Multi-target agricultural green production intelligent decision optimization method and system

Abstract

The invention discloses a multi-target agricultural green production intelligent decision optimization method and system, and relates to the technical field of production decision optimization. Comprising the following steps: information acquisition: acquiring specific index data information, obtaining index information items, respectively formulating specific evaluation indexes based on the index information items, obtaining evaluation information items, and respectively corresponding the evaluation information items to the index information items; demand acquisition: acquiring actual target information, and obtaining real-time target items. According to the method, specific index data information is acquired, specific evaluation indexes are respectively formulated based on the index data information, actual target information is acquired, related index information and evaluation information are matched, the acquired actual target information is matched according to index sorting items, and specific weight data are matched. When decision output is carried out, decision output tradeoff is carried out according to the weights occupied by different targets in the real-time target information, and then the optimization effect of the decision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production decision optimization, and specifically to a multi-objective agricultural green production intelligent decision optimization method and system. Background Art

[0002] Production intelligent decision optimization refers to the use of intelligent technology and data analysis tools to optimize the decisions made by enterprises in the production process in order to improve efficiency, reduce costs, improve product quality and adapt to market changes. In the agricultural production process, there are also different indicator data, such as environmental protection goals, sustainable development goals, etc. Under different indicator requirements, there will be different actual decisions. Therefore, different decision changes need to be made according to different indicators. Among them, the important thing to pay attention to in the environmental protection goals is the carbon emission issue, which needs to be adjusted according to specific decisions.

[0003] The patent publication number is CN112488869A, which is a multi-factor management decision-making system suitable for agricultural production. The decision data information and crop information are entered through the data input unit, the information is sorted and classified through the analysis and sorting module, and then transmitted to the central controller, and sent to the information matching module by the central controller. If the information matching module cannot extract similar information from the data storage center, the cloud data sends the information to the information matching module through the 5G transmission module for matching and comparison, and then the central controller sends the result directly to the result display module for screen display and viewing. The decision to be implemented is measured by means of a dynamic simulation environment, which ensures the accuracy of objective analysis, improves the comprehensiveness of the analysis of decision results, ensures the simplicity of subsequent production execution, and minimizes its losses, which can bring good development prospects for agricultural production.

[0004] In the agricultural production process, the above-mentioned and similar technical solutions have different indicator data, such as environmental protection goals, sustainable development goals, economic benefit goals, policy compliance goals, etc. Different actual decisions will be made under different indicator requirements. Some indicator data are dependent on each other, and some indicator data are dependent on each other. When there are multiple target requirements in the agricultural production process, it is necessary to combine different indicator data to execute decisions to prevent the indicator data from failing to meet the multiple target requirements due to the influence of the dependency relationship between the multiple targets, which will affect the execution and optimization of the decision. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-objective agricultural green production intelligent decision-making optimization method and system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a multi-objective agricultural green production intelligent decision-making optimization method and system, comprising: Information acquisition: Obtain specific indicator data information to obtain indicator information items, formulate specific evaluation indicators based on the indicator information items, obtain evaluation information items, and the evaluation information items correspond to the indicator information items respectively: Demand acquisition: obtain the actual target information, get the real-time target items, match the indicator information items and evaluation information items through the matching method, get the target indicator items and target evaluation items, and the target indicator items and target evaluation items are used to feedback the actual goals of agricultural production; Weight allocation: Based on the first allocation method, weights are allocated to the indicator information items to obtain the first ranking item, which is used to provide a basic comparison for the real-time target items. Based on the second allocation method, weights are allocated to the real-time target items to obtain the second ranking item, which is used to perform weight matching based on the actual goals of agricultural production feedback. Based on the second ranking item, the actual weight allocation data is output to obtain the target weight item, and decision adjustments are made based on the target weight item.

[0007] Furthermore, the indicator data information includes environmental protection goals, sustainable development goals, economic benefit goals and policy goals, and the evaluation information items include soil health index, water resource utilization efficiency, and carbon dioxide emissions. Corresponding to environmental protection goals, the evaluation information items also include yield per mu and crop diversity. Corresponding to sustainable development goals, the evaluation information items also include unit cost and sales revenue. Corresponding to economic benefit goals, the evaluation information items also include the ratio of compliance with green certification standards, subsidies and support obtained, and corresponding policy goals.

