A multi-objective optimization method and system for comprehensive reduction and regulation of regional microplastics

Abstract

The present invention discloses a multi-objective optimization method and system for comprehensive reduction and regulation of regional microplastics, including: defining the microplastic pollution risk level of each area to be regulated according to the microplastic pollution situation in different areas to be regulated; wherein there are one or more areas to be regulated; according to the microplastic pollution risk level, determining several reduction and regulation technologies from a reduction and regulation technology library to form an alternative solution set; wherein the reduction and regulation technology library includes several microplastic reduction and regulation technologies; conducting expert review on the alternative solution set, analyzing the results of the expert review according to the decision maker's preference, and obtaining the index weight value and the scheme evaluation value; according to the index weight value and the scheme evaluation value, determining the final control scheme combination of the area to be regulated. The present invention can efficiently solve the multi-objective decision-making problem in the process of comprehensive management of regional microplastics, realize technically feasible, economically reasonable, and environmentally friendly multi-objective collaborative management, and can be widely used in the field of environmental pollution treatment technology.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental pollution treatment, and in particular to a multi-objective optimization method and system for comprehensive reduction and regulation of regional microplastics. Background Art

[0002] Microplastics refer to plastic particles or fragments with a diameter of less than 5mm. They are an emerging environmental pollutant that is widely present in water, soil, air and other media, posing a potential threat to ecosystems and human health. The main characteristics of regional microplastic pollution are multi-source emissions, multiple pollutants, multiple physical and chemical processes, and multi-scale coupling. Compared with traditional single-type microplastic pollution control, it has more control factors, a wider control range, greater control uncertainty, and more sophisticated and complex control methods, making regional comprehensive microplastic reduction and regulation a difficult problem in the current microplastic pollution control work.

[0003] Multi-objective optimization methods have advantages in balancing and solving complex problems of multiple interrelated goals or indicators, and are an effective means to solve the problem of comprehensive reduction and regulation of regional microplastics. However, the current research on multi-objective optimization methods for regional microplastic regulation is still in its infancy, lacking systematic and targeted theories and methods, and it is difficult to solve the conflicts and trade-offs between multiple objectives. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present invention proposes an efficient multi-objective optimization method and system for comprehensive reduction and control of regional microplastics.

[0005] On the one hand, the embodiments of the present invention provide a multi-objective optimization method for comprehensive reduction and regulation of regional microplastics with high efficiency and wide application range, including:

[0006] Defining the microplastic pollution risk level of each area to be regulated according to the microplastic pollution conditions in different areas to be regulated; wherein there are one or more areas to be regulated;

[0007] According to the microplastic pollution risk level, a number of reduction control technologies are determined from the reduction control technology library to form a set of alternative solutions; wherein the reduction control technology library includes a number of microplastic reduction control technologies;

[0008] Conducting expert review on the set of alternative solutions, and analyzing the results of the expert review according to the decision maker's preference to obtain indicator weight values ​​and solution evaluation values;

[0009] According to the indicator weight values ​​and the scheme evaluation values, a final control scheme combination for the area to be controlled is determined.

[0010] Optionally, determining the microplastic pollution risk level according to the microplastic pollution situation in the area to be regulated includes:

[0011] Determine the predicted no-effect concentration of microplastics based on the environmental medium type and environmental medium characteristics of the area to be regulated;

[0012] Determine a number of sampling points, obtain the microplastic concentration at the sampling points, and calculate the microplastic pollution coefficient according to the microplastic concentration and the predicted no-effect concentration;

[0013] Calculating the pollution load index of the sampling point according to the microplastic pollution coefficient;

[0014] The pollution load index is compared with a preset pollution load threshold to determine the microplastic pollution risk level of the area to be regulated.

[0015] Optionally, according to the microplastic pollution risk level, a number of reduction control technologies are determined from a reduction control technology library to form a set of alternative solutions, including:

[0016] Determining the regulation target of the area to be regulated according to the microplastic pollution risk level;

[0017] According to the control target, the reduction control technologies in the reduction control technology library are screened, and several control schemes are determined to form a set of alternative schemes.

[0018] Optionally, the performing of expert review on the set of alternative solutions and analyzing the results of the expert review according to the decision maker's preference to obtain a solution evaluation value includes:

[0019] According to the control objectives of the area to be controlled, select indicators from the evaluation indicator library to form a decision indicator set;

[0020] Generate an expert evaluation table based on the set of alternative solutions and the set of decision indicators;

[0021] Convert the evaluation results in the expert evaluation table into characteristic single values ​​and fuzzy values;

[0022] Calculating the expert consistency between the scores of each expert and the consistency of the scores of each expert and the expert group according to the characteristic single value and the fuzzy value;

[0023] Determine an expert weight according to the expert consistency, and determine an expert summary coefficient according to the score consistency and the expert weight;

[0024] Establishing a program indicator matrix based on the expert summary coefficients;

[0025] Calculate the index weight values ​​of all the control schemes in the alternative scheme set according to the decision maker's preference or the scheme index matrix;

[0026] The scheme evaluation value of each of the control schemes is determined according to the scheme indicator matrix and the indicator weight value.

[0027] Optionally, determining a final control scheme combination for the area to be controlled according to the indicator weight value and the scheme evaluation value includes:

[0028] Combining the indicator weight values, the scheme evaluation values ​​are sorted to obtain a sorting result;

[0029] According to the ranking results, several control scheme combinations are determined to become the final control scheme combination.

