influencing factor, and combining the strategy layout factors and the projection results to determine the second influencing factor, and then optimizing the strategy according to the sub-adjustment suggestions to obtain a more reasonable strategy.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating the stability of an agricultural ecosystem based on the AFW-BRS model, characterized in that: include: Step 1: using model 1 to simulate the changes in biomass of various species before and after the use of chemical substances in the agricultural cycle, wherein the model 1 is an agricultural food web model, and the agricultural food web model is: a competition model and a LotkaVolterra model; Step 2: using Model 2 to simulate and evaluate the stability of the first ecosystem before and after the use of the chemical substance, wherein the Model 2 is a BRS evaluation model; Step 3: Use Model 1 to simulate and analyze the changes in the ecosystem after the introduction of the species. At the same time, use Model 2 to simulate and evaluate the stability of the second ecosystem after the introduction of the species. Step 4: Obtain the set of factors that affect the stability of agricultural ecosystems, and determine the organic agricultural strategy by combining the simulation results of Model 1 and Model 2; Step 5: Perform sensitivity analysis on the model one. When the model one meets the set standard, the organic agriculture strategy is sent to the agricultural end. Otherwise, the organic agriculture strategy is optimized. The AFW-BRS model is model one and model two.

2. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 1, characterized in that: The competition model is composed of a differential equation of producer ecological quantity, a differential equation of weed biomass, and an equation of the effect of chemical substances on species biomass; The LotkaVolterra model is composed of the Lotka-Kolmogorov equation, wherein the Lotka-Kolmogorov equation is used to model the variation pattern between pest and bird biomass.

3. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 2 is characterized in that: The construction of differential equations of producer ecological quantities includes: According to the relative utilization space of producers and farmland areas in the ecosystem, and combined with the competition relationship between producers and weeds, a blocked growth equation for the producer growth part is constructed; Use the sine function to construct an equation for the effect of seasonal factors on producer growth rate; According to the predation relationship between producers and pests, the influence of producers on seeds at the sowing stage, and the accumulation of pesticide pollution on soil, the loss equation of producers' biomass is constructed. Based on the random disturbance term of biomass, and combined with the blocked growth equation, the growth rate influence equation and the loss equation, the differential equation of the producer's ecological quantity is constructed.

4. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 2, characterized in that: Construction of differential equations for weed biomass, including: According to the competition between producers and weeds, the growth equation of weed biomass was constructed; Construct a weed growth equation based on the effects of seasonal climate; According to the inhibitory relationship between pesticides and weeds, the direct effect equation of pesticides on weeds is constructed; Based on the growth part equation, the weed growth equation and the direct impact equation, a differential equation for the weed biomass is constructed.

5. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 2, characterized in that: The construction of equations for the effects of chemicals on species biomass includes: Construct a cumulative equation for soil pollution caused by the number of times the agent is used; Based on the impulse function, the equation of biological change of weeds and pests after using pesticides was constructed; Based on the one-dimensional linear function, the equation of the effect of the drug on the biomass of birds was constructed; Based on the accumulation equation, the biological change equation and the equation for the impact on bird biomass, an equation for the impact of chemical substances on species biomass is constructed.

6. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 5, characterized in that: After constructing the equation for the effect of chemicals on species biomass, it also includes: Retrieving soil pollution concentrations of several detection areas at different agricultural stages in the agricultural cycle from a historical storage database, wherein the soil pollution concentration includes a first detection concentration after each spraying of the pesticide and a second detection concentration of the spraying pesticide at the end of a corresponding preset volatilization cycle; Marking the concentration curve according to the time point of spraying the pesticide, determining the light intensity of the marked point every day within the preset volatilization cycle, and determining the set volatilization concentration of the corresponding pesticide type under the agricultural stage of the corresponding marked point based on the light intensity set according to the stage-pharmaceutical type-intensity set-volatilization comparison table; Based on the set volatilization concentration of each marked point and the concentration difference between the first detection concentration and the second detection concentration, a concentration difference array of the same detection area in the corresponding agricultural stage is constructed. ,in, represents the set volatilization concentration of the ith annotation point in the uth agricultural stage; represents the first detected concentration of the i-th annotation point in the u-th agricultural stage; represents the second detected concentration of the i-th annotation point in the u-th agricultural stage; represents the concentration difference coefficient of the ith annotation point in the uth agricultural stage; Indicates the number of times the corresponding detection area is sprayed with pesticides in the u-th agricultural stage; Construct a concentration-volatilization difference function based on the concentration difference arrays under different agricultural stages; Retrieve from the historical storage database the first growth density of the producer after the corresponding drug spraying time and the second growth density of the producer before the corresponding drug spraying time corresponding to the drug type corresponding to each marked point; Obtaining the historical final growth state of the producer at each agricultural stage from the historical storage database, and determining the growth loss function in combination with the first growth density and the second growth density involved; ； in, represents the historical final growth state of the p-th detection area under the u-th agricultural stage; represents the state judgment value of the p-th detection area in the u-th agricultural stage; represents the first growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the second growth density corresponding to the i-th annotation point in the p-th detection area under the u-th agricultural stage; represents the density judgment function, and ; represents the growth loss function of all detected areas under the u-th agricultural stage; Indicates the total number of detection areas involved; Based on the growth loss function and the concentration volatilization difference function, a pollution fine-tuning function is generated, and based on the pollution fine-tuning function, an equation for the influence of chemical substances on species biomass is adjusted.

7. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 6, characterized in that: According to the concentration difference array under different agricultural stages, the concentration volatility difference function is constructed, including: Construct the phase volatilization difference function based on all the detection areas under the corresponding agricultural stages; ； according to The phase volatility difference function under each agricultural stage is adjusted, where: represents the variance based on all Ru; ； in, Indicates the p-th detection area under the u-th agricultural stage based on the corresponding concentration difference array The variance of the coefficient of difference; Indicates that the pth detection area exists in the uth agricultural stage the number of represents the stage volatility difference function under the u-th agricultural stage determined based on all detection areas; Indicates all The minimum value in ; It represents the concentration volatilization difference function under the u-th agricultural stage after adjusting the corresponding Ru; Represents the variance threshold.

8. The agricultural ecosystem stability assessment method based on the AFW-BRS model according to claim 1, characterized in that: Optimizing the organic farming strategy includes: Determine the sensitivity index of the model 1, and extract the index to be optimized whose sensitivity value does not meet the corresponding set standard from all the sensitivity indexes; Determine the first influencing factor based on each indicator to be optimized from the indicator-factor mapping table; Conduct a global strategic analysis of the organic farming strategy to determine the strategic layout factors involved; Projecting the strategic layout factor and the first influencing factor into a factor mapping table respectively, obtaining a second influencing factor mapped with each strategic layout factor, and determining a sub-adjustment suggestion based on the mapped second influencing factor according to a layout implementation rule; The organic farming strategy is optimized based on all sub-adjustment suggestions, and an optimized farming strategy is obtained and issued.

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