[0008] Furthermore, the actual target information includes indicator benchmarking data, and the matching method includes: according to the indicator benchmarking data, through the indicator information item, obtaining indicator data information corresponding to the indicator benchmarking data, to obtain the target indicator item; based on the target indicator item, obtaining the evaluation information item corresponding to the target indicator item, to obtain the target evaluation item, and the number of the target evaluation items is at least two.

[0009] Furthermore, the actual target information includes evaluation benchmarking data, and the matching method includes: according to the evaluation benchmarking data, through the evaluation information item, obtaining the evaluation information indicator information corresponding to the evaluation information item, to obtain the target evaluation item, the number of the target evaluation items is at least one, based on the target evaluation item, obtaining the indicator information item corresponding to the target evaluation item, to obtain the target indicator item.

[0010] Furthermore, the first allocation method includes: S1: Weight allocation: Based on the indicator information items, the weights of the indicator information items are allocated. The weight allocation formula is: ,in is the weight ratio of the i-th indicator information item; S2: Impact setting, set the impact value t, t is a weight adjustment factor, used to reflect how the importance of the target is affected. The adjusted weight formula is: , in Represents the adjustment factor corresponding to each goal, which can be set according to the importance of each goal in a specific situation; S3: Weight normalization. The adjusted weights may cause the sum to be different from 1, so the result needs to be normalized. , the normalized weight formula is: .

[0011] Furthermore, the initial weight distribution of the indicator information items is the initial setting value, and the weight proportion distribution is > > , sort the indicator information items based on the initial setting value to obtain the first sorting item.

[0012] Furthermore, the second allocation method includes: M1: Indicator acquisition: based on the target indicator item, obtain the weight data matching the indicator information in the target indicator item to obtain the target weight item; M2: Weight calculation, calculate the weight data of the individual target indicator item in the target weight item. The weight data formula is: , is the weight data of the fth target indicator in the target indicator item, It is the indicator information weight data corresponding to the target indicator item in the first sorting item.

[0013] Furthermore, the indicator acquisition method includes: based on the first ranking item and the target indicator item, obtaining the ranking position of the target indicator item in the first ranking item to obtain the target position item, and matching the target position item with the weight data corresponding to the target position item to obtain the target weight item.

[0014] Furthermore, a multi-objective agricultural green production intelligent decision-making optimization system uses the multi-objective agricultural green production intelligent decision-making optimization method described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The multi-objective agricultural green production intelligent decision-making optimization method and system obtains specific indicator data information, formulates specific evaluation indicators based on the indicator data information, distributes weights to the indicator information and sorts them according to the weight distribution results, and at the same time obtains actual target information, matches the relevant indicator information and evaluation information, matches the actual target information obtained according to the indicator sorting items, matches specific weight data, and when making decision outputs, weighs the decision output according to the weights of different targets in the real-time target information, thereby improving the optimization effect of the decision.