[0030] Optionally, in the step of calculating the expert consistency between the scores of each expert and the consistency of the scores of each expert and the expert group according to the feature single value and the fuzzy value, the calculation formula of the score consistency is:

[0031]

[0032] Among them, SIM(S k ,S (t+1) ) represents the kth expert opinion set S k and the expert group opinion set S (t+1) The consistency of the ratings; It indicates that for the i-th regulation scheme, the k-th expert supports the lower bound of the membership of the j-th decision indicator; It indicates that for the ith regulation scheme, the kth expert opposes the lower bound of the membership of the jth decision indicator; t is the number of iterations; It means that for the (t+1)th iteration, for the i-th regulation scheme, the expert group opinion supports the lower bound of the membership of the j-th decision indicator; It represents the lower bound of the degree of membership of the expert group against the jth decision indicator for the ith regulation plan at the (t+1)th iteration.

[0033] Optionally, in the step of determining the scheme evaluation value of each of the control schemes according to the scheme indicator matrix and the indicator weight value, the calculation formula of the scheme evaluation value is:

[0034]

[0035] Among them, V i represents the scheme evaluation value; σ j is the indicator weight value; AC is the program indicator matrix; n represents the total number of decision indicators; i represents the i-th control program; j represents the j-th decision indicator.

[0036] On the other hand, an embodiment of the present invention further provides a multi-objective optimization system for comprehensive reduction and regulation of regional microplastics, including:

[0037] The first module is used to define the corresponding microplastic pollution risk level according to the microplastic pollution situation in different areas to be regulated; wherein there are one or more areas to be regulated;

[0038] The second module is used to determine a number of reduction control technologies from the reduction control technology library according to the microplastic pollution risk level to form a set of alternative solutions; wherein the reduction control technology library includes a number of microplastic reduction control technologies;

[0039] The third module is used to conduct expert review on the set of alternative solutions, analyze the results of the expert review according to the decision maker's preference, and obtain the indicator weight value and the solution evaluation value;

[0040] The fourth module is used to determine the final control scheme combination of the area to be controlled according to the indicator weight value and the scheme evaluation value.

[0041] On the other hand, an embodiment of the present invention further provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above method.

[0042] On the other hand, an embodiment of the present invention further provides a computer storage medium, in which a program executable by a processor is stored. The program executable by the processor is used to implement the method as described above when executed by the processor.

[0043] The embodiments of the present invention have the following beneficial effects: Based on the comprehensive consideration of the differences in regional microplastic risk levels, the embodiments of the present invention set the microplastic pollution levels corresponding to different research areas, and established a regional microplastic comprehensive reduction control technology library and evaluation indicators. By combining expert evaluation opinions for multi-objective decision-making, the present invention can efficiently solve the multi-objective decision-making problem in the process of regional microplastic comprehensive governance, and achieve technically feasible, economically reasonable, and environmentally friendly multi-objective collaborative governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the technical solution 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 technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0045] Figure 1 It is a step diagram of a multi-objective optimization method for comprehensive reduction and regulation of regional microplastics provided by an embodiment of the present invention;

[0046] Figure 2It is a schematic diagram of the process of the multi-objective optimization method for comprehensive reduction and regulation of regional microplastics provided by an embodiment of the present invention;

[0047] Figure 3 is a flowchart of step S100 provided in an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of constructing a reduction control technology library and an evaluation index library provided by an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of microplastic concentration conversion provided by an embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of the ranking results of the regional microplastic control technology combination scheme with priority given to control efficiency provided by an embodiment of the present invention;

[0051] Figure 7 This is a schematic diagram of the sorting results of the regional microplastic control technology combination schemes with environmental impact priority provided by an embodiment of the present invention;

[0052] Figure 8 It is a structural schematic diagram of a multi-objective optimization system for comprehensive reduction and regulation of regional microplastics provided by an embodiment of the present invention;

[0053] Fig. 9 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 used to limit the present invention.

[0055] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100", "second / S200", etc. in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] Reference Figure 1 and Figure 2 , the method comprises the following steps:

[0058] S100. Define the microplastic pollution risk level of each area to be regulated according to the microplastic pollution situation in different areas to be regulated; there are one or more areas to be regulated.

[0059] Specifically, refer to Figure 3 , step S100 may include the following steps S110 to S140.

[0060] S110. Determine the predicted no-effect concentration of microplastics based on the environmental medium type and characteristics of the environmental medium in the area to be regulated.

[0061] Determine the predicted no-effect concentration (C) of microplastics based on the environmental medium type and characteristics of the area to be regulated. oi The environmental medium may be, for example, fresh water, sea water, soil, etc. In other embodiments, it may also be other environmental media, which is not limited in the embodiments of the present invention. Taking fresh water, sea water and soil as examples, the predicted no-effect concentration C of microplastics is oi Please refer to Table 1, which is a table of predicted no-effect concentrations of microplastics provided in an embodiment of the present invention:

[0062] Table 1

[0063] Environmental media freshwater ocean soil <![CDATA[Predicted no - effect concentration C oi > 99μg / L 72 μg / L 520-655 mg / kg

[0064] S120. Determine a number of sampling points, obtain the microplastic concentration at the sampling points, and calculate the microplastic pollution coefficient based on the microplastic concentration and the predicted no-effect concentration.

[0065] Determine the area to be regulated, set several sampling points in the area to be regulated, collect, process and calculate the concentration of microplastics C in the area to be regulated at the sampling points i Water bodies can be collected using net capture, sediments can be collected using mud samplers, and the number of microplastics in the calculation method can be replaced by the weight weighed by a microbalance.

[0066] According to the collected microplastic concentration C i and predicted no-effect concentration C of microplastics oi , and then convert it into a dimensionally consistent value to calculate the microplastic pollution coefficient CF i , microplastic pollution coefficient CF i The calculation formula is:

[0067]

[0068] In the formula, C iis the concentration of microplastics; C oi Predicting no-effect concentration for microplastics; CF i is the microplastic pollution coefficient.

[0069] S130. Calculate the pollution load index of the sampling point based on the microplastic pollution coefficient.

