CN117151466A - Regional micro-plastic comprehensive decrement regulation multi-objective optimization method and system - Google Patents
Regional micro-plastic comprehensive decrement regulation multi-objective optimization method and system Download PDFInfo
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Abstract
The invention discloses a regional micro-plastic comprehensive decrement regulation multi-objective optimization method and system, comprising the following steps: defining the micro-plastic pollution risk level of each region to be regulated according to the micro-plastic pollution conditions of different regions to be regulated; wherein one or more regions to be regulated are arranged; determining a plurality of decrement regulation technologies from the decrement regulation technology library according to the micro-plastic pollution risk level to form an alternative scheme set; wherein the weight-reducing control technology library comprises a plurality of microplastic weight-reducing control technologies; expert evaluation is carried out on the alternative scheme set, and the result of the expert evaluation is analyzed according to the preference of a decision maker, so that an index weight value and a scheme evaluation value are obtained; and determining the final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value. The invention can efficiently solve the multi-objective decision-making problem in the comprehensive treatment process of regional micro-plastics, realizes the multi-objective cooperative treatment with feasible technology, economy, rationality and environmental friendliness, and can be widely applied to the technical field of environmental pollution treatment.
Description
Technical Field
The invention relates to the technical field of environmental pollution treatment, in particular to a regional micro-plastic comprehensive decrement regulation multi-objective optimization method and system.
Background
Microplastic refers to plastic particles or fragments with the diameter smaller than 5mm, is an emerging environmental pollutant, widely exists in water, soil, air and other mediums, and forms a potential threat to the ecological system and human health. The main characteristics of regional microplastic pollution are multi-source emission, multi-pollutant, multi-physicochemical process and multi-scale mutual coupling. Compared with the traditional single type microplastic pollution control, the control factor is more, the control range is wider, the control uncertainty is larger, and the control means is finer and more complicated, so that the comprehensive reduction control of regional microplastic becomes a difficult problem in the current microplastic pollution control work.
The multi-objective optimization method has advantages in weighing and solving the complex problem of a plurality of mutually related targets or indexes, and is an effective means for solving the problem of comprehensive decrement regulation of regional micro-plastics. However, the current research on a regional micro-plastic regulation multi-objective optimization method is still in a starting stage, lacks systematic and targeted theory and method, and is difficult to solve the conflict and trade-off problem among multiple objectives.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention provides a high-efficiency region micro-plastic comprehensive decrement regulation multi-objective optimization method and system.
On one hand, the embodiment of the invention provides a high-efficiency and wide-application-range region microplastic comprehensive decrement regulation multi-objective optimization method, which comprises the following steps:
defining micro-plastic pollution risk levels of all the areas to be regulated according to the micro-plastic pollution conditions of different areas to be regulated; wherein one or more areas to be regulated are arranged;
determining a plurality of decrement regulation technologies from a decrement regulation technology library according to the micro-plastic pollution risk level to form an alternative scheme set; wherein the decrement control technology library comprises a plurality of microplastic decrement control technologies;
expert evaluation is carried out on the alternative scheme set, and the result of the expert evaluation is analyzed according to the preference of a decision maker to obtain an index weight value and a scheme evaluation value;
and determining the final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value.
Optionally, the determining the micro plastic pollution risk level according to the micro plastic pollution condition of the area to be regulated includes:
Determining the predicted ineffective concentration of the microplastic according to the type and the characteristics of the environmental medium of the region to be regulated;
determining a plurality of sampling points, obtaining the concentration of the microplastic at the sampling points, and calculating the microplastic pollution coefficient according to the concentration of the microplastic and the predicted non-effect concentration;
calculating a pollution load index of the sampling point according to the micro-plastic pollution coefficient;
and comparing the pollution load index with a preset pollution load threshold value, and determining the micro-plastic pollution risk level of the area to be regulated.
Optionally, determining a plurality of reduction control technologies from a reduction control technology library according to the micro plastic pollution risk level to form an alternative scheme set, including:
determining a regulation target of the region to be regulated according to the microplastic pollution risk level;
and screening the decrement regulation technologies in the decrement regulation technology library according to the regulation targets, and determining a plurality of regulation schemes to form an alternative scheme set.
Optionally, the performing expert review on the alternative scheme set, analyzing the result of the expert review according to the decision maker preference to obtain a scheme evaluation value, including:
Selecting indexes from an evaluation index library according to the regulation and control targets of the region to be regulated and controlled to form a decision index set;
generating an expert evaluation table based on the alternative scheme set and the decision index set;
converting the evaluation condition in the expert evaluation table into a feature single value and a fuzzy value;
calculating expert consistency among the scores of all the experts and the score consistency of all the experts and expert groups according to the feature single value and the fuzzy value;
determining expert weights according to the expert consistence, and determining expert summarization coefficients according to the scoring consistence and the expert weights;
establishing a scheme index matrix according to the expert summarization coefficient;
calculating index weight values of all the regulation and control schemes in the alternative scheme set according to decision maker preference or the scheme index matrix;
and determining the scheme evaluation value of each regulation scheme according to the scheme index matrix and the index weight value.
