A method and system for screening highly resistant plants based on ecological environment simulation

Abstract

The present invention is applicable to the field of plant resistance screening and provides a method and system for screening highly resistant plants based on ecological environment simulation. This method establishes an ecological environment model that simulates combined stresses, periodically collects plant physiological and biochemical phenotypes, environmental data, and resource consumption data, quantifies plant resistance behavior into environmental restoration asset points, calculates ecological liabilities based on resource consumption, and screens for primary highly resistant plants using net ecological benefit values. A final sequence is obtained through secondary screening using the resistance balance index. This method integrates ecological asset-liability thinking to comprehensively assess plant resistance and ecological benefits. The screening results are accurate and practical, making it suitable for fields such as ecological restoration and provides an efficient and scientific solution for screening highly resistant plants.

Description

Technical Field

[0001] The present invention belongs to the field of plant resistance screening, and in particular relates to a method and system for screening highly resistant plants based on ecological environment simulation. Background Art

[0002] With the development of remote sensing and spectral analysis technologies, the use of plant spectral characteristics to screen for suitable plants has gradually become a research hotspot. Currently, this technology has been widely used in various fields such as agricultural breeding and vegetation monitoring. By analyzing the relationship between plant reflectance spectra and physiological characteristics and environmental adaptability, it provides a new technical path for plant screening. Its application potential is particularly prominent in the study of plants in extreme environments.

[0003] As ecological and environmental problems become increasingly complex, resistance assessment under a single stress can no longer meet actual needs, and screening for highly resistant plants under a combined stress environment has become a research focus. Existing technologies mostly focus on the single determination of plant physiological and biochemical indicators, judging resistance by comparing the growth status of plants under specific stresses, and some schemes simulate limited environmental stresses for screening. However, these technologies have obvious limitations. They fail to comprehensively consider the ecological benefits brought by plant resistance and the resource costs consumed by growth, and the simulation of combined stress is not comprehensive enough. The screened plants may be difficult to continue to play a role in actual applications due to excessive resource consumption or unbalanced resistance. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for screening highly resistant plants based on ecological environment simulation, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is achieved by a method for screening highly resistant plants based on ecological environment simulation, the method comprising:

[0006] Establishing an ecological environment model that simulates a compound stress ecological environment, wherein the compound stress includes a combination of biotic stress and abiotic stress;

[0007] Periodically collect the physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, and continuously record and track the resource consumption data of each plant variety throughout its growth cycle;

[0008] Based on physiological and biochemical phenotypic data, the positive environmental effects of plant resistance behaviors are quantified into standardized environmental restoration asset points. Dynamic cost coefficients are then set and combined with resource consumption data to calculate and adjust the generated ecological debt points.

[0009] Calculate the net ecological benefit value of each tested plant variety and set a screening threshold. Based on the net ecological benefit value, dynamic cost coefficient and ecological liability point, select the first-level high-resistance plant varieties;

[0010] Extract data reflecting the plant's response to different stress factors from the physiological and biochemical phenotypic data, calculate the resistance balance index, evaluate the balance of plant resistance to different stress factors, conduct secondary screening on the first-level high-resistant plant varieties, and obtain the final high-resistant plant sequence.

[0011] As a further solution of the present invention, the establishment of an ecological environment model simulating a composite stress ecological environment specifically includes:

[0012] Constructing the ecological environment model and configuring abiotic stress parameters and biotic stress parameters;

[0013] Configure light intensity / spectrum, temperature, humidity, gas composition, soil physical and chemical properties, moisture pattern, and biotic stress parameters according to the characteristics of the target area;

[0014] Dynamically combine at least three abiotic stresses with one biotic stress to simulate a complex stress environment;

[0015] Environmental data within the ecological environment model is collected and fed back through sensors every 5 minutes.