[0070] According to the above microplastic pollution coefficient CF i , calculate the pollution load index PLI of the sampling point, the calculation formula is:

[0071]

[0072] S140. Compare the pollution load index with a pre-set pollution load threshold to determine the microplastic pollution risk level of the area to be regulated.

[0073] According to the preset pollution load threshold PLI value and the microplastic pollution risk level table, the pollution risk level of the current area to be regulated is obtained. The microplastic pollution risk level table can be configured with reference to Table 2, which is a microplastic pollution risk level table provided in an embodiment of the present invention:

[0074] Table 2

[0075]

[0076]

[0077] The microplastic risk level table of the embodiment of the present invention can be configured with specific numerical values ​​according to actual conditions, and Table 2 is only used as an example.

[0078] S200. According to the risk level of microplastic pollution, determine several reduction control technologies from the reduction control technology library to form a set of alternative solutions; wherein the reduction control technology library includes several microplastic reduction control technologies.

[0079] Specifically, step S200 may include the following steps S210 to S220.

[0080] S210. Determine the control target of the area to be controlled according to the pollution risk level.

[0081] According to the regional microplastic risk level, the research area is divided and the control targets of the corresponding areas are set. The control targets of each risk level can refer to the following ①~④:

[0082] ① “Level IV” areas: These areas are seriously polluted by microplastics. Microplastic prevention and control measures focus on improving governance efficiency and the degree of microplastic treatment, emphasizing rapid emergency treatment. Subsequent ecological governance will be considered after the concentration of microplastics in the area decreases. Such areas should appropriately relax the requirements for return on investment when making regulatory decisions.

[0083] ② "Level III" areas: Under the premise of ensuring that the current environmental impact is low, the governance efficiency should be guaranteed first, while taking into account the degree of microplastic treatment and sustainable development, and gradually improving the regional microplastic pollution situation. Certain investment cost factors need to be considered.

[0084] ③ “Level II” areas: Under the premise of ensuring the lowest current environmental impact, near-natural technologies should be used for microplastic pollution types. Attention should be paid to controlling the scale of investment, promoting plastic recycling and green substitution, and enhancing regional sustainable development capabilities.

[0085] ④ "Level I" area: Generally, no control measures are required to maintain extremely low levels of regional microplastic pollution.

[0086] S220. According to the control target, the reduction control technologies in the reduction control technology library are screened to determine several control schemes to form a set of alternative schemes.

[0087] Reference Figure 4 And Table 3, which is a regional microplastic comprehensive reduction and control technology library established by the embodiment of the present invention with reference to historical experience:

[0088] Table 3

[0089]

[0090]

[0091] Among them, reduction and substitution refers to reducing the production and use of plastic products from the source and promoting the application of recyclable and degradable alternative materials and products. The advantage is that it saves resources and energy and promotes the development of green consumption; the disadvantage is that it requires the development and promotion of recyclable and degradable alternative materials and products, which requires a large investment.

[0092] Interception and removal refers to strengthening the interception and removal of microplastics during the circulation, use and disposal of plastic products. The advantage is that it directly reduces the pollution of microplastics to the environment and promotes the development of a circular economy; the disadvantage is that it consumes manpower costs and the intercepted microplastics may re-enter the environment.

[0093] Degradation and remediation refers to the effective degradation and environmental remediation of microplastics that have entered the environment. The advantage is that it can effectively degrade and remove microplastics in the environment and restore the environment to its natural state. The disadvantage is that it requires overcoming scientific and technological difficulties and cost issues, and the degradation process may produce other substances that harm the environment.

[0094] S300, conducting expert review on the set of alternative solutions, analyzing the results of the expert review according to the decision maker's preferences, and obtaining indicator weight values ​​and solution evaluation values.

[0095] Specifically, step S300 may include the following steps S310 to S380.

[0096] S310. According to the control target of the area to be controlled, select indicators from the evaluation indicator library to form a decision indicator set.

[0097] Reference Figure 4 As shown, in the embodiment of the present invention, an evaluation index library of the regional microplastic comprehensive reduction control plan is supplemented and established, and the index connotation is stated for subsequent expert evaluation. The reference evaluation index library and index connotation can refer to Table 4, which is a schematic table of the evaluation index library provided in the embodiment of the present invention:

[0098] Table 4

[0099]

[0100] In other embodiments, the indicators in the evaluation indicator library can be increased or decreased, and the evaluation indicators can be configured according to actual conditions.

[0101] S320: Generate an expert evaluation table based on the set of alternative solutions and the set of decision indicators.

[0102] The generated expert evaluation table is used to collect the evaluation results of the alternative plans by each expert based on the decision indicator set.

[0103] S330, converting the evaluation results in the expert evaluation table into feature single values, and converting the feature single values ​​into feature fuzzy values.

[0104] S340, calculating the expert consistency between the scores of each expert and the consistency between the scores of each expert and the expert group according to the characteristic fuzzy value.

[0105] Steps S330 to S340 may generally include the following steps (1) to (5).

[0106] (1) Expert rating scales are distributed in the form of discussions and questionnaires. Experts evaluate the degree to which the solution set improves the decision indicator set. There are m alternative control solutions, n decision indicators, and K experts. The kth expert's evaluation opinion on the improvement of the jth decision indicator by the ith alternative solution is recorded as 1≤i≤m, 1≤j≤n, 1≤k≤K. Establish the opinion set of the kth expert and record it as S k **:

[0107] (1≤i≤m, 1≤j≤n, 1≤k≤K)

[0108] in, represents the evaluation opinion of the kth expert on the improvement of the jth decision indicator by the ith alternative plan, m and n represent the number of alternative plans and decision indicators respectively, and K is the total number of experts.