[0016] At the same time, by setting an adjustment factor separately for each indicator data information obtained, it can be set according to the importance of each goal in a specific situation. The key elements used to dynamically adjust the weights of each goal, the influencing factor can be set based on a variety of external factors or internal conditions, so that the weight data of different goals can be more accurately fine-tuned under the influence of different events, thereby making more detailed adjustments to the weights of different goals, which is more conducive to improving the optimization effect of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a logical step diagram of the present invention; Figure 2 It is a schematic diagram of the structure of the indicator information item of the present invention; Figure 3 It is a schematic diagram of the weight distribution structure of the present invention; Figure 4 This is a schematic diagram of the impact of the impact factor on the weight distribution of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In the process of agricultural production, when there are one or more goals, for example, it is necessary to reduce carbon emissions and costs at the same time, there are two goals at this time, one is the environmental protection goal, and the other is the sustainable development goal. At this time, these two goals are dependent on each other. When making decisions, it is necessary to consider the dependencies between different goals to optimize the decision to prevent the failure to achieve the "multi-goal" effect. When it is necessary to reduce carbon emissions and increase sales prices at the same time, there are also two goals at this time, one is the environmental protection goal and the other is the economic benefit goal. The decision or solution made at this time is for these two goals. It is a relationship of interdependence of gains and losses. When it is necessary to reduce carbon emissions, increase sales prices, and reduce costs at the same time, there are three goals at this time: environmental protection, economic benefits, and sustainable development. The decision or solution at this time is a relationship of interdependence of gains and losses. When a targeted decision is made for one of the goals, it is easy to have another chain reaction on the remaining goals, which will lead to the problem that the remaining goals cannot be completed or cannot exist at the same time, resulting in the inability to truly achieve the effect of "multiple goals". Therefore, when making decisions, , it is necessary to consider the dependencies between different goals in order to make better decision optimization. The technical solution provided by the present application first obtains specific indicator data information to obtain indicator information items, and formulates specific evaluation indicators based on the indicator information items to obtain evaluation information items. The evaluation information items correspond to the indicator information items respectively, obtain the actual target information, obtain the real-time target items, obtain the corresponding indicator information items and evaluation information items based on the real-time target items, obtain the target indicator items and the target evaluation items, assign weights to the indicator information items and sort them according to the weight assignment results to obtain indicator sorting items, match the real-time target items according to the indicator sorting items, and obtain To the evaluation matching items, the weights of the evaluation matching items are recalculated and allocated through the matching method to obtain the target weight items, and the real-time target items are output and fed back based on the target weight items, thereby completing the decision adjustment of the real-time target items, and setting an adjustment factor separately for each obtained indicator data information, which can be set according to the importance of each target in a specific situation, and is used to dynamically adjust the key elements of each target weight. The influencing factor can be set based on a variety of external factors or internal conditions, so that the weight data of different targets can be more accurately fine-tuned under the influence of different events, thereby making more detailed adjustments to the weights of different targets. Specifically in this application, Figure 1 As shown, steps S100-S400 are included.

[0020] Step S100: Acquire specific indicator data information to obtain indicator information items, formulate specific evaluation indicators based on the indicator information items, and obtain evaluation information items.

[0021] It should be noted that the specific indicator data information represents the general goals that need to be clarified in the agricultural production process. For example, reducing carbon emissions and reducing water waste are small goals in the general direction. The common general direction in the above examples is the environmental protection goal. Therefore, the specific indicator data information obtained is the general direction in agricultural production, and the evaluation criteria are small goals formulated based on the general direction of the indicator information items. It is an indicator evaluation based on the indicator information items.

[0022] like Figure 2 As shown, specifically, obtaining indicator data information includes steps S101-S104.

[0023] Step S101: Definition of environmental protection goals. The main factors affecting the specific agricultural production are obtained through big data during the agricultural production process. Through the acquisition, it is learned that the most important factors affecting agricultural development are soil and water quality factors. Among them, the soil health index and the use of water resources have the most direct impact on the development of agriculture. In addition, the carbon emissions caused by the use of fertilizers and various agricultural machinery in the agricultural production process are important products of the agricultural production process. Therefore, the soil health index, water resource utilization efficiency, and carbon dioxide emissions are all taken as small goals in a big direction. The specific big direction is the environmental protection goal.

[0024] Step S102: Definition of sustainable development goals. In the process of agricultural production, the purpose of development is to produce output. Therefore, the most important factor affecting agricultural development is the specific yield. The yield per mu is a direct indicator of agricultural production efficiency, reflecting the number of crops or products that can be produced per unit area of ​​land. Improving the yield per mu means higher resource utilization efficiency, including the effective use of inputs such as soil, water, and fertilizer. Achieving higher yields on limited arable land can reduce the pressure on the development of new arable land, thereby protecting the natural ecological environment and meeting the requirements of sustainable development. Similarly, species diversity is an important indicator of ecosystem health and stability. Diverse plant and animal species can provide a variety of ecosystem services, such as soil fertility, pest control, pollination, etc., thereby maintaining the long-term sustainability of agricultural production. Therefore, the yield per mu and crop diversity are small goals in a general direction, and the specific general direction is the sustainable development goal.

[0025] Step S103: Definition of economic benefit target. The cost of producing each unit of product covers raw materials, labor, operation and other expenses. Therefore, reducing unit cost means that the enterprise has improved efficiency in the production process, thereby improving economic benefits, and unit cost directly affects the profit margin of the enterprise. Lower unit cost improves the ability of enterprises to compete in the market and increases profit opportunities, which is crucial for overall economic benefits. Similarly, sales revenue refers to the total revenue obtained by the enterprise through the sale of products or services. It is a combination of market demand and enterprise production capacity. The increase in sales revenue indicates that the market is performing well and is conducive to the financial health of the enterprise. Therefore, unit cost and sales revenue are small goals in a general direction, and the specific general direction is the economic benefit target.