[0016] As a further solution of the present invention, the collection of physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, while continuously recording and tracking the resource consumption data of each plant variety throughout the growth cycle, specifically includes:

[0017] The ecological environment model is used to collect current environmental data of each plant variety every 15 minutes, and simultaneously collects physiological data of each plant variety under biotic and abiotic stresses to form physiological and biochemical phenotypic data;

[0018] The energy consumption of abiotic stress and the consumable materials of biotic stress for each plant variety were measured to form resource consumption data.

[0019] As a further solution of the present invention, the positive effects of the resistance behavior exhibited by plants on the environment are quantified into standardized environmental restoration asset points, and a dynamic cost coefficient is set. In combination with resource consumption data, the ecological debt points are calculated and adjusted to generate the ecological debt points, specifically including:

[0020] Based on physiological and biochemical phenotypic data, the positive effects of plant resistance on the environment are quantified as standardized environmental restoration asset points, where resistance to biotic stress corresponds to 10-30 restoration points per unit inhibition rate, and resistance to abiotic stress corresponds to 5-20 restoration points per unit improvement rate;

[0021] Set the initial value of the dynamic cost coefficient and calculate the dynamic cost coefficient in combination with the added value of the dynamic cost coefficient;

[0022] Combined with resource consumption data, set resource consumption-liability calculation standards and calculate ecological debt points.

[0023] As a further solution of the present invention, the net ecological benefit value of each tested plant variety is calculated, and a screening threshold is set to screen the first-level high-resistance plant varieties based on the net ecological benefit value, dynamic cost coefficient and ecological debt point, specifically including:

[0024] Summarize all environmental restoration asset points and ecological liability points every day and calculate the net ecological benefit value;

[0025] The screening threshold is set at 1.5 times the average net ecological benefit value of similar plant species in the target area, and an ecological debt warning value is set;

[0026] A first-level screening is performed based on the screening threshold to select plant varieties whose net ecological benefit values ​​throughout the growth cycle reach the screening threshold and whose dynamic cost coefficient does not exceed the preset ecological liability warning value as first-level high-resistant plant varieties.

[0027] As a further solution of the present invention, the secondary screening of the primary high-resistant plant varieties to obtain the final high-resistant plant sequence specifically includes:

[0028] Extract all the response data of plant varieties under abiotic stress and biotic stress from the physiological and biochemical phenotypic data, and quantify all the response data under abiotic stress into the overall resistance score to abiotic stress, and quantify all the response data under biotic stress into the overall resistance score to biotic stress;

[0029] Calculate the resistance balance index, and conduct secondary screening of the first-level high-resistance plant varieties based on a resistance balance index ≤ 0.3;

[0030] For the remaining plant varieties after the secondary screening, the weighted comprehensive scores were calculated and ranked from high to low to form the final high-resistance plant sequence.

[0031] Another object of the present invention is to provide a high-resistance plant screening system based on ecological environment simulation, the system comprising:

[0032] A model building module is used to build an ecological environment model that simulates a compound stress ecological environment, wherein the compound stress includes a combination of biotic stress and abiotic stress;

[0033] The automatic data collection module is used to periodically collect the physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, while continuously recording and tracking the resource consumption data of each plant variety throughout the growth cycle;

[0034] The parameter calculation module is used to quantify the positive effects of plant resistance on the environment into standardized environmental restoration asset points based on physiological and biochemical phenotypic data, set dynamic cost coefficients, and combine resource consumption data to calculate and adjust the generated ecological debt points;

[0035] The first-level screening module is used to calculate the net ecological benefit value of each tested plant variety and set the screening threshold. Based on the net ecological benefit value, dynamic cost coefficient and ecological debt point, the first-level high-resistance plant varieties are screened;

[0036] The secondary screening module is used to extract data reflecting the plant's response to different stress factors from the physiological and biochemical phenotypic data, calculate the resistance balance index, evaluate the balance of plant resistance to different stress factors, conduct secondary screening of the first-level high-resistant plant varieties, and obtain the final high-resistant plant sequence.