[0109] (2) Table 5 is a characteristic single value conversion table of evaluation opinions provided by an embodiment of the present invention. Referring to Table 5, the opinion set S k **Evaluation language in Convert to single-valued feature The set is denoted as S k *, S k The expression of * is:

[0110] (1≤i≤m, 1≤j≤n, 1≤k≤K)

[0111] in, The single value of the feature representing the transformation of the kth expert's opinion on how the i-th alternative improves the j-th decision indicator.

[0112] Table 5

[0113] Comments Code(s) Characteristic single value (n) Absolute improvement AI 1 Excellent improvement VI 0.9 Very good improvement GI 0.8 Better improvement FI 0.7 Moderate improvement MI 0.6 Partial Improvement I 0.5 A small part cannot be improved SU 0.4 Most cannot be improved LU 0.3 Basically no improvement BU 0.2 Cannot be improved NU 0.1 Absolutely no improvement AU 0

[0114] (3) Convert the single feature value into a Vague value, i.e., a fuzzy value. Vague value is a fuzzy value, which refers to a value between 0 and 1 in fuzzy logic that represents the degree of membership of a thing to a fuzzy concept.

[0115] There are m alternatives and n decision indicators. The characteristic single value of the k-th expert's evaluation of the j-th decision indicator of the ith alternative is Among them, the minimum value of the characteristic single value of all alternative solutions for the kth expert for the jth decision indicator is recorded as The maximum value is recorded as The opinion set S k * Convert all to Vague values The set is denoted as S k , (k=1,2,…,K).

[0116]

[0117] (1≤i≤m, 1≤j≤n, 1≤k≤K)

[0118] in, It represents the Vague value converted from the opinion of the kth expert on the i-th alternative to improve the j-th decision indicator. It indicates that the k-th expert supports the lower bound of the membership of the j-th decision indicator. It means that the k-th expert opposes the lower bound of the membership of the j-th decision indicator.

[0119] (4) Calculate any two opinion sets S k Each Vague value The degree of expert agreement between them. Taking experts k1 and k2 as an example, the opinion set S k1 The Vague value of the improvement of the jth decision indicator by the i-th alternative is recorded as Opinion Set S k2 It is recorded as Calculate the expert agreement SIM(S) between two opinion sets k1 ,S k2 ) is calculated as:

[0120]

[0121] in, are the lower bounds of the membership of expert k1 for and against the jth decision indicator for the i-th alternative, They are the lower bounds of the membership of expert k2 for and against the jth decision indicator for the ith alternative, respectively. The results can help judge the individual differences between expert opinions.

[0122] (5) There are K experts, m alternatives, and n decision indicators. k is the Vague set of the kth expert opinion, S (t +1) is the Vague set of expert group opinions after iteration, is the consistency of each expert's relative group opinion, and the calculation formula is as follows:

[0123]

[0124]

[0125] Wherein, t represents the iteration step, t = 0, 1, 2, ...; and All are expert opinion consistency, set initial consistency weight and Its set is represented by D, q is an integer, q>1. SIM(Sk ,S (t+1) ) is the kth opinion set S k and the expert group opinion set S (t+1) The score consistency of c-SIM(S k ,S (t+1) ), c is a constant, c≥1.

[0126] Calculate S k and S (t+1) The formula for calculating the consistency of the scores is:

[0127]

[0128] in, are the lower bounds of the membership of the kth expert in support and opposition to the jth decision indicator, respectively. These are the lower bounds of the membership of the group expert opinions supporting and opposing the j-th decision indicator in the (t+1)th iteration.

[0129] In order to obtain the best method for gathering expert opinions, the sum of the inconsistencies between the aggregated opinions and the individual opinions of the experts should be minimized. (t+1) =(d1,d2,…,d K ), if ||D (t+1) -D (t) ||≤ε, the iterative calculation stops, otherwise set t=t+1 and repeat this step, where ε is the iteration accuracy. The smaller the value of ε, the more the group opinion can reflect the expert's personal opinion.

[0130] S350, determining expert weights according to expert consistency, and determining expert summary coefficients according to score consistency and expert weights.

[0131] The summary coefficient H of each expert k The calculation formula is:

[0132]

[0133] Among them, ω k is the expert weight, select the kth opinion set result as a reference, and assign it a weight score of g k The remaining experts are then scaled against the reference expert and the scores of the remaining experts are determined. k is the consensus of expert opinions after iteration; α is used to adjust the weight of expert weight and consensus of expert opinions. Set the weight of each expert to K k ;

[0134] S360. Establish a program indicator matrix based on expert summary coefficients.

[0135] According to the summary coefficient H of each expert for each indicator k . Integrate and summarize the information and establish a plan-indicator matrix AC that integrates all expert opinions.

[0136]

[0137] Among them, H k is the summary coefficient of each expert for each indicator, It represents the Vague value converted from the opinion of the kth expert on the i-th alternative to improve the j-th decision indicator.

[0138] S370. Calculate the indicator weight values ​​of all control schemes in the alternative scheme set according to the decision maker's preference or the scheme indicator matrix.

[0139] Calculate the fitness f of the jth decision indicator of the i-th alternative ij ;

[0140] f ij =a ij -b ij

[0141] Among them, f ij represents the suitability of the jth decision indicator of the ith alternative, a ij 、b ij are the lower bounds of the membership degree supporting and opposing the jth decision indicator for the i-th alternative in the solution indicator matrix AC, respectively.

[0142] Using the information entropy method, calculate the entropy E of the jth decision indicator of the i-th alternative in the matrix M j ;

[0143]

[0144]

[0145] Among them, F ij is the ratio of the suitability of the ith alternative to the m alternatives; and 0≤E j ≤1.

[0146] Referring to the control target corresponding to the regional microplastic risk level, weight σ is assigned according to the importance of the decision indicator set. j For example, in “Level IV” pollution areas, the decision-making indicators of “microplastic treatment degree” and “management efficiency” are usually assigned higher values ​​than other indicators; if the decision maker has no preference for the importance of the decision indicator set, the indicator weight σ is calculated as follows: j :

[0147]

[0148] Among them, d j is the information deviation, d j =1-E j .