Optionally, the determining the final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value includes:
sorting the scheme evaluation values by combining the index weight values to obtain a sorting result;
And determining a plurality of regulation scheme combinations to form a final regulation scheme combination according to the sequencing result.
Optionally, in the step of calculating the degree of consistency of the scores of the experts and the expert group according to the feature single value and the fuzzy value, a calculation formula of the degree of consistency of the scores is:
wherein the SIM (S) k ,S (t+1) ) Representing the kth expert opinion set S k And expert group opinion collection S (t+1) Score agreement of (2);representing the membership lower bound of the j decision index supported by the kth expert for the ith regulation scheme; />Representing the membership lower bound of the kth expert against the jth decision index for the ith regulation scheme; t is the iteration number; />Representing iteration (t+1) times, and for the ith regulation scheme, supporting the membership lower bound of the jth decision index by expert group opinion; />Representing the iteration (t+1) times, and for the ith regulation scheme, the expert group opinion is against the membership lower bound of the jth decision index.
Optionally, in the step of determining the solution evaluation value of each regulation solution according to the solution index matrix and the index weight value, a calculation formula of the solution evaluation value is:
Wherein V is i Representing a scheme evaluation value; sigma (sigma) j Is an index weight value; AC is a scheme index matrix; n represents a decisionIndex total; i represents the ith regulatory scheme; j represents the j-th decision index.
On the other hand, the embodiment of the invention also provides a regional micro-plastic comprehensive decrement regulation multi-objective optimization system, which comprises:
the first module is used for defining corresponding microplastic pollution risk levels according to the microplastic pollution conditions of different areas to be regulated; wherein one or more areas to be regulated are arranged;
the second module is used for determining a plurality of decrement regulation technologies from the decrement regulation technology library according to the micro-plastic pollution risk level to form an alternative scheme set; wherein the decrement control technology library comprises a plurality of microplastic decrement control technologies;
the third module is used for carrying out expert review on the alternative scheme set, and analyzing the expert review result according to the preference of a decision maker to obtain an index weight value and a scheme evaluation value;
and a fourth module, configured to determine a final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value.
In another aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory; the memory is used for storing programs; the processor executes the program to implement the method as described above.
In another aspect, embodiments of the present invention also provide a computer storage medium in which a processor-executable program is stored, which when executed by a processor is configured to implement the method as described above.
The embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, on the basis of comprehensively considering the regional micro-plastic risk level difference, the micro-plastic pollution levels corresponding to different research regions are set, and a regional micro-plastic comprehensive decrement regulation technology library and evaluation indexes are established. By combining expert evaluation opinions to carry out multi-objective decision, the method can efficiently solve the multi-objective decision problem in the comprehensive treatment process of regional microplastic, and realizes multi-objective collaborative treatment with feasible technology, economy, rationality and environmental friendliness.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
FIG. 1 is a step diagram of a regional micro-plastic comprehensive decrement regulation multi-objective optimization method provided by an embodiment of the invention;
FIG. 2 is a schematic flow chart of a multi-objective optimization method for comprehensive decrement control of regional micro-plastics, which is provided by the embodiment of the invention;
Fig. 3 is a schematic flow chart of step S100 provided in the embodiment of the present invention;
FIG. 4 is a schematic diagram of construction of a weight-reduction control technology library and an evaluation index library according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of concentration conversion of microplastic provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of a sequencing result of a combination of regional micro-plastic control techniques with priority on abatement efficiency according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a combination scheme ordering result of regional micro-plastic conditioning techniques with environmental impact priority provided by an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a regional micro-plastic comprehensive decrement regulation multi-objective optimization system provided by an embodiment of the invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that although functional block diagrams are depicted as block diagrams, and logical sequences are shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the block diagrams in the system. The terms first/S100, second/S200, and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1 and 2, the method includes the steps of:
s100, defining micro-plastic pollution risk levels of all areas to be regulated according to the micro-plastic pollution conditions of different areas to be regulated; wherein, the area to be regulated is one or more.
Specifically, referring to fig. 3, step S100 may include the following steps S110 to S140.
S110, determining the predicted ineffective concentration of the micro plastic according to the type and the characteristics of the environmental medium of the area to be regulated.
Determining the predicted non-effect concentration C of the microplastic according to the type of the environmental medium and the characteristics of the environmental medium of the area to be regulated oi The environmental medium may be, for example, fresh water, sea water, soil, etc., and in other embodiments may be other environmental media, as the embodiments of the invention are not limited in this respect. Taking fresh water, seawater and soil as examples, the predicted non-effect concentration C of the microplastic oi Referring to table 1, table 1 is a table of predicted non-effect concentration of microplastic provided in the examples of the present invention:
TABLE 1
| Environmental medium | Fresh water | Oceans | Soil and method for producing soil |
| Predicting the concentration C without effect oi | 99μg/L | 72μg/L | 520-655mg/kg |
S120, determining a plurality of sampling points, obtaining the concentration of the microplastic at the sampling points, and calculating the microplastic pollution coefficient according to the concentration of the microplastic and the predicted ineffective concentration.