[0037] The beneficial effects of the present invention are:

[0038] This invention innovatively constructs a screening system based on an ecological balance sheet, extending plant resistance assessment to an eco-economic dimension. By establishing a dynamic quantitative model combining environmental restoration asset points and ecological liability points, and integrating financial thinking, it achieves accurate calculation of the positive effects of plant resistance behavior and resource consumption costs. Environmental restoration asset points quantify the plant's remediation effect on biotic and abiotic stresses, while ecological liability points, combined with dynamic cost coefficients, reflect the cumulative impact of resource consumption. The calculation of the net ecological benefit value ensures that the selected plants have both high resistance and resource utilization efficiency.

[0039] At the same time, the introduction of a resistance balance index assesses the balance of plant resistance to different stresses, preventing the selection of varieties with outstanding single resistance but weak overall adaptability. By simulating multiple complex stress environments and periodically collecting data, this system makes screening results more relevant to actual ecological scenarios. This provides plant varieties that are both environmentally adaptable and sustainable for applications such as ecological restoration and stress-resistant breeding, significantly improving the scientific and practical nature of screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for screening highly resistant plants based on ecological environment simulation provided by an embodiment of the present invention;

[0041] Figure 2 A flow chart of establishing an ecological environment model for simulating a complex stress ecological environment provided by an embodiment of the present invention;

[0042] Figure 3 A flowchart of collecting physiological and biochemical phenotype data and environmental data at corresponding moments provided by an embodiment of the present invention;

[0043] Figure 4 A flowchart of calculating and adjusting ecological debt points provided by an embodiment of the present invention;

[0044] Figure 5 A flow chart for screening first-level high-resistance plant varieties provided in an embodiment of the present invention;

[0045] Figure 6 A flow chart of secondary screening of primary high-resistance plant varieties provided in an embodiment of the present invention;

[0046] Figure 7 This is a structural block diagram of a high-resistance plant screening system based on ecological environment simulation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] Figure 1 Flowchart of the method for screening highly resistant plants based on ecological environment simulation provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0049] S100, establishing an ecological environment model for simulating a compound stress ecological environment, wherein the compound stress includes a combination of biotic stress and abiotic stress;

[0050] The model will integrate a multidimensional parameter library of abiotic stresses (such as temperature fluctuations, water stress, soil salinization, heavy metal pollution, and abnormal spectra) and biotic stresses (such as pathogen infection, insect pests, and competitive weeds), rather than simply superimposing a single factor.

[0051] The system automatically switches between sequences of at least three abiotic stresses and one biotic stress at different plant growth stages, simulating the uncertainty and cumulative effects of natural stresses. Simultaneously, a distributed sensor network collects environmental data every five minutes and feeds it back to a central control system in real time. This dynamically calibrates stress parameters to ensure the model aligns with the spatiotemporal evolution of the target region's ecological environment.

[0052] The core purpose of dynamically switching between sequences of at least three abiotic stresses and one biotic stress is to simulate the inherent temporal evolution of the natural environment and the complex interactions between stress factors. The adversities faced by plants in real ecological environments are not isolated or static, but rather form a chain reaction that varies with climate rhythms, growth stages, and biome dynamics.

[0053] This dynamic, interwoven stress network requires plants to coordinate their stress resistance across their growth cycle: rapidly establishing basic stress tolerance mechanisms during the seedling stage, balancing resource allocation to cope with ongoing stress during the vegetative growth phase, and ensuring that key physiological processes are not disrupted during the reproductive phase. By forcing multiple factor combinations to switch between each growth stage, the system can precisely replicate the stress transmission chain, forcing the plant to expose potential flaws in its resistance mechanisms.

[0054] like Figure 2 As shown, the establishment of an ecological environment model for simulating a complex stress ecological environment specifically includes:

[0055] S110, constructing the ecological environment model and configuring abiotic stress parameters and biotic stress parameters;

[0056] S120, configuring light intensity / spectrum, temperature, humidity, gas composition, soil physical and chemical properties, moisture pattern, and biotic stress parameters according to the characteristics of the target area;

[0057] S130, dynamically combining at least three abiotic stresses with one biotic stress to simulate a complex stress environment;

[0058] S140, collecting environmental data in the ecological environment model through sensors every 5 minutes and feeding back the data.