[0026] Step S104: Definition of policy objectives. Green certification standards are usually policy frameworks formulated by the state to promote sustainable development and ecological protection. The ratio of enterprises meeting these standards reflects the degree of their compliance with national policies. Enterprises that meet the standards usually enjoy advantages in terms of policy support and market access. Subsidies and support are important tools to promote the transformation of industries and enterprises towards green and sustainable development. For enterprises that receive subsidies, this not only improves the financial stability of the enterprise, but also helps reduce operating risks. Therefore, the ratio of meeting green certification standards and the acquisition of subsidies and support are regarded as small goals in a general direction. The specific general direction is the policy goal.

[0027] Step S200: Acquire actual target information, obtain real-time target items, match indicator information items and evaluation information items through a matching method, and obtain target indicator items and target evaluation items.

[0028] It should be noted that at different stages of agricultural production, the goals to be achieved are different and will change over time. Therefore, at different times, obtaining actual target information and matching relevant indicator information and evaluation information can better optimize relevant decisions.

[0029] Specifically, it includes steps S201-202.

[0030] Step S201: Obtain the indicator data information corresponding to the indicator benchmarking data through the indicator information item. When the actual target information includes the indicator benchmarking data, it means that the target at this time is a target in the general direction. For example, in a certain development period, the goal of agricultural development is to achieve an environmental protection goal. At this time, the target indicator item can be directly matched, that is, the environmental protection goal. Based on the general direction of the environmental protection goal, the corresponding evaluation information is matched, that is, the small goals included in the environmental protection goal, namely, soil health index, water resource utilization efficiency, and carbon dioxide emissions. At this time, the target indicator item is the environmental protection goal, and the target evaluation items are soil health index, water resource utilization efficiency, and carbon dioxide emissions.

[0031] Step S202: Obtain the evaluation information indicator information corresponding to the evaluation information item through the evaluation information item. When the actual target information includes evaluation benchmark data, it means that the target at this time is a small target. For example, in a certain development period, the goal of agricultural development is to achieve reduced carbon dioxide emissions and increased yield per mu. At this time, it can be directly matched to the target evaluation item, that is, carbon dioxide emissions and yield per mu, and then matched with the target indicator item corresponding to the target evaluation item, which is the environmental protection goal and sustainable development goal.

[0032] Step S300: weighting the indicator information items based on a first allocation method.

[0033] It should be noted that since the indicator information items have been determined as environmental protection goals, sustainable development goals, economic benefit goals and policy goals, it is necessary to allocate weights based on these four indicator information items. In the process of agricultural development, decisions should be optimized and adjusted based on specific target information and the allocated weights.

[0034] Specifically, the first allocation method includes steps S301-S303.

[0035] like Figure 3 As shown, step S301: weight allocation, based on the obtained indicator information items, the weights of the indicator information items are allocated, and the requirement is , that is, the sum of the weights of the indicator information items is 1, and is the weight ratio of the nth indicator information item. Since there are four indicator information items, the weight formula is: Since the initial weight distribution of the indicator information item is the initial setting value, the weight distribution is > > > ,in is 0.4, is 0.3, is 0.2, is 0.1, corresponding to environmental protection goals, sustainable development goals, economic benefit goals and policy goals respectively. The indicator information items are sorted based on the initial set values ​​to obtain the first sorting item.

[0036] S302: Influence setting: setting the influence value t, where t is a weight adjustment factor used to reflect how the importance of the target is affected. The adjusted weight formula is: , like Figure 4 As shown, Represents the adjustment factor corresponding to each target, which can be set according to the importance of each target in a specific situation. Since there are four indicator information items, there are also four impact values ​​t, which are, , , , They are 0.2, 0.3, 0.1, and 0.05 respectively. At this time, the adjustment weight of each target is calculated as: , , , .

[0037] Step S303: Weight normalization. The adjusted weights may result in the sum not being equal to 1, so the result needs to be normalized. , the normalized weight formula is: , since the normalized sum is 1.195, At this time, the final normalized weight is: , , , , According to the allocation and sorting of the weights of the four indicator information items, a first sorting item is obtained, and the first sorting item is used to provide a basic comparison for the real-time target item.