Determining a region to be regulated, setting a plurality of sampling points in the region to be regulated, and collecting, processing and calculating the concentration C of the microplastic in the region to be regulated in the sampling points i Wherein the water body can be captured and collected by using a net type, sediment can be collected by adopting a mud sampler, and the micro plastic technology in the calculation method can be replaced by the weight weighed by a micro balance.
According to the concentration C of the acquired microplastic i And predicted non-effect concentration C of microplastic oi Converting into a numerical value with consistent dimension, and calculating a microplastic pollution coefficient CF i Microplastic contamination coefficient CF i The calculation formula of (2) is as follows:
wherein C is i Is the concentration of microplastic; c (C) oi Predicting the concentration of the non-effect for the microplastic; CF (compact flash) i Is the pollution coefficient of micro plastic.
S130, calculating the pollution load index of the sampling point according to the micro plastic pollution coefficient.
According to the microplastic pollution coefficient CF i The pollution load index PLI of the sampling point is calculated, and the calculation formula is as follows:
and S140, comparing the pollution load index with a preset pollution load threshold value, and determining the micro-plastic pollution risk level of the area to be regulated.
And obtaining the pollution risk level of the current area to be regulated according to the preset pollution load threshold PLI value and the microplastic pollution risk level table. The micro plastic pollution risk level table can be configured with reference to table 2, and table 2 is a micro plastic pollution risk level table provided by the embodiment of the invention:
TABLE 2
The micro-plastic risk level table according to the embodiment of the present invention may be configured with specific values according to actual situations, and table 2 is merely an example.
S200, determining a plurality of decrement regulation technologies from a decrement regulation technology library according to the micro-plastic pollution risk level to form an alternative scheme set; wherein, the weight-reducing control technology library comprises a plurality of microplastic weight-reducing control technologies.
Specifically, step S200 may include the following steps S210 to S220.
S210, determining a regulation target of the region to be regulated according to the pollution risk level.
According to the regional micro-plastic risk level, dividing a research region and setting regulation targets of the corresponding region, wherein the regulation targets of each risk level can be referred to the following (1) to (4):
(1) "class IV" region: the microplastic pollution in the area is serious, the microplastic prevention and control measures focus on improving the treatment efficiency and the microplastic treatment degree, and the rapid emergency treatment is emphasized, so that the subsequent ecological treatment is considered after the concentration of the microplastic in the area is reduced. Such areas should properly relax the return on investment requirements in making regulatory decisions.
(2) "class III" region: on the premise of ensuring lower influence of the current environment, the treatment efficiency is ensured, the treatment degree and the sustainable development degree of the microplastic are simultaneously considered, the pollution condition of the microplastic in the area is gradually improved, and certain investment cost factors are required to be considered.
(3) "class II" region: on the premise of ensuring the lowest influence of the current environment, aiming at the pollution type of the microplastic, a near-natural technology is preferably adopted. The investment scale is controlled, the recycling and green replacement of plastics are promoted, and the sustainable development capability of the area is enhanced.
(4) "class I" region: generally, no treatment measures are needed to maintain the extremely low pollution state of regional microplastic.
S220, screening the decrement regulation technologies in the decrement regulation technology library according to the regulation targets, and determining a plurality of regulation schemes to form an alternative scheme set.
Referring to fig. 4 and table 3, table 3 is a table of a regional micro plastic comprehensive weight-reduction control technology library established with reference to historical experience in the embodiment of the present invention:
TABLE 3 Table 3
Wherein, the reduction substitution refers to reducing the production and use of plastic products from the source, and popularizing and applying recyclable and degradable substitute materials and products. The method has the advantages of saving resources and energy sources and promoting the development of green consumption; the disadvantage is the need to develop and popularize recyclable, degradable alternative materials and products, requiring significant capital investment.
Interception and removal refers to enhancing interception and removal of microplastic during circulation, use and disposal of plastic products. The method has the advantages of directly reducing the pollution of the micro plastic to the environment and promoting the development of circular economy; the disadvantage is the manpower cost and the possibility of re-entering the environment with the intercepted microplastic.
Degradation repair refers to the effective degradation and environmental repair of microplastic that has entered the environment. The method has the advantages that the method can effectively degrade and remove microplastic in the environment and restore the natural state of the environment. The disadvantage is that technological and cost problems need to be overcome and other environmentally hazardous substances may be generated during degradation.
And S300, expert evaluation is carried out on the alternative scheme set, and the result of the expert evaluation is analyzed according to the preference of a decision maker to obtain an index weight value and a scheme evaluation value.
Specifically, step S300 may include the following steps S310 to S380.
S310, selecting indexes from an evaluation index library according to the regulation and control targets of the region to be regulated and controlled to form a decision index set.
Referring to fig. 4, in the embodiment of the present invention, an evaluation index library of the regional micro-plastic comprehensive decrement regulation scheme is supplemented and built, the index connotation is written for the subsequent expert evaluation, and the reference evaluation index library and index connotation can refer to table 4, where table 4 is a schematic table of the evaluation index library provided in the embodiment of the present invention:
TABLE 4 Table 4
In other embodiments, the indexes in the evaluation index library may be increased or decreased, and the evaluation indexes may be configured according to actual situations.