[0059] S200, periodically collecting physiological and biochemical phenotypic data of each plant variety in the ecological environment model and environmental data at the corresponding moment, while continuously recording and tracking resource consumption data of each plant variety throughout its entire growth cycle;

[0060] In a complex stress environment, fluctuations in abiotic factors such as light, temperature, and humidity, and biotic factors such as pathogens and pests are continuous and interactive, and the physiological state of plants adjusts instantly to these fluctuations. A 15-minute data collection interval ensures that subtle changes in environmental factors (sudden increases in light intensity at noon, sudden drops in humidity at night) are captured while also simultaneously recording the physiological responses of plants to these changes, thereby establishing a precise spatiotemporal correspondence between environmental perturbations and physiological responses. This correspondence allows subsequent analysis of plant resistance mechanisms to clearly identify the stressor or stressors that trigger a particular resistance expression, as well as the correlation between the intensity of the response and the duration of the stress. This avoids misjudgment of resistance triggers due to data asynchrony and provides a traceable physiological basis for the subsequent conversion of resistance behaviors into environmental remediation assets.

[0061] There are essential differences in the forms of resource input by plants in response to different stresses: under abiotic stress, plants need to consume energy to maintain cellular homeostasis, and this type of consumption is reflected in energy consumption; under biotic stress, plants need to synthesize specific substances to resist damage, and this type of consumption is reflected in consumables. The resource consumption data formula introduces a weight coefficient to convert different types and intensities of energy consumption and consumables into a unified quantitative indicator (total daily resource consumption). The setting of the weight coefficient can be adjusted according to the actual impact of the stress factors in the target area to ensure that the resource input of different plant varieties is comparable. This quantitative method makes up for the defect of traditional screening that only focuses on resistance results and ignores costs. It makes it possible to clarify the resource price paid by plants to obtain certain restoration benefits when subsequently calculating ecological debt points, providing data support for evaluating the ecological and economic feasibility of plant resistance.

[0062] like Figure 3 As shown, the physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time are collected, and the resource consumption data of each plant variety throughout the growth cycle are continuously recorded and tracked, specifically including:

[0063] S210, collecting current environmental data of each plant variety every 15 minutes through the ecological environment model, and simultaneously collecting physiological data of each plant variety under biotic stress and abiotic stress to form physiological and biochemical phenotypic data;

[0064] S220, measuring the energy consumption of abiotic stress and consumable materials of biotic stress for each plant variety to generate resource consumption data.

[0065] Among them, for resource consumption data:

[0066] ;

[0067] is the total daily resource consumption, For the Daily energy consumption due to abiotic stress, is the energy consumption weight coefficient, For the Daily consumption of materials for biological stress, is the consumables weight coefficient, is the number of abiotic / biotic stress types.

[0068] S300, based on physiological and biochemical phenotypic data, quantifies the positive environmental effects of plant resistance behaviors into standardized environmental restoration asset points. Dynamic cost coefficients are then set and combined with resource consumption data to calculate and adjust ecological debt points.

[0069] Resistance to biotic stresses often directly impacts the balance of biological communities, maintaining ecosystem stability with immediate and cascading effects, thus receiving a higher coefficient. Resistance to abiotic stresses, on the other hand, reflects more gradual optimization of the abiotic environment, with a relatively slow release of effects, thus receiving a lower coefficient. This differentiated quantification ensures that asset points truly reflect the ecological value weight of different resistance performance indicators.