[0038] It should be added that in the multi-objective agricultural green production intelligent decision-making, the influencing factor t is the key element used to dynamically adjust the weights of each target. The influencing factor can be set based on a variety of external factors or internal conditions. The initial adjustment value is set to 0.05. For example: If the government introduces new environmental protection regulations or policies, it may lead to an increase in the priority of environmental protection goals, and the number of repetitions of environmental protection goals obtained in the target indicator item is 6, and the adjustment item is 6. At this time, The value of (the impact factor of environmental protection goals) is 0.3. If a region faces a serious threat of climate change, it may pay more attention to sustainable development goals. In the target indicator item, the number of repetitions of sustainable development goals is 5, and the adjustment item is 5. (Sustainable development impact factor) is set to 0.25. When the economic benefit target may be affected by changes in market demand, such as a sharp increase in the market price of a certain agricultural product, and the economic benefit target is repeated 4 times in the target indicator item, and the adjustment item is 4, then The value of (the impact factor of economic goals) is 0.2. If the enterprise faces strict policy compliance review, the weight of policy compliance goals may need to be increased. In the target indicator item, the number of repetitions of policy compliance goals is 3, and the adjustment item is 3. At this time, set =0.15, so the impact factor will change with the occurrence of various events. When the impact factors are 0.3, 0.25, 0.2, and 0.15 respectively, according to the formula , Calculate the weight of each indicator information item: , , , , At this time, the adjusted weights will cause the sum to be not equal to 1, so the result needs to be normalized. The normalized sum is 1.25. At this time, the final normalized weight is: , , , , According to the allocation and sorting of the weights of the four indicator information items, a first sorting item is obtained, and the first sorting item is used to provide a basic comparison for the real-time target item.

[0039] Step S400: weighting the real-time target items based on the second allocation method.

[0040] It should be noted that, since the weight allocation of the indicator information items has been completed, the real-time target information in the real-time target items will change and may not fully satisfy the indicator information items. For example, in the weight allocation and ranking results of the indicator information items, the weights of environmental protection goals, sustainable development goals, economic benefit goals and policy goals are 0.042, 0.326, 0.184 and 0.088, respectively, ranking first, second, third and fourth, respectively. However, in the real-time target information obtained, the target indicator items obtained according to the matching may only include environmental protection goals and sustainable development goals. Therefore, the weights need to be recalculated based on the real-time target information obtained.

[0041] Specifically, the second allocation method includes S401-S402.

[0042] Step S401: Indicator acquisition: based on the acquired real-time target information, target indicator items in the real-time target information are matched to obtain weight data corresponding to the indicator information items corresponding to the target indicator items.

[0043] It should be noted that in the changing real-time target information, the target indicator items and target evaluation items are matched according to the indicator information items and evaluation information items existing in the current real-time target information, and the weight data in the indicator information items are filtered out according to the target indicator items and target evaluation items.

[0044] Step S402: Calculate the weight data of the individual target index items in the target weight items according to the formula The weights of the target indicators in the real-time target information are calculated. When the impact values ​​t are 0.2, 0.3, 0.1, and 0.05, the corresponding weights of the environmental protection goal, sustainable development goal, economic benefit goal, and policy compliance goal are 0.402, 0.326, 0.184, and 0.088, respectively. When the target indicators in the real-time target information are environmental protection goals and sustainable development goals, the corresponding weights of the environmental protection goals and sustainable development goals in the real-time target information are: , .

[0045] The method for obtaining indicators includes: based on the first sorting item and the target indicator item, obtaining the sorting position of the target indicator item in the first sorting item to obtain the target position item, and matching the target position item with the weight data corresponding to the target position item to obtain the target weight item.

[0046] A multi-objective agricultural green production intelligent decision-making optimization system uses the above-mentioned multi-objective agricultural green production intelligent decision-making optimization method.