S320, generating an expert evaluation table based on the alternative scheme set and the decision index set.
The generated expert evaluation table is used for collecting results of evaluation of alternative schemes by each expert based on the decision index set.
S330, converting the evaluation condition in the expert evaluation table into a feature single value, and converting the feature single value into a feature fuzzy value.
And S340, calculating the consistency degree of the experts among the scores of the experts and the consistency degree of the scores of the experts and the expert group according to the feature fuzzy values.
Steps S330 to S340 may generally include the following steps (1) to (5).
(1) The expert scoring scale is issued in the form of interview and questionnaire, the expert evaluates the degree of improving the decision index set by aiming at the scheme set, m alternative modulation and control schemes are provided, n decision indexes and K-bit expert are provided, and the evaluation opinion of the kth expert on the ith alternative scheme for improving the jth decision index is recorded as followsI is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n, and K is more than or equal to 1 and less than or equal to K. Establishing opinion aggregation of the kth expert as S k **：
(1≤i≤m，1≤j≤n，1≤k≤K)
Wherein,the method is characterized in that evaluation opinions of the kth expert on the jth decision index of the ith alternative scheme improvement are represented, m and n respectively represent the number of the alternative scheme and the decision index, and K is the total number of the experts.
(2) Table 5 is a characteristic single value conversion table of evaluation opinions provided in the embodiment of the present invention, and the opinion collection S is described with reference to Table 5 k * Evaluation language in:conversion to characteristic Mono value>Its set is denoted as S k *，S k * Expression of (2)The method comprises the following steps:
(1≤i≤m，1≤j≤n，1≤k≤K)
wherein,a feature order value representing the opinion transformation of the kth expert on the ith alternative to improve the jth decision index.
TABLE 5
| Evaluation opinion | Code(s) | Characteristic single value (n) |
| Absolute improvement | AI | 1 |
| Excellent improvement | VI | 0.9 |
| Well improve | GI | 0.8 |
| Better improve | FI | 0.7 |
| Moderate improvement | MI | 0.6 |
| Partial improvement | I | 0.5 |
| Small parts cannot improve | SU | 0.4 |
| Most of them cannot be improved | LU | 0.3 |
| Can not be improved basically | BU | 0.2 |
| Cannot improve | NU | 0.1 |
| Can not be improved at all | AU | 0 |
(3) The feature single value is converted into a feature Vague value, namely a feature fuzzy value. The Vague value is a fuzzy value, and means that a membership degree of a certain object to a fuzzy concept is represented by a numerical value between 0 and 1 in fuzzy logic.
The method comprises the steps of sharing m alternatives, n decision indexes, and evaluating a characteristic single value of a jth decision index of an ith alternative by a kth expert as followsWherein the minimum value of the feature single value in all alternatives of the kth expert for the jth decision index is marked as +.>Maximum value is marked as->Gathering opinion S k * Is->All conversion to the Vague value +.>Its set is denoted as S k ，(k＝1,2,…,K)。
(1≤i≤m，1≤j≤n，1≤k≤K)
Wherein,conversion of the kth expert opinion on the ith alternative improvement jth decision index into a Vague value, _a->Indicating that the kth expert supports the membership lower bound of the jth decision index,/for> Representing the lower bound of membership of the kth expert against the jth decision criterion,/->
(4) Calculating any twoPersonal opinion collection S k Each Vague value of (a)Expert agreement between. Taking k1 and k2 experts as examples, opinion collection S k1 The Vague value of the ith alternative in (a) to improve the jth decision index is noted asOpinion collection S k2 Then record as +.>Calculates the expert' S consistency SIM of the two opinion sets (S k1 ,S k2 ) The calculation formula of (2) is as follows:
wherein,expert k1 supports and opposes the lower bound of membership of the jth decision criterion for the ith alternative, respectively,/for->The lower bounds of membership of the j-th decision index are supported and opposed for the i-th alternative by expert k2, respectively, and the result can help judge personal differences between expert opinions.
(5) There are K experts, m alternatives, n decision indices. S is S k Vague set of kth expert opinion, S (t +1) For the iterative expert group opinion Vague set,for the consistency of the opinion of each expert relative to the group, the calculation formula is as follows:
where t represents an iterative step, t=0, 1,2, …;and->Expert opinion consistency, initial consistency weight is set> And->The set is represented by D, q is an integer, and q is more than 1.SIM (S) k ,S (t+1) ) For the kth opinion set S k And expert group opinion collection S (t+1) Is a degree of scoring uniformity of c-SIM (S k ,S (t+1) ) C is a constant, and c is more than or equal to 1.
Calculation S k And S is (t+1) The calculation formula of the scoring consistency is as follows:
wherein,the k-th expert supports and resists the membership lower bound of the j-th decision index,then respectively iterate (t+1)) Next, the community expert opinion supports and opposes the membership lower bound of the j-th decision index.