[0149] S380. Determine the scheme evaluation value of each control scheme according to the scheme indicator matrix and the indicator weight value.

[0150] Based on the indicator weight value σ j , calculate the evaluation value V of each solution i ;

[0151]

[0152] Among them, σ j is the indicator weight, AC is the scheme-indicator matrix integrating all expert opinions, and the evaluation value Vi i is a Vague value format, so the advantage function V(f ij )=a ij -b ij +1 for converting it to a real score result.

[0153] S400: Determine a final control scheme combination for the area to be controlled based on the indicator weight values ​​and the scheme evaluation values.

[0154] S410. Sort the scheme evaluation values ​​in combination with the indicator weight values ​​to obtain a sorting result.

[0155] S420. Determine, based on the sorting results, a number of control scheme combinations to become a final control scheme combination.

[0156] On the other hand, Figure 8 As shown, an embodiment of the present invention provides a multi-objective optimization system for comprehensive reduction and regulation of regional microplastics, including:

[0157] The first module is used to determine the pollution risk level according to the microplastic pollution situation in the area to be regulated; wherein there are one or more areas to be regulated;

[0158] The second module is used to determine a number of reduction control technologies from the reduction control technology library according to the pollution risk level to form a set of alternative solutions; among which, the reduction control technology library includes a number of microplastic reduction control technologies;

[0159] The third module is used to conduct expert review of the alternative solution set, and analyze the results of the expert review according to the decision maker's preferences to obtain the indicator weight value and solution evaluation value;

[0160] The fourth module is used to determine the final combination of control schemes for the area to be controlled based on the indicator weight values ​​and scheme evaluation values.

[0161] On the other hand, Fig. 9 As shown, an embodiment of the present invention further provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above method.

[0162] On the other hand, an embodiment of the present invention further provides a computer storage medium, in which a program executable by a processor is stored. The program executable by the processor is used to implement the above method when executed by the processor.

[0163] The embodiments of the present invention have the following beneficial effects: based on comprehensive consideration of the differences in regional microplastic risk levels, microplastic pollution control targets corresponding to different research areas are set, and a regional microplastic comprehensive reduction control technology library and evaluation index library are established. By combining the consistency of expert evaluation opinions with the importance of indicators for multi-objective decision-making, the method and system of the present invention can effectively solve the multi-objective decision-making problem in the process of regional microplastic comprehensive governance, and achieve technically feasible, economically reasonable, and environmentally friendly multi-objective collaborative governance.

[0164] The following is a further introduction to the multi-objective optimization method for comprehensive reduction and control of regional microplastics in accordance with an embodiment of the present invention in combination with actual application scenarios:

[0165] First, the area to be regulated was determined to be a river section in a certain city. The current microplastic concentration in this river section is 3000-5000 pieces / cubic meter, of which low-density polyethylene and polypropylene account for 70%, and the rest are high-density polyethylene, polystyrene and polyvinyl chloride. The main particle size is <0.25mm. Four experts participated in the evaluation as an example.

[0166] In this application scenario, the steps of the embodiment of the present invention may specifically be the following steps 1 to 12:

[0167] 1. Reference Figure 5 The microplastic concentration was converted to about 20 mg / L, and the pollution coefficient CF was calculated. i =20000 / 99=202.02, calculate the load index According to the microplastic pollution risk level table, the area is divided into "Level II" microplastic pollution area.

[0168] 2. Expert evaluation criteria for designing microplastic pollution prevention and control programs.

[0169] Based on historical experience, the current mainstream microplastic pollution control technologies are collected according to the reduction and substitution strategy, interception and removal strategy, and degradation and restoration strategy, and a regional microplastic comprehensive reduction and control technology library is established.

[0170] The pollution risk level of microplastics is selected as the decision variable. Combined with the current regional microplastic pollution control experience and technical summary, a technical combination of microplastic pollution control is carried out. The microplastic pollution control plan in this study area is mainly formulated in the following five aspects: return on investment, degree of microplastic treatment, degree of environmental impact, governance efficiency, and degree of sustainable development.

[0171] 3. Design expert evaluation form.

[0172] According to the microplastic pollution control targets in the "Level II" regions, a set of alternative solutions with higher feasibility, A = {production of biodegradable plastics (A1), production of natural material substitutes (A2), manual screening (A3), electrocoagulation removal (A4), activated carbon removal (A5), membrane separation removal (A6), bacterial degradation (A7), algae degradation (A8), photocatalytic degradation (A9)}, was selected from the current mainstream microplastic pollution control technology library; at the same time, a decision indicator set C = {return on investment (C1), degree of microplastic treatment (C2), degree of environmental impact (C3), governance efficiency (C4), degree of sustainable development (C5)} was set.

[0173] Based on this, the "Expert Evaluation Form for Regional Microplastic Pollution Comprehensive Reduction and Control Plan" is designed. The reference table design can refer to Table 6, which is an example of an expert evaluation table in an application scenario provided by an embodiment of the present invention:

[0174] Table 6

[0175]

[0176] It should be noted that the following content can also be annotated in the table: Please score the corresponding attributes of each plan: absolute improvement (AI), extremely good improvement (VI), very good improvement (GI), relatively good improvement (FI), moderate improvement (MI), partial improvement (I), slightly unimprovable (SU), mostly unimprovable (LU), basically unimprovable (BU), unimprovable (NU), absolutely unimprovable (AU).

[0177] 4. Summarize the expert rating opinions.

[0178] An expert evaluation meeting was held and the above-mentioned "Expert Evaluation Form for Regional Microplastic Pollution Comprehensive Reduction and Control Plan" was distributed.