[0070] When plants cope with combined stresses, resource consumption is not uniform. As the growth cycle lengthens, the continued effects of stress factors lead to increasing costs for maintaining plant resistance. By incorporating the number of days in the growth cycle and the stress intensity coefficient, the dynamic cost coefficient allows cost calculations to dynamically match actual consumption patterns. This avoids the limitation of static coefficients that cannot reflect cost changes under long-term stress, allowing subsequent liability calculations to more closely reflect the true cost curve of plant growth.

[0071] Combining resource consumption data with dynamic cost coefficients to calculate ecological debt points essentially transforms a plant's resource inputs into a cost ledger that can be offset against asset points. Resource consumption data reflects the actual cost of maintaining plant resistance, while the dynamic cost coefficient weights these costs based on time and stress intensity. The resulting ecological debt point, derived by multiplying the two, reflects both the absolute amount of resource consumption and the context in which it occurs. This design approach breaks the one-sidedness of traditional assessments that focus solely on resistance effects and ignore resource inputs, thereby truly reflecting the balance between benefits and costs.

[0072] like Figure 4 As shown, the positive effects of plant resistance on the environment are quantified as standardized environmental restoration asset points, and dynamic cost coefficients are set. Combined with resource consumption data, ecological debt points are calculated and adjusted, specifically including:

[0073] S310, based on physiological and biochemical phenotypic data, quantify the positive effects of plant resistance behavior on the environment as standardized environmental restoration asset points, where resistance to biotic stress corresponds to 10-30 restoration points per unit inhibition rate, and resistance to abiotic stress corresponds to 5-20 restoration points per unit improvement;

[0074] Among them, for environmental restoration asset points:

[0075] ;

[0076] For a single-day environmental restoration asset point, is the biological stress repair coefficient, 10-30 points / unit, is the biotic stress inhibition rate, is the abiotic stress repair coefficient, 5-20 points / unit, Improved amount for abiotic stress;

[0077] S320, set the initial value of the dynamic cost coefficient , and calculate the dynamic cost coefficient in combination with the added value of the dynamic cost coefficient :

[0078] ;

[0079] in, is the number of days of the growth cycle, is the composite stress intensity coefficient;

[0080] Dynamic cost coefficient With the number of days of growth cycle and composite stress intensity coefficient Increasing, reflecting the cost amplification effect of a long-term high-stress environment;

[0081] S330, combined with resource consumption data, sets resource consumption-liability calculation standards and calculates ecological debt points :

[0082] .

[0083] S400, calculating the net ecological benefit value of each tested plant variety, setting a screening threshold, and screening first-level high-resistance plant varieties based on the net ecological benefit value, dynamic cost coefficient, and ecological liability point;

[0084] Environmental restoration asset points reflect the environmental benefits of plant resistance, while ecological liabilities reflect the resource costs of maintaining resistance. The difference between the two (net ecological benefit) transcends the limitations of focusing solely on resistance performance or resource consumption, visualizing the balance between plant contributions and costs. This daily summary ensures that fluctuations in benefits are captured throughout the plant's growth cycle.

[0085] The average level of similar plants in the target area is the baseline for plant resistance under the ecological environment of the region. Using 1.5 times of the average level as the threshold ensures that the selected varieties have clear performance advantages, while also being in line with local ecological characteristics, thus avoiding blindly high standards that are divorced from the actual environment. The ecological debt warning value set simultaneously adds restrictions to the screening from the cost side to prevent the situation of high assets but extremely high liabilities.

[0086] The requirement of a full growth cycle avoids the one-sidedness of drawing conclusions based solely on data from a certain stage. Some plants may perform well under short-term stress, but as the growth cycle lengthens, resistance mechanisms fatigue or resources are over-consumed, and the net benefit will gradually fall below the threshold. Such varieties are difficult to continue to play a role in actual applications, and full-cycle evaluation can effectively identify their instability; if the dynamic cost coefficient does not exceed the warning value, further checks will be made from the cost dimension to ensure that the selected varieties not only have outstanding benefits, but also have cost increases that are within a controllable range, avoiding cost out-of-control due to long-term stress.