In order to obtain the optimal expert opinion aggregation method, the sum of the disagreement between the collected opinions and the personal opinions of each expert should be minimized. Order D (t+1) ＝(d 1 ,d 2 ,…,d K ) If I D (t+1) -D (t) And if the value of epsilon is smaller, the group opinion can reflect the expert personal opinion.
S350, determining expert weights according to the expert consistency, and determining expert summarization coefficients according to the scoring consistency and the expert weights.
Summarizing coefficient H of each expert k The calculation formula of (2) is as follows:
wherein omega k Selecting the kth opinion result as a reference for expert weight, and assigning a weight score of g to the kth opinion result k . The remaining experts are then scaled against the reference expert and the scores of the remaining experts are determined. d, d k The consistency of the expert opinion after iteration; alpha is used to adjust the specific gravity of both expert weights and expert opinion agreements. In addition, according toSetting the expert weight omega of each bit with the total number of K k ；
S360, establishing a scheme index matrix according to expert summarization coefficients.
According to the sum coefficient H of each expert for each index k . Integrating the summarized information to establish a scheme-index matrix AC integrating all expert opinions.
Wherein H is k For each expert's summary coefficients for the respective indicators,a Vague value representing the conversion of the kth expert opinion on the ith alternative improvement jth decision criterion.
And S370, calculating index weight values of all the regulation and control schemes in the alternative scheme set according to decision maker preference or scheme index matrix.
Calculating the fitness f of the j decision index of the i alternative ij ；
f ij ＝a ij -b ij
Wherein f ij Indicating the fitness of the jth decision index of the ith alternative, a ij 、b ij The ith alternative scheme in the scheme index matrix AC supports and opposes the membership lower bound of the jth decision index, respectively.
Calculating entropy E of a j decision index of an i-th alternative scheme in the matrix M by using an information entropy method j ；
Wherein F is ij Is the ratio of the fitness of the ith alternative to the m alternatives; and 0.ltoreq.E j ≤1。
Weighting sigma according to importance degree of decision index set by reference to regulation and control targets corresponding to regional micro-plastic risk levels j For example, a "grade IV" contaminated area typically assigns higher decision metrics to "microplastic treatment" and "abatement efficiency" than other metrics; if the decision maker has no preference for the importance of the decision index set, the index weight sigma is calculated as follows j ：
Wherein d j For information deviation degree d j ＝1-E j 。
S380, determining the scheme evaluation value of each regulation scheme according to the scheme index matrix and the index weight value.
Based on index weight value sigma j Calculating evaluation value V of each scheme i ；
Wherein sigma j For index weight, AC is a scheme-index matrix integrating all expert opinions, and evaluation value Vi i In the form of Vague values, therefore uses the dominance function V (f ij )＝a ij -b ij +1 converts it into a real score result.
S400, determining a final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value.
S410, sorting the evaluation values of the schemes by combining the index weight values to obtain a sorting result.
S420, determining a plurality of regulation scheme combinations to form a final regulation scheme combination according to the sequencing result.
On the other hand, as shown in fig. 8, an embodiment of the present invention provides a regional micro-plastic comprehensive decrement regulation multi-objective optimization system, including:
the first module is used for determining pollution risk level according to the microplastic pollution condition of the area to be regulated; wherein, the area to be regulated has one or more;
the second module is used for determining a plurality of decrement regulation technologies from the decrement regulation technology library according to the pollution risk level to form an alternative scheme set; wherein the weight-reducing control technology library comprises a plurality of microplastic weight-reducing control technologies;
the third module is used for carrying out expert review on the alternative scheme set, and analyzing the expert review result according to the preference of the decision maker to obtain an index weight value and a scheme evaluation value;
and the fourth module is used for determining the final regulation and control scheme combination of the region to be regulated and controlled according to the index weight value and the scheme evaluation value.
On the other hand, as shown in fig. 9, an embodiment of the present invention further provides an electronic device, including: a processor and a memory; the memory is used for storing programs; the processor executes the program to implement the method as described above.
In another aspect, embodiments of the present invention also provide a computer storage medium in which a processor-executable program is stored, which when executed by a processor is configured to implement the method as above.
The embodiment of the invention has the following beneficial effects: on the basis of comprehensively considering the regional micro-plastic risk level difference, micro-plastic pollution regulation and control targets corresponding to different research regions are set, and a regional micro-plastic comprehensive decrement regulation and control technology library and an evaluation index library are established. The method and the system can effectively solve the multi-objective decision problem in the comprehensive treatment process of regional microplastic by combining the consistency of expert evaluation opinions and the index importance to realize the multi-objective collaborative treatment with feasible technology, economy, rationality and environmental friendliness.
The method for optimizing the regional micro plastic comprehensive decrement regulation and control multi-objective according to the embodiment of the invention is further described below by combining with actual application scenes:
Firstly, determining a region to be regulated as an urban river reach, wherein the current microplastic concentration of the river reach is 3000-5000 pieces/cubic meter, the low-density polyethylene and the polypropylene account for 70 percent, the high-density polyethylene, the polystyrene and the polyvinyl chloride account for the rest, the main particle size is less than 0.25mm, and four experts participate in evaluation as examples.