[0179] This application scenario takes 4 experts as an example, and summarizes the expert evaluation opinions and expresses them in abbreviated form as shown in Table 7 below. Table 7 is a schematic table of expert evaluation opinions provided by an embodiment of the present invention:

[0180] Table 7

[0181] k1 C1 C2 C3 C4 C5 k2 C1 C2 C3 C4 C5 A1 MI BU GI NU VI A1 GI BU MI NU FI A2 FI BU VI BU AI A2 I VI AI MI AI A3 GI AI FI AI I A3 SU GI GI GI LU A4 BU GI BU I BU A4 I MI MI GI I A5 MI FI MI I I A5 LU SU I FI I A6 BU FI FI VI MI A6 I MI I GI I A7 LU I MI LU MI A7 LU BU MI BU GI A8 MI MI LU I MI A8 BU BU MI NU GI A9 BU FI MI VI LU A9 BU BU I GI I k3 C1 C2 C3 C4 C5 k4 C1 C2 C3 C4 C5 A1 GI FI VI GI SU A1 FI AI GI BU FI A2 GI MI NU GI I A2 FI GI AI MI AI A3 BU AU SU NU BU A3 I LU GI NU BU A4 GI VI GI I LU A4 FI GI NU FI NU A5 GI NU BU I NU A5 VI I MI I MI A6 BU I AU I FI A6 I FI FI I I A7 BU I VI SU FI A7 I VI NU FI SU A8 BU SU I SU VI A8 GI VI I GI FI A9 BU SU LU I I A9 SU GI VI VI I

[0182] 5. Conversion of expert evaluation scores.

[0183] According to the Vague value and evaluation opinion conversion rule of the embodiment of the present invention, the evaluation opinions in the evaluation table 7 are first converted into feature single values, and then converted into Vague values ​​for subsequent statistics and weighted calculations. The Vague set of the kth expert opinion is denoted as S k , the Vague value conversion results of the four experts' evaluations are shown in Table 8 below. Table 8 is a result table of Vague value conversion for the evaluation opinions in Table 7 provided by an embodiment of the present invention:

[0184] Table 8

[0185]

[0186]

[0187] 6. Calculate the expert agreement between experts.

[0188] For the opinion set S k , calculate the expert agreement between every two experts, k1, k2, k3, k4 represent the first, second, third, and fourth experts respectively, and calculate the expert agreement M(S) between each pair of experts k ,S k′ ), k≠k', k∈[0,K], k'∈[0,K]. The expert agreement calculation results are shown in Table 9 below. Table 9 is a table of expert agreement calculation results between every two experts provided by an embodiment of the present invention, wherein the larger the M value, the higher the agreement between the two experts.

[0189] Table 9

[0190]

[0191]

[0192] In this application scenario, the expert agreement between any two expert opinions is higher than 0.50, the evaluations are relatively consistent, and both opinions can be adopted.

[0193] 7. Iteratively optimize the expert group opinion set and calculate the consistency of expert opinions.

[0194] In order to obtain the best expert opinion aggregation method, the sum of the inconsistencies between the aggregated opinions and the individual opinions of the experts should be minimized. The opinion set of the kth expert is S k , the expert group opinion after iteration is recorded as S (t+1) The consistency of their scores is SIM(S k ,S (t+1) ), where t represents the iteration step, t = 0, 1, 2, .... The relative consistency of expert opinions is Setting initial consistency weights The set is represented by D. Take c = 1, q = 2, and calculate the expert group opinion set S (t+1) Consistency with expert opinions

[0195]

[0196]

[0197] Let D (t+1) =(d1,d2,…,d K ), if ||D (t+1) -D (t) ||≤0.001, the iterative calculation stops, otherwise set t=t+1 and repeat this step. In this application scenario, when the fifth iteration is performed, ||D is satisfied. (t+1) -D (t) ||≤0.001, the results refer to Table 10, which is an iterative calculation record table provided by an embodiment of the present invention:

[0198] Table 10

[0199] <![CDATA[D (t+1) ]]> k1 k2 k3 k4 <![CDATA[d (0) ]]> 0.250 0.250 0.250 0.250 <![CDATA[d (1) ]]> 0.301 0.262 0.228 0.202 <![CDATA[d (2) ]]> 0.285 0.245 0.226 0.205 <![CDATA[d (3) ]]> 0.289 0.250 0.227 0.205 <![CDATA[d (4) ]]> 0.288 0.249 0.227 0.205 <![CDATA[d (5) ]]> 0.288 0.249 0.227 0.205

[0200] At this time, the expert group opinion set S obtained after iteration (5) See Table 11 below, which is a schematic table of expert group opinion sets for the fifth iteration provided by an embodiment of the present invention:

[0201] Table 11

[0202]

[0203]

[0204] 8. Calculate the expert summary coefficient.

[0205] according to 0≤α≤1Calculate the summary coefficient H of each expert k In this application scenario, the experience background and professional level of each expert are assumed to be consistent, so the expert weight ω is defined k =1 / K=0.25, α=0.5, the calculation results refer to Table 12, which is an expert summary coefficient table provided by an embodiment of the present invention:

[0206] Table 12

[0207] Hk C1 C2 C3 C4 C5 k1 0.241 0.267 0.291 0.260 0.281 k2 0.261 0.220 0.231 0.231 0.239 k3 0.214 0.275 0.231 0.258 0.228 k4 0.284 0.238 0.247 0.252 0.252

[0208] 9. Establish a matrix to summarize expert opinions.

[0209] According to the summary coefficient H of each expert for each indicator k , establish the matrix AC that integrates all expert opinions:

[0210] (1≤i≤m, 1≤j≤n)

[0211] The integration and summary coefficient H of this application scenario k and Vague set matrix, the results are shown in Table 13 below:

[0212] Table 13

[0213]

[0214] 10. Empowerment of information entropy indicators.