[0087] Through the quantitative hedging of the net ecological benefit value, a comprehensive assessment of the ecological value of plants is achieved, which neither ignores the environmental improvements brought about by resistance nor condones excessive resource consumption; the threshold setting based on the average level of similar plants in the target area makes the screening results more in line with actual application scenarios and has direct promotion value; and the dual standards of the entire growth cycle and cost warning ensure the resistance stability and ecological economy of the selected varieties, and effectively avoids varieties that do not meet actual needs, such as short-term compliance but long-term failure, high returns but high costs, from entering subsequent links.

[0088] like Figure 5 As shown, the net ecological benefit value of each tested plant variety is calculated, and a screening threshold is set. Based on the net ecological benefit value, dynamic cost coefficient and ecological liability point, the first-level high-resistance plant variety is screened, specifically including:

[0089] S410: Summarize all daily environmental restoration asset points and ecological liability points and calculate the net ecological benefit value :

[0090] ;

[0091] Total daily resource consumption of high-energy-consuming products More, therefore need to generate higher single-day environmental restoration asset points , in order to meet the net ecological benefit value Greater than the screening threshold;

[0092] S420: Set a screening threshold of 1.5 times the average net ecological benefit value of similar plant species in the target area, and set an ecological debt warning value;

[0093] S430, performing a first-level screening based on the screening threshold, screening out plant varieties whose net ecological benefit values ​​throughout the entire growth cycle reach the screening threshold and whose dynamic cost coefficients do not exceed the preset ecological liability warning value, as first-level high-resistance plant varieties.

[0094] S500: Extract the data reflecting the plant's response to different stress factors from the physiological and biochemical phenotypic data, calculate the resistance balance index, evaluate the balance of the plant's resistance to different stress factors, conduct a secondary screening of the first-level high-resistant plant varieties, and obtain the final high-resistant plant sequence.

[0095] The mechanisms of abiotic and biotic stresses faced by plants under combined stress differ significantly: abiotic stress primarily tests a plant's physiological regulation, while biotic stress focuses on defense mechanisms. Disparate response data cannot directly reflect the overall level of resistance in either category. Through weighted comprehensive quantification, abiotic and biotic stress response data are combined into an overall resistance score, enabling direct comparison of the two resistance categories and providing a quantitative basis for subsequent balance assessments.

[0096] The resistance balance index was calculated and ≤0.3 was used as the screening basis to eliminate varieties with biased resistance and ensure that plants can cope with complex stresses in a balanced manner. In the actual ecological environment, plants face multiple types of stress. If a variety only performs well in one type of stress (such as Very high but Very low), it cannot adapt to the complex environment. For example, in grassland restoration under the combined stress of drought and root rot, the inhibition rate of forage to root rot (biological stress) is extremely high ( high), but leaves wilt rapidly under drought (abiotic stress) ( Low resistance scores may pass primary screening (net ecological benefit meets the target), but in practice, they will die due to their inability to cope with drought. The resistance balance index intuitively reflects the degree of balance by comparing the difference between the two resistance scores to the sum of the two scores. The lower the ratio, the more closely matched the two resistance types are, and the more comprehensive the ability to withstand the pressure of combined stresses, avoiding a decline in overall adaptability due to a weakness in one resistance type.

[0097] Calculating a weighted composite score and ranking the varieties after secondary screening aims to prioritize them based on balanced resistance and ensure the final ranking is directly applicable to practical applications. The weighted composite score combines the overall level and balance of the two types of resistance. The weights can be adjusted based on the actual proportion of stress factors in the target area, making the ranking results tailored to specific scenarios and facilitating the subsequent selection of optimal varieties based on actual needs.