In this application scenario, the steps of the embodiment of the present invention may specifically be the following steps 1 to 12:
1. referring to the conversion of FIG. 5, conversion of the microplastic concentration was performed to obtain a microplastic concentration of about 20mg/L, and the pollution coefficient CF was calculated i =20000/99= 202.02, calculating a load indexThe areas are divided into 'II-level' microplastic pollution areas according to a pollution risk level table of the microplastic.
2. Expert judgment standards for designing microplastic pollution control schemes.
According to historical experience, the current mainstream microplastic pollution regulation and control technology is respectively collected according to a decrement substitution strategy, an interception removal strategy and a degradation restoration strategy, and a regional microplastic comprehensive decrement regulation and control technology library is established.
And (5) selecting the pollution risk level of the micro plastic as a decision variable. Combining the current regional micro plastic pollution regulation experience and technical summary, and carrying out the technical combination of micro plastic pollution regulation, the research regional micro plastic pollution regulation scheme is mainly formulated aiming at the following five aspects: return on investment, degree of microplastic treatment, degree of environmental impact, treatment efficiency, and degree of sustainable development.
3. Expert evaluation forms were designed.
According to the micro-plastic pollution regulation and control target of the 'II-level' area, screening out an alternative scheme set A= { for producing biodegradable plastic (A1), producing natural material substitute (A2), manually screening out (A3), electrically flocculating out (A4), removing active carbon (A5), removing membrane separation (A6), bacterial degradation (A7), algae degradation (A8) and photocatalytic degradation (A9) from a micro-plastic pollution regulation and control technology library which is currently mainstream; meanwhile, a decision index set C= { return on investment (C1), a microplastic treatment degree (C2), an environmental influence degree (C3), treatment efficiency (C4), and a sustainable development degree (C5) } are set.
According to the design of expert evaluation table of comprehensive reduction control scheme of regional micro plastic pollution, reference table design can be referred to table 6, and table 6 is an example of expert evaluation table in an application scene provided by the embodiment of the invention:
TABLE 6
The following may be noted in the table: please score for each scheme's corresponding attributes: absolute Improvement (AI), excellent improvement (VI), excellent improvement (GI), better improvement (FI), medium Improvement (MI), partial improvement (I), small partial no improvement (SU), large partial no improvement (LU), basic no improvement (BU), no improvement (NU), absolute no improvement (AU).
4. And summarizing expert scoring opinion.
And (3) holding an expert evaluation conference, and issuing the expert evaluation list of the regional microplastic pollution comprehensive decrement control scheme.
Taking 4 experts as an example, the application scene summarizes expert evaluation opinions and uses shorthand to express the following table 7, and the table 7 is an expert evaluation opinion schematic table provided by the embodiment of the invention:
TABLE 7
| 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 |
5. Expert evaluation score conversion.
According to the Vague value and the evaluation opinion transformation rule of the embodiment of the invention, the evaluation opinions in the evaluation table 7 are firstly transformed into feature single values and then transformed into Vague values so as to be convenient for subsequent statistics and weighted calculation. The Vague set of the kth expert opinion is denoted as S k The Vague value conversion results of the four expert evaluations are shown in the following table 8, and table 8 is a table of results of Vague value conversion for the evaluation opinions in table 7 provided in the embodiment of the present invention:
TABLE 8
/>
6. And calculating expert consistency among the experts.
For opinion collection S k Calculating expert coincidence degree between every two experts, k1, k2, k3, k4 representing the first, second, third, and fourth expert respectively, calculating expert coincidence degree M of scores between every two (S k ,S k′ )，k≠k’，k∈[0,K]，k’∈[0,K]. The expert agreement calculation results are shown in table 9 below, and table 9 is a table of expert agreement calculation results between every two experts provided in the embodiment of the present invention, wherein the larger the M value is, the higher the agreement representing the opinion of the two experts is.
TABLE 9
/>
In the application scene, the expert consistency of any two expert opinions is higher than 0.50, the evaluation is more consistent, and the opinion can be adopted.
7. And iteratively optimizing the expert group opinion collection, and calculating the consistency of the expert opinions.
In order to obtain the optimal expert opinion aggregation method, the sum of the disagreement between the collected opinions and the personal opinions of each expert should be minimized. The opinion set of the kth expert is S k Recording the expert group opinion after iteration as S (t+1) The degree of consistency of the scores of the two is SIM (S k ,S (t+1) ) Where t represents an iterative step, t=0, 1,2, …. The expert opinion is recorded as relative consistencySetting initial consistency weight +.>The set of which is denoted D. Taking c=1 and q=2, calculating expert group opinion sets S (t+1) Expert opinion degree->/>
Order D (t+1) ＝(d 1 ,d 2 ,…,d K ) If I D (t+1) -D (t) And if the I is less than or equal to 0.001, stopping iterative computation, otherwise, enabling t=t+1, and repeating the step. In the application scene, when iteration is carried out for the fifth time, the requirement of D is met (t+1) -D (t) Referring to Table 10 for results, table 10 is an iterative calculation record table provided by embodiments of the present invention:
table 10
| D (t+1) | k1 | k2 | k3 | k4 |
| d (0) | 0.250 | 0.250 | 0.250 | 0.250 |
| d (1) | 0.301 | 0.262 | 0.228 | 0.202 |
| d (2) | 0.285 | 0.245 | 0.226 | 0.205 |
| d (3) | 0.289 | 0.250 | 0.227 | 0.205 |
| d (4) | 0.288 | 0.249 | 0.227 | 0.205 |
| d (5) | 0.288 | 0.249 | 0.227 | 0.205 |
At this time, expert group opinion sets S obtained after iteration (5) Table 11 below, table 11 is a schematic table of expert group opinion sets for iterations through the 5 th time provided by an embodiment of the present invention:
TABLE 11
8. And calculating expert summarization coefficients.
According toAlpha is more than or equal to 0 and less than or equal to 1, and the summarizing coefficient H of each expert is calculated k . Wherein, the application scene is provided with the expertise background and the expertise degree of each expert to be consistent, so that the expert weight omega is defined k =1/k=0.25, α=0.5, and the calculation results refer to table 12, table 12 is an expert summary coefficient table provided in the embodiment of the present invention:
table 12
| 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 |
9. And establishing a summary expert opinion matrix.
According to the sum coefficient H of each expert for each index k Establishing a matrix AC integrating all expert opinions:
(1≤i≤m，1≤j≤n)
the application scene integrates the summarization coefficient H k And Vague set matrix, the results are shown in table 13 below:
TABLE 13
10. The information entropy index is weighted.
The application scene is based on a scheme-index matrix AC integrating all expert opinions, and the fitness degree F of the j decision index of the i alternative scheme is calculated in sequence according to a calculation method of entropy weight in the expert opinion summarizing and multi-target scheme ordering method (3) ij And entropy E j By degree of deviation of information d j Calculating the weight sigma of each decision index j The results are shown in Table 14:
TABLE 14
11. And calculating a scheme evaluation value.
Based on the matrix AC, based on the respective index weight value sigma j The evaluation values of the respective schemes were calculated according to the following formula:
/>
In the application scene, the evaluation value calculation results of all schemes are shown in table 15:
TABLE 15
The scheme evaluation value is in the form of a Vague value, and for comparison, the dominance function V (f ij )＝a ij -b ij +1 converts the Vague value to a real number. The conversion results are shown in Table 16 below:
table 16
| Scheme set | Score value | Scheme set | Score value |
| Production of biodegradable Plastic (A1) | 1.00 | Membrane separation and removal (A6) | 0.79 |
| Production of natural material substitute (A2) | 1.26 | Bacterial degradation (A7) | 0.58 |
| Manual screening (A3) | 0.83 | Algae degradation (A8) | 0.69 |
| Electric flocculation removal (A4) | 0.87 | Photocatalytic degradation (A9) | 0.82 |
| Activated carbon removal (A5) | 0.78 |
12. And sequencing to obtain a final regulation scheme combination.
And sequencing the schemes according to the scheme dominance results, and evaluating the scheme advantages and disadvantages by combining indexes, wherein the results are shown in fig. 6. At this time, the index weight value of the "treatment efficiency" is the largest. Under the condition, the combination of the technology of producing natural material substitutes "+" producing biodegradable plastics "+" electric flocculation removal "is ranked before in the treatment scheme, and in addition, under the condition of economy and feasibility, the technology of manually sieving out, photocatalytic degradation, membrane separation removal and the like can be added, so that the treatment efficiency is further improved, and the method has expected better effect on the rapid treatment of microplastic. Through research decision flow, the result of alternative scheme sequencing comprehensively reflects the satisfaction degree of each index and preference opinions of different experts, and the result has higher reference value for the selection of final regulation measures.
The regional regulation and control scheme aiming at different microplastic pollution degrees can be obtained by modifying the index weight. For example, according to the regulation and control target when the regional micro-plastic pollution degree is II-level, the scheme has the highest requirement on the index of the environmental influence degree, and can be weighted sigma by a decision maker j The above-mentioned step 11 is repeated = {0.20,0.10,0.50,0.05,0.15}, and as a result, as shown in fig. 7, except for the recycling of plastics such as "producing natural material substitute", "producing biodegradable plastic", and the like, and the green substitution mode, the sorting of "manual screening", "activated carbon removal", and "bacterial degradation" is prioritized over other schemes, and this scheme is applicable to the case where the decision maker preference index is the minimum environmental impact.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the embodiments described above, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. The regional micro-plastic comprehensive decrement regulation multi-objective optimization method is characterized by comprising the following steps of:
defining micro-plastic pollution risk levels of all the areas to be regulated according to the micro-plastic pollution conditions of different areas to be regulated; wherein one or more areas to be regulated are arranged;
determining a plurality of decrement regulation technologies from a decrement regulation technology library according to the micro-plastic pollution risk level to form an alternative scheme set; wherein the decrement control technology library comprises a plurality of microplastic decrement control technologies;
Expert evaluation is carried out on the alternative scheme set, and the result of the expert evaluation is analyzed according to the preference of a decision maker to obtain an index weight value and a scheme evaluation value;
and determining the final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value.