[0047] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A multi-objective agricultural green production intelligent decision-making optimization method, characterized in that: include: Information acquisition: Obtain specific indicator data information to obtain indicator information items, formulate specific evaluation indicators based on the indicator information items, obtain evaluation information items, and the evaluation information items correspond to the indicator information items respectively: Demand acquisition: obtain the actual target information, get the real-time target items, match the indicator information items and evaluation information items through the matching method, get the target indicator items and target evaluation items, and the target indicator items and target evaluation items are used to feedback the actual goals of agricultural production; Weight allocation: Based on the first allocation method, weights are allocated to the indicator information items to obtain the first ranking item, which is used to provide a basic comparison for the real-time target items. Based on the second allocation method, weights are allocated to the real-time target items to obtain the second ranking item, which is used to perform weight matching based on the actual goals of agricultural production feedback. Based on the second ranking item, the actual weight allocation data is output to obtain the target weight item, and decision adjustments are made based on the target weight item.

2. A multi-objective agricultural green production intelligent decision-making optimization method according to claim 1, characterized in that: The indicator data information includes environmental protection goals, sustainable development goals, economic benefit goals and policy goals. The evaluation information items include soil health index, water resource utilization efficiency, and carbon dioxide emissions. Corresponding to environmental protection goals, the evaluation information items also include yield per mu and crop diversity. Corresponding to sustainable development goals, the evaluation information items also include unit cost and sales revenue. Corresponding to economic benefit goals, the evaluation information items also include the ratio of compliance with green certification standards, subsidies and support obtained, and corresponding policy goals.

3. The multi-objective agricultural green production intelligent decision-making optimization method according to claim 1 is characterized by: The actual target information includes indicator benchmarking data, and the matching method includes: according to the indicator benchmarking data, obtaining indicator data information corresponding to the indicator benchmarking data through indicator information items to obtain target indicator items; based on the target indicator items, obtaining evaluation information items corresponding to the target indicator items to obtain target evaluation items, and the number of target evaluation items is at least two.

4. A multi-objective agricultural green production intelligent decision-making optimization method according to claim 1, characterized in that: The actual target information includes evaluation benchmarking data, and the matching method includes: according to the evaluation benchmarking data, through the evaluation information item, obtaining the evaluation information indicator information corresponding to the evaluation information item, to obtain the target evaluation item, the number of the target evaluation items is at least one, based on the target evaluation item, obtaining the indicator information item corresponding to the target evaluation item, to obtain the target indicator item.

5. The multi-objective agricultural green production intelligent decision-making optimization method according to claim 1 is characterized by: The first allocation method includes: S1: Weight allocation: Based on the indicator information items, the weights of the indicator information items are allocated. The weight allocation formula is: ,in is the weight ratio of the i-th indicator information item; S2: Impact setting, set the impact value t, t is a weight adjustment factor, used to reflect how the importance of the target is affected. The adjusted weight formula is: ， in Represents the adjustment factor corresponding to each goal, which is set according to the importance of each goal in a specific situation; S3: Weight normalization. The adjusted weights may cause the sum to be different from 1, so the result needs to be normalized. , the normalized weight formula is: .

6. A multi-objective agricultural green production intelligent decision-making optimization method according to claim 5, characterized in that: The adjustment factor The method of obtaining is: set the initial adjustment value, obtain the adjustment item based on the number of repetitions of the target indicator item, and obtain the adjustment factor based on the combination of the adjustment item and the initial adjustment value. .

7. The multi-objective agricultural green production intelligent decision-making optimization method according to claim 1 is characterized by: The initial weight distribution of the indicator information item is the initial setting value, and the weight ratio distribution is > > , sort the indicator information items based on the initial setting value to obtain the first sorting item.

8. The multi-objective agricultural green production intelligent decision-making optimization method according to claim 1 is characterized by: The second allocation method includes: M1: Indicator acquisition: based on the target indicator item, obtain the weight data matching the indicator information in the target indicator item to obtain the target weight item; M2: Weight calculation, calculate the weight data of the individual target indicator item in the target weight item. The weight data formula is: , is the weight data of the fth target indicator in the target indicator item, It is the indicator information weight data corresponding to the target indicator item in the first sorting item.

9. A multi-objective agricultural green production intelligent decision-making optimization method according to claim 8, characterized in that: The indicator acquisition method includes: based on the first sorting item and the target indicator item, acquiring the sorting position of the target indicator item in the first sorting item to obtain the target position item, and matching the target position item with weight data corresponding to the target position item to obtain the target weight item.

10. A multi-objective agricultural green production intelligent decision-making optimization system, characterized by: A multi-objective agricultural green production intelligent decision-making optimization method as described in any one of claims 1-9 is used.

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