[0098] like Figure 6 As shown, the secondary screening of the first-level high-resistant plant varieties to obtain the final high-resistant plant sequence specifically includes:

[0099] S510: Extract all response data of plant varieties under abiotic stress and biotic stress from physiological and biochemical phenotypic data, and quantify all response data under abiotic stress into an overall resistance score to abiotic stress. , all response data under biotic stress are quantified into an overall resistance score to biotic stress :

[0100] ;

[0101] in, is the single stress response data, is the coercion weight, is the number of abiotic / biotic stress factors;

[0102] S520, calculate resistance balance index The first-level high-resistance plant varieties were screened for the second time based on the resistance balance index ≤ 0.3:

[0103] ;

[0104] like:

[0105] when =80, =60, =0.14 (passed screening);

[0106] like =90, =40 then ==0.38 (eliminated).

[0107] S530, for the remaining plant varieties after the secondary screening, calculate the weighted comprehensive scores and sort them from high to low to form a final high-resistance plant sequence.

[0108] Figure 7 This is a structural diagram of a high-resistance plant screening system based on ecological environment simulation provided by an embodiment of the present invention, such as Figure 7 As shown, the system includes:

[0109] A model building module 100 is used to build an ecological environment model for simulating a compound stress ecological environment, wherein the compound stress includes a combination of biotic stress and abiotic stress;

[0110] The automatic data collection module 200 is used to periodically collect the physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, and continuously record and track the resource consumption data of each plant variety throughout the entire growth cycle;

[0111] Parameter calculation module 300 is used to quantify the positive environmental effects of plant resistance behaviors as standardized environmental restoration asset points based on physiological and biochemical phenotypic data, set dynamic cost coefficients, and calculate and adjust ecological debt points based on resource consumption data.

[0112] The first-level screening module 400 is used to calculate the net ecological benefit value of each tested plant variety and set a screening threshold to screen the first-level high-resistance plant varieties based on the net ecological benefit value, dynamic cost coefficient and ecological debt point;

[0113] The secondary screening module 500 is used to extract data reflecting the plant's response to different stress factors from the physiological and biochemical phenotypic data, calculate the resistance balance index, evaluate the balance of the plant's resistance to different stress factors, perform secondary screening on the first-level high-resistant plant varieties, and obtain the final high-resistant plant sequence.

[0114] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for screening highly resistant plants based on ecological environment simulation, characterized in that: The method comprises: Establishing an ecological environment model that simulates a compound stress ecological environment, wherein the compound stress includes a combination of biotic stress and abiotic stress; Periodically collect the physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, and continuously record and track the resource consumption data of each plant variety throughout its growth cycle; Based on physiological and biochemical phenotypic data, the positive effects of plant resistance on the environment are quantified into standardized environmental restoration asset points. Dynamic cost coefficients are set and combined with resource consumption data to calculate and adjust the generated ecological debt points. Specifically, this includes: Based on physiological and biochemical phenotypic data, the positive effects of plant resistance on the environment were quantified as standardized environmental restoration asset points, where resistance to biotic stresses corresponded to 10-30 restoration points per unit inhibition rate, and resistance to abiotic stresses corresponded to 5-20 restoration points per unit improvement rate. For environmental restoration asset points: ; For a single-day environmental restoration asset point, is the biological stress repair coefficient, 10-30 points / unit, is the biotic stress inhibition rate, is the abiotic stress repair coefficient, 5-20 points / unit, Improved amount for abiotic stress; Set the initial value of the dynamic cost factor , and calculate the dynamic cost coefficient in combination with the added value of the dynamic cost coefficient : ;in, is the number of days of the growth cycle, is the composite stress intensity coefficient; dynamic cost coefficient With the number of days of growth cycle and composite stress intensity coefficient Increasing, reflecting the cost amplification effect of a long-term high-stress environment; Combined with resource consumption data, set resource consumption-liability calculation standards and calculate ecological debt points : ;in, is the total amount of resource consumption per day; calculate the net ecological benefit value of each tested plant species , , and set screening thresholds to screen first-level high-resistance plant varieties based on net ecological benefit value, dynamic cost coefficient and ecological liability point; Extract data reflecting the plant's response to different stress factors from the physiological and biochemical phenotypic data, calculate the resistance balance index, evaluate the balance of plant resistance to different stress factors, conduct secondary screening on the first-level high-resistant plant varieties, and obtain the final high-resistant plant sequence.