2. The method for optimizing the comprehensive decrement control of the regional micro-plastic according to claim 1, wherein the determining the micro-plastic pollution risk level according to the micro-plastic pollution condition of the region to be controlled comprises the following steps:
determining the predicted ineffective concentration of the microplastic according to the type and the characteristics of the environmental medium of the region to be regulated;
determining a plurality of sampling points, obtaining the concentration of the microplastic at the sampling points, and calculating the microplastic pollution coefficient according to the concentration of the microplastic and the predicted non-effect concentration;
calculating a pollution load index of the sampling point according to the micro-plastic pollution coefficient;
and comparing the pollution load index with a preset pollution load threshold value, and determining the micro-plastic pollution risk level of the area to be regulated.
3. The method for optimizing a regional micro-plastic comprehensive decrement control multi-objective according to claim 1, wherein determining a plurality of decrement control technologies from a decrement control technology library according to the micro-plastic pollution risk level to form an alternative scheme set comprises:
Determining a regulation target of the region to be regulated according to the microplastic pollution risk level;
and screening the decrement regulation technologies in the decrement regulation technology library according to the regulation targets, and determining a plurality of regulation schemes to form an alternative scheme set.
4. The regional micro-plastic comprehensive decrement regulation multi-objective optimization method according to claim 1, wherein the expert review is performed on the alternative scheme set, and the result of the expert review is analyzed according to decision maker preference to obtain a scheme evaluation value, and the method comprises the following steps:
selecting indexes from an evaluation index library according to the regulation and control targets of the region to be regulated and controlled to form a decision index set;
generating an expert evaluation table based on the alternative scheme set and the decision index set;
converting the evaluation condition in the expert evaluation table into a feature single value, and converting the feature single value into a feature fuzzy value;
calculating the consistency of the expert among the scores of all the experts and the consistency of the scores of all the experts and the expert group according to the characteristic fuzzy values;
determining expert weights according to the expert consistence, and determining expert summarization coefficients according to the scoring consistence and the expert weights;
Establishing a scheme index matrix according to the expert summarization coefficient;
calculating index weight values of all the regulation and control schemes in the alternative scheme set according to decision maker preference or the scheme index matrix;
and determining the scheme evaluation value of each regulation scheme according to the scheme index matrix and the index weight value.
5. The method for optimizing a regional micro-plastic comprehensive decrement regulation multi-objective according to claim 1, wherein the determining the final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value comprises:
sorting the scheme evaluation values by combining the index weight values to obtain a sorting result;
and determining a plurality of regulation scheme combinations to form a final regulation scheme combination according to the sequencing result.
6. The method for optimizing regional microplastic comprehensive decrement regulation and control multiple objectives according to claim 4, wherein in the step of calculating the expert agreement between the scores of each expert and the score agreement between each expert and the expert group according to the feature single value and the fuzzy value, the calculation formula of the score agreement is as follows:
wherein the SIM (S) k ,S (t+1) ) Representing the kth expert opinion set S k And expert group opinion collection S (t+1) Score agreement of (2);representing the membership lower bound of the j decision index supported by the kth expert for the ith regulation scheme; />Representing the membership lower bound of the kth expert against the jth decision index for the ith regulation scheme; t is the iteration number; />Representing iteration (t+1) times, and for the ith regulation scheme, supporting the membership lower bound of the jth decision index by expert group opinion; />Representing the iteration (t+1) times, and for the ith regulation scheme, the expert group opinion is against the membership lower bound of the jth decision index.
7. The method for optimizing regional micro-plastic comprehensive decrement regulation and control multiple objectives according to claim 4, wherein in the step of determining the solution evaluation value of each regulation and control solution according to the solution index matrix and the index weight value, a calculation formula of the solution evaluation value is:
wherein V is i Representing a scheme evaluation value; sigma (sigma) j Is an index weight value; AC is a scheme index matrix; n represents the total number of decision indexes; i represents the ith regulatory scheme; j represents the j-th decision index.
8. The regional microplastic comprehensive decrement regulation multi-objective optimization system is characterized by comprising:
The first module is used for defining corresponding microplastic pollution risk levels according to the microplastic pollution conditions of different areas to be regulated; wherein one or more areas to be regulated are arranged;
the second module is used for determining a plurality of decrement regulation technologies from the decrement regulation technology library according to the micro-plastic pollution risk level to form an alternative scheme set; wherein the decrement control technology library comprises a plurality of microplastic decrement control technologies;
the third module is used for carrying out expert review on the alternative scheme set, and analyzing the expert review result according to the preference of a decision maker to obtain an index weight value and a scheme evaluation value;
and a fourth module, configured to determine a final regulation scheme combination of the region to be regulated according to the index weight value and the scheme evaluation value.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program implements the method of any one of claims 1 to 7.
10. A computer storage medium in which a processor executable program is stored, characterized in that the processor executable program is for implementing the method according to any one of claims 1 to 7 when being executed by the processor.
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