[0215] This application scenario is based on the scheme-index matrix AC that integrates all expert opinions. According to the calculation method of entropy weight in an expert opinion summary and multi-objective scheme ranking method (3), the suitability F of the jth decision indicator of the i-th alternative scheme is calculated in turn. ij and entropy E j , through the information deviation d j Calculate the weight σ of each decision indicator j , the results are shown in Table 14:

[0216] Table 14

[0217]

[0218] 11. Calculate the scheme evaluation value.

[0219] Based on the matrix AC, based on the weight value σ of each indicator j , calculate the evaluation value of each scheme according to the following formula:

[0220]

[0221] In this application scenario, the evaluation value calculation results of each solution are shown in Table 15:

[0222] Table 15

[0223]

[0224] At this time, the scheme evaluation value is in the form of Vague value. In order to facilitate comparison, the advantage function V(f ij )=a ij -b ij +1 converts the Vague value into a real number. The conversion results are shown in Table 16:

[0225] Table 16

[0226] Solution Set Points Solution Set Points Production of biodegradable plastics (A1) 1.00 Membrane separation and removal (A6) 0.79 Producing natural material alternatives (A2) 1.26 Bacterial degradation (A7) 0.58 Manual screening (A3) 0.83 Algae degradation (A8) 0.69 Electrocoagulation removal (A4) 0.87 Photocatalytic degradation (A9) 0.82 Activated carbon removal (A5) 0.78

[0227] 12. Sort and get the final control plan combination.

[0228] According to the results of the scheme advantage, the schemes are ranked and the pros and cons of the schemes are evaluated by combining the indicators. The results are as follows: Figure 6 As shown. At this time, the weight value of the indicator "management efficiency" is the largest. Under this condition, the technical combination of "production of natural material substitutes" + "production of biodegradable plastics" + "electrocoagulation removal" ranks high in the management plan. In addition, under economically feasible conditions, technologies such as "artificial screening", "photocatalytic degradation" and "membrane separation removal" can be added to further improve the management efficiency, which is expected to have a good effect on the rapid disposal of microplastics. By studying the decision-making process, it can be found that the ranking results of the alternative plans comprehensively reflect the degree of satisfaction of each indicator and the preferences of different experts. The results have a high reference value for the selection of the final regulatory measures.

[0229] Modifying the indicator weights can result in regional control plans for different microplastic pollution levels. For example, according to the control target for regional microplastic pollution level “Level II”, the plan has the highest demand for the “environmental impact level” indicator, which can be determined by the decision maker’s weighting σ j ={0.20,0.10,0.50,0.05,0.15}, repeat the above step 11, the result is as follows Figure 7 As shown, in addition to plastic recycling and green alternatives such as "producing natural material substitutes" and "producing biodegradable plastics", "artificial screening", "activated carbon removal" and "bacterial degradation" are ranked ahead of other options. This option is suitable when the decision maker's preference indicator is the minimum environmental impact.

[0230] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0231] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0232] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0233] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0234] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0235] Although the 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 defined by the claims and their equivalents.

[0236] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A multi-objective optimization method for comprehensive reduction and regulation of regional microplastics, characterized in that: include: Defining the microplastic pollution risk level of each area to be regulated according to the microplastic pollution conditions in the areas to be regulated; wherein there are one or more areas to be regulated; According to the microplastic pollution risk level, a number of reduction control technologies are determined from the reduction control technology library to form a set of alternative solutions; wherein the reduction control technology library includes a number of microplastic reduction control technologies; Conducting expert review on the set of alternative solutions, and analyzing the results of the expert review according to the decision maker's preference to obtain indicator weight values ​​and solution evaluation values; Determine the final control scheme combination of the area to be controlled according to the indicator weight value and the scheme evaluation value; Among them, the microplastic pollution risk level of each area to be regulated is defined according to the microplastic pollution situation in different areas to be regulated, including: Determine the predicted no-effect concentration of microplastics based on the environmental medium type and environmental medium characteristics of the area to be regulated; Determine a number of sampling points, obtain the microplastic concentration at the sampling points, and calculate the microplastic pollution coefficient according to the microplastic concentration and the predicted no-effect concentration; Calculating the pollution load index of the sampling point according to the microplastic pollution coefficient; Comparing the pollution load index with a preset pollution load threshold to determine the microplastic pollution risk level of the area to be regulated; Among them, the calculation formula of the microplastic pollution coefficient is: The calculation formula of the pollution load index is: Among them, C i is the concentration of microplastics; C oi Predicting no-effect concentration for microplastics; CF i is the microplastic pollution coefficient; PLI is the pollution load index; According to the microplastic pollution risk level, several reduction and control technologies are determined from the reduction and control technology library to form a set of alternative solutions, including: Determining the regulation target of the area to be regulated according to the microplastic pollution risk level; According to the control target, the reduction control technologies in the reduction control technology library are screened to determine several control schemes to form a set of alternative schemes; The expert review of the alternative solution set and the analysis of the expert review results according to the decision maker's preference to obtain a solution evaluation value include: According to the control objectives of the area to be controlled, select indicators from the evaluation indicator library to form a decision indicator set; Generate an expert evaluation table based on the set of alternative solutions and the set of decision indicators; Convert the evaluation results in the expert evaluation table into a single feature value, and convert the single feature value into a fuzzy feature value; Calculating the expert agreement between the scores of each expert and the agreement between the scores of each expert and the expert group according to the characteristic fuzzy value; Determine an expert weight according to the expert consistency, and determine an expert summary coefficient according to the score consistency and the expert weight; Establishing a program indicator matrix based on the expert summary coefficients; Calculate the index weight values ​​of all the control schemes in the alternative scheme set according to the decision maker's preference or the scheme index matrix; Determining the scheme evaluation value of each of the control schemes according to the scheme indicator matrix and the indicator weight value; Wherein, determining the final control scheme combination of the area to be controlled according to the indicator weight value and the scheme evaluation value includes: Combining the indicator weight values, the scheme evaluation values ​​are sorted to obtain a sorting result; Determine, according to the ranking results, a number of control scheme combinations to become a final control scheme combination; Among them, in the step of calculating the expert consistency between the scores of each expert and the score consistency between each expert and the expert group according to the feature single value and the fuzzy value, the calculation formula of the score consistency is: Among them, SIM(S k , S (t+1) ) represents the kth expert opinion set S k and the expert group opinion set S (t+1) The consistency of the ratings; It indicates that for the i-th regulation scheme, the k-th expert supports the lower bound of the membership of the j-th decision indicator; It indicates that for the ith regulation scheme, the kth expert opposes the lower bound of the membership of the jth decision indicator; t is the number of iterations; It means that for the (t+1)th iteration, for the i-th regulation scheme, the expert group opinion supports the lower bound of the membership of the j-th decision indicator; It means that for the (t+1)th iteration, for the i-th regulation scheme, the lower bound of the degree of membership of the expert group against the j-th decision indicator; Among them, in the step of determining the scheme evaluation value of each of the control schemes according to the scheme indicator matrix and the indicator weight value, the calculation formula of the scheme evaluation value is: Among them, V i represents the scheme evaluation value; σ j is the indicator weight value; AC is the program indicator matrix; n represents the total number of decision indicators; i represents the i-th control program; j represents the j-th decision indicator.