2. The method according to claim 1, characterized in that The establishment of an ecological environment model simulating a composite stress ecological environment specifically includes: Constructing the ecological environment model and configuring abiotic stress parameters and biotic stress parameters; Configure light intensity / spectrum, temperature, humidity, gas composition, soil physical and chemical properties, moisture pattern, and biotic stress parameters according to the characteristics of the target area; Dynamically combine at least three abiotic stresses with one biotic stress to simulate a complex stress environment; Environmental data within the ecological environment model is collected and fed back through sensors every 5 minutes.

3. The method according to claim 2, characterized in that The collection of physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, while continuously recording and tracking the resource consumption data of each plant variety throughout the growth cycle, specifically includes: The ecological environment model is used to collect current environmental data of each plant variety every 15 minutes, and simultaneously collects physiological data of each plant variety under biotic and abiotic stresses to form physiological and biochemical phenotypic data; The energy consumption of abiotic stress and the consumable materials of biotic stress for each plant variety were measured to form resource consumption data.

4. The method according to claim 3, characterized in that The net ecological benefit value of each tested plant variety is calculated, and a screening threshold is set. Based on the net ecological benefit value, dynamic cost coefficient and ecological liability point, the first-level high-resistance plant variety is screened, specifically including: Summarize all environmental restoration asset points and ecological liability points every day and calculate the net ecological benefit value; The screening threshold is set at 1.5 times the average net ecological benefit value of similar plant species in the target area, and an ecological debt warning value is set; A first-level screening is performed based on the screening threshold to select plant varieties whose net ecological benefit values ​​throughout the growth cycle reach the screening threshold and whose dynamic cost coefficient does not exceed the preset ecological liability warning value as first-level high-resistant plant varieties.

5. The method according to claim 4, characterized in that The secondary screening of the first-level high-resistance plant varieties to obtain the final high-resistance plant sequence specifically includes: Extract all the response data of plant varieties under abiotic stress and biotic stress from the physiological and biochemical phenotypic data, and quantify all the response data under abiotic stress into the overall resistance score to abiotic stress, and quantify all the response data under biotic stress into the overall resistance score to biotic stress; Calculate the resistance balance index, and conduct secondary screening of the first-level high-resistance plant varieties based on a resistance balance index ≤ 0.3; For the remaining plant varieties after the secondary screening, the weighted comprehensive scores were calculated and ranked from high to low to form the final high-resistance plant sequence.

6. A high-resistance plant screening system based on ecological environment simulation, characterized in that: To implement the method for screening highly resistant plants based on ecological environment simulation as described in claims 1-5, the system comprises: A model building module is used to build an ecological environment model that simulates a compound stress ecological environment, wherein the compound stress includes a combination of biotic stress and abiotic stress; The automatic data collection module is used to periodically collect the physiological and biochemical phenotypic data of each plant variety in the ecological environment model and the environmental data at the corresponding time, while continuously recording and tracking the resource consumption data of each plant variety throughout the growth cycle; The parameter calculation module is used to quantify the positive effects of plant resistance on the environment into standardized environmental restoration asset points based on physiological and biochemical phenotypic data, set dynamic cost coefficients, and calculate and adjust the generated ecological debt points in combination with resource consumption data; The first-level screening module is used to calculate the net ecological benefit value of each tested plant variety and set the screening threshold. Based on the net ecological benefit value, dynamic cost coefficient and ecological debt point, the first-level high-resistance plant varieties are screened; The secondary screening module is used to extract data reflecting the plant's response to different stress factors from the physiological and biochemical phenotypic data, calculate the resistance balance index, evaluate the balance of plant resistance to different stress factors, conduct secondary screening of the first-level high-resistant plant varieties, and obtain the final high-resistant plant sequence.

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