2. A multi-objective optimization system for comprehensive reduction and regulation of regional microplastics, characterized in that: include: The first module is used to define the microplastic pollution risk level of each area to be regulated according to the microplastic pollution situation in different areas to be regulated; wherein there are one or more areas to be regulated; The second module is used to determine a number of reduction control technologies from the reduction control technology library according to the microplastic pollution risk level to form a set of alternative solutions; wherein the reduction control technology library includes a number of microplastic reduction control technologies; The third module is used to conduct expert review on the set of alternative solutions, analyze the results of the expert review according to the decision maker's preference, and obtain the indicator weight value and the solution evaluation value; The fourth module is used to determine the final control scheme combination of the area to be controlled according to the indicator weight value and the scheme evaluation value; Among them, the microplastic pollution risk level of each area to be regulated is defined according to the microplastic pollution situation in different areas to be regulated, including: Determine the predicted no-effect concentration of microplastics based on the environmental medium type and environmental medium characteristics of the area to be regulated; Determine a number of sampling points, obtain the microplastic concentration at the sampling points, and calculate the microplastic pollution coefficient according to the microplastic concentration and the predicted no-effect concentration; Calculating the pollution load index of the sampling point according to the microplastic pollution coefficient; Comparing the pollution load index with a preset pollution load threshold to determine the microplastic pollution risk level of the area to be regulated; Among them, the calculation formula of the microplastic pollution coefficient is: The calculation formula of the pollution load index is: Among them, C i is the concentration of microplastics; C oi Predicting no-effect concentration for microplastics; CF i is the microplastic pollution coefficient; PLI is the pollution load index; According to the microplastic pollution risk level, several reduction and control technologies are determined from the reduction and control technology library to form a set of alternative solutions, including: Determining the regulation target of the area to be regulated according to the microplastic pollution risk level; According to the control target, the reduction control technologies in the reduction control technology library are screened to determine several control schemes to form a set of alternative schemes; The expert review of the alternative solution set and the analysis of the expert review results according to the decision maker's preference to obtain a solution evaluation value include: According to the control objectives of the area to be controlled, select indicators from the evaluation indicator library to form a decision indicator set; Generate an expert evaluation table based on the set of alternative solutions and the set of decision indicators; Convert the evaluation results in the expert evaluation table into a single feature value, and convert the single feature value into a fuzzy feature value; Calculating the expert agreement between the scores of each expert and the agreement between the scores of each expert and the expert group according to the characteristic fuzzy value; Determine an expert weight according to the expert consistency, and determine an expert summary coefficient according to the score consistency and the expert weight; Establishing a program indicator matrix based on the expert summary coefficients; Calculate the index weight values ​​of all the control schemes in the alternative scheme set according to the decision maker's preference or the scheme index matrix; Determining the scheme evaluation value of each of the control schemes according to the scheme indicator matrix and the indicator weight value; Wherein, determining the final control scheme combination of the area to be controlled according to the indicator weight value and the scheme evaluation value includes: Combining the indicator weight values, the scheme evaluation values ​​are sorted to obtain a sorting result; Determine, according to the ranking results, a number of control scheme combinations to become a final control scheme combination; Among them, in the step of calculating the expert consistency between the scores of each expert and the score consistency between each expert and the expert group according to the feature single value and the fuzzy value, the calculation formula of the score consistency is: Among them, SIM(S k , S (t+1) ) represents the kth expert opinion set S k and the expert group opinion set S (t+1) The consistency of the ratings; It indicates that for the i-th regulation scheme, the k-th expert supports the lower bound of the membership of the j-th decision indicator; It indicates that for the ith regulation scheme, the kth expert opposes the lower bound of the membership of the jth decision indicator; t is the number of iterations; It means that for the (t+1)th iteration, for the i-th regulation scheme, the expert group opinion supports the lower bound of the membership of the j-th decision indicator; It means that for the (t+1)th iteration, for the i-th regulation scheme, the lower bound of the degree of membership of the expert group against the j-th decision indicator; Among them, in the step of determining the scheme evaluation value of each of the control schemes according to the scheme indicator matrix and the indicator weight value, the calculation formula of the scheme evaluation value is: Among them, V i represents the scheme evaluation value; σ j is the indicator weight value; AC is the program indicator matrix; n represents the total number of decision indicators; i represents the i-th control program; j represents the j-th decision indicator.

3. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to claim 1.

4. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to claim 1 when executed by the processor.

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